Method to Annotate Arrhythmias by Deep Network

This study targets to automatically annotate on arrhythmia by deep network. The investigated types include sinus rhythm, asystole (Asys), supraventricular tachycardia (Tachy), ventricular flutter or fibrillation (VF/VFL), ventricular tachycardia (VT). Methods: 13s limb lead ECG chunks from MIT malignant ventricular arrhythmia database (VFDB) and MIT normal sinus rhythm database were partitioned into subsets for 5-fold cross validation. These signals were resampled to 200Hz, filtered to remove baseline wandering, projected to 2D gray spectrum and then fed into a deep network with brand-new structure. In this network, a feature vector for a single time point was retrieved by residual layers, from which latent representation was extracted by variational autoencoder (VAE). These front portions were trained to meet a certain threshold in loss function, then fixed while training procedure switched to remaining bidirectional recurrent neural network (RNN), the very portions to predict an arrhythmia category. Attention windows were polynomial lumped on RNN outputs for learning from details to outlines. And over sampling was employed for imbalanced data. The trained model was wrapped into docker image for deployment in edge or cloud. Conclusion: Promising sensitivities were achieved in four arrhythmias and good precision rates in two ventricular arrhythmias were also observed. Moreover, it was proven that latent representation by VAE, can significantly boost the speed of convergence and accuracy

Causal role of immune cells on risk of Parkinson’s disease: a Mendelian randomization study

BackgroundPrevious observational studies have suggested a correlation between immune cells and Parkinson’s disease (PD), yet specific investigations into the causal relationship between the two remain limited. This study aims to explore this potential causal relationship.MethodsWe utilized genome-wide association study (GWAS) data on immune cells and Parkinson’s Disease, conducting a two-sample Mendelian randomization (MR) analysis using single nucleotide polymorphisms (SNPs). To estimate causality, we employed inverse variance weighting (IVW), MR-Egger, and weighted median (WM) methods. For sensitivity analysis, we used Cochran’s Q-test, MR-Egger intercept, leave-one-out analysis, and funnel plots.ResultsAfter false discovery rate (FDR) correction, the effects of PD on immune cells, and vice versa, were not statistically significant. These include CX3CR1 on CD14+ CD16-monocyte (OR = 0.91, 95% CI = 0.86–0.96, p = 0.0003 PFDR = 0.152), CD62L-CD86+ myeloid DC AC (OR = 0.93, 95% CI = 0.89–0.97, p = 0.0005, PFDR = 0.152),CD11b on Mo (OR = 1.08, 95% CI = 1.03–1.13, p = 0.001, PFDR = 0.152), CD38 on igd+ cd24− (OR = 1.14, 95% CI = 1.06–1.23, p = 0.001, PFDR = 0.152), D14+ cd16+ monocyte %monocyte (OR = 1.10, 95% CI = 1.04–1.17, p = 0.001, PFDR = 0.159). Additionally, PD may be causally related to the immune phenotype of CM CD8br %T cell (beta = 0.10, 95% CI = 1.14–1.16, p = 0.0004, PFDR = 0.151), SSC-A on monocyte (beta = 0.11, 95% CI = 1.15–1.18, p = 0.0004, PFDR = 0.1 SSC-A on monocyte). No pleiotropy was determined.ConclusionThis study suggested a potential causal link between immune cells and Parkinson’s Disease through the MR method, which could provide a new direction for the mechanistic research and clinical treatment of PD

Large-scale analyses of heat shock transcription factors and database construction based on whole-genome genes in horticultural and representative plants

Heat shock transcription factor (Hsf) plays a critical role in regulating heat resistance. Here, 2950 Hsf family genes were identified from 111 horticultural and representative plants. More Hsf genes were detected in higher plants than in lower plants. Based on all Hsf genes, we constructed a phylogenetic tree, which indicated that Hsf genes of each branch evolved independently after species differentiation. Furthermore, we uncovered the evolutionary trajectories of Hsf genes by motif analysis. There were only six motifs (M1–M6) in lower plants, and then four novel motifs (M7–M10) appeared in higher plants. However, the motifs of some Hsf genes were lost in higher plants, indicating that Hsf genes have undergone sequence variation during their evolution. The number of Hsf genes lost was greater than the number of genes that were duplicated after whole-genome duplication in higher plants. The heat response network was constructed using 24 Hsf genes and 2421 downstream and 222 upstream genes of Arabidopsis. Further enrichment analysis revealed that Hsf genes and other transcription factors interacted with each other in the response to heat stress. Global expression maps were illustrated for Hsf genes under various abiotic and biotic stresses and several developmental stages in Arabidopsis. Syntenic and phylogenetic analyses were conducted using Hsf genes of Arabidopsis and the pan-genome of 18 Brassica rapa accessions. We also performed expression pattern analysis of Hsf and six Hsp family genes using expression values from different tissues and heat treatments in B. rapa. The interaction network between the Hsf and Hsp gene families was constructed in B. rapa, and several core genes were detected in the network. Finally, we constructed an Hsf database (http://hsfdb.bio2db.com) for researchers to retrieve Hsf gene family information. Therefore, our study will provide rich resources for the study of the evolution and function of Hsf genes

