Publications

Chou, J & Hao, J 2016, 'Marine biomaterials as drug delivery system for osteoporosis and bone tissue regeneration' in Handbook of Bioceramics and Biocomposites, Springer, Germany, pp. 1309-1332.
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© Springer International Publishing Switzerland 2016.There is currently an urgent need to develop sustainable and therapeutically relevant advanced drug delivery systems to treat the prevalence of ongoing human diseases and ailments. The effectiveness of such system will depend primarily on the properties and characteristics of the carrier material. In this chapter, marine materials are investigated as potential drug delivery carriers for bone tissue engineering and in the treatment of osteoporosis. This chapter will explore the unique structures of marine materials that set it apart from its synthetic counterparts and the conversion to biocompatible calcium phosphates combined with synthetic modifications, and case studies will demonstrate the potential clinical applications.

Rana, M, Li, L & Su, SW 2016, 'Microgrid State Estimation Using the IoT with 5G Technology' in Mavromoustakis, CX, Mastorakis, G & Batalla, JM (eds), Internet of Things (IoT) in 5G Mobile Technologies, Springer, Germany, pp. 175-196.

Ahadi, A, Brennan, S, Kennedy, P, Hutvagner, G & Tran, N 2016, 'Long non-coding RNAs harboring miRNA seed regions are enriched in prostate cancer exosomes', Scientific Reports, vol. 6, pp. 1-14.
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Long non-coding RNAs (lncRNAs) form the largest transcript class in the human transcriptome. These lncRNA are expressed not only in the cells, but they are also present in the cell-derived extracellular vesicles such as exosomes. The function of these lncRNAs in cancer biology is not entirely clear, but they appear to be modulators of gene expression. In this study, we characterize the expression of lncRNAs in several prostate cancer exosomes and their parental cell lines. We show that certain lncRNAs are enriched in cancer exosomes with the overall expression signatures varying across cell lines. These exosomal lncRNAs are themselves enriched for miRNA seeds with a preference for let-7 family members as well as miR-17, miR-18a, miR-20a, miR-93 and miR-106b. The enrichment of miRNA seed regions in exosomal lncRNAs is matched with a concomitant high expression of the same miRNA. In addition, the exosomal lncRNAs also showed an over representation of RNA binding protein binding motifs. The two most common motifs belonged to ELAVL1 and RBMX. Given the enrichment of miRNA and RBP sites on exosomal lncRNAs, their interplay may suggest a possible function in prostate cancer carcinogenesis

Ahadi, A, Hutvagner, G & Connerty, P 2016, 'RNA Binding Proteins in the miRNA Pathway', International Journal of Molecular Sciences, vol. 17, no. 31, pp. 1-16.
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microRNAs (miRNAs) are short ~22 nucleotides (nt) ribonucleic acids which post-transcriptionally regulate gene expression. miRNAs are key regulators of all cellular processes, and the correct expression of miRNAs in an organism is crucial for proper development and cellular function. As a result, the miRNA biogenesis pathway is highly regulated. In this review, we outline the basic steps of miRNA biogenesis and miRNA mediated gene regulation focusing on the role of RNA binding proteins (RBPs). We also describe multiple mechanisms that regulate the canonical miRNA pathway, which depends on a wide range of RBPs. Moreover, we hypothesise that the interaction between miRNA regulation and RBPs is potentially more widespread based on the analysis of available high-throughput datasets.

Ahadi, A, Khoury, S, losseva, M & Tran, N 2016, 'A comparative analysis of lncRNAs in prostate cancer exosomes and theirparental cell lines', Genomics Data, vol. 9, pp. 7-9.
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Prostate cancer is the second leading cancer in men world-wide. Due to its heterogeneous nature, a considerable amount of research effort has been dedicated in identifying effective clinical biomarkers with a focus on proteins, messenger RNA and microRNAs. However, there is limited data on the role and expression of long noncoding RNAs (lncRNAs) in prostate cancer exosomes. This array dataset which is linked to our publication describes the profiling of human lncRNAs in prostate cancer and their exosomes from five different cell lines. From this dataset, we identified a list of statistically significant prostate cancer lncRNAs which are differentially expressed in the exosomes compared to their parent cell lines. This dataset has been deposited into Gene Expression Omnibus (GSE81034).

Ahadi, A, Khoury, S, Tran, N & Zhang, X 2016, 'Expression of microRNAs in HPV negative tonsil cancers and their regulation of PDCD4', Genomics Data, vol. 8, pp. 93-96.
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Global rates of tonsil cancer have been increasing since the turn of the millennia, however we still have a limited understanding of the genes and pathways which control this disease. This array dataset which is linked to our publication (Zhang et al., 2015) describes the profiling of human miRNAs in tonsil and normal adjacent tissues. With this dataset, we identified a list of microRNA (miRNA) which were highly over represented in tonsil cancers and showed that several miRNAs were able to regulate the tumour suppressor PDCD4 in a temporal manner. The dataset has been deposited into Gene Expression Omnibus (GSE75630).

Alarkawi, D, Bliuc, D, Nguyen, TV, Eisman, JA & Center, JR 2016, 'Contribution of Lumbar Spine BMD to Fracture Risk in Individuals With T-Score Discordance', Journal of Bone and Mineral Research, vol. 31, no. 2, pp. 274-280.
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© 2015 American Society for Bone and Mineral Research. Fracture risk estimates are usually based on femoral neck (FN) BMD. It is unclear how to address T-score discordance, where lumbar spine (LS) T-score is lower than FN T-score. The objective of this work was to examine the impact of LS BMD on fracture risk, in individuals with lower LS T-score than FN T-score. Participants aged 60+ years from the Dubbo Osteoporosis Epidemiology Study with LS and FN BMD measured at first visit, and were followed from 1989 to 2014. Five-hundred and seventy-three (573) of 2270 women and 131 of 1373 men had lower LS than FN T-score by ≥0.6 standard deviation (SD) (low-LS group based on least significant change). In low-LS women, each 1 SD lower LS T-score than FN was associated with a 30% increase in fracture risk (hazard ratio [HR] 1.30; 95% CI, 1.11 to 1.45). For low-LS men there was a 20% nonsignificant increase in fracture risk for each 1 SD lower LS than FN T-score (HR 1.20; 95% CI, 0.10 to 1.67). Low-LS women had greater absolute fracture risks than the rest of the women. This increased risk was more apparent for lower levels of FN T-score and in older age groups. At an FN T-score of -2, low-LS women had a 3%, 10%, and 23% higher 5-year absolute fracture risk than non-low LS women in the 60 to 69 year, 70 to 79 year, and 80+ years age-groups, respectively. Furthermore, an osteoporotic LS T-score increased 5-year absolute fracture risk for women with normal or osteopenic FN T-score by 10% to 13%. Men in the low-LS group had very few fractures; therefore, a meaningful analyses of fracture risk could not be conducted. This study shows the significant contribution of lower LS BMD to fracture risk over and above FN BMD in women. A LS BMD lower than FN BMD should be incorporated into fracture risk calculators at least for women in older age-groups.

Al-Dmour, H & Al-Ani, A 2016, 'A steganography embedding method based on edge identification and XOR coding', EXPERT SYSTEMS WITH APPLICATIONS, vol. 46, pp. 293-306.
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Al-Dmour, H & Al-Ani, A 2016, 'Quality optimized medical image information hiding algorithm that employs edge detection and data coding', COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol. 127, pp. 24-43.
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Alzoubi, Y, Gill, AQ & Al-ani, A 2016, 'Empirical studies of geographically distributed agile development communication challenges: A systematic review', Information and Management, vol. 53, no. 1, pp. 22-37.
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There is increasing interest in studying and applying geographically distributed agile development (GDAD). Much has been published on GDAD communication. There is a need to systematically review and synthesize the literature on GDAD communication challenges. Using the SLR approach and applying customized search criteria derived from the research questions, 21 relevant empirical studies were identified and reviewed in this paper. The data from these papers were extracted to identify communication challenges and the techniques used to overcome these challenges. The findings of this research serve as a resource for GDAD practitioners and researchers when setting future research priorities and directions.

Andronescu, E, Brown, JM, Oktar, FN, Agathopoulos, S, Chou, J & Obata, A 2016, 'Nanomaterials for Medical Applications: Benefits and Risks', JOURNAL OF NANOMATERIALS.
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Argha, A, Li, L, Su, S & Nguyen, HT 2016, 'Stabilising the networked control systems involving actuation and measurement consecutive packet losses', IET Control Theory & Applications, vol. 10, no. 11, pp. 1269-1280.
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This study is devoted to the problem of designing a robust output-feedback discrete-time sliding mode control (ODSMC) for the networked systems involving both measuring and actuating data packet losses. Packet losses in the networked control systems (NCSs) have been modelled by utilising the probability and the characteristics of the sources and the destinations. Here, the well-known Bernoulli random binary distribution is used to model consecutive packet losses in the NCSs. In this study, first, a robust observer-based discrete-time sliding mode control is proposed for the NCSs including random packet losses. The packet losses occur in the channels from the sensors to the controller and the channels from the controller to the actuators. Then, using the notion of exponential mean square stability, the boundedness of the obtained closed-loop system is analysed with a linear matrix inequality approach. Our proposed robust ODSMC can be applied to unstable NCSs, and there is no need to stabilise the underlying system in advance. Illustrative examples are presented to show the effectiveness of the proposed approach.

Argha, A, Li, L, Su, SW & Nguyen, HT 2016, 'On LMI-based sliding mode control for uncertain discrete-time systems', Journal of the Franklin Institute, vol. 353, no. 15, pp. 3857-3875.
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In this paper, a new approach to design a robust discrete-time sliding mode control (DSMC) is proposed for uncertain discrete-time systems. To this end, an LMI approach is used to develop a new framework to design the sliding function which is linear to the state. Our proposed robust DSMC can be applied to unstable systems, and also there is no need to stabilize the underlying system first. It has been argued in the literature that for the systems involving balanced external disturbances, using switching component is not needed. In this paper, it is shown that with the assumption of smoothness of the external disturbances, a different form of switching element in the controller can outperform the so-called linear controller in terms of the thickness of the boundary layer around the sliding function and the ultimate bound on the system state. Also, this paper extends the idea of disturbance estimation to the uncertain discrete-time systems. The disturbance estimator is exploited in the controller design and the boundedness of the obtained closed-loop system is analyzed. Also, two novel forms of variable structure DSMC are suggested in this paper.

Asadnia, M, Kottapalli, AGP, Karavitaki, KD, Warkiani, ME, Miao, J, Corey, DP & Triantafyllou, M 2016, 'From Biological Cilia to Artificial Flow Sensors: Biomimetic Soft Polymer Nanosensors with High Sensing Performance', Scientific Reports, vol. 6.
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© The Author(s) 2016. We report the development of a new class of miniature all-polymer flow sensors that closely mimic the intricate morphology of the mechanosensory ciliary bundles in biological hair cells. An artificial ciliary bundle is achieved by fabricating bundled polydimethylsiloxane (PDMS) micro-pillars with graded heights and electrospinning polyvinylidenefluoride (PVDF) piezoelectric nanofiber tip links. The piezoelectric nature of a single nanofiber tip link is confirmed by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). Rheology and nanoindentation experiments are used to ensure that the viscous properties of the hyaluronic acid (HA)-based hydrogel are close to the biological cupula. A dome-shaped HA hydrogel cupula that encapsulates the artificial hair cell bundle is formed through precision drop-casting and swelling processes. Fluid drag force actuates the hydrogel cupula and deflects the micro-pillar bundle, stretching the nanofibers and generating electric charges. Functioning with principles analogous to the hair bundles, the sensors achieve a sensitivity and threshold detection limit of 300 mV/(m/s) and 8 μm/s, respectively. These self-powered, sensitive, flexible, biocompatibale and miniaturized sensors can find extensive applications in navigation and maneuvering of underwater robots, artificial hearing systems, biomedical and microfluidic devices.

Avellino, R, Havermans, M, Erpelinck, C, Sanders, MA, Hoogenboezem, R, van de Werken, HJG, Rombouts, E, van Lom, K, van Strien, PMH, Gebhard, C, Rehli, M, Pimanda, J, Beck, D, Erkeland, S, Kuiken, T, de Looper, H, Groeschel, S, Touw, I, Bindels, E & Delwel, R 2016, 'An autonomous CEBPA enhancer specific for myeloid-lineage priming and neutrophilic differentiation', BLOOD, vol. 127, no. 24, pp. 2991-3003.
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Balasankar, G, Luximon, A & Al-Jumaily, A 2016, 'Current conservative management and classification of club foot: A review.', Journal of Pediatric Rehabilitation Medicine, vol. 9, no. 4, pp. 257-264.
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Clubfoot, known as congenital talipes equinovarus, is one of the complex paediatric foot deformity with the incidence of 1 in every 1000 live births. It consists of four complex foot abnormalities such as forefoot adductus, midfoot cavus, and hindfoot varus and ankle equinus. There are a number of surgical techniques (soft tissue releases, arthrodesis) used to correct clubfoot. However currently the conservative management (manipulation, serial casting, and braces) of clubfoot is considered as the best choice and it is widely accepted among orthopaedists. Clubfoot treated with surgical techniques might suffer various complications such as soft tissues contractures, neurovascular complications, infections, and shortening of the limbs. Although conservative method is generally considered as an effective method, it is still challenging to cure clubfoot in advance stages. Also, the classification of the initial severity of clubfoot is essential to evaluate the outcome of the treatment. In this review, the aim is to review the different types of conservative method and the assessment of clubfoot severity.

Bigdeli, B, Goliaei, B, Masoudi-Khoram, N, Jooyan, N, Nikoofar, A, Rouhani, M, Haghparast, A & Mamashli, F 2016, 'Enterolactone: A novel radiosensitizer for human breast cancer cell lines through impaired DNA repair and increased apoptosis', TOXICOLOGY AND APPLIED PHARMACOLOGY, vol. 313, pp. 180-194.
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Bliuc, D, Tran, T, Alarkawi, D, Nguyen, TV, Eisman, JA & Center, JR 2016, 'Secular Changes in Postfracture Outcomes Over 2 Decades in Australia: A Time-Trend Comparison of Excess Postfracture Mortality in Two Birth Controls Over Two Decades', JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM, vol. 101, no. 6, pp. 2475-2483.
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Botezatu, L, Michel, LC, Makishima, H, Schroeder, T, Germing, U, Haas, R, van der Reijden, B, Marneth, AE, Bergevoet, SM, Jansen, JH, Przychodzen, B, Wlodarski, M, Niemeyer, C, Platzbecker, U, Ehninger, G, Unnikrishnan, A, Beck, D, Pimanda, J, Hellstrom-Lindberg, E, Malcovati, L, Boultwood, J, Pellagatti, A, Papaemmanuil, E, Le Coutre, P, Kaeda, J, Opalka, B, Moroy, T, Duehrsen, U, Maciejewski, J & Khandanpour, C 2016, 'GFI1(36N) as a therapeutic and prognostic marker for myelodysplastic syndrome', EXPERIMENTAL HEMATOLOGY, vol. 44, no. 7, pp. 590-595.
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Carter, DR, Sutton, SK, Pajic, M, Murray, J, Sekyere, EO, Fletcher, J, Beckers, A, De Preter, K, Speleman, F, George, RE, Haber, M, Norris, MD, Cheung, BB & Marshall, GM 2016, 'Glutathione biosynthesis is upregulated at the initiation of MYCN-driven neuroblastoma tumorigenesis', Molecular Oncology, vol. 10, no. 6, pp. 866-878.
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© 2016 . The MYCN gene is amplified and overexpressed in a large proportion of high stage neuroblastoma patients and has been identified as a key driver of tumorigenesis. However, the mechanism by which MYCN promotes tumor initiation is poorly understood. Here we conducted metabolic profiling of pre-malignant sympathetic ganglia and tumors derived from the TH-MYCN mouse model of neuroblastoma, compared to non-malignant ganglia from wildtype littermates. We found that metabolites involved in the biosynthesis of glutathione, the most abundant cellular antioxidant, were the most significantly upregulated metabolic pathway at tumor initiation, and progressively increased to meet the demands of tumorigenesis. A corresponding increase in the expression of genes involved in ribosomal biogenesis suggested that MYCN-driven transactivation of the protein biosynthetic machinery generated the necessary substrates to drive glutathione biosynthesis. Pre-malignant sympathetic ganglia from TH-MYCN mice had higher antioxidant capacity and required glutathione upregulation for cell survival, when compared to wildtype ganglia. Moreover, in vivo administration of inhibitors of glutathione biosynthesis significantly delayed tumorigenesis when administered prophylactically and potentiated the anticancer activity of cytotoxic chemotherapy against established tumors. Together these results identify enhanced glutathione biosynthesis as a selective metabolic adaptation required for initiation of MYCN-driven neuroblastoma, and suggest that glutathione-targeted agents may be used as a potential preventative strategy, or as an adjuvant to existing chemotherapies in established disease.

