An improved ShuffleNetV2 method based on ensemble self-distillation for tomato leaf diseases recognition.

Introduction: Timely and accurate recognition of tomato diseases is crucial for improving tomato yield. While large deep learning models can achieve high-precision disease recognition, these models often have a large number of parameters, making them difficult to deploy on edge devices. To address this issue, this study proposes an ensemble self-distillation method and applies it to the lightweight model ShuffleNetV2.

Improving long short-term memory (LSTM) networks for arbitrage spread forecasting: integrating cuckoo and zebra algorithms in chaotic mapping space for enhanced accuracy.

Long short-term memory (LSTM) networks, widely used for financial time series forecasting, face challenges in arbitrage spread prediction, especially in hyperparameter tuning for large datasets. These issues affect model complexity and adaptability to market dynamics. Existing heuristic algorithms for LSTM often struggle to capture the complex dynamics of futures spread data, limiting prediction accuracy.

Muscle Fiber-Inspired High-Performance Strain Sensors for Motion Recognition and Control.

The rapid development of wearable technology, flexible electronics, and human-machine interaction has brought about revolutionary changes to the fields of motion analysis and physiological monitoring. Sensors for detecting human motion and physiological signals have become a hot topic of current research. Inspired by the muscle fiber structure, this paper proposed a highly stable strain sensor that was composed of stretchable Spandex fibers (SPF), multiwalled carbon nanotubes (MWCNTs), and silicone rubber (Ecoflex).

Thermodynamically Dependent Behavior in Gas Transport in Two-Dimensional Graphene Nanochannels.

Gas transport through nanochannels has aroused significant interest in many fields. Recently, "ballistic transport" of gas was observed through a two-dimensional graphene nanochannel, and it causes a peculiar enhancement compared to the predictions of the Knudson theory. Many studies attributed this effect to the specular reflection caused by the atomically smooth surface of the channel.

Highly Sensitive and Wide Detection Range Thermoplastic Polyurethane/Graphene Nanoplatelets Multifunctional Strain Sensor with a Porous and Crimped Network Structure.

High-performance flexible strain sensors have tremendous potential applications in wearable devices and health monitoring. However, developing a flexible strain sensor with high sensitivity over a wide strain range remains a significant challenge. In this study, a fibrous membrane with a porous and crimped structure was designed as the substrate material for TPU/GNPs flexible strain sensors.

Ab Initio Prediction of 3-D Conformations for Protein Long Loops with High Accuracy and Applications to Antibody CDRH3 Modeling.

Residue-level potentials of mean force were widely used for protein backbone refinements to avoid simultaneous sampling of side-chain conformations. The interaction energy between the reduced side chains and backbone atoms was not considered explicitly. In this study, we developed novel methods to calculate the residue-atom interaction energy in combination with atomic and residue-level terms.

Global identifiability of latent class models with applications to diagnostic test accuracy studies: A Gröbner basis approach.

Identifiability of statistical models is a fundamental regularity condition that is required for valid statistical inference. Investigation of model identifiability is mathematically challenging for complex models such as latent class models. Jones et al.

Identification of individuals by trait prediction using whole-genome sequencing data.

Prediction of human physical traits and demographic information from genomic data challenges privacy and data deidentification in personalized medicine. To explore the current capabilities of phenotype-based genomic identification, we applied whole-genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a cohort of 1,061 participants of diverse ancestry. Individually, for a large fraction of the traits, their predictive accuracy beyond ancestry and demographic information is limited.

Evaluating whole genome sequence data from the Genetic Absence Epilepsy Rat from Strasbourg and its related non-epileptic strain.

Objective: The Genetic Absence Epilepsy Rats from Strasbourg (GAERS) are an inbreed Wistar rat strain widely used as a model of genetic generalised epilepsy with absence seizures. As in humans, the genetic architecture that results in genetic generalized epilepsy in GAERS is poorly understood. Here we present the strain-specific variants found among the epileptic GAERS and their related Non-Epileptic Control (NEC) strain.

Delay-Dependent Global Exponential Stability for Delayed Recurrent Neural Networks.