Large-scale analyses of heat shock transcription factors and database construction based on whole-genome genes in horticultural and representative plants

Heat shock transcription factor (Hsf) plays a critical role in regulating heat resistance. Here, 2950 Hsf family genes were identified from 111 horticultural and representative plants. More Hsf genes were detected in higher plants than in lower plants. Based on all Hsf genes, we constructed a phylogenetic tree, which indicated that Hsf genes of each branch evolved independently after species differentiation. Furthermore, we uncovered the evolutionary trajectories of Hsf genes by motif analysis. There were only six motifs (M1–M6) in lower plants, and then four novel motifs (M7–M10) appeared in higher plants. However, the motifs of some Hsf genes were lost in higher plants, indicating that Hsf genes have undergone sequence variation during their evolution. The number of Hsf genes lost was greater than the number of genes that were duplicated after whole-genome duplication in higher plants. The heat response network was constructed using 24 Hsf genes and 2421 downstream and 222 upstream genes of Arabidopsis. Further enrichment analysis revealed that Hsf genes and other transcription factors interacted with each other in the response to heat stress. Global expression maps were illustrated for Hsf genes under various abiotic and biotic stresses and several developmental stages in Arabidopsis. Syntenic and phylogenetic analyses were conducted using Hsf genes of Arabidopsis and the pan-genome of 18 Brassica rapa accessions. We also performed expression pattern analysis of Hsf and six Hsp family genes using expression values from different tissues and heat treatments in B. rapa. The interaction network between the Hsf and Hsp gene families was constructed in B. rapa, and several core genes were detected in the network. Finally, we constructed an Hsf database (http://hsfdb.bio2db.com) for researchers to retrieve Hsf gene family information. Therefore, our study will provide rich resources for the study of the evolution and function of Hsf genes

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Nonlinear Analysis on Traffic Flow Based on Catastrophe and Chaos Theory

We applied catastrophe and chaos theory to analyze the traffic nonlinear characteristics of expressway condition. Catastrophe theory was generally used to explore the mathematical relationships among the traffic data collected from highway conditions, which could not be appropriate for the urban expressway conditions. Traffic flow data collected from the 3rd ring road expressway in Beijing was used to build flow-density model and speed-density Greenshields model. Then the density was discussed based on the traffic wave speed function with cusp catastrophe theory; in particular, density conditions on median lanes and shoulder lanes were deeply discussed. Meanwhile the chaotic characteristics were analyzed based on the traffic temporal sequence data collected from 29 detectors located at the 3rd ring road expressway, and C-C method was used to reconstruct the phase space and the largest Lyapunov exponents were estimated by Wolf method and the small data sets method. The results indicated that the traffic operation catastrophe density on the median lanes was a bit higher than that on the shoulder lanes; additionally chaotic characteristics obviously existed in the local corridor composed of 29 detectors in the 3rd ring road expressway traffic flow system

Identification of Contributing Factors for Driver’s Perceptual Bias of Aggressive Driving in China

Aggressive driving is common across the world. While most aggressive driving is conscious, some aggressive driving behavior may be unconscious on part of motor vehicle drivers. Perceptual bias of aggressive driving behavior is one of the main causes of traffic accidents. This paper focuses on identifying impact factors related to aggressive driving perceptual bias. Questionnaire data from 690 drivers, collected from a drivers’ retraining course administered by the Traffic Management Bureau in Nanjing, China, were used to collect drivers’ socioeconomic characteristics, personality traits, and external environment data. Actual penalty points were considered as an objective indicator and Gaussian mixture model (GMM) was used to cluster an objective indicator into different levels. The driving anger expression (DAX) was used to measure drivers’ self-assessment of aggressive driving behavior and then to identify perceptual biases. Then a binary logistic model was estimated to explore the influence of different factors on drivers’ perceptual bias of aggressive driving behavior. Results showed that bus drivers were less likely to have perceptual bias of aggressive driving behavior. Truck drivers, drivers with an extraversion characteristic, and drivers who have dissatisfaction with road infrastructure and actual work were likely to have a perceptual bias. The findings are potentially beneficial for proposing targeted countermeasures to identify dangerous drivers and improve drivers’ safety awareness