Chan, KY & Ling, SH 2016, 'A forward selection based fuzzy regression for new product development that correlates engineering characteristics with consumer preferences', JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, vol. 30, no. 3, pp. 1869-1880.
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Chandrakanthan, V, Yeola, A, Kwan, JC, Oliver, RA, Qiao, Q, Kang, YC, Zarzour, P, Beck, D, Boelen, L, Unnikrishnan, A, Villanueva, JE, Nunez, AC, Knezevic, K, Palu, C, Nasrallah, R, Carnell, M, Macmillan, A, Whan, R, Yu, Y, Hardy, P, Grey, ST, Gladbach, A, Delerue, F, Ittner, L, Mobbs, R, Walkley, CR, Purton, LE, Ward, RL, Wong, JWH, Hesson, LB, Walsh, W & Pimanda, JE 2016, 'PDGF-AB and 5-Azacytidine induce conversion of somatic cells into tissue-regenerative multipotent stem cells', PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, vol. 113, no. 16, pp. E2306-E2315.
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Chaudhuri, PK, Ebrahimi Warkiani, M, Jing, T, Kenry & Lim, CT 2016, 'Microfluidics for research and applications in oncology', Analyst, vol. 141, no. 2, pp. 504-524.
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© The Royal Society of Chemistry 2016. Cancer is currently one of the top non-communicable human diseases, and continual research and developmental efforts are being made to better understand and manage this disease. More recently, with the improved understanding in cancer biology as well as the advancements made in microtechnology and rapid prototyping, microfluidics is increasingly being explored and even validated for use in the detection, diagnosis and treatment of cancer. With inherent advantages such as small sample volume, high sensitivity and fast processing time, microfluidics is well-positioned to serve as a promising platform for applications in oncology. In this review, we look at the recent advances in the use of microfluidics, from basic research such as understanding cancer cell phenotypes as well as metastatic behaviors to applications such as the detection, diagnosis, prognosis and drug screening. We then conclude with a future outlook on this promising technology.

Chou, J, Ito, T, Otsuka, M, Ben-Nissan, B & Milthorpe, B 2016, 'The effectiveness of the controlled release of simvastatin from β-TCP macrosphere in the treatment of OVX mice.', Journal of tissue engineering and regenerative medicine, vol. 10, no. 3, pp. E195-E203.
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Simvastatin, a cholesterol treatment drug, has been shown to stimulate bone regeneration. As such, there has been an increase interest in the development of suitable materials and systems for the delivery of simvastatin. Without the appropriate dosage of simvastatin, the therapeutic effects on bone growth will be significantly reduced. Furthermore, similar to many pharmaceutical compounds, at high concentration simvastatin can cause various adverse side-effects. Given the associated side-effects with the usage of simvastatin, the development of suitable controlled drug release system is pertinent. Calcium phosphate in particularly beta-tricalcium phosphate (β-TCP) has been extensively studied and used as a carrier material for drug delivery system. In this study, Foraminifera exoskeletons were used as calcium carbonate precursor materials, which were hydrothermally converted to β-TCP as a carrier material for simvastatin. Natural marine exoskeletons posses interconnected and uniformly porous network capable of improving drug loading and release rate. To prolong the release of simvastatin, an apatite coating was made around the β-TCP sample and in vitro release studies in simulated body fluid (SBF) showed a significant decrease in release rate. Osteoporotic mice were used to examine the compare therapeutic effectiveness of β-TCP, β-TCP with simvastatin, apatite-coated β-TCP with simvastatin and direct injection of simvastatin near the right femur of the mice. Localized and systemic effect were compared with the femur of the non-implanted side (left) and showed that β-TCP with or without simvastatin was able to induce significant bone formation over 6 weeks. Mechanical analysis showed that apatite-coated β-TCP with simvastatin produced significantly stronger bones compared with other experimental groups. This study shows that natural exoskeletons with the appropriate structure can be successfully used as a drug delivery system for simvastatin and can its release ca...

Chou, J, Komuro, M, Hao, J, Kuroda, S, Hattori, Y, Ben-Nissan, B, Milthorpe, B & Otsuka, M 2016, 'Bioresorbable zinc hydroxyapatite guided bone regeneration membrane for bone regeneration', Clinical Oral Implants Research, vol. 27, no. 3, pp. 354-360.
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Connerty, P, Bajan, S, Remenyi, J, Fuller-Pace, FV & Hutvagner, G 2016, 'The miRNA biogenesis factors, p72/DDX17 and KHSRP regulate the protein level of Ago2 in human cells', BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS, vol. 1859, no. 10, pp. 1299-1305.
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Cropley, JE, Eaton, SA, Aiken, A, Young, PE, Giannoulatou, E, Ho, JWK, Buckland, ME, Keam, SP, Hutvagner, G, Humphreys, DT, Langley, KG, Henstridge, DC, Martin, DIK, Febbraio, MA & Suter, CM 2016, 'Male-lineage transmission of an acquired metabolic phenotype induced by grand-paternal obesity', Molecular Metabolism, vol. 5, no. 8, pp. 699-708.
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© 2016 The Author(s).Objective: Parental obesity can induce metabolic phenotypes in offspring independent of the inherited DNA sequence. Here we asked whether such non-genetic acquired metabolic traits can be passed on to a second generation that has never been exposed to obesity, even as germ cells. Methods: We examined the F1, F2, and F3 a/a offspring derived from F0 matings of obese prediabetic A vy /a sires and lean a/a dams. After F0, only lean a/a mice were used for breeding. Results: We found that F1 sons of obese founder males exhibited defects in glucose and lipid metabolism, but only upon a post-weaning dietary challenge. F1 males transmitted these defects to their own male progeny (F2) in the absence of the dietary challenge, but the phenotype was largely attenuated by F3. The sperm of F1 males exhibited changes in the abundance of several small RNA species, including the recently reported diet-responsive tRNA-derived fragments. Conclusions: These data indicate that induced metabolic phenotypes may be propagated for a generation beyond any direct exposure to an inducing factor. This non-genetic inheritance likely occurs via the actions of sperm noncoding RNA.

Daryasari, MP, Akhgar, MR, Mamashli, F, Bigdeli, B & Khoobi, M 2016, 'Chitosan-folate coated mesoporous silica nanoparticles as a smart and pH-sensitive system for curcumin delivery', RSC ADVANCES, vol. 6, no. 107, pp. 105578-105588.
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Ekong, U, Lam, HK, Xiao, B, Ouyang, G, Liu, H, Chan, KY & Ling, SH 2016, 'Classification of epilepsy seizure phase using interval type-2 fuzzy support vector machines', Neurocomputing, vol. 199, pp. 66-76.
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An interval type-2 fuzzy support vector machine (IT2FSVM) is proposed to solve a classification problem which aims to classify three epileptic seizure phases (seizure-free, pre-seizure and seizure) from the electroencephalogram (EEG) captured from patients with neurological disorder symptoms. The effectiveness of the IT2FSVM classifier is evaluated based on a set of EEG samples which are collected from 10 patients at Peking university hospital. The EEG samples for the three seizure phases were captured by the 112 2-s 19 channel EEG epochs, where each patient was extracted for each sample. Feature extraction was used to reduce the feature vector of the EEG samples to 45 elements and the EEG samples with the reduced features are used for training the IT2FSVM classifier. The classification results obtained by the IT2FSVM are compared with three traditional classifiers namely Support Vector Machine, k-Nearest Neighbor and naive Bayes. The experimental results show that the IT2FSVM classifier is able to achieve superior learning capabilities with respect to the uncontaminated samples when compared with the three classifiers. In order to validate the level of robustness of the IT2FSVM, the original EEG samples are contaminated with Gaussian white noise at levels of 0.05, 0.1, 0.2 and 0.5. The simulation results show that the IT2FSVM classifier outperforms the traditional classifiers under the original dataset and also shows a high level of robustness when compared to the traditional classifiers with white Gaussian noise applied to it.

Ekren, N, Gunduz, O, Celik, S, Ayata, B, Sahin, YM, Chou, J, Ben-Nissan, B, Salman, S, Gokce, H & Oktar, FN 2016, 'Production of apatite from snail shells for biomedical engineering applications', Key Engineering Materials, vol. 696, pp. 51-56.
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© 2016 Trans Tech Publications, Switzerland. Bioceramics is very important application for dental and orthopedic procedures. Beside all these normal procedures traffic accidents are requiring increasing number of graft, prostheses and orthosis applications. Bioceramics can be produced from local and natural sources with various methods. Those can be produced from various bone structures through calcination (at high temperatures) or with diluted hydrochloric acid (HCl) application & freeze drying. Beside these methods calcite and aragonite structures like from sea shells and egg shells bioceramic production can be realized through mechanochemical processing via a simple hot-plate or ultrasonic equipment. A fresh water snail shell (Zebra Nerite Snail-Neritina natalensis) was prepared as bioceramic production source. The resulting hydroxyapatite (HA) powders were obtained without any impurities. At two varying temperature of 865 and 885 0C the snail shells was transformed to HA bioceramics. Scanning electron microscopy (SEM), X-ray diffraction (XRD) and differential thermal analysis (TG/DTA) were evaluated.

Fabian, J, Opitz, D, Althoff, K, Lodrini, M, Hero, B, Volland, R, Beckers, A, Preter, KD, Decock, A, Patil, N, Abba, M, Kopp-Schneider, A, Astrahantseff, K, Wünschel, J, Pfeil, S, Ercu, M, Künkele, A, Hu, J, Thole, T, Schweizer, L, Mechtersheimer, G, Carter, D, Cheung, BB, Popanda, O, Deimling, AV, Koster, J, Versteeg, R, Schwab, M, Marshall, GM, Speleman, F, Erb, U, Zoeller, M, Allgayer, H, Simon, T, Fischer, M, Kulozik, AE, Eggert, A, Witt, O, Schulte, JH & Deubzer, HE 2016, 'MYCN and HDAC5 transcriptionally repress CD9 to trigger invasion and metastasis in neuroblastoma', Oncotarget, vol. 7, no. 41, pp. 66344-66359.
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The systemic and resistant nature of metastatic neuroblastoma renders it largely incurable with current multimodal treatment. Clinical progression stems mainly from the increasing burden of metastatic colonization. Therapeutically inhibiting the migration-invasion-metastasis cascade would be of great benefit, but the mechanisms driving this cycle are as yet poorly understood. In-depth transcriptome analyses and ChIP-qPCR identified the cell surface glycoprotein, CD9, as a major downstream player and direct target of the recently described GRHL1 tumor suppressor. CD9 is known to block or facilitate cancer cell motility and metastasis dependent upon entity. High-level CD9 expression in primary neuroblastomas correlated with patient survival and established markers for favorable disease. Low-level CD9 expression was an independent risk factor for adverse outcome. MYCN and HDAC5 colocalized to the CD9 promoter and repressed transcription. CD9 expression diminished with progressive tumor development in the TH-MYCN transgenic mouse model for neuroblastoma, and CD9 expression in neuroblastic tumors was far below that in ganglia from wildtype mice. Primary neuroblastomas lacking MYCN amplifications displayed differential CD9 promoter methylation in methyl-CpG-binding domain sequencing analyses, and highlevel methylation was associated with advanced stage disease, supporting epigenetic regulation. Inducing CD9 expression in a SH-EP cell model inhibited migration and invasion in Boyden chamber assays. Enforced CD9 expression in neuroblastoma cells transplanted onto chicken chorioallantoic membranes strongly reduced metastasis to embryonic bone marrow. Combined treatment of neuroblastoma cells with HDAC/ DNA methyltransferase inhibitors synergistically induced CD9 expression despite hypoxic, metabolic or cytotoxic stress. Our results show CD9 is a critical and indirectly druggable suppressor of the invasion-metastasis cycle in neuroblastoma.

Frost, SA & Nguyen, TV 2016, 'Uncertain effects of calcium and vitamin D supplementation on fracture risk reduction', Osteoporosis International, vol. 27, no. 8, pp. 2647-2648.
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Ganesan, B, Fong, KNK, Ameersing, L & Al-Jumaily, A 2016, 'Kinetic and Kinematic analysis of gait pattern of 13 year old children with unilateral genu valgum', European Review for Medical and Pharmacological Sciences, vol. 20, no. 15, pp. 3168-3171.
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Abstract OBJECTIVE: Genu valgum is a common knee deformity in growing children. It alters the alignment of the lower extremity, body posture, and gait pattern of the children. Understanding of kinematic and kinetic parameters of gait in genu valgum is essential for planning and implementing the intervention to correcting the valgus deformity. The aim of this paper is to investigate the kinetic and kinematic gait differences in children with genu valgum. PATIENTS AND METHODS: A 13-year old girl with left side unilateral genu valgum and a closely matched healthy counterpart were recruited to compare the kinetic and kinematic parameters of their gait performances, and they were captured by The VICON motion analysis system. RESULTS: The results showed that the child with genu valgum had lower left and right knee angles (39.6˚; 30.2˚) and higher ankle angles (35.6˚; 28.4˚) than the healthy subject (64.2˚, 60.2˚). In addition, the child with genu valgum had lower moments on the left side of the knee (42.1 mm.N) than unaffected right knee (73.9 mm.N). Also, the ground reaction force was (0.7 N) lower in the affected knee of the child with genu valgum than the normal subject. CONCLUSIONS: This study revealed that there were decreased knee and ankle moments and lower knee and ankle ground reaction forces in the affected genu valgum extremity when compared with the healthy counterpart. These changes might be responsible for the altering gait pattern of the child with genu valgum.

Gourlay, ML, Overman, RA, Fine, JP, Filteau, G, Cawthon, PM, Schousboe, JT, Orwoll, ES, Wilt, TJ, Nguyen, TV, Lane, NE, Szulc, P, Taylor, BC, Dam, TT, Nielson, CM, Cauley, JA, Barrett-Connor, E, Fink, HA, Lapidus, JA, Kado, DM, Diem, SJ & Ensrud, KE 2016, 'Time to Osteoporosis and Major Fracture in Older Men. The MrOS Study', American Journal of Preventive Medicine, vol. 50, no. 6, pp. 727-736.
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© 2016 American Journal of Preventive Medicine. Introduction: For older men who undergo bone mineral density (BMD) testing, the optimal osteoporosis screening schedule is unknown. Time-to-disease estimates are necessary to inform screening intervals. Methods: A prospective cohort study of 5,415 community-dwelling men aged ≥65 years without hip or clinical vertebral fracture or antifracture treatment at baseline was conducted. Participants had concurrent BMD and fracture follow-up between 2000 and 2009, and additional fracture follow-up through 2014. Data were analyzed in 2015. Time to incident osteoporosis (lowest T-score ≤ -2.50) for men without baseline osteoporosis, and time to hip or clinical vertebral fracture or major osteoporotic fracture for men without or with baseline osteoporosis, were estimated. Results: Nine men (0.2%) with BMD T-scores >-1.50 at baseline developed osteoporosis during follow-up. The adjusted estimated time for 10% to develop osteoporosis was 8.5 (95% CI=6.7, 10.9) years for those with moderate osteopenia (lowest T-score, -1.50 to -1.99) and 2.7 (95% CI=2.1, 3.4) years for those with advanced osteopenia (lowest T-score, -2.00 to -2.49) at baseline. The adjusted times for 3% to develop a first hip or clinical vertebral fracture ranged from 7.1 (95% CI=6.0, 8.3) years in men with baseline T-scores > -1.50 to 1.7 (95% CI=1.0, 3.1) years in men with baseline osteoporosis. Conclusions: Men aged 65 years and older with femoral neck, total hip, and lumbar spine BMD T-scores >-1.50 on a first BMD test were very unlikely to develop osteoporosis during follow-up. Additional BMD testing may be most informative in older men with T-scores ≤-1.50.

