Research on Yaw Stability Control Strategy for Distributed Drive Electric Trucks.

With the advancement of vehicle electrification and intelligence, distributed drive electric trucks have emerged as the preferred choice for heavy-duty electric trucks. However, the control of yaw stability remains a significant issue. To tackle this concern, this study introduces a layered control strategy for yaw moment.

Native diversity buffers against severity of non-native tree invasions.

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies. Here, leveraging global tree databases, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity.

Deciphering peanut complex genomes paves a way to understand its origin and domestication.

Peanut (Arachis) is a key oil and protein crop worldwide with large genome. The genomes of diploid and tetraploid peanuts have been sequenced, which were compared to decipher their genome structures, evolutionary, and life secrets. Genome sequencing efforts showed that different cultivars, although Bt homeologs being more privileged in gene retention and gene expression.

Class-Specific Distribution Alignment for semi-supervised medical image classification.

Despite the success of deep neural networks in medical image classification, the problem remains challenging as data annotation is time-consuming, and the class distribution is imbalanced due to the relative scarcity of diseases. To address this problem, we propose Class-Specific Distribution Alignment (CSDA), a semi-supervised learning framework based on self-training that is suitable to learn from highly imbalanced datasets. Specifically, we first provide a new perspective to distribution alignment by considering the process as a change of basis in the vector space spanned by marginal predictions, and then derive CSDA to capture class-dependent marginal predictions on both labeled and unlabeled data, in order to avoid the bias towards majority classes.

Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment.

Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are often unevenly distributed in medical images, which severely affects the classification performance on minority classes.

May microbial ecological baseline exist in continental groundwater?

Background: Microbes constitute almost the entire biological community in subsurface groundwater and play an important role in ecological evolution and global biogeochemical cycles. Ecological baseline as a fundamental reference with less human interference has been investigated in surface ecosystems such as soils, rivers, and ocean, but the existence of groundwater microbial ecological baseline (GMEB) is still an open question so far.

Results: Based on high-throughput sequencing information derived from national monitoring of 733 newly constructed wells, we find that bacterial communities in pristine groundwater exhibit a significant lateral diversity gradient and gradually approach the topsoil microbial latitudinal diversity gradient with decreasing burial depth of phreatic water.

Identification of cell subpopulations associated with disease phenotypes from scRNA-seq data using PACSI.

Background: Single-cell RNA sequencing (scRNA-seq) has revolutionized the transcriptomics field by advancing analyses from tissue-level to cell-level resolution. Despite the great advances in the development of computational methods for various steps of scRNA-seq analyses, one major bottleneck of the existing technologies remains in identifying the molecular relationship between disease phenotype and cell subpopulations, where "disease phenotype" refers to the clinical characteristics of each patient sample, and subpopulation refer to groups of single cells, which often do not correspond to clusters identified by standard single-cell clustering analysis. Here, we present PACSI, a method aimed at distinguishing cell subpopulations associated with disease phenotypes at the single-cell level.

Wood Veneer Defect Detection Based on Multiscale DETR with Position Encoder Net.

Wood is one of the main building materials. However, defects on veneers result in substantial waste of wood resources. Traditional veneer defect detection relies on manual experience or photoelectric-based methods, which are either subjective and inefficient or need substantial investment.

Prediction of composting maturity and identification of critical parameters for green waste compost using machine learning.

Ensuring the maturity of green waste compost is crucial to composting processes and quality control of compost products. However, accurate prediction of green waste compost maturity remains a challenge, as there are limited computational methods available. This study aimed to address this issue by employing four machine learning models to predict two indicators of green waste compost maturity: seed germination index (GI) and T value.

DETIRE: a hybrid deep learning model for identifying viral sequences from metagenomes.

A metagenome contains all DNA sequences from an environmental sample, including viruses, bacteria, archaea, and eukaryotes. Since viruses are of huge abundance and have caused vast mortality and morbidity to human society in history as a type of major pathogens, detecting viruses from metagenomes plays a crucial role in analyzing the viral component of samples and is the very first step for clinical diagnosis. However, detecting viral fragments directly from the metagenomes is still a tough issue because of the existence of a huge number of short sequences.

Application of artificial intelligence-based methods in bioelectrochemical systems: Recent progress and future perspectives.

Bioelectrochemical Systems (BESs) leverage microbial metabolic processes to either produce electricity by degrading organic matter or consume electricity to assist metabolism, and can be used for various applications such as energy production, wastewater treatment, and bioremediation. Given the intricate mechanisms of BESs, the application of artificial intelligence (AI)-based methods have been proposed to enhance the performance of BESs due to their capability to identify patterns and gain insights through data analysis. This review focuses on the analysis and comparison of AI algorithms commonly used in BESs, including artificial neural network (ANN), genetic programming (GP), fuzzy logic (FL), support vector regression (SVR), and adaptive neural fuzzy inference system (ANFIS).

DeepITEH: a deep learning framework for identifying tissue-specific eRNAs from the human genome.

Motivation: Enhancers are vital cis-regulatory elements that regulate gene expression. Enhancer RNAs (eRNAs), a type of long noncoding RNAs, are transcribed from enhancer regions in the genome. The tissue-specific expression of eRNAs is crucial in the regulation of gene expression and cancer development.

Programmable, Changeable, Origami Cellulose Films for Magnetically Controllable Soft Robots.

