A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification

Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) neglect the non-Euclidean topology and causal dynamics of brain connectivity across time. In this paper, a deep probabilistic spatiotemporal framework developed based on variational Bayes (DSVB) is proposed to learn time-varying topological structures in dynamic brain FC networks for autism spectrum disorder (ASD) identification. The proposed framework incorporates a spatial-aware recurrent neural network to capture rich spatiotemporal patterns across dynamic FC networks, followed by a fully-connected neural network to exploit these learned patterns for subject-level classification. To overcome model overfitting on limited training datasets, an adversarial training strategy is introduced to learn graph embedding models that generalize well to unseen brain networks. Evaluation on the ABIDE resting-state functional magnetic resonance imaging dataset shows that our proposed framework significantly outperformed state-of-the-art methods in identifying ASD. Dynamic FC analyses with DSVB learned embeddings reveal apparent group difference between ASD and healthy controls in network profiles and switching dynamics of brain states

Age-related changes in Serum Growth Hormone, Insulin-like Growth Factor-1 and Somatostatin in System Lupus Erythematosus

BACKGROUND: Systemic lupus erythematosus is an age- and gender-associated autoimmune disorder. Previous studies suggested that defects in the hypothalamic/pituitary axis contributed to systemic lupus erythematosus disease progression which could also involve growth hormone, insulin-like growth factor-1 and somatostatin function. This study was designed to compare basal serum growth hormone, insulin-like growth factor-1 and somatostatin levels in female systemic lupus erythematosus patients to a group of normal female subjects. METHODS: Basal serum growth hormone, insulin-like growth factor-1 and somatostatin levels were measured by standard radioimmunoassay. RESULTS: Serum growth hormone levels failed to correlate with age (r(2 )= 3.03) in the entire group of normal subjects (i.e. 20 – 80 years). In contrast, serum insulin-like growth factor-1 levels were inversely correlated with age (adjusted r(2 )= 0.092). Of note, serum growth hormone was positively correlated with age (adjusted r(2 )= 0.269) in the 20 – 46 year range which overlapped with the age range of patients in the systemic lupus erythematosus group. In that regard, serum growth hormone levels were not significantly higher compared to either the entire group of normal subjects (20 – 80 yrs) or to normal subjects age-matched to the systemic lupus erythematosus patients. Serum insulin-like growth factor-1 levels were significantly elevated (p < 0.001) in systemic lupus erythematosus patients, but only when compared to the entire group of normal subjects. Serum somatostatin levels differed from normal subjects only in older (i.e. >55 yrs) systemic lupus erythematosus patients. CONCLUSIONS: These results indicated that systemic lupus erythematosus was not characterized by a modulation of the growth hormone/insulin-like growth factor-1 paracrine axis when serum samples from systemic lupus erythematosus patients were compared to age- matched normal female subjects. These results in systemic lupus erythematosus differ from those previously reported in other musculoskeletal disorders such as rheumatoid arthritis, osteoarthritis, fibromyalgia, diffuse idiopathic skeletal hyperostosis and hypermobility syndrome where significantly higher serum growth hormone levels were found. Somatostatin levels in elderly systemic lupus erythematosus patients may provide a clinical marker of disease activity in these patients