Guo, Q, Zhang, Y, Celler, BG & Su, SW 2016, 'Backstepping Control of Electro-Hydraulic System Based on Extended-State-Observer With Plant Dynamics Largely Unknown', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 63, no. 11, pp. 6909-6920.
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Hassanzadeh-Barforoushi, A, Shemesh, J, Farbehi, N, Asadnia, M, Yeoh, GH, Harvey, RP, Nordon, RE & Warkiani, ME 2016, 'A rapid co-culture stamping device for studying intercellular communication', Scientific Reports, vol. 6.
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© 2016 The Author(s). Regulation of tissue development and repair depends on communication between neighbouring cells. Recent advances in cell micro-contact printing and microfluidics have facilitated the in-vitro study of homotypic and heterotypic cell-cell interaction. Nonetheless, these techniques are still complicated to perform and as a result, are seldom used by biologists. We report here development of a temporarily sealed microfluidic stamping device which utilizes a novel valve design for patterning two adherent cell lines with well-defined interlacing configurations to study cell-cell interactions. We demonstrate post-stamping cell viability of >95%, the stamping of multiple adherent cell types, and the ability to control the seeded cell density. We also show viability, proliferation and migration of cultured cells, enabling analysis of co-culture boundary conditions on cell fate. We also developed an in-vitro model of endothelial and cardiac stem cell interactions, which are thought to regulate coronary repair after myocardial injury. The stamp is fabricated using microfabrication techniques, is operated with a lab pipettor and uses very low reagent volumes of 20 μl with cell injection efficiency of >70%. This easy-to-use device provides a general strategy for micro-patterning of multiple cell types and will be important for studying cell-cell interactions in a multitude of applications.

Hattori, Y, Mori, H, Chou, J & Otsuka, M 2016, 'Mechanochemical synthesis of zinc-apatitic calcium phosphate and the controlled zinc release for bone tissue engineering', DRUG DEVELOPMENT AND INDUSTRIAL PHARMACY, vol. 42, no. 4, pp. 595-601.
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Hesari, Z, Soleimani, M, Atyabi, F, Sharifdini, M, Nadri, S, Warkiani, ME, Zare, M & Dinarvand, R 2016, 'A hybrid microfluidic system for regulation of neural differentiation in induced pluripotent stem cells', Journal of Biomedical Materials Research - Part A, vol. 104, no. 6, pp. 1534-1543.
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© 2016 Wiley Periodicals, Inc. Controlling cellular orientation, proliferation, and differentiation is valuable in designing organ replacements and directing tissue regeneration. In the present study, we developed a hybrid microfluidic system to produce a dynamic microenvironment by placing aligned PDMS microgrooves on surface of biodegradable polymers as physical guidance cues for controlling the neural differentiation of human induced pluripotent stem cells (hiPSCs). The neuronal differentiation capacity of cultured hiPSCs in the microfluidic system and other control groups was investigated using quantitative real time PCR (qPCR) and immunocytochemistry. The functionally of differentiated hiPSCs inside hybrid system's scaffolds was also evaluated on the rat hemisected spinal cord in acute phase. Implanted cell's fate was examined using tissue freeze section and the functional recovery was evaluated according to the Basso, Beattie, and Bresnahan (BBB) locomotor rating scale. Our results confirmed the differentiation of hiPSCs to neuronal cells on the microfluidic device where the expression of neuronal-specific genes was significantly higher compared to those cultured on the other systems such as plain tissue culture dishes and scaffolds without fluidic channels. Although survival and integration of implanted hiPSCs did not lead to a significant functional recovery, we believe that combination of fluidic channels with nanofiber scaffolds provides a great microenvironment for neural tissue engineering, and can be used as a powerful tool for in situ monitoring of differentiation potential of various kinds of stem cells.

Hesson, LB, Ng, B, Zarzour, P, Srivastava, S, Kwok, C-T, Packham, D, Nunez, AC, Beck, D, Ryan, R, Dower, A, Ford, CE, Pimanda, JE, Sloane, MA, Hawkins, NJ, Bourke, MJ, Wong, JWH & Ward, RL 2016, 'Integrated Genetic, Epigenetic, and Transcriptional Profiling Identifies Molecular Pathways in the Development of Laterally Spreading Tumors', MOLECULAR CANCER RESEARCH, vol. 14, no. 12, pp. 1217-1228.
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Ho-Pham, LT, Do, TT, Campbell, LV & Nguyen, TV 2016, 'HbA(1c)-Based Classification Reveals Epidemic of Diabetes and Prediabetes in Vietnam', DIABETES CARE, vol. 39, no. 7, pp. E93-E94.
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Ho-Pham, LT, Hans, D, Doan, MC, Mai, LD & Nguyen, TV 2016, 'Genetic determinant of trabecular bone score (TBS) and bone mineral density: A bivariate analysis', BONE, vol. 92, pp. 79-84.
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Ho-Pham, LT, Lai, TQ, Mai, LD, Doan, MC & Nguyen, TV 2016, 'Body Composition in Individuals with Asymptomatic Osteoarthritis of the Knee', Calcified Tissue International, vol. 98, no. 2, pp. 165-171.
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© 2015, Springer Science+Business Media New York. Greater body mass index (BMI) is associated with a greater risk of osteoarthritis (OA). This study sought to investigate whether the association is mediated by fat mass or lean mass. The study involved 170 men and 488 women aged between 20 and 90 (average age: 55) who were randomly recruited from Ho Chi Minh City, Vietnam. The presence of knee OA was radiographically diagnosed based on the Kellgren–Lawrence criteria. Lean mass (LM) and fat mass (FM) were obtained from the DXA whole body scan (Hologic QDR-4500). The relationship between OA, LM, and FM was analyzed by a series of multiple linear regression models which take into account the effects of gender and age. As expected, men and women with knee OA were older than those without OA (65 vs 51 year in men, and 64 vs 52 year in women). After adjusting for age, OA was associated with greater FM and percent body fat (PBF), but the association was only observed in women, not in men. There was no statistically significant difference in LM between OA and non-OA individuals. Moreover, after adjusting for age and BMI or PBF, bone density in OA patients was not significantly different from non-OA individuals. Women with OA of the knee have greater fat mass than non-OA individuals, and that there is no significant difference in bone density between OA and non-OA individuals. Thus, the association between body mass index and OA is mainly mediated by fat mass.

Huang, Y, Thoms, JAI, Tursky, ML, Knezevic, K, Beck, D, Chandrakanthan, V, Suryani, S, Olivier, J, Boulton, A, Glaros, EN, Thomas, SR, Lock, RB, MacKenzie, KL, Bushweller, JH, Wong, JWH & Pimanda, JE 2016, 'MAPK/ERK2 phosphorylates ERG at serine 283 in leukemic cells and promotes stem cell signatures and cell proliferation', LEUKEMIA, vol. 30, no. 7, pp. 1552-1561.
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Inan, AT, Gunduz, O, Sahin, YM, Ekren, N, Salman, S, Chou, J, Ben-Nissan, B, Gokce, H & Oktar, FN 2016, 'Novel Bioceramic production via mechanochemical conversion from plate limpet (Tectura scutum)-Shells', Key Engineering Materials, vol. 696, pp. 45-50.
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© 2016 Trans Tech Publications, Switzerland. Calcium phosphates are very important biomaterials for orthopaedic and dental applications. Hydroxyapatite (HA) is one of the important phases used for grafting. Those are produced from synthetic and natural sources with various methods. Especially nano-bioceramics can be produced through calcitic and aragonitic structures (i.e. mussel shells, sea snail shells, land snail shells and sea urchin shells). The plate limpet shells were used. The plate limpet is a gastropod, a soft-bodied invertebrate (an animal without a backbone) that is protected by a very hard, flattened conical shell. In this study the Plate Limpet (Tectura scutum) shells were obtained from a local gift store in Istanbul. The habitation of these limpets broadens from south Alaska down to California-Mexico. First the exact 97 % CaCO3 content was measured with thermal analysis (DTA/TGA). Here in this study agitation was preformed on a hot-plate. First the temperature was fixed at 80 °C for 15 min. Then equivalent amount to 1.92 mmol CaO H3PO4was added dropwise for affecting HA phase formation and the reaction was set on a hotplate for 8 hours. The dried sediments HA part was divided into 2 groups. One group was sintered to 835 °C and second group to 855 °C. Here x-ray diffraction and scanning electron microscope (SEM) analyses were performed. From the study various HA phases and TCP phases were obtained. A previous study done with Atlantic Deer Cowrie encourages nanobioceramic production from natural sources. This study proposes that mechanochemical agitation with very simple way for producing nano-sized calcium phosphates for future bioengineering scaffold applications.

Karami, C, Adli, AH, Zhand, S, Tabarraei, A, Talei, R, Saeidi, M & Moradi, A 2016, 'Study of Genotype, Subtype and Mutation in the S Gene in Hepatitis B Patients Co-infected with HIV in Iran', JUNDISHAPUR JOURNAL OF MICROBIOLOGY, vol. 9, no. 12.
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Kelishomi, ZH, Goliaei, B, Mandavi, H, Nikoofar, A, Rahimi, M, Moosavi-Movahedi, AA, Mamashli, F & Bigdeli, B 2016, 'Antioxidant activity of low molecular weight alginate produced by thermal treatment', FOOD CHEMISTRY, vol. 196, pp. 897-902.
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Khoo, BL, Chaudhuri, PK, Ramalingam, N, Tan, DSW, Lim, CT & Warkiani, ME 2016, 'Single-cell profiling approaches to probing tumor heterogeneity', International Journal of Cancer, vol. 139, no. 2, pp. 243-255.
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© 2016 UICC. Tumor heterogeneity is a major hindrance in cancer classification, diagnosis and treatment. Recent technological advances have begun to reveal the true extent of its heterogeneity. Single-cell analysis (SCA) is emerging as an important approach to detect variations in morphology, genetic or proteomic expression. In this review, we revisit the issue of inter- and intra-tumor heterogeneity, and list various modes of SCA techniques (cell-based, nucleic acid-based, protein-based, metabolite-based and lipid-based) presently used for cancer characterization. We further discuss the advantages of SCA over pooled cell analysis, as well as the limitations of conventional techniques. Emerging trends, such as high-throughput sequencing, are also mentioned as improved means for cancer profiling. Collectively, these applications have the potential for breakthroughs in cancer treatment.

Khoram, NM, Bigdeli, B, Nikoofar, A & Goliaei, B 2016, 'Caffeic Acid Phenethyl Ester Increases Radiosensitivity of Estrogen Receptor-Positive and -Negative Breast Cancer Cells by Prolonging Radiation-Induced DNA Damage', JOURNAL OF BREAST CANCER, vol. 19, no. 1, pp. 18-25.
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Khushaba, RN, Al-Timemy, A, Al-Ani, A & Al-Jumaily, A 2016, 'Myoelectric feature extraction using temporal-spatial descriptors for multifunction prosthetic hand control.', Conf Proc IEEE Eng Med Biol Soc, vol. 2016, pp. 1696-1699.
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We tackle the challenging problem of myoelectric prosthesis control with an improved feature extraction algorithm. The proposed algorithm correlates a set of spectral moments and their nonlinearly mapped version across the temporal and spatial domains to form accurate descriptors of muscular activity. The main processing step involves the extraction of the Electromyogram (EMG) signal power spectrum characteristics directly from the time-domain for each analysis window, a step to preserve the computational power required for the construction of spectral features. The subsequent analyses involve computing 1) the correlation between the time-domain descriptors extracted from each analysis window and a nonlinearly mapped version of it across the same EMG channel; representing the temporal evolution of the EMG signals, and 2) the correlation between the descriptors extracted from differences of all possible combinations of channels and a nonlinearly mapped version of them, focusing on how the EMG signals from different channels correlates with each other. The proposed Temporal-Spatial Descriptors (TSDs) are validated on EMG data collected from six transradial amputees performing 11 classes of finger movements. Classification results showed significant reductions (at least 8%) in classification error rates compared to other methods.

Kulasinghe, A, Perry, C, Warkiani, ME, Blick, T, Davies, A, O'Byrne, K, Thompson, EW, Nelson, CC, Vela, I & Punyadeera, C 2016, 'Short term ex-vivo expansion of circulating head and neck tumour cells', Oncotarget, vol. 7, no. 37, pp. 60101-60109.
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Minimally invasive techniques are required for the identification of head and neck cancer (HNC) patients who are at an increased risk of metastasis, or are not responding to therapy. An approach utilised in other solid cancers is the identification and enumeration of circulating tumour cells (CTCs) in the peripheral blood of patients. Low numbers of CTCs has been a limiting factor in the HNC field to date. Here we present a methodology to expand HNC patient derived CTCs ex-vivo. As a proof of principle study, 25 advanced stage HNC patient bloods were enriched for circulating tumour cells through negative selection and cultured in 2D and 3D culture environments under hypoxic conditions (2% O2, 5% CO2). CTCs were detected in 14/25 (56%) of patients (ranging from 1-15 CTCs/5 mL blood). Short term CTC cultures were successfully generated in 7/25 advanced stage HNC patients (5/7 of these cultures were from HPV+ patients). Blood samples from which CTC culture was successful had higher CTC counts (p = 0.0002), and were predominantly from HPV+ patients (p = 0.007). This is, to our knowledge, the first pilot study to culture HNC CTCs ex-vivo. Further studies are warranted to determine the use of short term expansion in HNC and the role of HPV in promoting culture success.

Lal, S, Hall, RM & Tipper, JL 2016, 'A novel method for isolation and recovery of ceramic nanoparticles and metal wear debris from serum lubricants at ultra-low wear rates', ACTA BIOMATERIALIA, vol. 42, pp. 420-428.
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Ling, SH, Chan, KY, Leung, FHF, Jiang, F & Nguyen, H 2016, 'Quality and robustness improvement for real world industrial systems using a fuzzy particle swarm optimization', Engineering Applications of Artificial Intelligence, vol. 47, pp. 68-80.
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© 2015 Elsevier Ltd. This paper presents a novel fuzzy particle swarm optimization with cross-mutated (FPSOCM) operation, where a fuzzy logic system developed based on the knowledge of swarm intelligence is proposed to determine the inertia weight for the swarm movement of particle swarm optimization (PSO) and the control parameter of a newly introduced cross-mutated operation. Hence, the inertia weight of the PSO can be adaptive with respect to the search progress. The new cross-mutated operation intends to drive the solution to escape from local optima. A suite of benchmark test functions are employed to evaluate the performance of the proposed FPSOCM. Experimental results show empirically that the FPSOCM performs better than the existing hybrid PSO methods in terms of solution quality, robustness, and convergence rate. The proposed FPSOCM is evaluated by improving the quality and robustness of two real world industrial systems namely economic load dispatch system and self-provisioning systems for communication network services. These two systems are employed to evaluate the effectiveness of the proposed FPSOCM as they are multi-optima and non-convex problems. The performance of FPSOCM is found to be significantly better than that of the existing hybrid PSO methods in a statistical sense. These results demonstrate that the proposed FPSOCM is a good candidate for solving product or service engineering problems which have multi-optima or non-convex natures.

Ling, SH, San, PP & Nguyen, HT 2016, 'Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes.', ISA Transactions, vol. 64, pp. 440-446.
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Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM.

Nasrallah, R, Fast, EM, Solaimani, P, Knezevic, K, Eliades, A, Patel, R, Thambyrajah, R, Unnikrishnan, A, Thoms, J, Beck, D, Vink, CS, Smith, A, Wong, J, Shepherd, M, Kent, D, Roychoudhuri, R, Paul, F, Klippert, J, Hammes, A, Willnow, T, Göttgens, B, Dzierzak, E, Zon, LI, Lacaud, G, Kouskoff, V & Pimanda, JE 2016, 'Identification of novel regulators of developmental hematopoiesis using Endoglin regulatory elements as molecular probes.', Blood, vol. 128, no. 15, pp. 1928-1939.
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Enhancers are the primary determinants of cell identity, and specific promoter/enhancer combinations of Endoglin (ENG) have been shown to target blood and endothelium in the embryo. Here, we generated a series of embryonic stem cell lines, each targeted with reporter constructs driven by specific promoter/enhancer combinations of ENG, to evaluate their discriminative potential and value as molecular probes of the corresponding transcriptome. The Eng promoter (P) in combination with the -8/+7/+9-kb enhancers, targeted cells in FLK1 mesoderm that were enriched for blast colony forming potential, whereas the P/-8-kb enhancer targeted TIE2+/c-KIT+/CD41- endothelial cells that were enriched for hematopoietic potential. These fractions were isolated using reporter expression and their transcriptomes profiled by RNA-seq. There was high concordance between our signatures and those from embryos with defects at corresponding stages of hematopoiesis. Of the 6 genes that were upregulated in both hemogenic mesoderm and hemogenic endothelial fractions targeted by the reporters, LRP2, a multiligand receptor, was the only gene that had not previously been associated with hematopoiesis. We show that LRP2 is indeed involved in definitive hematopoiesis and by doing so validate the use of reporter gene-coupled enhancers as probes to gain insights into transcriptional changes that facilitate cell fate transitions.

