Efficient Alternating Minimization Solvers for Wyner Multi-View Unsupervised Learning

In this work, we adopt Wyner common information framework for unsupervised multi-view representation learning. Within this framework, we propose two novel formulations that enable the development of computational efficient solvers based on the alternating minimization principle. The first formulation, referred to as the {\em variational form}, enjoys a linearly growing complexity with the number of views and is based on a variational-inference tight surrogate bound coupled with a Lagrangian optimization objective function. The second formulation, i.e., the {\em representational form}, is shown to include known results as special cases. Here, we develop a tailored version from the alternating direction method of multipliers (ADMM) algorithm for solving the resulting non-convex optimization problem. In the two cases, the convergence of the proposed solvers is established in certain relevant regimes. Furthermore, our empirical results demonstrate the effectiveness of the proposed methods as compared with the state-of-the-art solvers. In a nutshell, the proposed solvers offer computational efficiency, theoretical convergence guarantees (local minima), scalable complexity with the number of views, and exceptional accuracy as compared with the state-of-the-art techniques. Our focus here is devoted to the discrete case and our results for continuous distributions are reported elsewhere

Comparison of bypass surgery and drug-eluting stenting in diabetic patients with left main and/or multivessel disease: A systematic review and meta-analysis of randomized and nonrandomized studies

Background: With advances in theinterventional field, the choice between coronary artery bypass grafting (CABG) and percutaneous coronary intervention with drug-eluting stents (PCI-DES) for the diabetic subset with left main (LM) and/or multivessel disease (MVD) remains consistently controversial. Methods and results: We conducted a systematic review of randomized controlled trials (RCTs) and observational controlled trials (OCTs) comparing the two strategies for the diabetic subset with LM and/or MVD. PubMed, EMBASE, CENTRAL databases, Google Scholar and SinoMed were systematically searched for eligible studies without language and publica­tion restrictions. We identified 19 trials (4 randomized and 15 nonrandomized), enrolling 5,805 patients in OCTs and 3,060 patients in RCTs, respectively. PCI-DES was associated with higher mortality compared with CABG (11.7% DES vs. 9.1% CABG, RR 1.23, 95% CI 1.00–1.53, p = 0.06). Patients after PCI-DES had higher prevalence of myocardial infarction (MI) when compared with CABG (8.5% DES vs. 4.6% CABG, RR 1.68, 95% CI 1.20–2.37, p = 0.003). PCI-DES patients were at substantially lower risk of stroke (2.0% DES vs. 3.9% CABG, RR 0.51, 95% CI 0.39–0.67, p < 0.00001), but at several-fold higher risk of repeat revascularization (19.0% DES vs. 6.3% CABG, RR 2.95, 95% CI 2.46–3.55, p < 0.00001). The OCT patients risked a lower mortality as compared to the RCT patients (9.6% OCTs vs. 11.9% RCTs, RR 0.81, 95% CI 0.71–0.92, p = 0.001). Conclusions: CABG for patients with diabetes mellitus and LM and/or MVD had advan­tages over PCI-DES in all-cause death, nonfatal MI, and repeat revascularization, but the substantial disadvantage in nonfatal stroke. The high-selected patients (RCTs) risked a higher mortality than the real-world patients (OCTs)

The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting

Wireless fingerprinting refers to a device identification method leveraging hardware imperfections and wireless channel variations as signatures. Beyond physical layer characteristics, recent studies demonstrated that user behaviours could be identified through network traffic, e.g., packet length, without decryption of the payload. Inspired by these results, we propose a multi-layer fingerprinting framework that jointly considers the multi-layer signatures for improved identification performance. In contrast to previous works, by leveraging the recent multi-view machine learning paradigm, i.e., data with multiple forms, our method can cluster the device information shared among the multi-layer features without supervision. Our information-theoretic approach can be extended to supervised and semi-supervised settings with straightforward derivations. In solving the formulated problem, we obtain a tight surrogate bound using variational inference for efficient optimization. In extracting the shared device information, we develop an algorithm based on the Wyner common information method, enjoying reduced computation complexity as compared to existing approaches. The algorithm can be applied to data distributions belonging to the exponential family class. Empirically, we evaluate the algorithm in a synthetic dataset with real-world video traffic and simulated physical layer characteristics. Our empirical results show that the proposed method outperforms the state-of-the-art baselines in both supervised and unsupervised settings

