Genomic Characteristics of Stx2e-Producing Escherichia coli Strains Derived from Humans, Animals, and Meats

Shiga toxin (Stx) can be classified into two types, Stx1 and Stx2, and different subtypes. Stx2e is a subtype commonly causing porcine edema disease and rarely reported in humans. The purpose of this study was to analyze the prevalence and genetic characteristics of Stx2e-producing Escherichia coli (Stx2e-STEC) strains from humans compared to strains from animals and meats in China. Stx2e-STEC strains were screened from our STEC collection, and whole-genome sequencing was performed to characterize their genetic features. Our study showed a wide distribution of Stx2e-STEC among diverse hosts and a higher proportion of Stx2e-STEC among human STEC strains in China. Three human Stx2e-STEC isolates belonged to O100:H30, Onovel26:H30, and O8:H9 serotypes and varied in genetic features. Human Stx2e-STECs phylogenetically clustered with animal- and food-derived strains. Stx2e-STEC strains from animals and meat showed multidrug resistance, while human strains were only resistant to azithromycin and tetracycline. Of note, a high proportion (55.9%) of Stx2e-STEC strains, including one human strain, carried the heat-stable and heat-labile enterotoxin-encoding genes st and lt, exhibiting a STEC/enterotoxigenic E. coli (ETEC) hybrid pathotype. Given that no distinct genetic feature was found in Stx2e-STEC strains from different sources, animal- and food-derived strains may pose the risk of causing human disease.

Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction

Feature selection reduces the dimension of input variables by eliminating irrelevant features. We propose feature selection techniques based on a genetic algorithm, which is a metaheuristic inspired by a natural selection process. We compare two types of feature selection for predicting a stock market index and cryptocurrency price. The first method is a newly devised genetic filter involving a fitness function designed to increase the relevance between the target and the selected features and decrease the redundancy between the selected features. The second method is a genetic wrapper, whereby we can find the better feature subsets related to KOPSI by exploring the solution space more thoroughly. Both genetic feature selection methods improved the predictive performance of various regression functions. Our best model was applied to predict the KOSPI, cryptocurrency price, and their respective trends after COVID-19.

Emergence of IncHI2 Plasmid-Harboring blaNDM-5 from Porcine Escherichia coli Isolates in Guangdong, China

Carbapenem resistance has posed potential harmful risks to human and animals. The objectives of this study were to understand the prevalence of blaNDM-5 in pigs and investigate the molecular characteristics of NDM-5-producing Escherichia coli isolates in Guangdong province in China. Carbapenem-resistant E. coli isolates were isolated from pigs and obtained using MacConkey plates containing 0.5 mg/L meropenem. Conjugation assay and antimicrobial susceptibility testing were conducted for the isolates and their transconjugants. Whole-genome sequence (WGS) was used to analyze the plasmid genetic feature. A total of five blaNDM-5-carrying E. coli isolates were obtained in the present investigations. They belonged to five ST types. The blaNDM-5 genes were found to be in IncX3 and IncHI2 plasmid. The IncX3 plasmid was 46,161 bp in size and identical to other reports. IncHI2 plasmid was 246,593 bp in size and similar to other IncHI2-ST3 plasmids. It consisted of a typical IncHI2 plasmid backbone region and a multiresistance region (MRR). The blaNDM-5 was closely associated with the IS3000-ISAba125-blaNDM-5-bleMBL-trpF-tat-IS26 unit. We first reported the blaNDM-5-carrying IncHI2 in E. coli isolates recovered from pigs and revealed the molecular characterization. Continued surveillance for the dissemination of blaNDM-5 among food-producing animals is required.

Genetic Feature Fusion for Object Skeleton Detection

Object skeleton detection requires the convolutional neural networks to recognize objects and their parts in the cluttered background, overcome the image definition degradation brought by the pooling layers, and predict the location of skeleton pixels in different scale granularity. Most existing object skeleton detection methods take great efforts into the designing of side-output networks for multiscale feature fusion. Despite the great progress achieved by them, there are still many problems that hinder the development of object skeleton detection, such as the manually designed network is labor-intensive and the network initialization depends on models pretrained on large-scale datasets. To alleviate these issues, we propose a genetic NAS method to automatically search on a newly designed architecture search space for adaptive multiscale feature fusion. Furthermore, we introduce a symmetric encoder-decoder search space based on reversing the VGG network, in which the decoder can reuse the ImageNet pretrained model of VGG. The searched networks improve the performance of the state-of-the-art methods on commonly used skeleton detection benchmarks, which proves the efficacy of our method.

Disentangling genetic feature selection and aggregation in transcriptome-wide association studies

ABSTRACTThe success of transcriptome-wide association studies (TWAS) has led to substantial research towards improving its core component of genetically regulated expression (GReX). GReX links expression information with phenotype by serving as both the outcome of genotype-based expression models and the predictor for downstream association testing. In this work, we demonstrate that current linear models of GReX inadvertently combine two separable steps of machine learning - feature selection and aggregation - which can be independently replaced to improve overall power. We show that the monolithic approach of GReX limits the adaptability of TWAS methodology and practice, especially given low expression heritability.