Pavesi, A, Adriani, G, Tay, A, Warkiani, ME, Yeap, WH, Wong, SC & Kamm, RD 2016, 'Engineering a 3D microfluidic culture platform for tumor-treating field application', Scientific Reports, vol. 6.
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The limitations of current cancer therapies highlight the urgent need for a more effective therapeutic strategy. One promising approach uses an alternating electric field; however, the mechanisms involved in the disruption of the cancer cell cycle as well as the potential adverse effects on non-cancerous cells must be clarified. In this study, we present a novel microfluidic device with embedded electrodes that enables the application of an alternating electric field therapy to cancer cells in a 3D extracellular matrix. To demonstrate the potential of our system to aid in designing and testing new therapeutic approaches, cancer cells and cancer cell aggregates were cultured individually or co-cultured with endothelial cells. The metastatic potential of the cancer cells was reduced after electric field treatment. Moreover, the proliferation rate of the treated cancer cells was lower compared with that of the untreated cells, whereas the morphologies and proliferative capacities of the endothelial cells were not significantly affected. These results demonstrate that our novel system can be used to rapidly screen the effect of an alternating electric field on cancer and normal cells within an in vivo-like microenvironment with the potential to optimize treatment protocols and evaluate synergies between tumor-treating field treatment and chemotherapy.

Perera, D, Poulos, RC, Shah, A, Beck, D, Pimanda, JE & Wong, JWH 2016, 'Differential DNA repair underlies mutation hotspots at active promoters in cancer genomes', NATURE, vol. 532, no. 7598, pp. 259-+.
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Pham, HM, Nguyen, ND, Center, JR, Eisman, JA & Nguyen, TV 2016, 'Contribution of Quadriceps Weakness to Fragility Fracture: A Prospective Study', JOURNAL OF BONE AND MINERAL RESEARCH, vol. 31, no. 1, pp. 208-214.
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Rafeie, M, Zhang, J, Asadnia, M, Li, W & Warkiani, ME 2016, 'Multiplexing slanted spiral microchannels for ultra-fast blood plasma separation', Lab on a Chip, vol. 16, no. 15, pp. 2791-2802.
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© The Royal Society of Chemistry 2016. Blood and blood products are critical components of health care. Blood components perform distinct functions in the human body and thus the ability to efficiently fractionate blood into its individual components (i.e., plasma and cellular components) is of utmost importance for therapeutic and diagnostic purposes. Although conventional approaches like centrifugation and membrane filtration for blood processing have been successful in generating relatively pure fractions, they are largely limited by factors such as the required blood sample volume, component purity, clogging, processing time and operation efficiency. In this work, we developed a high-throughput inertial microfluidic system for cell focusing and blood plasma separation from small to large volume blood samples (1-100 mL). Initially, polystyrene beads and blood cells were used to investigate the inertial focusing performance of a single slanted spiral microchannel as a function of particle size, flow rate, and blood cell concentration. Afterwards, blood plasma separation was conducted using an optimised spiral microchannel with relatively large dimensions. It was found that the reject ratio of the slanted spiral channel is close to 100% for blood samples with haematocrit (HCT) values of 0.5% and 1% under an optimal flow rate of 1.5 mL min-1. Finally, through a unique multiplexing approach, we built a high-throughput system consisting of 16 spiral channels connected together, which can process diluted samples with a total flow rate as high as 24 mL min-1. The proposed multiplexed system can surmount the shortcomings of previously reported microfluidic systems for plasma separation and cell sorting in terms of throughput, yield and operation efficiency.

Ramalingam, N, Warkiani, ME, Ramalingam, N, Keshavarzi, G, Hao-Bing, L & Hai-Qing, TG 2016, 'Numerical and experimental study of capillary-driven flow of PCR solution in hybrid hydrophobic microfluidic networks', Biomedical Microdevices, vol. 18, no. 4.
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© 2016, Springer Science+Business Media New York. Capillary-driven microfluidics is essential for development of point-of-care diagnostic micro-devices. Polymerase chain reaction (PCR)-based micro-devices are widely developed and used in such point-of-care settings. It is imperative to characterize the fluid parameters of PCR solution for designing efficient capillary-driven microfluidic networks. Generally, for numeric modelling, the fluid parameters of PCR solution are approximated to that of water. This procedure leads to inaccurate results, which are discrepant to experimental data. This paper describes mathematical modeling and experimental validation of capillary-driven flow inside Poly-(dimethyl) siloxane (PDMS)-glass hybrid micro-channels. Using experimentally measured PCR fluid parameters, the capillary meniscus displacement in PDMS-glass microfluidic ladder network is simulated using computational fluid dynamic (CFD), and experimentally verified to match with the simulated data.

Rana, MM, Li, L & Su, SW 2016, 'An Adaptive-Then-Combine Dynamic State Estimation Considering Renewable Generations in Smart Grids', IEEE Journal on Selected Areas in Communications, vol. 34, no. 12, pp. 3954-3961.
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© 1983-2012 IEEE.The penetration of renewable distributed energy resources, such as wind turbine, has been dramatically increased in distribution networks. Due to the intermittent property, the wind power generation patterns vary, which may risk distribution network operations. So, it is intrinsically necessary to monitor wind turbines in a distributed way. This paper presents an adaptive-Then-combine distributed dynamic approach for monitoring the grid under lossy communication links between the wind turbines and energy management system. First, the wind turbine is represented by a state-space linear model, with sensors deployed to obtain the system state information. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining estimation results with weighting factors which are calculated by minimizing the estimation error covariance based on semidefinite programming. Finally, the convergence analysis indicates that the estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the wind turbine and the IEEE 6-bus distribution system.

Rana, MM, Li, L & Su, SW 2016, 'Microgrid state estimation and control using Kalman filter and semidefinite programming technique', International Energy Journal, vol. 16, no. 2, pp. 47-56.
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The design of environment-friendly microgrids at the smart distribution level requires a stable behaviour for multiple state operations. This paper develops a Kalman filter based optimal feedback control method for the microgrid state estimation and stabilization. First, the microgrid is modelled by a discrete-time state space equation. Then the cost-effective smart sensors are deployed in order to obtain the required system information. From the communication point of view, the recursive systematic convolution code is adopted to add the redundancy in the system. At the end, the soft output Viterbi decoder is used to recover the system information from the noisy measurements and transmission uncertainties. Thereafter, the Kalman filter is utilized to estimate the system states, which acts as a precursor for applying the control algorithm. Finally, this paper proposes an optimal feedback control method to stabilize the microgrid based on semidefinite programming. The performance of the proposed approach is demonstrated by extensive numerical simulations.

Remenyi, J, Bajan, S, Fuller-Pace, FV, Arthur, JSC & Hutvagner, G 2016, 'The loop structure and the RNA helicase p72/DDX17 influence the processing efficiency of the mice miR-132', SCIENTIFIC REPORTS, vol. 6.
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Richmond, J, Robbins, A, Evans, K, Beck, D, Kurmasheva, RT, Billups, CA, Carol, H, Heatley, S, Sutton, R, Marshall, GM, White, D, Pimanda, J, Houghton, PJ, Smith, MA & Lock, RB 2016, 'Acute Sensitivity of Ph-like Acute Lymphoblastic Leukemia to the SMAC-Mimetic Birinapant', CANCER RESEARCH, vol. 76, no. 15, pp. 4579-4591.
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Sadr, N, Huvanandana, J, Doan, TN, Kalra, C, McEwan, A & de Chazal, P 2016, 'Reducing false arrhythmia alarms in the ICU using multimodal signals and robust QRS detection', PHYSIOLOGICAL MEASUREMENT, vol. 37, no. 8, pp. 1340-1354.
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Seeman, E & Nguyen, TV 2016, 'Bone remodeling markers: so easy to measure, so difficult to interpret', OSTEOPOROSIS INTERNATIONAL, vol. 27, no. 1, pp. 33-35.
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Shao, J, Qin, J, Bishop, AN, Huang, T-Z & Zheng, WX 2016, 'A novel analysis on the efficiency of hierarchy among leader-following systems', AUTOMATICA, vol. 73, pp. 215-222.
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Shields, BJ, Jackson, JT, Metcalf, D, Shi, W, Huang, Q, Garnham, AL, Glaser, SP, Beck, D, Pimanda, JE, Bogue, CW, Smyth, GK, Alexander, WS & McCormack, MP 2016, 'Acute myeloid leukemia requires Hhex to enable PRC2-mediated epigenetic repression of Cdkn2a', GENES & DEVELOPMENT, vol. 30, no. 1, pp. 78-91.
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Stratton-Powell, AA, Pasko, KM, Brockett, CL & Tipper, JL 2016, 'The Biologic Response to Polyetheretherketone (PEEK) Wear Particles in Total Joint Replacement: A Systematic Review', CLINICAL ORTHOPAEDICS AND RELATED RESEARCH, vol. 474, no. 11, pp. 2394-2404.
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Styrkarsdottir, U, Thorleifsson, G, Eiriksdottir, B, Gudjonsson, SA, Ingvarsson, T, Center, JR, Nguyen, TV, Eisman, JA, Christiansen, C, Thorsteinsdottir, U, Sigurdsson, G & Stefansson, K 2016, 'Two Rare Mutations in the COL1A2 Gene Associate With Low Bone Mineral Density and Fractures in Iceland', JOURNAL OF BONE AND MINERAL RESEARCH, vol. 31, no. 1, pp. 173-179.
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Styrkarsdottir, U, Thorleifsson, G, Gudjonsson, SA, Sigurdsson, A, Center, JR, Lee, SH, Nguyen, TV, Kwok, TCY, Lee, JSW, Ho, SC, Woo, J, Leung, P-C, Kim, B-J, Rafnar, T, Kiemeney, LA, Ingvarsson, T, Koh, J-M, Tang, NLS, Eisman, JA, Christiansen, C, Sigurdsson, G, Thorsteinsdottir, U & Stefansson, K 2016, 'Sequence variants in the PTCH1 gene associate with spine bone mineral density and osteoporotic fractures.', Nature Communications, vol. 7, pp. 1-8.
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Bone mineral density (BMD) is a measure of osteoporosis and is useful in evaluating the risk of fracture. In a genome-wide association study of BMD among 20,100 Icelanders, with follow-up in 10,091 subjects of European and East-Asian descent, we found a new BMD locus that harbours the PTCH1 gene, represented by rs28377268 (freq. 11.4-22.6%) that associates with reduced spine BMD (P=1.0 × 10(-11), β=-0.09). We also identified a new spine BMD signal in RSPO3, rs577721086 (freq. 6.8%), that associates with increased spine BMD (P=6.6 × 10(-10), β=0.14). Importantly, both variants associate with osteoporotic fractures and affect expression of the PTCH1 and RSPO3 genes that is in line with their influence on BMD and known biological function of these genes. Additional new BMD signals were also found at the AXIN1 and SOST loci and a new lead SNP at the EN1 locus.

Sutton, SK, Carter, DR, Kim, P, Tan, O, Arndt, GM, Zhang, XD, Baell, J, Noll, BD, Wang, S, Kumar, N, McArthur, GA, Cheung, BB & Marshall, GM 2016, 'A novel compound which sensitizes BRAF wild-type melanoma cells to vemurafenib in a TRIM16-dependent manner', Oncotarget, vol. 7, no. 32, pp. 52166-52178.
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There is an urgent need for better therapeutic options for advanced melanoma patients, particularly those without the BRAFV600E/K mutation. In melanoma cells, loss of TRIM16 expression is a marker of cell migration and metastasis, while the BRAF inhibitor, vemurafenib, induces melanoma cell growth arrest in a TRIM16-dependent manner. Here we identify a novel small molecule compound which sensitized BRAF wild-type melanoma cells to vemurafenib. High throughput, cell-based, chemical library screening identified a compound (C012) which significantly reduced melanoma cell viability, with limited toxicity for normal human fibroblasts. When combined with the BRAFV600E/K inhibitor, vemurafenib, C012 synergistically increased vemurafenib potency in 5 BRAFWT and 4 out of 5 BRAFV600E human melanoma cell lines (Combination Index: CI < 1), and, dramatically reduced colony forming ability. In addition, this drug combination was significantly anti-tumorigenic in vivo in a melanoma xenograft mouse model. The combination of vemurafenib and C012 markedly increased expression of TRIM16 protein, and knockdown of TRIM16 significantly reduced the growth inhibitory effects of the vemurafenib and C012 combination. These findings suggest that the combination of C012 and vemurafenib may have therapeutic potential for the treatment of melanoma, and, that reactivation of TRIM16 may be an effective strategy for patients with this disease.

Tay, A, Pavesi, A, Yazdi, SR, Lim, CT & Warkiani, ME 2016, 'Advances in microfluidics in combating infectious diseases', Biotechnology Advances, vol. 34, no. 4, pp. 404-421.
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© 2016 Elsevier Inc. One of the important pursuits in science and engineering research today is to develop low-cost and user-friendly technologies to improve the health of people. Over the past decade, research efforts in microfluidics have been made to develop methods that can facilitate low-cost diagnosis of infectious diseases, especially in resource-poor settings. Here, we provide an overview of the recent advances in microfluidic devices for point-of-care (POC) diagnostics for infectious diseases and emphasis is placed on malaria, sepsis and AIDS/HIV. Other infectious diseases such as SARS, tuberculosis, and dengue are also briefly discussed. These infectious diseases are chosen as they contribute the most to disability-adjusted life-years (DALYs) lost according to the World Health Organization (WHO). The current state of research in this area is evaluated and projection toward future applications and accompanying challenges are also discussed.

Thoms, JAI, Knezevic, K, Liu, JJ, Glaros, EN, Thai, T, Qiao, Q, Campbell, H, Packham, D, Huang, Y, Papathanasiou, P, Tunningley, R, Whittle, B, Yeung, AWS, Chandrakanthan, V, Hesson, L, Chen, V, Wong, JWH, Purton, LE, Ward, RL, Thomas, SR & Pimanda, JE 2016, 'Arrested Hematopoiesis and Vascular Relaxation Defects in Mice with a Mutation in Dhfr', MOLECULAR AND CELLULAR BIOLOGY, vol. 36, no. 8, pp. 1222-1236.
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Tran, NT 2016, 'Cancer Exosomes as miRNA Factories', Trends in Cancer, vol. 2, no. 7, pp. 329-331.
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miRNAs modulate gene expression while exosomes are extracellular cargo vessels that transport miRNAs and other materials to surrounding cells. When exosomes are taken up by recipient cells, the released miRNAs can modulate immune responses, inhibit apoptosis, and promote angiogenesis to maintain tumor growth. Central to this regulation is the processing of the primary transcripts into active miRNAs, which occurs exclusively within mammalian cells. Challenging this dogma is the discovery that Dicer and Ago2, key components of miRNA processing, are also present inside exosomes. While the exact nature of this processing requires extensive proof, it is an exciting notion that exogenous miRNA factories could exist outside the canonical boundaries of mammalian cells.

Unnikrishnan, A, Guan, YF, Huang, Y, Beck, D, Thoms, JAI, Peirs, S, Knezevic, K, Ma, S, de Walle, IV, de Jong, I, Ali, Z, Zhong, L, Raftery, MJ, Taghon, T, Larsson, J, MacKenzie, KL, Van Vlierberghe, P, Wong, JWH & Pimanda, JE 2016, 'A quantitative proteomics approach identifies ETV6 and IKZF1 as new regulators of an ERG-driven transcriptional network.', Nucleic Acids Research, vol. 44, no. 22, pp. 10644-10661.
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Aberrant stem cell-like gene regulatory networks are a feature of leukaemogenesis. The ETS-related gene (ERG), an important regulator of normal haematopoiesis, is also highly expressed in T-ALL and acute myeloid leukaemia (AML). However, the transcriptional regulation of ERG in leukaemic cells remains poorly understood. In order to discover transcriptional regulators of ERG, we employed a quantitative mass spectrometry-based method to identify factors binding the 321 bp ERG +85 stem cell enhancer region in MOLT-4 T-ALL and KG-1 AML cells. Using this approach, we identified a number of known binders of the +85 enhancer in leukaemic cells along with previously unknown binders, including ETV6 and IKZF1. We confirmed that ETV6 and IKZF1 were also bound at the +85 enhancer in both leukaemic cells and in healthy human CD34+ haematopoietic stem and progenitor cells. Knockdown experiments confirmed that ETV6 and IKZF1 are transcriptional regulators not just of ERG, but also of a number of genes regulated by a densely interconnected network of seven transcription factors. At last, we show that ETV6 and IKZF1 expression levels are positively correlated with expression of a number of heptad genes in AML and high expression of all nine genes confers poorer overall prognosis.