Trends in lipid-modifying agent use in 83 countries

Background and aims: Lipid-modifying agents (LMAs) are increasingly used to reduce lipid levels and prevent cardiovascular events but the magnitude of their consumption in different world regions is unknown. We aimed to describe recent global trends in LMA consumption and to explore the relationship between country-level LMA consumption and cholesterol concentrations. / Methods: This cross-sectional and ecological study used monthly pharmaceutical sales data from January 2008 to December 2018 for 83 countries from the IQVIA Multinational Integrated Data Analysis System and total and non-high-density lipoprotein (non-HDL) cholesterol concentrations from the NCD Risk Factor Collaboration. Compound annual growth rate (CAGR) was used to assess changes in LMA consumption over time. / Results: From 2008 to 2018, use of LMAs increased from 7,468 to 11,197 standard units per 1000 inhabitants per year (CAGR 4.13%). An estimated 173 million people used LMAs in 2018. Statins were the most used class of LMA and their market share increased in 75% of countries between 2008 and 2018. From 2013 to 2018, consumption of low-density lipoprotein lowering therapies increased (statins 3.99%; ezetimibe 4.01%; proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors 104.47%). Limited evidence supports a clear relationship between country-level changes in LMA consumption and mean total and non-HDL cholesterol concentrations in 2008 versus 2018. / Conclusions: Since 2008, global access to LMAs, especially statins, has improved. In line with international lipid guideline recommendations, recent trends indicate growth in the use of statins, ezetimibe, and PCSK9 inhibitors. Country-level patterns of LMA use and total and non-HDL cholesterol varied considerably

Harnessing technology and molecular analysis to understand the development of cardiovascular diseases in Asia: a prospective cohort study (SingHEART)

BACKGROUND: Cardiovascular disease (CVD) imposes much mortality and morbidity worldwide. The use of "deep learning", advancements in genomics, metabolomics, proteomics and devices like wearables have the potential to unearth new insights in the field of cardiology. Currently, in Asia, there are no studies that combine the use of conventional clinical information with these advanced technologies. We aim to harness these new technologies to understand the development of cardiovascular disease in Asia. METHODS: Singapore is a multi-ethnic country in Asia with well-represented diverse ethnicities including Chinese, Malays and Indians. The SingHEART study is the first technology driven multi-ethnic prospective population-based study of healthy Asians. Healthy male and female subjects aged 21-69 years old without any prior cardiovascular disease or diabetes mellitus will be recruited from the general population. All subjects are consented to undergo a detailed on-line questionnaire, basic blood investigations, resting and continuous electrocardiogram and blood pressure monitoring, activity and sleep tracking, calcium score, cardiac magnetic resonance imaging, whole genome sequencing and lipidomic analysis. Outcomes studied will include mortality and cause of mortality, myocardial infarction, stroke, malignancy, heart failure, and the development of co-morbidities. DISCUSSION: An initial target of 2500 patients has been set. From October 2015 to May 2017, an initial 683 subjects have been recruited and have completed the initial work-up the SingHEART project is the first contemporary population-based study in Asia that will include whole genome sequencing and deep phenotyping: including advanced imaging and wearable data, to better understand the development of cardiovascular disease across different ethnic groups in Asia

Biomechanics of human fetal hearts with critical aortic stenosis

Critical aortic stenosis (AS) of the fetal heart causes a drastic change in the cardiac biomechanical environment. Consequently, a substantial proportion of such cases will lead to a single-ventricular birth outcome. However, the biomechanics of the disease is not well understood. To address this, we performed Finite Element (FE) modelling of the healthy fetal left ventricle (LV) based on patient-specific 4D ultrasound imaging, and simulated various disease features observed in clinical fetal AS to understand their biomechanical impact. These features included aortic stenosis, mitral regurgitation (MR) and LV hypertrophy, reduced contractility, and increased myocardial stiffness. AS was found to elevate LV pressures and myocardial stresses, and depending on severity, can drastically decrease stroke volume and myocardial strains. These effects are moderated by MR. AS alone did not lead to MR velocities above 3 m/s unless LV hypertrophy was included, suggesting that hypertrophy may be involved in clinical cases with high MR velocities. LV hypertrophy substantially elevated LV pressure, valve flow velocities and stroke volume, while reducing LV contractility resulted in diminished LV pressure, stroke volume and wall strains. Typical extent of hypertrophy during fetal AS in the clinic, however, led to excessive LV pressure and valve velocity in the FE model, suggesting that reduced contractility is typically associated with hypertrophy. Increased LV passive stiffness, which might represent fibroelastosis, was found to have minimal impact on LV pressures, stroke volume, and wall strain. This suggested that fibroelastosis could be a by-product of the disease progression and does not significantly impede cardiac function. Our study demonstrates that FE modelling is a valuable tool for elucidating the biomechanics of congenital heart disease and can calculate parameters which are difficult to measure, such as intraventricular pressure and myocardial stresses

