Topology Consistency of Disease-specific Differential Co-regulatory Networks

Background: Sets of differentially expressed genes often contain driver genes that induce disease processes. However, various methods for identifying differentially expressed genes yield quite different results. Thus, we investigated whether this affects the identification of key players in regulatory networks derived by downstream analysis from lists of differentially expressed genes. Results: While the overlap between the sets of significant differentially expressed genes determined by DESeq, edgeR, voom and VST was only 26% in liver hepatocellular carcinoma and 28% in breast invasive carcinoma, the topologies of the regulatory networks constructed using the TFmiR webserver for the different sets of differentially expressed genes were found to be highly consistent with respect to hub-degree nodes, minimum dominating set and minimum connected dominating set. Conclusions: The findings suggest that key genes identified in regulatory networks derived by systematic analysis of differentially expressed genes may be a more robust basis for understanding diseases processes than simply inspecting the lists of differentially expressed genes

Incremental Transfer Learning in Two-pass Information Bottleneck based Speaker Diarization System for Meetings

The two-pass information bottleneck (TPIB) based speaker diarization system operates independently on different conversational recordings. TPIB system does not consider previously learned speaker discriminative information while diarizing new conversations. Hence, the real time factor (RTF) of TPIB system is high owing to the training time required for the artificial neural network (ANN). This paper attempts to improve the RTF of the TPIB system using an incremental transfer learning approach where the parameters learned by the ANN from other conversations are updated using current conversation rather than learning parameters from scratch. This reduces the RTF significantly. The effectiveness of the proposed approach compared to the baseline IB and the TPIB systems is demonstrated on standard NIST and AMI conversational meeting datasets. With a minor degradation in performance, the proposed system shows a significant improvement of 33.07% and 24.45% in RTF with respect to TPIB system on the NIST RT-04Eval and AMI-1 datasets, respectively.Comment: 5 pages, 2 figures, To appear in Proc. ICASSP 2019, May 12-17, 2019, Brighton, U

The Urine Metabolome of Young Autistic Children Correlates with Their Clinical Profile Severity

Autism diagnosis is moving from the identification of common inherited genetic variants to a systems biology approach. The aims of the study were to explore metabolic perturbations in autism, to investigate whether the severity of autism core symptoms may be associated with specific metabolic signatures; and to examine whether the urine metabolome discriminates severe from mild-to-moderate restricted, repetitive, and stereotyped behaviors. We enrolled 57 children aged 2–11 years; thirty-one with idiopathic autism and twenty-six neurotypical (NT), matched for age and ethnicity. The urine metabolome was investigated by gas chromatography-mass spectrometry (GC-MS). The urinary metabolome of autistic children was largely distinguishable from that of NT children; food selectivity induced further significant metabolic dierences. Severe autism spectrum disorder core deficits were marked by high levels of metabolites resulting from diet, gut dysbiosis, oxidative stress, tryptophan metabolism, mitochondrial dysfunction. The hierarchical clustering algorithm generated two metabolic clusters in autistic children: 85–90% of children with mild-to-moderate abnormal behaviors fell in cluster II. Our results open up new perspectives for the more general understanding of the correlation between the clinical phenotype of autistic children and their urine metabolome. Adipic acid, palmitic acid, and 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid can be proposed as candidate biomarkers of autism severity

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy.

Peer reviewe

The Implementation of Enhanced Microgrid using Mayfly Algorithm based PID Controller

Micro grids, comprised of distributed generation units, are designed to function independently of the main grid. To ensure stable operation in isolated mode, precise control of system is essential. Common challenges faced by standalone microgrids include maintaining stability of the system with balancing the load and generation from renewable energy sources and preventing fluctuations.  Primary objective of paper to develop and execute an auxiliary controller capable of regulating system within a networked microgrid environment. Intermittent nature of renewable energy sources can lead to fluctuations in system frequency and power flow variations in tie line. To mitigate these challenges and balance the nonlinear output from renewable sources, Mayfly Algorithm (MA)-optimized Proportional-Integral-Derivative (PID) controller is proposed and implemented. Validation results demonstrate that the proposed MA-PID controller effectively regulates system in response to varying load demands and renewable energy sources