Warkiani, MEB, Khoo, BLU, Wu, L, Tay, AKAP, Bhagat, AASS, Han, J & Lim, CTE 2016, 'Ultra-fast, label-free isolation of circulating tumor cells from blood using spiral microfluidics', Nature protocols, vol. 11, no. 1, pp. 134-148.
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Circulating tumor cells (CTCs) are rare cancer cells that are shed from primary or metastatic tumors into the peripheral blood circulation. Phenotypic and genetic characterization of these rare cells can provide important information to guide cancer staging and treatment, and thus further research into their characteristics and properties is an area of considerable interest. In this protocol, we describe detailed procedures for the production and use of a label-free spiral microfluidic device to allow size-based isolation of viable CTCs using hydrodynamic forces that are present in curvilinear microchannels. This spiral system enables us to achieve ≥ 85% recovery of spiked cells across multiple cancer cell lines and 99.99% depletion of white blood cells in whole blood. The described spiral microfluidic devices can be produced at an extremely low cost using standard microfabrication and soft lithography techniques (2-3 d), and they can be operated using two syringe pumps for lysed blood samples (7.5 ml in 12.5 min for a three-layered multiplexed chip). The fast processing time and the ability to collect CTCs from a large patient blood volume allows this technique to be used experimentally in a broad range of potential genomic and transcriptomic applications.

Yuwono, M, Qin, Y, Zhou, J, Guo, Y, Celler, BG & Su, SW 2016, 'Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model', ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, vol. 47, pp. 88-100.
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Zarepour, E, Hassan, M, Chou, CT & Ebrahimi Warkiani, M 2016, 'Characterizing terahertz channels for monitoring human lungs with wireless nanosensor networks', Nano Communication Networks, vol. 9, pp. 43-57.
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© 2016 We characterize terahertz wireless channels for extracting data from nanoscale sensors deployed within human lungs. We discover that the inhalation and exhalation of oxygen and carbon dioxide causes periodic variation of the absorption coefficient of the terahertz channel. Channel absorption drops to its minimum near the end of inhalation, providing a window of opportunity to extract data with minimum transmission power. We propose an algorithm for nanosensors to estimate the periodic channel by observing signal-to-noise ratio of the beacons transmitted from the data sink. Using real respiration data from multiple subjects, we demonstrate that the proposed algorithm can estimate the minimum absorption interval of the periodic channel with 98.5% accuracy. Our analysis shows that by confining all data collections during the estimated low-absorption window of the periodic channel, nanosensors can reduce power consumption by six orders of magnitude. Finally, we demonstrate that for wireless communications within human lungs, 0.1–0.12 THz is the least absorbing spectrum within the terahertz band.

Zhang, J, Yan, S, Yuan, D, Alici, G, Nguyen, NT, Ebrahimi Warkiani, M & Li, W 2016, 'Fundamentals and applications of inertial microfluidics: A review', Lab on a Chip, vol. 16, no. 1, pp. 10-34.
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© The Royal Society of Chemistry 2016. In the last decade, inertial microfluidics has attracted significant attention and a wide variety of channel designs that focus, concentrate and separate particles and fluids have been demonstrated. In contrast to conventional microfluidic technologies, where fluid inertia is negligible and flow remains almost within the Stokes flow region with very low Reynolds number (Re 蠐 1), inertial microfluidics works in the intermediate Reynolds number range (~1 < Re < ~100) between Stokes and turbulent regimes. In this intermediate range, both inertia and fluid viscosity are finite and bring about several intriguing effects that form the basis of inertial microfluidics including (i) inertial migration and (ii) secondary flow. Due to the superior features of high-throughput, simplicity, precise manipulation and low cost, inertial microfluidics is a very promising candidate for cellular sample processing, especially for samples with low abundant targets. In this review, we first discuss the fundamental kinematics of particles in microchannels to familiarise readers with the mechanisms and underlying physics in inertial microfluidic systems. We then present a comprehensive review of recent developments and key applications of inertial microfluidic systems according to their microchannel structures. Finally, we discuss the perspective of employing fluid inertia in microfluidics for particle manipulation. Due to the superior benefits of inertial microfluidics, this promising technology will still be an attractive topic in the near future, with more novel designs and further applications in biology, medicine and industry on the horizon.

Zhang, X, Gee, HG, Rose, BR, Lee, CS, Clark, JC, Elliott, ME, Gamble, JG, Cairns, MJC, Harris, AH, Khoury, SK & Tran, NT 2016, 'Regulation of the tumour suppressor PDCD4 by miR-499 and miR-21 in oropharyngeal cancers', BMC Cancer, vol. 16.
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Background The rates of oropharyngeal cancers such as tonsil cancers are increasing. The tumour suppressor protein Programmed Cell Death Protein 4 (PDCD4) has been implicated in the development of various human cancers and small RNAs such as microRNAs (miRNAs) can regulate its expression. However the exact regulation of PDCD4 by multiple miRNAs in oropharyngeal squamous cell carcinoma (SCC) is not well understood. Results Using two independent oropharyngeal SCC cohorts with a focus on the tonsillar region, we identified a miRNA profile differentiating SCC tissue from normal. Both miR-21 and miR-499 were highly expressed in tonsil SCC tissues displaying a loss of PDCD4. Interestingly, expression of the miRNA machinery, Dicer1, Drosha, DDX5 (Dead Box Helicase 5) and DGCR8 (DiGeorge Syndrome Critical Region Gene 8) were all elevated by greater than 2 fold in the tonsil SCC tissue. The 3’UTR of PDCD4 contains three binding-sites for miR-499 and one for miR-21. Using a wild-type and truncated 3’UTR of PDCD4, we demonstrated that the initial suppression of PDCD4 was mediated by miR-21 whilst sustained suppression was mediated by miR-499. Moreover the single miR-21 site was able to elicit the same magnitude of suppression as the three miR-499 sites. Conclusion This study describes the regulation of PDCD4 specifically in tonsil SCC by miR-499 and miR-21 and has documented the loss of PDCD4 in tonsil SCCs. These findings highlight the complex interplay between miRNAs and tumour suppressor gene regulation and suggest that PDCD4 loss may be an important step in tonsillar carcinogenesis.

Adil, S, Anwar, T, Al-Jumaily, A & Adel 2016, 'Extreme Learning Machine based sEMG for Drop-Foot after Stroke Detection', Information Science and Technology (ICIST), 2016 Sixth International Conference on, International Conference on Information Science and Technology, IEEE, Dalian, China, pp. 18-22.
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Drop Foot (FD) is the inability to raising the foot due to the weakness of paralysis dorsiflexion muscle, this disability is caused by stroke. Recognize FD of the patient and provide a treatment as they needed is an important requirement for rehabilitation. One of the recognize techniques for FD event is based on using the surface electromyography (sEMG) signal. Utilizing sEMG signal can help to provide patient the specific rehabilitation treatment in specific time since it can detect the FD event before it happen. This paper has investigated the ability of Extreme Learning Machine (ELM) method to classify the sickness and healthy muscles on the leg, based on sEMG, that yield the FD. The performance is compared with Support Vector Machine (SVM) and Neural Network (NN). Classification accuracy with ELM is much better than SVM and NN giving the results with up to 97% classification accuracy using two channels on each side of the leg.

Adil, S, Jumaily, AA & Anam, K 2016, 'AW-ELM-based Crouch Gait recognition after ischemic stroke', International Conference on Electronic Devices, Systems, and Applications, International Conference on Electronic Devices, Systems and Applications, IEEE, Ras Al Khaimah, United Arab Emirates, pp. 1-4.
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© 2016 IEEE. Crouch Gait (CG) can be observed in the hemiplegia persons after ischemic stroke. Walking with Crouch Gait (CG) shown a large gaits disorder. This paper explores the use of adaptive wavelet extreme learning machine (AW-ELM) to classifying different gait conditions for hemiplegia and healthy subjects. Three participants having a Crouch Gait problem with categories of Mild, Moderate, and Severe gait conditions, also, one Healthy person are used their data in this work. The recognition system extracting number of time and frequency domain features for dimensionality reduction. While for the classification stage, the common Extreme Learning Machine (ELM) classifiers are used. AW-ELM achieved maximum testing accuracy up to 91.149 % and with using majority vote post-processing the accuracy achieves 91.547 %.

Al Widyan, F, Al-Ani, A, Kirchner, N & Zeibots, M 2016, 'A Bottleneck Investigation at Escalator Entry at the Brisbane Central Train Station', Australasian Transport Research Forum 2016 Proceedings, Australasian Transport Research Forum, ATRF, Melbourne, Australia, pp. 1-9.
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Escalators are an essential for passenger’s movements through multi-level rail station concourse environments. Despite the access benefits that escalators provide, they can make travel time longer and pose some challenges when bottlenecks appear at entry. Studying the passenger behaviour of bottlenecks at escalator entrances is essential for planning, designing and control of engineering transportation systems. In this paper we investigate passenger route choice behaviour while approaching an escalator-stair infrastructure set at Brisbane Central train station. A model of an escalator entry bottleneck is formulated. The developed model can explain the queuing characteristics of the bottlenecks and can be readily used to predict congested state occurrence at escalator entry bottleneck. Accurate prediction of bottlenecks occurring around escalators and the estimation of escalator capacity are obtained based on real field data collected from Brisbane Central train station. Results have provided significant insights and computational tools for understanding many features of escalator bottlenecks. Remarkably, escalator capacity at bottleneck points affects the duration and severity of the congested period.

Al-Ani, A, Koprinska, I, Naik, GR & Khushaba, RN 2016, 'A dynamic channel selection algorithm for the classification of EEG and EMG data', Proceedings of the International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Vancouver, BC, Canada, pp. 4076-4081.
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© 2016 IEEE.The multichannel nature of EEG and EMG data poses a big challenge to the development of automatic EEG/EMG analysis and classification systems. Due to the 'curse of dimensionality' problem, the analysis and classification of several channels may not lead to the desired performance. Accordingly, a number of algorithms have been proposed to identify small 'static' subsets of channels that are capable of differentiating between samples of different classes. However, the identification of small subsets of relevant channels may not always be possible, where for certain applications the smaller the number of channels the less chance that sufficient information is provided. We propose in this paper a dynamic channel selection algorithm that identifies a channel (or a subset of channels) for each time segment of the signal that is relevant to the class of that particular time segment. To achieve this, we embraced the dynamic classifier selection methodology, and particularly the multiple classifier behaviour approach. Each EEG/EMG channel can be chosen to represent a certain unseen time segment of the signal based on the performance, or local accuracy, of its nearest neighbours in the set of training time segments. Results obtained using EEG data of a four-class alertness state classification problem reveal that the proposed approach is capable of achieving competitive performance compared to a traditional static channel selection based method. The algorithm also produced very encouraging results when used to classify EMG data collected from nine transradial amputees performing six classes of movements.

Al-Dmour, H & Al-Ani, A 2016, 'MR Brain Image Segmentation Based on Unsupervised and Semi-Supervised Fuzzy Clustering Methods', 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016, Digital Image Computing Techniques and Applications, IEEE, Gold Coast, Australia, pp. 1-7.
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© 2016 IEEE.In medical imaging applications, the segmentation of Magnetic Resonance (MR) brain images plays a crucial role for measuring and visualizing the anatomical structures of interest. In general, the brain image segmentation aims to divide the image pixels into non-overlapping homogeneous regions for analyzing the changes in the brain for surgical planning. Several supervised and unsupervised clustering methods have been developed over the years to segment the magnetic resonance brain image. However, most of these methods have certain limitations such as requiring user interaction and high computational complexity. In this context, this paper proposes a methodology that combines semi-supervised and unsupervised classification techniques for achieving efficient and fully-automatic segmentation of brain images. Firstly, the algorithm applies a median filter to remove the noise inherent in MR images prior to the clustering step. Secondly, the background of the MR image is removed by using a global thresholding technique. Thirdly, we utilize the subtractive clustering method to overcome the deficiency of randomly initialized Fuzzy C-Means (FCM) parameters. This method is used for estimating the clustering number and to generate the initial centers, which is used as initialization parameter for FCM clustering. Finally, a semi-supervised algorithm with Standard Fuzzy Clustering is selected to divide the brain MR image into different classes based on the generated membership function from FCM. The efficiency of the proposed method is demonstrated on various MR brain images and compared with some of the well-known clustering techniques.

Al-Fatlawi, AH, Jabardi, MH & Ling, SH 2016, 'Efficient diagnosis system for Parkinson's disease using deep belief network', Evolutionary Computation (CEC), 2016 IEEE Congress on, IEEE Congress on Evolutionary Computation, IEEE, Piscataway, USA, pp. 1324-1330.
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In this paper, a deep belief network (DBN) has been adopted as an efficient technique to diagnosis the Parkinson's disease (PD). This diagnosis has been established based on the speech signal of the patients. Through the distinguishing and analyzing of the speech signal, the DBN has the ability to diagnose Parkinson's disease. To realize the diagnosis of Parkinson's disease by using DBN, the proposed system has been trained and tested with voices from a number of patients and healthy people. A feature extraction process has been prepared to be inputted to the deep belief network (DBN) which is used to create a template matching of the voices. In this paper, DBN is used to classify the Parkinson's disease which composes two stacked Restricted Boltzmann Machines (RBMs) and one output layer. Two stages of learning need to be applied to optimize the networks' parameters. The first stage is unsupervised learning which uses RBMs to overcome the problem that can cause because of the random value of the initial weights. Secondly, backpropagation algorithm is used as a supervised learning for the fine tuning. To illustrate the effectiveness of the proposed system, the experimental results are compared with different approaches and related works. The overall testing accuracy of the proposed system is 94% which is better than all of the compared methods. In short, the DBN is an effective method to diagnosis Parkinson's disease by using the speech signal.

Al-Kaysi, A, Al-Ani, A, Loo, CK, Breakspear, M & Boonstra, T 2016, 'Predicting Brain Stimulation Treatment Outcomes of DepressedPatients Through the Classification of EEG Oscillations', 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando Florida-USA, pp. 5266-5269.
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Major depressive disorder (MDD) is a mental disorder that is characterized by negative thoughts, mood and behavior. Transcranial direct current stimulation (tDCS) has recently emerged as a promising brain-stimulation treatment for MDD. A standard tDCS treatment involves numerous sessions that run over a few weeks, however, not all participants respond to this type of treatment. This study aims to predict which patients improve in mood and cognition in response to tDCS treatment by analyzing electroencephalography (EEG) of MDD patients that was collected at the start of tDCS treatment. This is achieved through classifying power spectral density (PSD) of resting-state EEG using support vector machine (SVM), linear discriminate analysis (LDA) and extreme learning machine (ELM). Participants were labelled as improved/not improved based on the change in mood and cognitive scores. The obtained classification results of all channel pair combinations are used to identify the most relevant brain regions and channels for this classification task. We found the frontal channels to be particularly informative for the prediction of the clinical outcome of the tDCS treatment. Subject independent results reveal that our proposed method enables the correct identification of the treatment outcome for seven of the ten participants for mood improvement and nine of ten participants for cognitive improvement. This represents an encouraging sign that EEG-based classification may help to tailor the selection of patients for treatment with tDCS brain stimulation.

Alqudah, HM, Cao, K, Zhang, T, Haddad, A, Su, S, Celler, B & Nguyen, H 2016, 'Cardiovascular Fitness Strengthening Using Portable Device', 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, USA, pp. 2692-2695.
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The paper describes a reliable and valid Portable Exercise Monitoring system developed using TI eZ430-Chronos watch, which can control the exercise intensity through audio stimulation in order to increase the Cardiovascular fitness strengthening.

Anam, K & Al-Jumaily, A 2016, 'Adaptive myoelectric pattern recognition for arm movement in different positions using advanced online sequential extreme learning machine', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, Florida, United States, pp. 900-903.
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© 2016 IEEE.The performance of the myoelectric pattern recognition system sharply decreases when working in various limb positions. The issue can be solved by cumbersome training procedure that can anticipate all possible future situations. However, this procedure will sacrifice the comfort of the user. In addition, many unpredictable scenarios may be met in the future. This paper proposed a new adaptive myoelectric pattern recognition using advance online sequential extreme learning (AOS-ELM) for classification of the hand movements to five different positions. AOS-ELM is an improvement of OS-ELM that can verify the adaptation validity using entropy. The proposed adaptive MPR was able to classify eight different classes from eleven subjects by accuracy of 95.42 % using data from one position. After learning the data from whole positions, the performance of the proposed system is 86.13 %. This performance was better than the MPR that employed original OS-ELM, but it was worse than the MPR that utilized the batch classifiers. Nevertheless, the adaptation mechanism of AOS-ELM is preferred in the real-time application.