This paper deals with the global exponential stability for delayed recurrent neural networks (DRNNs). By constructing an augmented Lyapunov-Krasovskii functional and adopting the reciprocally convex combination approach and Wirtinger-based integral inequality, delay-dependent global exponential stability criteria are derived in terms of linear matrix inequalities. Meanwhile, a general and effective method on global exponential stability analysis for DRNNs is given through a lemma, where the exponential convergence rate can be estimated.

Clinical application of whole-genome sequencing in patients with primary immunodeficiency.

This report illustrates the value of whole genome sequencing (WGS) in elucidating the genetic cause of disease in patients with primary immunodeficiency (PID). As sequencing costs decline, we predict that utilization of next generation sequencing (NGS) in the clinical setting will increase.

Using ERDS to infer copy-number variants in high-coverage genomes.

Although there are many methods available for inferring copy-number variants (CNVs) from next-generation sequence data, there remains a need for a system that is computationally efficient but that retains good sensitivity and specificity across all types of CNVs. Here, we introduce a new method, estimation by read depth with single-nucleotide variants (ERDS), and use various approaches to compare its performance to other methods. We found that for common CNVs and high-coverage genomes, ERDS performs as well as the best method currently available (Genome STRiP), whereas for rare CNVs and high-coverage genomes, ERDS performs better than any available method.

Prioritizing genetic variants for causality on the basis of preferential linkage disequilibrium.

To date, the widely used genome-wide association studies (GWASs) of the human genome have reported thousands of variants that are significantly associated with various human traits. However, in the vast majority of these cases, the causal variants responsible for the observed associations remain unknown. In order to facilitate the identification of causal variants, we designed a simple computational method called the "preferential linkage disequilibrium (LD)" approach, which follows the variants discovered by GWASs to pinpoint the causal variants, even if they are rare compared with the discovery variants.

The association of genetic variants with hepatic steatosis in patients with genotype 1 chronic hepatitis C infection.

Background: Single-nucleotide polymorphisms (SNPs) in the IL28B and PNPLA3 gene regions have been associated with hepatic steatosis in genotype 1 (G1) chronic HCV infection but their clinical impacts remain to be determined.

Aim: We sought to validate these associations and to explore their impact on treatment response to peginterferon and ribavirin therapy.

Methods: A total of 972 G1 HCV-infected Caucasian patients were genotyped for the SNPs rs12979860 (IL28B) and rs2896019 (PNPLA3).

Genome-wide association study of interferon-related cytopenia in chronic hepatitis C patients.

Background & Aims: Interferon-alfa (IFN)-related cytopenias are common and may be dose-limiting. We performed a genome wide association study on a well-characterized genotype 1 HCV cohort to identify genetic determinants of peginterferon-α (pegIFN)-related thrombocytopenia, neutropenia, and leukopenia.

Methods: 1604/3070 patients in the IDEAL study consented to genetic testing.

SVA: software for annotating and visualizing sequenced human genomes.

Summary: Here we present Sequence Variant Analyzer (SVA), a software tool that assigns a predicted biological function to variants identified in next-generation sequencing studies and provides a browser to visualize the variants in their genomic contexts. SVA also provides for flexible interaction with software implementing variant association tests allowing users to consider both the bioinformatic annotation of identified variants and the strength of their associations with studied traits. We illustrate the annotation features of SVA using two simple examples of sequenced genomes that harbor Mendelian mutations.

A genome-wide comparison of the functional properties of rare and common genetic variants in humans.

One of the longest running debates in evolutionary biology concerns the kind of genetic variation that is primarily responsible for phenotypic variation in species. Here, we address this question for humans specifically from the perspective of population allele frequency of variants across the complete genome, including both coding and noncoding regions. We establish simple criteria to assess the likelihood that variants are functional based on their genomic locations and then use whole-genome sequence data from 29 subjects of European origin to assess the relationship between the functional properties of variants and their population allele frequencies.

The characterization of twenty sequenced human genomes.

We present the analysis of twenty human genomes to evaluate the prospects for identifying rare functional variants that contribute to a phenotype of interest. We sequenced at high coverage ten "case" genomes from individuals with severe hemophilia A and ten "control" genomes. We summarize the number of genetic variants emerging from a study of this magnitude, and provide a proof of concept for the identification of rare and highly-penetrant functional variants by confirming that the cause of hemophilia A is easily recognizable in this data set.