Despite their success in identifying genes that affect complex disease or traits, current genome-wide association studies (GWASs) based on a single SNP analysis are too simple to elucidate comprehensive picture of the genetic architecture phenotypes. A simultaneous large number SNPs, although statistically challenging, especially with small samples, is crucial for modeling.We propose two-stage procedure multi-SNP modeling and GWASs, by first producing 'preconditioned' response variable using...

SEMG signal is a bioelectrical produced by the contraction of human surface muscles. Human-computer interaction based on great significance in field rehabilitation robots. In this study, feature extraction method activated muscle regionis pr oposed, which study regionin forearm and hand movement. At same time, main research object multi-object intergroup closer to practical application environment. The new extracted fused with sample entropy wavelength obtain better features. After combining...

Although genome-wide association studies (GWAS) have proven powerful for comprehending the genetic architecture of complex traits, they are challenged by a high dimension single-nucleotide polymorphisms (SNPs) as predictors, presence environmental factors, and longitudinal or functional natures many traits diseases. To address these challenges, we propose high-dimensional varying-coefficient model incorporating aspects phenotypic into GWAS to formulate so-called fGWAS. The Bayesian group...

Real-time and efficient driver distraction detection is of great importance for road traffic safety assisted driving. The design a real-time lightweight model crucial in-vehicle edge devices that have limited computational resources. However, most existing approaches focus on lighter more architectures, ignoring the cost losing tiny target performance comes with lightweighting. In this paper, we present MTNet, detector scenarios. MTNet consists multidimensional adaptive feature extraction...

Equivariance has been a long-standing concern in various fields ranging from computer vision to physical modeling. Most previous methods struggle with generality, simplicity, and expressiveness — some are designed ad hoc for specific data types, too complex be accessible, sacrifice flexible transformations. In this work, we propose novel simple framework achieve equivariance point cloud analysis based on the message passing (graph neural network) scheme. We find equivariant property could...

Molecular docking is a key task in computational biology that has attracted increasing interest from the machine learning community. While existing methods have achieved success, they generally treat each protein-ligand pair isolation. Inspired by biochemical observation ligands binding to same target protein tend adopt similar poses, we propose \textsc{GroupBind}, novel molecular framework simultaneously considers multiple protein. This introducing an interaction layer for group of and...

In this paper, we propose a revenue optimization framework integrating demand learning and dynamic pricing for firms in monopoly or oligopoly markets. We introduce state-space model management problem, which incorporates game-theoretic dynamics nonparametric techniques estimating the evolution of underlying state variables. Under framework, stringent assumptions are removed. develop new algorithm using Markov chain Monte Carlo methods to estimate parameters, unobserved variables, functional...

With the recent advent of high-throughput genotyping techniques, genetic data for genome-wide association studies (GWAS) have become increasingly available, which entails development efficient and effective statistical approaches. Although many such approaches been developed used to identify single-nucleotide polymorphisms (SNPs) that are associated with complex traits or diseases, few able detect gene–gene interactions among different SNPs. Genetic interactions, also known as epistasis,...

We used in situ atomic force microscope (AFM) to explore the exquisite structure of mitochondrial membranes under quasi-native conditions. The outer surface membrane is slightly rough and protein-embedded inside, while inner smooth intermembrane space protein-covered on matrix side.

Many phenomena of fundamental importance to biology and biomedicine arise as a dynamic curve, such organ growth HIV dynamics. The genetic mapping these traits is challenged by longitudinal variables measured at irregular possibly subject‐specific time points, in which case nonnegative definiteness the estimated covariance matrix needs be guaranteed. We present semiparametric approach for within mixture‐model setting jointly modeling mean structures data. Penalized spline used model functions...

Gesture recognition is one of the most promising subjects in field computer vision and artificial intelligence; development it will have a profound influence on research robot control Human-Machine Interface (HMI) image segmentation key step recognition. This paper based Kinect sensor developed by Microsoft gives an introduction its hardware structure measuring method depth camera. At same time we collect colour hand gesture images as samples, to introduce traditional skin HSV YCbCr space....

Aiming at the problem that stereo matching accuracy is easily affected by noise and amplitude distortion, a algorithm based on HSV color space improved census transform proposed. In cost calculation stage, image first converted from RGB to space; moreover, hue channel used as primitive establish absolute difference (HAD) function, which reduces amount of enhances robustness matching. Then, solve traditional overrelying central pixel improve resistance algorithm, an method neighborhood...

Multivalent tetraploids that include many plant species, such as potato, sugarcane, and rose, are of paramount importance to agricultural production biological research. Quantitative trait locus (QTL) mapping in multivalent is challenged by their unique cytogenetic properties, double reduction. We develop a statistical method for tetraploid QTLs considering these properties. This built the mixture model-based framework implemented with EM algorithm. The allows simultaneous estimation QTL...

Functional mapping is a statistical method for quantitative trait loci (QTLs) that regulate the dynamic pattern of biological trait. This integrates mathematical aspects complexity into mixture model genetic and tests effects QTLs by comparing genotype-specific curve parameters. As way quantitatively specifying behavior system, differential equations have proven to be powerful modeling unraveling biochemical, molecular, cellular mechanisms process, such as rhythms. The equipment functional...