Serum selenium concentration is associated with metabolic factors in the elderly: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Selenium is an essential micronutrient known for its antioxidant function. However, the association of serum selenium with lipid profiles and fasting glucose are inconsistent in populations with average intake of selenium. Furthermore, there were few studies conducted specifically for the elderly. This study examined the relationship of serum selenium concentration with serum lipids and fasting glucose in the Taiwanese elderly population.</p> <p>Methods</p> <p>This was a cross-sectional study of 200 males and females aged 65-85 years (mean 71.5 ± 4.6 years) from Taipei, Taiwan. Serum selenium was measured by inductively coupled plasma-mass spectrometer. The association between serum selenium and metabolic factors was examined using a multivariate linear regression analysis after controlling several confounders.</p> <p>Results</p> <p>The mean serum selenium concentration was 1.14 μmol/L, without significant difference between sexes. Total cholesterol, triglycerides, and LDL cholesterol increased significantly with serum selenium concentration (<it>P </it>< 0.001, <it>P </it>< 0.05 and <it>P </it>< 0.001, respectively) after adjusting for age, gender, anthropometric indices, lifestyle factors, and cardio-vascular risk factors in several linear regression models. Furthermore, there was a significantly positive association between serum selenium and serum fasting glucose concentrations (<it>P </it>< 0.05).</p> <p>Conclusions</p> <p>Total cholesterol, triglycerides, and LDL cholesterol, and fasting serum glucose concentrations increased significantly with serum selenium concentration in the Taiwanese elderly. The underlying mechanism warrants further research.</p

Novel Technology for Bio-diesel Production from Cooking and Waste Cooking Oil by Microwave Irradiation

AbstractIn the transitional process, acid or base catalysts are common technology to produce bio-diesel from waste cooking oil; however, the catalysts only can be use one time. Highly reaction time is requirement for the transitional technology. For improvement these concern issues, this study applied a novel technology to create bio-diesel product from cooking oil and waste cooking oil by microwave irritation. The microwave irradiation can provide strong power and reach reaction temperature in a short time. The SrO catalyst is a heterogeneous catalyst which is not dissolution into any liquid solution therefore, it can be recycling and reusing again.In this research, the optimum conditions were using commercial SrO, 40 to 180seconds reaction time, around 80oC reaction temperature, 6 methanol to oil ratio, and 1000W microwave power output. 99% and 93% biodiesel conversion efficiency for cooking oil and waste cooking oil were reached within in these conditions. The determined specifications of obtained biodiesel according to ASTM D6751 and EN14214 standards were in accordance with the required limits. As a conclusion, the present study indicates that derived fuel promises being an alternative for diesel, and could be used in engines without a major modification due to its qualifications

Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration

[[abstract]]Background Protein-ligand interactions are key processes in triggering and controlling biological functions within cells. Prediction of protein binding regions on the protein surface assists in understanding the mechanisms and principles of molecular recognition. In silico geometrical shape analysis plays a primary step in analyzing the spatial characteristics of protein binding regions and facilitates applications of bioinformatics in drug discovery and design. Here, we describe the novel software, PLB-SAVE, which uses parallel processing technology and is ideally suited to extract the geometrical construct of solid angles from surface atoms. Representative clusters and corresponding anchors were identified from all surface elements and were assigned according to the ranking of their solid angles. In addition, cavity depth indicators were obtained by proportional transformation of solid angles and cavity volumes were calculated by scanning multiple directional vectors within each selected cavity. Both depth and volume characteristics were combined with various weighting coefficients to rank predicted potential binding regions. Results Two test datasets from LigASite, each containing 388 bound and unbound structures, were used to predict binding regions using PLB-SAVE and two well-known prediction systems, SiteHound and MetaPocket2.0 (MPK2). PLB-SAVE outperformed the other programs with accuracy rates of 94.3% for unbound proteins and 95.5% for bound proteins via a tenfold cross-validation process. Additionally, because the parallel processing architecture was designed to enhance the computational efficiency, we obtained an average of 160-fold increase in computational time. Conclusions In silico binding region prediction is considered the initial stage in structure-based drug design. To improve the efficacy of biological experiments for drug development, we developed PLB-SAVE, which uses only geometrical features of proteins and achieves a good overall performance for protein-ligand binding region prediction. Based on the same approach and rationale, this method can also be applied to predict carbohydrate-antibody interactions for further design and development of carbohydrate-based vaccines. PLB-SAVE is available at http://save.cs.ntou.edu.tw.[[booktype]]電子