Anwar, T & Al Jumaily, A 2016, 'EMG signal based knee joint torque estimation', 2016 International Conference on Systems in Medicine and Biology, ICSMB 2016, International Conference on Systems in Medicine and Biology, IEEE, Kharagpur, India, pp. 182-185.
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© 2016 IEEE. Although Lower Limb Robotic Rehabilitation device exhibit a great prospect in the rehabilitation of impaired limb, yet it has not been widely applied to clinical rehabilitation of the patient with impairment. This is mostly due to insufficient bidirectional information interaction between exoskeleton and patient. The intended action data that can be extracted from surface electromyography (sEMG) signal may include the intended posture, intended torque, intended knee joint angle and intended desired impedance of the patient. Capturing intended knee joint torque from sEMG signal is one of the necessary parameter to achieve a smooth Human Machine Interaction force in a multilayer control mechanism. In this paper, a new technique to estimate Knee joint torque using SVM has been proposed that has used wavelet feature. The estimator is able to estimate required knee joint torque to lift 5kg, 12kg and 19kg weight using extensor and flexor muscles. Based on weight-Torque relationship, greater the weight, greater the torque is required to lift the weight. The estimator has classified required torque of 5kg, 12kg and 19kg with accuracy of 98.7296%, 86.0254% and 95.6443% respectively. The estimator can also be used to estimate torque about knee joint at different joint angle.

Anwar, T & Al-Jumaily, A 2016, 'ANFIS to estimate damping coefficient from EMG to optimize the interaction force', 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems, IEEE, Vancouver, Canada.
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Although Lower Limb Robotic Rehabilitation device exhibit a great prospect in the rehabilitation of impaired limb, yet it has not been widely applied to clinical rehabilitation. This is mostly due to the insufficient bidirectional information interaction between exoskeleton and patient. In the shared control at the interaction point, it is very important that the deficiency of impaired lower limb in sharing the knee joint dynamics (Capturing of the intended action of the patient) is extracted beforehand to estimate as to how much assistance the robotic exoskeleton would provide. The intended action data that can be extracted from EMG signal may include the intended posture, intended torque, intended knee joint angle, intended knee joint torque and impedance parameter. In this paper, an application of Adaptive Network Based Fuzzy Inference System (ANFIS) has been proposed for proprioceptive feedback on the status of the interaction force at the patient robotic exoskeleton interaction point. ANFIS has been used to model the relationship between input and output. Interaction forces, rate of change in surface electromyography (EMG) signal are two inputs to ANFIS model and impedance parameters damping coefficients (also stiffness) is output. Impedance control law has damping as one of the tuning parameter. The resultant total torque is calculated from this law. The proposed model is able to estimate damping and demonstrate decent accuracy in modulating the knee joint dynamics to minimize the interaction force at the Patient Exoskeleton interaction point.

Anwar, T & Al-Jumaily, A 2016, 'ANFIS to Estimate Damping Coefficient from EMG to Optimize the Interaction Force', Microelectronics, Computing and Communications (MicroCom), 2016 International Conference on, International Conference on Microelectronics, Computing and Communications, IEEE.
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Although Lower Limb Robotic Rehabilitation device exhibit a great prospect in the rehabilitation of impaired limb, yet it has not been widely applied to clinical rehabilitation. This is mostly due to the insufficient bidirectional information interaction between exoskeleton and patient. In the shared control at the interaction point, it is very important that the deficiency of impaired lower limb in sharing the knee joint dynamics (Capturing of the intended action of the patient) is extracted beforehand to estimate as to how much assistance the robotic exoskeleton would provide. The intended action data that can be extracted from EMG signal may include the intended posture, intended torque, intended knee joint angle, intended knee joint torque and impedance parameter. In this paper, an application of Adaptive Network Based Fuzzy Inference System (ANFIS) has been proposed for proprioceptive feedback on the status of the interaction force at the patient robotic exoskeleton interaction point. ANFIS has been used to model the relationship between input and output. Interaction forces, rate of change in surface electromyography (EMG) signal are two inputs to ANFIS model and impedance parameters damping coefficients (also stiffness) is output. Impedance control law has damping as one of the tuning parameter. The resultant total torque is calculated from this law. The proposed model is able to estimate damping and demonstrate decent accuracy in modulating the knee joint dynamics to minimize the interaction force at the Patient Exoskeleton interaction point.

Anwar, T & Al-Jumaily, A 2016, 'System Identification and Damping Coefficient Estimation from EMG based on ANFIS to Optimize Human Exoskeleton Interaction', 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems, IEEE, Vancouver, Canada.
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Although Lower Limb Robotic Rehabilitation device exhibit a great prospect in the rehabilitation of impaired limb, yet it has not been widely applied to clinical rehabilitation due to lack of identification of the system that can build a relationship relating knee joint dynamics to EMG signal. As a result, the insufficient bidirectional information interaction between exoskeleton and patient, the adaptive collaboration is very much absent. In the shared control situation at the interaction point, it is very important that the deficiency of impaired lower limb at knee joint dynamics (Capturing of the intended action of the patient) is extracted beforehand to estimate as to how much assistance need to be provided by the robotic exoskeleton. The intended action data that can be extracted from EMG signal may include the intended posture, intended torque, intended knee joint angle, intended knee joint torque and impedance parameter. In this paper, an application of Adaptive Network based Fuzzy Inference System (ANFIS) has been proposed to identify a proprioceptive feedback system which plays the role of inverse dynamics in the closed loop controlled Robotic Rehabilitation Device. The identified ANFIS inverse dynamic model updates the current status of the interaction force at the patient robotic exoskeleton interaction point. Interaction forces, rate of change in surface electromyography (EMG) signal, Extracted RMS (Root Mean Square) are three input patterns to ANFIS model and impedance parameters damping coefficients, stiffness are the output. The proposed model is able to estimate damping and demonstrate decent accuracy in modulating the knee joint dynamics to minimize the interaction force at the Patient Exoskeleton interaction point.

Argha, A, Li, L, Su, SW & Nguyen, H 2016, 'H-2-Based Optimal Sparse Sliding Mode Control for Networked Control Systems', 2016 55th IEEE Conference on Decision and Control, IEEE Conference on Decision and Control, IEEE, Las Vegas, NV, pp. 6826-6831.
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This paper is devoted to the problem of designing a sparsely distributed sliding mode control for networked systems. Indeed, this note employs a distributed sliding mode control framework by exploiting (some of) other subsystems' information to improve the performance of each local controller so that it can widen the applicability region of the given scheme. To do so, different from the traditional schemes in the literature, a novel approach is proposed to design the sliding surface, in which the level of required control effort is taken into account during the sliding surface design based on the ℋ2 control. We then use this novel scheme to provide an innovative less-complex procedure that explores sparse control networks to satisfy the underlying control objective.

Argha, A, Su, SW & Savkin, A 2016, 'Optimal actuator/sensor selection through dynamic output feedback', Proceedings of the 2016 IEEE 55th Conference on Decision and Control, CDC 2016, IEEE Conference on Decision and Control, IEEE, Las Vegas, USA, pp. 3624-3629.
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© 2016 IEEE.This paper is devoted to the problem of optimal selection of a subset of available actuators/sensors through a multi-channel H22 dynamic output feedback controller for continuous linear time invariant systems. Incorporating two extra terms for penalizing the number of actuators and sensors into the optimization objective function, we develop an iterative process to identify the favorable row/column-wise sparse DOF gains. Employing the identified structure, we solve the constructed row/column structured multi-channel H22 DOF problem in order to derive a gain that exploits optimum number of sensors/actuators by which the closed-loop stability is maintained and the performance degradation of the closed-loop system is restricted. Through an example we demonstrate the remarkable performance and broad applicability of the proposed approach.

Argha, A, Su, SW, Savkin, A & Celler, B 2016, 'H-2/H-infinity Based Sliding Mode Control: A Partial Eigenstructure Assignment Method', 2016 55th IEEE Conference on Decision and Control, IEEE Conference on Decision and Control, IEEE, Las Vegas, NV, pp. 5354-5359.
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This paper is devoted to the problem of designing a sliding surface for an underlying system, while ”2/H∞ performance specifications of the closed-loop system are under control. This scheme is different from a large number of the existing methods in the literature for the sliding surface design, in the sense that it will penalize the required level of control effort to maintain sliding. This novel scheme consists of two stages. First, exploiting a certain partial eigenstructure assignment method, a state feedback gain is selected that ensures precise locations for some of the closed-loop system poles while minimizing the H∞-norm (”2-norm) of a specific closed-loop transfer function and satisfying an ”2-norm (H∞-norm) constraint on the same or another closed-loop transfer function. Following this, the second stage derives the sliding surface and thereby the control law associated with the particular state feedback designed in the first stage by using one of an approach developed for this purpose. We present a numerical example to demonstrate the remarkable performance of the proposed scheme.

Argha, A, Ye, L, Su, SW, Nguyen, H & Celler, BG 2016, 'Heart rate regulation during cycle-ergometer exercise using damped parameter estimation method', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, Florida, United States, pp. 2676-2679.
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© 2016 IEEE.This paper is devoted to the problem of heart rate regulation using a model-based control strategy and a realtime damped parameter estimation scheme. The controller is a time-varying integral sliding mode controller. A recursive damped parameter estimation method is also developed, by incorporation of a weighting upon the one-step parameter variation, which in contrast to the conventional parameter estimation schemes (e.g. recursive least squares (RLS) method) can avoid the occurrence of the so-called blowup phenomena. The calculated control signals are transmitted to the subjects employing a synchronized biofeedback mechanism. The proposed control and estimation scheme were experimentally verified using twelve healthy male subjects and the results demonstrated that the designed scheme is able to regulate the HR of the exercising subjects to a predetermined HR profile preventing overshooting in the HR responses.

Argha, A, Ye, L, Su, SW, Nguyen, H & Celler, BG 2016, 'Real-time modelling of heart rate response during exercise using a novel constrained parameter estimation method', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, Florida, United States, pp. 2680-2683.
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© 2016 IEEE.This paper is devoted to the problem of real-time heart rate (HR) response modelling during treadmill exercise. A novel recursive constrained parameter estimation method is developed which in contrast to the conventional parameter estimation schemes (e.g. recursive least squares (RLS) method) can avoid the occurrence of the so-called blowup phenomena. By incorporation of a weighting upon 1) parameter variation relative to a priori HR response knowledge, 2) one-step parameter variation, into the objective function, an estimation scheme is obtained that in the absence of exciting data can avoid blowup. The proposed estimation scheme were experimentally verified using eight healthy male subjects and the results demonstrated that the designed scheme is able to identify the HR response of the exercising subjects in a real-time manner. As HR response is naturally a time-varying dynamics, the proposed online modelling method is of importance for the HR regulation during exercises, using a feedback controller with a desirable level of performance.

Attwater, DJ, Stenzel, MH, Min, EH, Zhao, J, Hutvagner, GJ & Ting, SRSIMON 2016, 'Cationic Polymers based on Fructose and Galactose Moieties for Nucleic Acids Delivery', 36th Australasian Polymer Symposium, Lorne, Australia.
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Cationic polymers and glycopolymers were synthesised using the RAFT technique. Combining cationic polymers with glycopolymers has great potential in targeted nucleic acid delivery.1,2 However, many obstacles prevent the use of cationic glycopolymers as vectors including low success in nucleic acid delivery and high toxicity of the cationic polymer. This project aims to investigate RAFT synthesis of cationic glycopolymers with galactose or fructose carbohydrates, their binding ability with their specific lectins and with negatively charged nucleic acids. The cationic polymer poly(2-(dimethylamino)ethyl methacrylate) (PDMAEMA) was synthesised using RAFT polymerisation. The galactose monomer, 2-(2’,3’,4’,6’-tetra-O-acetyl-β-D-galactosyloxy)ethyl methacrylate (AcGalEMA), and the fructose monomer, 1-O-methacryloyl-2,3:4,5-di-O-isopropylidene--D-fructopyranose (1-O-MAiPFru)3, were polymerised with PDMAEMA to form cationic glycopolymers. Chain extension was confirmed using proton nuclear magnetic spectroscopy (NMR) and gel permeation chromatography (GPC). Gel permeation chromatography was also performed to determine the polydispersity index (uniformity) of the polymers. The protected glycopolymer blocks were modified by deacetylation of the galactose block and acid deprotection of the fructose block. Characterisation of the modified cationic glycopolymers was achieved using proton nuclear magnetic spectroscopy for confirmation of deacetylation/deprotection, and dynamic light scattering to determine the sizes of the diblock copolymers. The zeta potential (ionic charge) of the diblock copolymers was recorded. Aggregation assays between the cationic glycopolymers and plant lectins were assessed. The galactose-containing glycopolymers were conjugated with peanut agglutinin lectin and the fructose-containing glycopolymers were conjugated with lectin from Ulex europaeus. The assays were analysed using dynamic light spectroscopy and ultraviolet-visible spectroscopy. Complexa...

Barforoushi, AH, Shemesh, J, Farbehi, N, Asadnia, M, Yeoh, GH, Nordon, RE & Warkiani, ME 2016, 'A rapid microfluidic stamping device for studying cardiac stem cells and endothelial cells co-culture', 20th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2016, pp. 1069-1070.
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Many biological processes in the body are regulated by synchronized activity between two cell types. Recent advances in cell μcontact printing have facilitated the in-vitro study of homotypic and heterotypic cell-cell interaction. However, these techniques are still complicated to perform and are seldom used by biologists. We report here development of a novel microfluidic stamping device for patterning two adherent cell lines with well-defined interlacing configurations to study cell-cell spatial interactions. To demonstrate the stamp's capabilities, we developed an in-vitro model of endothelial and cardiac mesenchymal stem cell interactions, which are thought to regulate coronary repair after myocardial injury.

Beck, D, Thoms, J, Palu, C, Herold, T, Shah, A, Olivier, J, Boelen, L, Huang, Y, Chacon, D, Brown, A, Babic, M, Hahn, C, Perugini, M, Zhou, X, Huntly, B, Berdel, W, Woermann, B, Buechner, T, Hiddemann, W, Bohlander, S, Scott, H, Lewis, I, D'Andrea, R, Wong, J & Pimanda, J 2016, 'Integrative analysis of lincrna expression in 922 acute myeloid leukemia patients reveals multiple prognostic gene signatures', Haematologica, pp. 208-209.

Bigdeli, B, Delavari, B, Daryan, SS, Saboury, AA & Goliaei, B 2016, 'Biophysical and Molecular Investigation of the Interaction between Enterolactone and Human Serum Albumin', BIOPHYSICAL JOURNAL, 60th Annual Meeting of the Biophysical-Society, CELL PRESS, Los Angeles, CA, pp. 49A-49A.
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Boddupalli, A, Hossain, MD, Gozasht, F, Ling, SSH & Sanagavarapu, AM 2016, 'Techniques for RF Localization of Wireless Capsule Endoscopy', Proceedings of the 2016 18th International Conference on Electromagnetics in Advanced Applications, ICEAA 2016, International Conference on Electromagnetics in Advanced Applications, IEEE, Cairns, pp. 856-859.
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Candra, H, Yuwono, M, Rifai Chai, Nguyen, HT & Su, S 2016, 'Classification of facial-emotion expression in the application of psychotherapy using Viola-Jones and Edge-Histogram of Oriented Gradient.', 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, United States, pp. 423-426.
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Psychotherapy requires appropriate recognition of patient's facial-emotion expression to provide proper treatment in psychotherapy session. To address the needs this paper proposed a facial emotion recognition system using Combination of Viola-Jones detector together with a feature descriptor we term Edge-Histogram of Oriented Gradients (E-HOG). The performance of the proposed method is compared with various feature sources including the face, the eyes, the mouth, as well as both the eyes and the mouth. Seven classes of basic emotions have been successfully identified with 96.4% accuracy using Multi-class Support Vector Machine (SVM). The proposed descriptor E-HOG is much leaner to compute compared to traditional HOG as shown by a significant improvement in processing time as high as 1833.33% (p-value = 2.43E-17) with a slight reduction in accuracy of only 1.17% (p-value = 0.0016).