Magnetic soft robots composed of stimuli-responsive materials are promising for biomedical engineering applications; however, typical responsive materials are fabricated with nondegradable polymeric substrates. In this study, we report a flexible, biodegradable, and magnetically sensitive cellulose film (M-film) that can be utilized for magnetically controllable soft robots (M-robots) with programmable locomotion, cargo delivery, and remote wireless operation functions. The M-film with good foldability, origami, and magnetic properties is synthesized by a simple paper-making process using cellulose nanofibers, additive sodium alginate, and BaFeO particles.

Molecular Cloning and Characterization of WRKY12, A Pathogen Induced WRKY Transcription Factor from .

WRKY transcription factors (TFs), which are plant-specific TFs, play significant roles in plant defense. Here, a pathogen-induced WRKY gene, named , which was the homologous gene of , was isolated from . The gene has a total length of 645 nucleotides and an open reading frame (ORF) encoding 214 amino acid polypeptides.

A computational study of the influence of nanoparticle shape on clathrin-mediated endocytosis.

Nanoparticles have been widely used in biomedical applications such as gene/drug delivery, molecular imaging and diagnostics. Among the physicochemical properties, shape is a vital design parameter for tuning the cell uptake of nanoparticles. However, the regulatory mechanism remains elusive due to the complexity of the cell membrane and multiple pathways of cell uptake.

Classification of early mechanical damage over time in pears based on hyperspectral imaging and transfer learning.

Mechanical damage of fresh fruit caused by compression and collision during harvesting and transportation is an urgent problem in the agricultural industry. The purpose of this work was to detect early mechanical damage of pears using hyperspectral imaging technology and advanced modeling techniques of transfer learning and convolutional neural networks. The visible/near-infrared hyperspectral imaging system was applied to obtain the intact and damaged pears at three time points (2, 12, and 24 h) after compression or collision damage.

Distract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition.

This paper presents a novel facial expression recognition network, called Distract your Attention Network (DAN). Our method is based on two key observations in biological visual perception. Firstly, multiple facial expression classes share inherently similar underlying facial appearance, and their differences could be subtle.

Functional Mapping of Genes Modulating Plant Shade Avoidance Using Leaf Traits.

Shade avoidance syndrome (SAS) refers to a set of plant responses that increases light capture in dense stands. This process is crucial for plants in natural and agricultural environments as they compete for resources and avoid suboptimal conditions. Although the molecular, biochemical, and physiological mechanisms underlying the SAS response have been extensively studied, the genetic basis of developmental variation in leaves in regard to leaf area, petiole length, and leaf length (i.

Effects of Konjac Glucomannan/ε-Polylysine Hydrochloride/Ferulic Acid Composite Coating on the Freshness Preservation Performance and Flavor of Refrigerated Sea Bass Fillets.

Coating preservation has a remarkable effect on the preservation of aquatic products. This work prepared a composite coating using konjac glucomannan (KGM) as the film-forming matrix and ε-polylysine hydrochloride (ε-PL) and ferulic acid (FA) as the preservative. Three types of treated sea bass (KGM, KGM-ε-PL, and KGM-ε-PL-FA) and untreated sea bass were stored at 4 °C for 20 days to compare freshness changes under different treatment conditions.

Research on trajectory tracking and body attitude control of autonomous ground vehicle based on differential steering.

The differential steering can be used not only as the backup system of steer-by-wire, but also as the only steering system. Because the differential steering is realized through the differential moment between the coaxial left and right driving wheels, the sharp reduction of the load on the inner driving wheel will directly lead to the failure of the differential steering when the four-wheel independent drive electric vehicle approaches the rollover. Therefore, this paper not only realizes the trajectory tracking of autonomous ground vehicle through the differential steering, but also puts forward the body attitude control to improve the handling stability.

Construction and sustained release of konjac glucomannan/naringin composite gel spheres.

Objective: To improve the bioavailability of active substances and reduce the toxic and side effects on the human body, natural biological macromolecules are used to load active substances and control their release speed in different environments of the human body. In this study, mesoporous silica (MSN) was combined with konjac glucomannan (KGM) and sodium alginate (AC) to prepare pH-sensitive konjac glucomannan/sodium alginate-mesoporous silica loaded naringin gel spheres (KS/MSN). On this basis, the structure, morphology, and release properties of the composite gel spheres were characterized.

Evaluating adoption of climate smart agricultural practices among farmers in the Fujian Province, China.

This study examined the adaptation level of climate-smart agricultural (CSA) practices among the farmers and the factors influencing the adoption of CSA practices in the Fujian Province, China. In this study, questionnaire survey data was analyzed, and 600 respondents were randomly selected as sample through randomly sampling technique. Descriptive statistics, adoption level index (ALI), and binary logit model were used for data analyses.

Screening and Mechanism of Novel Angiotensin-I-Converting Enzyme Inhibitory Peptides in Seed Meal: A Computer-Assisted Experimental Study Method.

Angiotensin-I-converting enzyme (ACE) inhibitors are used extensively to control hypertension. In this study, a computer-assisted experimental approach was used to screen ACE-inhibiting peptides from seed meal (XSM). The process conditions for XSM hydrolysis were optimized through the orthogonal experimental method combined with a database.

Utilization of current pyrolysis technology to convert biomass and manure waste into biochar for soil remediation: A review.

Traditional disposal of animal manures and lignocellulosic biomass is restricted by its inefficiency and sluggishness. To advance the carbon management and greenhouse gas mitigation, this review scrutinizes the effect of pyrolysis in promoting the sustainable biomass and manure disposal as well as stimulating the biochar industry development. This review has examined the advancement of pyrolysis of animal manure (AM) and lignocellulosic biomass (LB) in terms of efficiency, cost-effectiveness, and operability.