Ellagic Acid, the Active Compound of Phyllanthus urinaria, Exerts In Vivo Anti-Angiogenic Effect and Inhibits MMP-2 Activity

This study aimed to assess the potential anti-angiogenic mechanism of Phyllanthus urinaria (P. urinaria) and characterize the major compound in P. urinaria that exerts anti-angiogenic effect. The water extract of P. urinaria and Ellagic Acid were used to evaluate the anti-angiogenic effect in chorioallantoic membrane (CAM) in chicken embryo and human vascular endothelial cells (HUVECs). The matrix metalloproteinase-2 (MMP-2) activity was determined by gelatin zymography. The mRNA expressions of MMP-2, MMP-14 and tissue inhibitor of metalloproteinase-2 (TIMP-2) were analyzed by reverse transcription polymerase chain reaction (RT-PCR). Level of MMP-2 proteins in conditioned medium or cytosol was determined by western blot analysis. We confirmed that P. urinaria's in vivo anti-angiogenic effect was associated with a reduction in MMP-2 activity. Ellagic acid, one of the major polyphenolic components as identified in P. urinaria by high performance liquid chromatography mass spectrometry (HPLC/MS), exhibited the same anti-angiogenic effect in vivo. Both P. urinaria and Ellagic Acid inhibited MMP-2 activity in HUVECs with unchanged mRNA level. The mRNA expression levels of MMP-14 and TIMP-2 were not altered either. Results from comparing the change of MMP-2 protein levels in conditioned medium and cytosol of HUVECs after the P. urinaria or Ellagic Acid treatment revealed an inhibitory effect on the secretion of MMP-2 protein. This study concluded that Ellagic Acid is the active compound in P. urinaria to exhibit anti-angiogenic activity and to inhibit the secretion of MMP-2 protein from HUVECs

Sex differences in patients with COVID-19: a retrospective cohort study and meta-analysis

BACKGROUND: Accumulated evidence revealed that male was much more likely to higher severity and fatality by SARS-CoV-2 infection than female patients, but few studies and meta-analyses have evaluated the sex differences of the infection and progression of COVID-19 patients. AIM: We aimed to compare the sex differences of the epidemiological and clinical characteristics in COVID-19 patients; and to perform a meta-analysis evaluating the severe rate, fatality rate, and the sex differences of the infection and disease progression in COVID-19 patients. METHODS: We analyzed clinical data of patients in Changchun Infectious Hospital and Center, Changchun, Northeast China; and searched PubMed, Embase, Web of Science, and Cochrane Library without any language restrictions for published articles that reported the data of sex-disaggregated, number of severe, and death patients on the confirmed diagnosis of adult COVID-19 patients. RESULTS: The pooled severe rate and fatality rate of COVID-19 were 22.7% and 10.7%. Male incidence in the retrospective study was 58.1%, and the pooled incidence in male was 54.7%. CONCLUSION: The pooled severe rate in male and female of COVID-19 was 28.2% and 18.8%, the risky of severe and death was about 1.6folds higher in male compared with female, especially for older patients (&gt; 50 y)

Central and Peripheral Changes in FOS Expression in Schizophrenia Based on Genome-Wide Gene Expression

Schizophrenia is a chronic, debilitating neuropsychiatric disorder. Multiple transcriptomic gene expression profiling analysis has been used to identify schizophrenia-associated genes, unravel disease-associated biomarkers, and predict clinical outcomes. We aimed to identify gene expression regulation, underlying pathways, and their roles in schizophrenia pathogenesis. We searched the Gene Expression Omnibus (GEO) database for microarray studies of fibroblasts, lymphoblasts, and post-mortem brains of schizophrenia patients. Our analysis demonstrated high FOS expression in non-neural peripheral samples and low FOS expression in brain tissues of schizophrenia patients compared with healthy controls. FOS exhibited predictive value for schizophrenia patients in these datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that “amphetamine addiction” was among the top 10 significantly enriched KEGG pathways. FOS and FOSB, which are implicated in the amphetamine addiction pathway, were up-regulated in schizophrenia fibroblast samples. Protein–protein interaction (PPI) network analysis revealed that proteins closely interacting with FOS-encoded protein were also involved in the amphetamine addiction pathway. Pearson correlation test indicated that FOS showed positive correlation with genes in the amphetamine pathway. The results revealed that FOS was acceptable as a biomarker for schizophrenia and may be involved in schizophrenia pathogenesis