Cao, K, Ye, L, Alqudah, HM, Szymanski, J, Zhou, J & Su, SW 2016, 'Dynamical estimation of key cardiac-respiratory variables by using commercialized wearable sensors', Proceedings of the 3rd IASTED International Conference on Telehealth and Assistive Technology, TAT 2016, IASTED Telehealth and Assistive Technology, ACTA Press, Zurich, Switzerland, pp. 1-4.
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This paper investigates the estimation of key cardiac-respiratory variables (e.g.,$VO_2$) by using commercialised wearable sensors such as SensorTag and iPhone. The main aim of this study is to use inexpensive and user-friendly wearable sensors rather than expensive and cumbersome equipment (e.g., metabolic analyser). This study also aims to explore the possibility of using only embedded sensors of smart-phone to dynamically estimate oxygen consumption during moderate exercises. The major focus of this research is the modelling of the linear dynamic component. In order to capture the variance of linear dynamic characteristics (e.g., the time constant and steady state gain), we proposed a least square estimation algorithm equipped with automated equilibrium detection function. Finally, the effectiveness of the proposed approaches has been well demonstrated by experimental results.

Chai, R, Naik, G, Nguyen, TN, Ling, SH, Tran, Y & Nguyen, HT 2016, 'Selecting Optimal Electroencephalography Channels for Mental Tasks Classification: An approach using ICA', 2016 IEEE Congress on Evolutionary Computation (CEC), IEEE Congress on Evolutionary Computation, IEEE, Vancouver, Canada, pp. 1331-1335.
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This paper presents a systematic method to select optimal electroencephalography (EEG) channels for three mental tasks-based brain-computer interface (BCI) classification. A blind source separation (BSS) technique based on independent component analysis (ICA) with its back-projecting of the scalp map was used for selecting the optimal EEG channels. The three mental tasks included: mental letter composing, mental arithmetic and mental Rubik's cube rolling. Based on a power spectral density (PSD), the features of the two-channel EEG data were extracted, and then were classified by Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map showed that the prominent channels could be selected for dominant features from original six EEG channels (C3, C4, P3, P4, O1, O2) to four dominant channels (P3, O1, C4, O2) with the best two EEG channels selection at O1&C4. Two channel combinations classification yielded to the best two EEG channels of O1&C4 with an accuracy of 76.4%, followed by P3&O2 with an accuracy of 74.5%; P3&C4 with an accuracy of 71.9% and O1&O2 with an accuracy of 70%.

Chai, R, Tran, Y, Naik, GR, Nguyen, TN, Ling, SH, Craig, A & Nguyen, HT 2016, 'Classification of EEG-based Mental Fatigue using Principal Component Analysis and Bayesian Neural Network', Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2016 (EMBC), International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, pp. 4654-4657.
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This paper presents an electroencephalography (EEG) based- classification of between pre- and post- mental load tasks for mental fatigue detection from 65 healthy participants. During the data collection, eye closed and eye open tasks were collected before and after conducting the mental load tasks. For the computational intelligence, the system uses the combination of principal component analysis (PCA) as the dimension reduction method of the original 26 channels of EEG data, power spectral density (PSD) as feature extractor and Bayesian neural network (BNN) as classifier. After applying the PCA, the dimension of the data is reduced from 26 EEG channels in 6 principal components (PCs) with above 90% of information retained. Based on this reduced dimension of 6 PCs of data, during eyes open, the classification pre-task (alert) vs. post-task (fatigue) using Bayesian neural network resulted in sensitivity of 76.8 %, specificity of 75.1% and accuracy of 76%. Also based on data from the 6 PCs, during eye closed, the classification between pre- and post- task resulted in a sensitivity of 76.1%, specificity of 74.5% and accuracy of 75.3%. Further, the classification results of using only 6 PCs data are comparable to the result using the original 26 EEG channels. This finding will help in reducing the computational complexity of data analysis based on 26 channels of EEG for mental fatigue detection.

Deghat, M, Lampiri, E & Bishop, AN 2016, 'Sensor Fault Detection for the Roll Dynamic Model of a Generic Delta-Wing Aircraft', Proceedings of the 2016 Australian Control Conference (AUCC), Australian Control Conference, IEEE, Newcastle, Australia, pp. 317-322.
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This paper proposes a fault detection algorithm for the roll dynamic model of a generic delta-wing aircraft. It is assumed that the system model has some noise/uncertainties and the measured roll angle and roll rate used by the control law are faulty/under attack. The proposed fault detection algorithm employs a fault detection estimator and adaptive thresholds to detect the occurrence of a fault in the sensor measurements. Simulation results are presented to show the performance of the proposed algorithm.

Gozasht, F, Boddupalli, A, Ling, SSH & Mohan, AS 2016, 'Singly-fed shaped planar inverted-F antenna for circular polarization', Proceedings of the AMS 2016 - 2016 2nd Australian Microwave Symposium, Conference Proceedings, Australian Microwave Symposium, IEEE, Adelaide, South Australia, Australia, pp. 33-34.
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© 2016 IEEE.We propose a singly-fed planar inverted-F antenna (PIFA), with double-shorting planes and a tapered patch for radiating circular polarization. FEKO® simulations are employed to optimize the geometry of the antenna. The antenna prototype resonates at 2.52GHz and has a measured impedance bandwidth of around 240MHz. The calculated 3dB axial ratio bandwidth falls within the impedance bandwidth, demonstrating satisfactory circular polarization characteristics.

Ho-Pham, LT, Lai, TQ, Nguyen, MTT & Nguyen, T 2016, 'Relationship Between Body Mass Index And Percent Body Fat In Vietnamese: Implications For The Diagnosis Of Obesity', Osteoporosis International, Springer Verlag (Germany), pp. S734-S735.

Hosseini, S & Al-Jumaily, A 2016, 'Active force control system for tremor suppression in elbow joint', IRIS 2016 - 2016 IEEE 4th International Symposium on Robotics and Intelligent Sensors: Empowering Robots with Smart Sensors, IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), IEEE, Japan, pp. 140-145.
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© 2016 IEEE. Tremor is a neurological disorder characterized by involuntary oscillations. Difficulties associated with tremor in patients with Parkinson's disease have motivated the researchers to work on developing various methods for tremor suppression. Active Force Control (AFC) method for tremor attenuation in human body parts is considered in this work. This paper proposes a new AFC system based on a piezoelectric actuator. A one-degree-of-freedom musculoskeletal model of the elbow joint with two links and one joint is developed. The model includes two muscles, biceps, and triceps as the flexor and the extensor of the elbow joint. First, simulation of the tremor generation in the model is performed and then the performance of AFC system for suppressing elbow joint tremor is investigated. A single piezoelectric actuator is embedded in AFC system for controlling the behaviour of the classic proportional-derivative controller. MATLAB Simulink is used to analyse the model. Results show that the AFC-based system with a piezoelectric actuator and a PD controller is very effective in suppressing the human hand tremor.

Ibrahim, MFI & Al-Jumaily, AA 2016, 'ICA based feature learning and feature selection', International Conference on Electronic Devices, Systems, and Applications, International Conference on Electronic Devices, Systems and Applications, IEEE, Ras Al Khaimah, United Arab Emirates, pp. 1-4.
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© 2016 IEEE. Feature extraction is playing a major role in bio signal processing. Feature identification and selection has two approaches. The common approach is engineering handcraft which is based on user experience and application area. While the other approach is feature learning that based on making the system identify and select the best features suit the application. The idea behind feature learning is to avoid dealing with any feature extraction or reduction algorithms and to train the suggested model on learning with avoiding the exposure to feature extraction which is mainly based on researcher experience. In this paper, Independent component analysis (ICA) will be implemented as a feature learning technique to learn the model extract the features from the input data. Deep learning approach will be proposed by implementing ICA to learn features. In the proposed model, the raw data will be read then represented by using different signal representation as Spectrogram, Wavelet and Wavelet Packet. Then, the new represented data will be fed to Independent component analysis layer to generate features and finally, the performance of the suggested scheme will be evaluated by applying different classifiers such as Support Vector Machine, Extreme Learning Machine and Discriminate Analysis. And As an improving step for the results, classifier fusion layer will be implemented to select the most accurate result for both training and testing set. Classifier fusion layer resulted in a promising training and testing accuracies. On the other side, Feature Selection is the process of selecting subset of features from a pool of features.

Ibrahim, MFI & Al-Jumaily, AA 2016, 'PCA indexing based feature learning and feature selection', 2016 8th Cairo International Biomedical Engineering Conference, CIBEC 2016, Cairo International Biomedical Engineering Conference, pp. 68-71.
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© 2016 IEEE. Feature extraction is playing a major role in bio signal processing. Feature identification and selection has two approaches. The common approach is engineering handcraft which is based on user experience and application area. While the other approach is feature learning that based on making the system identify and select the best features suit the application. The idea behind feature learning is to avoid dealing with any feature extraction or reduction algorithms and to train the suggested model. In this paper, principal component analysis (PCA) will be implemented as a feature learning technique to learn the model extract the features from the input data. Deep learning approach will be proposed by implementing PCA to learn features. In the proposed model, the raw data will be read then represented by using different signal representation as Spectrogram, Wavelet and Wavelet Packet. The new represented data will be fed to principal component analysis layer to generate features and finally, the performance of the suggested scheme will be evaluated by applying different classifiers such as Support Vector Machine, Extreme Learning Machine and Discriminate Analysis classification. As an improving step for the results, classifier fusion layer will be implemented to select the most accurate result for both training and testing set. Classifier fusion layer resulted in a promising training and testing accuracies. On the other side, Feature Selection is the procedure of picking out or choosing subgroup of features from a pool of features. Indices may be a good indicator or guider in determining which features to be combined with each other in order to avoid redundancy.

Khushaba, RN, Al-Ani, A, Al-Timemy, A & Al-Jumaily, A 2016, 'A Fusion of Time-Domain Descriptors for Improved Myoelectric Hand Control', PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), IEEE Symposium Series on Computational Intelligence (IEEE SSCI), IEEE, Athens, GREECE.
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Khushaba, RN, Al-Timemy, A, Al-Ani, A & Al-Jumaily, A 2016, 'Myoelectric feature extraction using temporal-spatial descriptors for multifunction prosthetic hand control.', 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, Florida, United States, pp. 1696-1699.
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We tackle the challenging problem of myoelectric prosthesis control with an improved feature extraction algorithm. The proposed algorithm correlates a set of spectral moments and their nonlinearly mapped version across the temporal and spatial domains to form accurate descriptors of muscular activity. The main processing step involves the extraction of the Electromyogram (EMG) signal power spectrum characteristics directly from the time-domain for each analysis window, a step to preserve the computational power required for the construction of spectral features. The subsequent analyses involve computing 1) the correlation between the time-domain descriptors extracted from each analysis window and a nonlinearly mapped version of it across the same EMG channel; representing the temporal evolution of the EMG signals, and 2) the correlation between the descriptors extracted from differences of all possible combinations of channels and a nonlinearly mapped version of them, focusing on how the EMG signals from different channels correlates with each other. The proposed Temporal-Spatial Descriptors (TSDs) are validated on EMG data collected from six transradial amputees performing 11 classes of finger movements. Classification results showed significant reductions (at least 8%) in classification error rates compared to other methods.

Ly, QT, Handojoseno, AMA, Gilat, M, Nguyen, TN, Chai, R, Tran, Y, Lewis, SJG & Nguyen, HT 2016, 'Detection of Gait Initiation Failure in Parkinson’s Disease Patients using EEG Signals', Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2016, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, USA, pp. 1599-1602.
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Gait Initiation Failure (GIF) is one of the most disabling gait disturbances seen in advanced Parkinson’s disease (PD). Gait Initiation is a complex motor task that requires motor and cognitive processing to enable the correct selection, timing and scaling of movement. Failure to initiate the first step often precipitates falls and leads to significant morbidity. However, the brain mechanisms underlying GIF remain unknown. This study utilized an ambulatory electroencephalography (EEG) technique to investigate the brain dynamic changes underlying GIF and aims to detect the occurrence of GIF in four PD patients. We sought to determine whether episodes of GIF might be associated with a characteristic brain signal that could be detected by surface EEG. This preliminary investigation analyzed the EEG signals through power spectra density (PSD) and centroid frequency (CF) to show that the GIF episodes were associated with significant increases in the high beta band (21-38Hz) across the central, frontal, occipital and parietal EEG sites. By implementing PSD and CF as input features with two-layer Back Propagation neural networks as a classifier, the proposed system was able to detect GIF events with a classification performance of 84.27% sensitivity and 84.80% accuracy. This is the first study to show cortical dynamic changes associated with GIF in Parkinson’s disease, providing valuable information to enhance the performance of future GIF detection that could be translated into clinical practice.

Ly, QT, Handojoseno, AMA, Gilat, M, Nguyen, TN, Chai, R, Tran, Y, Lewis, SJG & Nguyen, HT 2016, 'Identifying Montages that Best Detect the Electroencephalogram Power Spectrum Alteration during Freezing of Gait in Parkinson’s Disease Patients', Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2016 EMBC, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, USA, pp. 6094-6097.
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Our research team has previously used four Electroencephalography (EEG) leads to successfully detect and predict Freezing of Gait (FOG) in Parkinson’s disease (PD). However, it remained to be determined whether these four sensor locations that were arbitrarily chosen based on their role in motor control are indeed the most optimal for FOG detection. The aim of this study was therefore to determine the most optimal location and combination of sensors to detect FOG amongst a 32-channel EEG montage using our EEG classification system. EEG measures, including power spectral density, centroid frequency and power spectral entropy, were extracted from 7 patients with PD and FOG during a series of Timed up and Go tasks. By applying a feed-forward neural networks to classify EEG data, the obtained results showed that even a small number of electrodes suffice to construct a FOG detector with expected performance, which is comparable to the use of a full 32 channels montage. This finding therefore progresses the realization of a FOG detection system that can be effectively implemented on a daily basis for FOG prevention, improving the quality of life for many patients with PD.

Manuel, IL, Bishop, AN, Anderson, BDO & Yu, C 2016, 'Controlling the shape and scale of triangular formations using landmarks and bearing-only sensing', Proceedings of the Control Conference (CCC), 2016 35th Chinese, Chinese Control Conference, IEEE, Chengdu, China, pp. 7532-7537.
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© 2016 TCCT.This work considers the scenario where three agents that can sense only bearings use two landmarks to control their formation shape. We define a method of relating the known distance separating the landmarks back to the edge lengths of the triangular formation. The result is used to define a formation control law that incorporates inter-agent distance constraints. We prove a strong exponential convergence result and show how one can extend the controller such that global stability from any initial position is possible.

Masood, A & Al-Jumaily, A 2016, 'Semi-advised learning model for skin cancer diagnosis based on histopathalogical images.', 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, Florida, United States, pp. 631-634.
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Computer aided classification of skin cancer images is an active area of research and different classification methods has been proposed so far. However, the supervised classification models based on insufficient labeled training data can badly influence the diagnosis process. To deal with the problem of limited labeled data availability this paper presents a semi advised learning model for automated recognition of skin cancer using histopathalogical images. Deep belief architecture is constructed using unlabeled data by making efficient use of limited labeled data for fine tuning done the classification model. In parallel an advised SVM algorithm is used to enhance classification results by counteracting the effect of misclassified data using advised weights. To increase generalization capability of the model, advised SVM and Deep belief network are trained in parallel. Then the results are aggregated using least square estimation weighting. The proposed model is tested on a collection of 300 histopathalogical images taken from biopsy samples. The classification performance is compared with some popular methods and the proposed model outperformed most of the popular techniques including KNN, ANN, SVM and semi supervised algorithms like Expectation maximization algorithm and transductive SVM based classification model.

Nurhanim, K, Elamvazuthi, I, Izhar, LI, Ganesan, T & Su, SW 2016, 'Development of A Model For sEMG based Joint-Torque Estimation using Swarm Techniques', Proceedings of the 2nd IEEE International Symposium on Robotics and Manufacturing Automation, IEEE International Symposium on Robotics and Manufacturing Automation, IEEE, Ipoh, Malaysia, pp. 1-6.
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Over the years, numerous researchers have explored the relationship between surface electromyography (sEMG) signal with joint torque that would be useful to develop a suitable controller for rehabilitation robot. This research focuses on the transformation of sEMG signal by adopting a mathematical model to find the estimated joint torque of knee extension. Swarm techniques such as Particle Swarm Optimization (PSO) and Improved Particle Swarm Optimization (IPSO) were adapted to optimize the mathematical model for estimated joint torque. The correlation between the estimated joint torque and actual joint torque were determined by Coefficient of Determination (R2) and fitness value of Sum Squared Error (SSE). The outcome of the research shows that both the PSO and IPSO have yielded promising results.