Predicting a small molecule-kinase interaction map: A machine learning approach

<p>Abstract</p> <p>Background</p> <p>We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features.</p> <p>Results</p> <p>A Support Vector Machine (SVM) as well as a decision tree algorithm (C5/See5) is used to learn models based on the available features which in turn can be used for the classification of new kinase-inhibitor pair test instances. We evaluate our approach using different feature sets and parameter settings for the employed classifiers. Moreover, the paper introduces a new way of evaluating predictions in such a setting, where different amounts of information about the binding partners can be assumed to be available for training. Results on an external test set are also provided.</p> <p>Conclusions</p> <p>In most of the cases, the presented approach clearly outperforms the baseline methods used for comparison. Experimental results indicate that the applied machine learning methods are able to detect a signal in the data and predict binding affinity to some extent. For SVMs, the binding prediction can be improved significantly by using features that describe the active site of a kinase. For C5, besides diversity in the feature set, alignment scores of conserved regions turned out to be very useful.</p

High-resolution digital phenotypes from consumer wearables and their applications in machine learning of cardiometabolic risk markers: cohort study

Background: Consumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized. Objective: We aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk. Methods: We introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events. Results: We found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P<.001 in each case). Furthermore, heart rate dynamics from different activity states contain distinct information (maximum absolute correlation coefficient of 0.15). Heart rate dynamics in sedentary states are most predictive of lipid abnormalities and obesity, whereas patterns in active states are most predictive of blood pressure abnormalities (P<.001). Moreover, in comparison with standard measures, higher resolution patterns in wearable heart rate recordings are better able to represent subtle physiological dynamics related to genomic risk for cardiometabolic disease (improvement of 11.9%-22.0% in Brier scores; P<.001). Finally, illustrative case studies reveal connections between these high-resolution phenotypes and actualized clinical events, even for borderline profiles lacking apparent cardiometabolic risk markers. Conclusions: High-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management

Epstein-Barr Virus Associated Modulation of Wnt Pathway Is Not Dependent on Latent Membrane Protein-1

Previous studies have indicated that Epstein-Barr virus (EBV) can modulate the Wnt pathway in virus-infected cells and this effect is mediated by EBV-encoded oncogene latent membrane protein 1 (LMP1). Here we have reassessed the role of LMP1 in regulating the expression of various mediators of the canonical Wnt cascade. Contradicting the previous finding, we found that the levels of E-cadherin, β-catenin, Glycogen Synthase Kinase 3ß (GSK3β), axin and α-catenin were not affected by the expression of LMP1 sequences from normal B cells or nasopharyngeal carcinoma. Moreover, we also show that LMP1 expression had no detectable effect on the E-cadherin and β-catenin interaction and did not induce transcriptional activation of β-catenin. Taken together these studies demonstrate that EBV-mediated activation of Wnt pathway is not dependent on the expression of LMP1

Kocuria kristinae infection associated with acute cholecystitis

BACKGROUND: Kocuria, previously classified into the genus of Micrococcus, is commonly found on human skin. Two species, K. rosea and K. kristinae, are etiologically associated with catheter-related bacteremia. CASE PRESENTATION: We describe the first case of K. kristinae infection associated with acute cholecystitis. The microorganism was isolated from the bile of a 56-year old Chinese man who underwent laparoscopic cholecystectomy. He developed post-operative fever that resolved readily after levofloxacin treatment. CONCLUSION: Our report of K. kristinae infection associated with acute cholecystitis expands the clinical spectrum of infections caused by this group of bacteria. With increasing number of recent reports describing the association between Kocuria spp. and infectious diseases, the significance of their isolation from clinical specimens cannot be underestimated. A complete picture of infections related to Kocuria spp. will have to await the documentation of more clinical cases

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente