Exploiting the chaotic behaviour of atmospheric models with reconfigurable architectures

Reconfigurable architectures are becoming mainstream: Amazon, Microsoft and IBM are supporting such architectures in their data centres. The computationally intensive nature of atmospheric modelling is an attractive target for hardware acceleration using reconfigurable computing. Performance of hardware designs can be improved through the use of reduced-precision arithmetic, but maintaining appropriate accuracy is essential. We explore reduced-precision optimisation for simulating chaotic systems, targeting atmospheric modelling, in which even minor changes in arithmetic behaviour will cause simulations to diverge quickly. The possibility of equally valid simulations having differing outcomes means that standard techniques for comparing numerical accuracy are inappropriate. We use the Hellinger distance to compare statistical behaviour between reduced-precision CPU implementations to guide reconfigurable designs of a chaotic system, then analyse accuracy, performance and power efficiency of the resulting implementations. Our results show that with only a limited loss in accuracy corresponding to less than 10% uncertainty in input parameters, the throughput and energy efficiency of a single-precision chaotic system implemented on a Xilinx Virtex-6 SX475T Field Programmable Gate Array (FPGA) can be more than doubled

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review

Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren’t any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts

Paediatric radiology seen from Africa. Part I: providing diagnostic imaging to a young population

Article approval pendingPaediatric radiology requires dedicated equipment, specific precautions related to ionising radiation, and specialist knowledge. Developing countries face difficulties in providing adequate imaging services for children. In many African countries, children represent an increasing proportion of the population, and additional challenges follow from extreme living conditions, poverty, lack of parental care, and exposure to tuberculosis, HIV, pneumonia, diarrhoea and violent trauma. Imaging plays a critical role in the treatment of these children, but is expensive and difficult to provide. The World Health Organisation initiatives, of which the World Health Imaging System for Radiography (WHIS-RAD) unit is one result, needs to expand into other areas such as the provision of maintenance servicing. New initiatives by groups such as Rotary and the World Health Imaging Alliance to install WHIS-RAD units in developing countries and provide digital solutions, need support. Paediatric radiologists are needed to offer their services for reporting, consultation and quality assurance for free by way of teleradiology. Societies for paediatric radiology are needed to focus on providing a volunteer teleradiology reporting group, information on child safety for basic imaging, guidelines for investigations specific to the disease spectrum, and solutions for optimising imaging in children

Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples

Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them

GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals

Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead to misdiagnoses. Hence, noninvasive methods such as electroencephalographic (EEG) recordings of PD patients can be an alternative biomarker. In this study, a deep-learning model is proposed for automated PD diagnosis. EEG recordings of 16 healthy controls and 15 PD patients were used for analysis. Using Gabor transform, EEG recordings were converted into spectrograms, which were used to train the proposed two-dimensional convolutional neural network (2D-CNN) model. As a result, the proposed model achieved high classification accuracy of 99.46% (±0.73) for 3-class classification (healthy controls, and PD patients with and without medication) using tenfold cross-validation. This indicates the potential of proposed model to simultaneously automatically detect PD patients and their medication status. The proposed model is ready to be validated with a larger database before implementation as a computer-aided diagnostic (CAD) tool for clinical-decision support.</jats:p

Survival of HIV-Infected Adolescents on Antiretroviral Therapy in Uganda: Findings from a Nationally Representative Cohort in Uganda

CITATION: Bakanda, C. et al. 2011. Survival of HIV-infected adolescents on antiretroviral therapy in Uganda : findings from a nationally representative cohort in Uganda. PLoS ONE, 6(4): e19261, doi:10.1371/journal.pone.0019261.The original publication is available at http://journals.plos.org/plosoneBackground: Adolescents have been identified as a high-risk group for poor adherence to and defaulting from combination antiretroviral therapy (cART) care. However, data on outcomes for adolescents on cART in resource-limited settings remain scarce. Methods: We developed an observational study of patients who started cART at The AIDS Service Organization (TASO) in Uganda between 2004 and 2009. Age was stratified into three groups: children (≤10 years), adolescents (11-19 years), and adults (≥20 years). Kaplan-Meier survival curves were generated to describe time to mortality and loss to follow-up, and Cox regression used to model associations between age and mortality and loss to follow-up. To address loss to follow up, we applied a weighted analysis that assumes 50% of lost patients had died. Findings: A total of 23,367 patients were included in this analysis, including 810 (3.5%) children, 575 (2.5%) adolescents, and 21 982 (94.0%) adults. A lower percentage of children (5.4%) died during their cART treatment compared to adolescents (8.5%) and adults (10%). After adjusting for confounding, other features predicted mortality than age alone. Mortality was higher among males (p<0.001), patients with a low initial CD4 cell count (p<0.001), patients with advanced WHO clinical disease stage (p<0.001), and shorter duration of time receiving cART (p<0.001). The crude mortality rate was lower for children (22.8 per 1000 person-years; 95% CI: 16.1, 29.5), than adolescents (36.5 per 1000 person-years; 95% CI: 26.3, 46.8) and adults (37.5 per 1000 person-years; 95% CI: 35.9, 39.1). Interpretation: This study is the largest assessment of adolescents receiving cART in Africa. Adolescents did not have cART mortality outcomes different from adults or children. © 2011 Bakanda et al.http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0019261Publisher's versio

High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines.

Hundreds of genetically characterized cell lines are available for the discovery of genotype-specific cancer vulnerabilities. However, screening large numbers of compounds against large numbers of cell lines is currently impractical, and such experiments are often difficult to control. Here we report a method called PRISM that allows pooled screening of mixtures of cancer cell lines by labeling each cell line with 24-nucleotide barcodes. PRISM revealed the expected patterns of cell killing seen in conventional (unpooled) assays. In a screen of 102 cell lines across 8,400 compounds, PRISM led to the identification of BRD-7880 as a potent and highly specific inhibitor of aurora kinases B and C. Cell line pools also efficiently formed tumors as xenografts, and PRISM recapitulated the expected pattern of erlotinib sensitivity in vivo

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.</jats:p

Translational research into gut microbiota: new horizons on obesity treatment: updated 2014

Obesity is currently a pandemic of worldwide proportions affecting millions of people. Recent studies have proposed the hypothesis that mechanisms not directly related to the human genome could be involved in the genesis of obesity, due to the fact that, when a population undergoes the same nutritional stress, not all individuals present weight gain related to the diet or become hyperglycemic. The human intestine is colonized by millions of bacteria which form the intestinal flora, known as gut flora. Studies show that lean and overweight human may present a difference in the composition of their intestinal flora; these studies suggest that the intestinal flora could be involved in the development of obesity. Several mechanisms explain the correlation between intestinal flora and obesity. The intestinal flora would increase the energetic extraction of non-digestible polysaccharides. In addition, the lipopolysaccharide from intestinal flora bacteria could trigger a chronic sub-clinical inflammatory process, leading to obesity and diabetes. Another mechanism through which the intestinal flora could lead to obesity would be through the regulation of genes of the host involved in energy storage and expenditure. In the past five years data coming from different sources established causal effects between intestinal microbiota and obesity/insulin resistance, and it is clear that this area will open new avenues of therapeutic to obesity, insulin resistance and DM2