Rafeie, M, Zhang, J, Asadnia, M, Li, W & Warkiani, ME 2016, 'Multiplexing slanted spiral microchannels for ultra-fast blood plasma separation', 20th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2016, pp. 906-907.
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The ability to efficiently fractionate blood into its individual components is crucial for therapeutic and diagnostic purposes. Here, we developed an inertial microfluidic system for cell focusing and blood plasma separation. First, polystyrene beads and blood cells were used to investigate the inertial focusing performance of a single slanted spiral microchannel as a function of particle size, flow rate, and the haematocrit (HCT) concentration. Next, we built a high-throughput system consisted of 16 interconnected spiral channels which can process diluted samples at 24 mL/min. The proposed multiplexed system surmounts shortcomings of previously-reported microfluidic systems concerning throughput, yield and operation efficiency.

Rana, M, Li, LI & Su, SW 2016, 'Cyber Attack Protection and Control in Microgrids Using Channel Code and Semidefinite Programming', IEEE Power and Energy Society General Meeting, IEEE Power and Energy Society General Meeting, IEEE, USA.
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The smart grid has been considered as a nextgeneration power system to modernize the traditional grid to improve its security, connectivity and sustainability. Unfortunately, the grid is susceptible to malicious cyber attacks, which can create serious technical, economical and control problems in power network operations. In contrast to the traditional cyber attack minimization techniques, this paper proposes a recursive systematic convolutional (RSC) code and Kalman filter based method in the context of microgrids. Specifically, the proposed RSC code is used to add redundancy in the microgrid states, and the log maximum a posterior is used to recover the state information which is affected by random noises and cyber attacks. Once the estimated states are obtained, a semidefinite programming based optimal feedback controller is proposed to regulate the system states. Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate and control the system states.

Rana, M, Li, LI & Su, SW 2016, 'Distributed Condition Monitoring of Renewable Microgrids Using Adaptive-then-Combine Algorithm', IEEE Power and Energy Society General Meeting, IEEE Power and Energy Society General Meeting, IEEE, USA.
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Rana, MM, Li, L & Su, SW 2016, 'Distributed dynamic state estimation considering renewable generation and packet losses', 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, International Conference on Control, Automation, Robotics and Vision, IEEE, Phuket, Thailand, pp. 1-6.
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© 2016 IEEE.The penetration of renewable distributed energy resources such as wind turbine has been dramatically integrated in distribution networks due to clean, sustainable, and economical green energy. Due to intermittent wind speed, the power generation patterns vary which can risk the distribution network operation. So, it is intrinsically required to monitor the wind turbines in a distributed way. This paper presents an adaptive-then-combine distributed dynamic approach for monitoring the grid under lossy communication link between the wind turbines and energy management system. Firstly, the wind turbine is represented by a state-space linear equation, with sensors deployed to obtain system state information. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining estimation results with weighting factors which are calculated by minimizing estimation error covariance based on semi-definite programming. Finally, the convergence analysis indicates that estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the turbine model. The research is valuable for green energy technologies, households and interconnected industrial information societies, and also provides the knowledge towards a smart and secure green energy future.

Rana, MM, Li, L & Su, SW 2016, 'Distributed dynamic state estimation over a lossy communication network with an application to smart grids', 2016 IEEE 55th Conference on Decision and Control, CDC 2016, IEEE Conference on Decision and Control, IEEE, Las Vegas, USA, pp. 6657-6662.
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© 2016 IEEE.In contrast to the traditional centralised power system state estimation methods, this paper investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. Specifically, the power system incorporating microgrids is modelled as a state-space linear equation where sensors are deployed to obtain measurements. Basically, the sensing information is transmitted to the energy management system through a lossy communication network where measurements are lost. As the system states are unavailable, so the estimation is essential to know the overall operating conditions of the electricity network. The proposed estimator is based on the mean squared error between the actual state and its estimate. To obtain the distributed estimation, the optimal local and neighbouring gains are computed to reach a consensus estimation after exchanging their information with the neighbouring estimators. Then the convergence of the developed algorithm is theoretically proved. Afterwards, a distributed controller is designed based on the semidefinite programming approach. Simulation results demonstrate the accuracy of the developed approaches under the condition of missing measurements.

Rana, MM, Li, L & Su, SW 2016, 'Microgrid protection and control through reliable smart grid communication systems', Proceedings of the 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, International Conference on Control, Automation, Robotics and Vision, IEEE, Phuket, Thailand, pp. 1-6.
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© 2016 IEEE.Due to dramatically rising energy demand worldwide power system is often run near the operational and technical limits, where unexpected trivial disturbances can cause possibly massive blackouts. Cyber attacks on smart grid communication networks are one of the impending threats to cause large-scale cascading outage. In contrast to the traditional cyber attack protection techniques, this paper presents a recursive systematic convolutional code based defending technique from the signal processing perspective. This code introduces redundancy in the system for protecting the grid information. Furthermore, an optimal control law is designed to stabilize the power network. Specifically, the performance index for control is converted to a convex semidefinite programming problem. The proposed controller can work well for any initial values. The efficacy of the developed approach is verified through numerical simulations. Results show that the proposed strategy has stronger attack protection performance and the controller can stabilize the grid in a fairly short time. This approach provides a fundamental framework for the design of the smart grid energy management system and reliable communication infrastructure scheme with renewable integration applications.

Roxby, DN, Tran, N, Yu, P-L & Nguyen, HT 2016, 'Effect of Growth Solution, Membrane Size and Array Connection on Microbial Fuel Cell Power Supply for Medical Devices', Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2016 EMBC, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, USA, pp. 1946-1949.
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Implanted biomedical devices typically last a number of years before their batteries are depleted and a surgery is required to replace them. A Microbial Fuel Cell (MFC) is a device which by using bacteria, directly breaks down sugars to generate electricity. Conceptually there is potential to continually power implanted medical devices for the lifetime of a patient. To investigate the practical potential of this technology, H-Cell Dual Chamber MFCs were evaluated with two different growth solutions and measurements recorded for maximum power output both of individual MFCs and connected MFCs. Using Luria-Bertani media and connecting MFCs in a hybrid series and parallel arrangement with larger membrane sizes showed the highest power output and the greatest potential for replacing implanted batteries.

San, PP, Ling, SH & Nguyen, HT 2016, 'Deep Learning Framework for Detection of Hypoglycemic Episodes in Children with Type 1 Diabetes', Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2016 (EMBC), International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, pp. 3503-3506.
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Most Type 1 diabetes mellitus (T1DM) patients have hypoglycemia problem. Low blood glucose, also known as hypoglycemia, can be a dangerous and can result in unconsciousness, seizures and even death. In recent studies, heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal are found as the most common physiological parameters to be effected from hypoglycemic reaction. In this paper, a state-of-the-art intelligent technology namely deep belief network (DBN) is developed as an intelligent diagnostics system to recognize the onset of hypoglycemia. The proposed DBN provides a superior classification performance with feature transformation on either processed or un-processed data. To illustrate the effectiveness of the proposed hypoglycemia detection system, 15 children with Type 1 diabetes were volunteered overnight. Comparing with several existing methodologies, the experimental results showed that the proposed DBN outperformed and achieved better classification performance.

San, PP, Ling, SH, Chai, R, Tran, Y, Craig, A & Nguyen, HT 2016, 'EEG-based Driver Fatigue Detection using Hybrid Deep Generic Model', Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2016 EMBC, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, USA, pp. 800-803.
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Classification of electroencephalography (EEG)- based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)- based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG.

Savitha, R, Chan, KY, San, PP, Ling, SH & Suresh, S 2016, 'A Hybrid Deep Boltzmann Functional Link Network for Classification Problems', PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), IEEE Symposium Series on Computational Intelligence (IEEE SSCI), IEEE, Athens, GREECE.
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Shi, Y, Tuan, HD & Su, S 2016, 'Nonconvex spectral algorithm for solving BMI on the reduced order H∞ control', Proceedings - 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016, IEEE International Conference on Control System, Computing and Engineering, IEEE, Batu Ferringhi, Malaysia, pp. 316-319.
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© 2016 IEEE. The design of reduced-order H∞ control can be transformed into an optimization problem with bilinear matrix inequality (BMI) constraints, which is an NP-hard problem. We propose a method to equivalently transfer the BMI constraint into a convex LMI constraint plus a matrix-rank constraint. The optimization with matrix-rank constraint is iteratively solved by a sequence of semidefinite programming (SDP) problems. Simulations on several benchmark systems show that our algorithm is practical and efficient.

Shi, Y, Tuan, HD, Su, SW & Savkin, AV 2016, 'Multiple matrix rank constrained optimization for optimal power flow over large scale transmission networks', SMARTGREENS 2016 - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems, International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), SCITEPRESS – Science and Technology Publications, Rome, Italy, pp. 384-389.
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© Copyright 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.The optimal power flow (OPF) problem for power transmission networks is an NP-hard optimization problem with numerous quadratic equality and indefinite quadratic inequality constraints on bus voltages. The existing nonlinear solvers often fail in yielding a feasible solution. In this paper, we follow our previously developed nonsmooth optimization approach to address this difficult large-scale OPF problem, which is an iterative process to generate a sequence of improved solutions that converge to an optimal solution. Each iteration calls an SDP of a moderate dimension. Intensive simulations for OPF over networks with a large number of buses are provided to demonstrate the efficiency of our approach.

Su, SW, Ye, L, Lei, D & Nguyen, HT 2016, 'An online recursive autocalibration of triaxial accelerometer', Proceedings of the 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, Florida, USA, pp. 2038-2041.
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In this paper, we proposed a novel method for autocalibration of triaxial Micro-Electro-Mechanical systems (MEMS) accelerometer that does not require any sophisticated laboratory facilities. In particular, this method is an online calibration method which can be conveniently implemented with the accuracy of MEMS accelerometer being significantly improved. The procedure exploits the fact that the output vector of the accelerometer must match the local gravity in static state condition. To achieve online calibration, the model as well as the cost function are linearized at the beginning, and an online recursive method is then utilized to identify the unknown parameters and remove the bias caused by linearization. This online recursive method is based on damped recursive least square estimation (DRLS), which can significantly reduce the calculation complexity comparing to nonlinear optimization method. In addition, the unknown parameters can be solved in a short time and the estimated parameters can remain stable during calibration. Experimentally, this method was tested by comparing the output results before and after calibration in different condition. It showed that the output, after calibrated by the proposed method, is more accurate with respect to raw output using default factory parameters.

Tran, V & Al-Jumaily, A 2016, 'Non-Contact Real-Time Estimation of Intrapulmonary Pressure and Tidal Volume for Chronic Heart Failure Patients', 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Lake Buena Vista (Orlando), Florida USA, pp. 3564-3567.
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Long-term continuous patient monitoring is required in many health systems for monitoring and analytical diagnosing purposes. It has been recognized that these types of monitoring systems have shortcomings related to patient comfort and/or functionality. Non-contact monitoring systems have been developed to address some of these shortcomings. One of such systems is non-contact physiological vital signs assessments for Chronic Heart Failure (CHF) patients. This paper presents a novel pulmonary ventilation model that defines the relationship between the intrapulmonary pressure and the chest displacement. A novel intrapulmonary pressure and tidal volume estimation algorithm is also proposed. A database consisting of twenty CHF patients with New York Heart Association (NYHA) Heart Failure Classification Class II & III; whose underwent full Polysomnography (PSG) analysis for diagnosis of sleep apnea, disordered sleep, or both, was selected for the verification of the proposed model and algorithm. The proposed algorithm analyzes the non-contact sensor data and estimate the patient’s intrapulmonary pressure and tidal volume. The output of the algorithm is compared with the gold-standard PSG recordings. Across all twenty CHF patients’ recordings with mean recorded sleep duration of 7.76 hours, the tidal volume estimation median accuracy achieved 83.13% with a median error of 57.32 milliliters. A potential application would be non-contact continuous monitoring of intrapulmonary pressure and tidal volume during sleep in the home.

Wang, Y, Yu, F, Bishop, A & Yu, C 2016, 'Adaptive tracking algorithm for a rigid spacecraft with input saturation', Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016, Chinese Control and Decision Conference, IEEE, Yinchuan, China, pp. 1605-1610.
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© 2016 IEEE. In this paper, the tracking problem for a rigid spacecraft with input saturation is investigated. Due to the complexity of the dynamic of a rigid spacecraft, a hierarchical control scheme, dividing the model of a rigid spacecraft into translational level and attitude level, is designed. The controllers for translational dynamic and attitude dynamic of the six-freedom-degrees spacecraft are successively proposed respectively. The input saturation is also considered in designing the two controllers. For translational level, a kind of special saturation function is employed to get the controller whose amplitude is constrained. For the attitude dynamic, a new adaptive quaternion-based control scheme is proposed to withstand the effect of bounded uncertain items and input saturation. Finally, simulation results are provided to show the effectiveness of the proposed scheme.

Wedyan, M & Al-Jumaily, A 2016, 'Early diagnosis autism based on upper limb motor coordination in high risk subjects for autism', IRIS 2016 - 2016 IEEE 4th International Symposium on Robotics and Intelligent Sensors: Empowering Robots with Smart Sensors, IEEE International Symposium on Robotics and Intelligent Sensors, IEEE, Tokyo, Japan, pp. 13-18.
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© 2016 IEEE. Detection of autism in the beginning of infant life is very important to increase child treatment outcomes. There are many signs that help to diagnosis autism based on observation of great difficulty in communicating, delayed speech, and difficulty in forming relationships with other people. Attempts to discover other early symptoms and signs do not stop and tried to reach the early detection of autism. One of these signs is based on examination of child motor movements, such as kinematic for both gait and upper limb. This study aims to know how a basic upper-limb motion would be helpful to accurately classify High-Risk (HR) Infants for autism and infants with Low-Risk (LR) for autism. Also, the paper examined the task that throws a small ball into a transparent plastic tub followed by inserting the ball into a clear tube open at both ends performed by HR and LR subjects between the ages of 12 and 36 months. Then the data was analyzed by using Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The results showed encouraging results in comparison to other state or art.

Wedyan, M & Al-Jumaily, A 2016, 'Upper Limb Motor Coordination based Early Diagnosis in High Risk Subjects for Autism', PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), IEEE Symposium Series on Computational Intelligence (IEEE SSCI), IEEE, Athens, GREECE.
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Wong, GY, Leung, FHF & Ling, SSH 2016, 'Identification of protein-ligand binding site using multi-clustering and support vector machine', IECON Proceedings (Industrial Electronics Conference), Annual Conference of the IEEE Industrial Electronics Society, IEEE, Florence, Italy, pp. 939-944.
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© 2016 IEEE.Multi-clustering has been widely used. It acts as a pre-training process for identifying protein-ligand binding in structure-based drug design. Then, the Support Vector Machine (SVM) is employed to classify the sites most likely for binding ligands. Three types of attributes are used, namely geometry-based, energy-based, and sequence conservation. Comparison is made on 198 drug-target protein complexes with LIGSITECSC, SURFNET, Fpocket, Q-SiteFinder, ConCavity, and MetaPocket. The results show an improved success rate of up to 86%.

Ye, L, Cao, K, Guo, J, Huang, X, Beadle, P, Argha, A, Piccardi, M, Zhang, G & Su, SW 2016, 'Inertial sensor based post fall analysis for false alarming reduction', Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics, IASTED Telehealth and Assistive Technology, Acta Press, Zurich, Switzerland, pp. 36-43.
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One of the major public health problems among elderly people is falling injury. This study investigates fall detection and prevention by using inertial sensors for which the major existing challenging is how to significantly reduce false alarming in order to enhance the acceptance of elderly users during rehabilitation and daily exercises. Different from most existing approaches in the literature, the behavior after falling will be analyzed in details, which can not only greatly reduce false alarming, but also significantly improves the accuracy of the assessment of the severity of falling injuries.

Zhang, T, Su, S & Nguyen, HT 2016, 'The Hybrid Bio-Inspired Aerial Vehicle: Concept and SIMSCAPE Flight Simulation', Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2016 (EMBC), International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, Florida, USA, pp. 2107-2110.
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This paper introduces a Silver Gull-inspired hybrid aerial vehicle, the Super Sydney Silver Gull (SSSG), which is able to vary its structure, under different manoeuvre requirements, to implement three flight modes: the flapping wing flight, the fixed wing flight, and the quadcopter flight (the rotary wing flight of Unmanned Air Vehicle). Specifically, through proper mechanism design and flight mode transition, the SSSG can imitate the Silver Gull’s flight gesture during flapping flight, save power consuming by switching to the fixed wing flight mode during long-range cruising, and hover at targeted area when transferring to quadcopter flight mode. Based on the aerodynamic models, the Simscape, a product of MathWorks, is used to simulate and analyse the performance of the SSSG’s flight modes. The entity simulation results indicate that the created SSSG’s 3D model is feasible and ready to be manufactured for further flight tests.