Proteomic Analysis of Differentially Expressed Proteins in A549 Cells Infected with H9N2 Avian Influenza Virus.

Influenza A viruses (IAVs) are highly contagious pathogens that cause zoonotic disease with limited availability of antiviral therapies, presenting ongoing challenges to both public health and the livestock industry. Unveiling host proteins that are crucial to the IAV life cycle can help clarify mechanisms of viral replication and identify potential targets for developing alternative host-directed therapies. Using a four-dimensional (4D), label-free methodology coupled with bioinformatics analysis, we analyzed the expression patterns of cellular proteins that changed following H9N2 virus infection.

SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification.

Introduction: In clinical, the echocardiogram is the most widely used for diagnosing heart diseases. Different heart diseases are diagnosed based on different views of the echocardiogram images, so efficient echocardiogram view classification can help cardiologists diagnose heart disease rapidly. Echocardiogram view classification is mainly divided into supervised and semi-supervised methods.

Differential responses of plant and microbial respiration to extreme precipitation and drought during spring and summer in the Eurasian meadow steppe.

Increasing extreme precipitation and drought events along changes in their seasonal patterns due to climate change are expected to have profound consequences for carbon cycling. However, how these climate extremes impact ecosystem respiration (R) and whether these impacts differ between seasons remain unclear. Here, we reveal the responses of R and its components to extreme precipitation and drought in spring and summer by conducting a five-year manipulative experiment in a temperate meadow steppe.

Convergent-Diffusion Denoising Model for multi-scenario CT Image Reconstruction.

A generic and versatile CT Image Reconstruction (CTIR) scheme can efficiently mitigate imaging noise resulting from inherent physical limitations, substantially bolstering the dependability of CT imaging diagnostics across a wider spectrum of patient cases. Current CTIR techniques often concentrate on distinct areas such as Low-Dose CT denoising (LDCTD), Sparse-View CT reconstruction (SVCTR), and Metal Artifact Reduction (MAR). Nevertheless, due to the intricate nature of multi-scenario CTIR, these techniques frequently narrow their focus to specific tasks, resulting in limited generalization capabilities for diverse scenarios.

GATv2EPI: Predicting Enhancer-Promoter Interactions with a Dynamic Graph Attention Network.

Background: The enhancer-promoter interaction (EPI) is a critical component of gene regulatory networks, playing a significant role in understanding the complexity of gene expression. Traditional EPI prediction methods focus on one-to-one interactions, neglecting more complex one-to-many and many-to-many patterns. To address this gap, we utilize graph neural networks to comprehensively explore all interaction patterns between enhancers and promoters, capturing complex regulatory relationships for more accurate predictions.

GLNet: global-local feature network for wheat leaf disease image classification.

Addressing the issues with insufficient multi-scale feature perception and incomplete understanding of global information in traditional convolutional neural networks for image classification of wheat leaf disease, this paper proposes a global local feature network, i.e. GLNet, which adopts a unique global-local convolutional neural network architecture, realizes the comprehensive capturing of multi-scale features in an image by processing the global feature block and local feature block in parallel and integrating the information of both of them with the help of a feature fusion block.

Forest pest monitoring and early warning using UAV remote sensing and computer vision techniques.

Unmanned aerial vehicle (UAV) remote sensing has revolutionized forest pest monitoring and early warning systems. However, the susceptibility of UAV-based object detection models to adversarial attacks raises concerns about their reliability and robustness in real-world deployments. To address this challenge, we propose SC-RTDETR, a novel framework for secure and robust object detection in forest pest monitoring using UAV imagery.

Physiological mechanisms of Carya illinoensis tolerance to manganese stress.

Manganese (Mn) is an essential element for plant growth but can be toxic at high levels. Pecan (Carya illinoensis), an important nut-producing species, has been observed to exhibit tolerance to high Mn levels. In this study, pecan seedlings were exposed to a nutrient solution containing either 2 μM (control) or 1000 μM (excess) MnSO to investigate the physiological mechanisms.

Conference 2024: Building an innovative scientific research ecosystem for microbiome and One Health.

The Conference 2024 provides a platform to promote the development of an innovative scientific research ecosystem for microbiome and One Health. The four key components - Technology, Research (Biology), Academic journals, and Social media - form a synergistic ecosystem. Advanced technologies drive biological research, which generates novel insights that are disseminated through academic journals.

Chlorophyll-Based Optogenetics to Control Membraneless Organelles.

Membraneless organelles (MLOs) formed via protein phase separation have garnered significant attention recently due to their relevance to cellular physiology and pathology. However, there is a lack of tools available to study their behavior and control their bioactivity in complex biological systems. This chapter describes a new optogenetic tool based on water-soluble chlorophyll protein (WSCP), a red light-induced singlet oxygen-generating protein, to control synthetic MLOs.

Visual codon: a user-friendly Python program for viewing and optimizing gene GC content.

Due to the codon bias of different species, codon optimization is usually carried out in the process of heterologous protein expression. At present, there are a variety of codon optimization tools. However, the optimized sequences may still have high or low points of local guanine and cytosine (GC) content, which is not conducive to the primer design of gene subcloning, and also makes it difficult to perform the experiment of synthesizing the whole gene with DNA fragments by polymerase chain reaction (PCR) reaction.

GAADE: identification spatially variable genes based on adaptive graph attention network.

The rapid advancement of spatial transcriptomics (ST) sequencing technology has made it possible to capture gene expression with spatial coordinate information at the cellular level. Although many methods in ST data analysis can detect spatially variable genes (SVGs), these methods often fail to identify genes with explicit spatial expression patterns due to the lack of consideration for spatial domains. Considering spatial domains is crucial for identifying SVGs as it focuses the analysis of gene expression changes on biologically relevant regions, aiding in the more accurate identification of SVGs associated with specific cell types.

Meta learning for mutant HLA class I epitope immunogenicity prediction to accelerate cancer clinical immunotherapy.

Accurate prediction of binding between human leukocyte antigen (HLA) class I molecules and antigenic peptide segments is a challenging task and a key bottleneck in personalized immunotherapy for cancer. Although existing prediction tools have demonstrated significant results using established datasets, most can only predict the binding affinity of antigenic peptides to HLA and do not enable the immunogenic interpretation of new antigenic epitopes. This limitation results from the training data for the computational models relying heavily on a large amount of peptide-HLA (pHLA) eluting ligand data, in which most of the candidate epitopes lack immunogenicity.

Control Intracellular Protein Condensates with Light.

Protein phase transitions are gaining traction among biologists for their wide-ranging roles in biological regulation. However, achieving precise control over these phenomena in vivo remains a formidable task. Optogenetic techniques present us with a potential means to control protein phase behavior with spatiotemporal precision.

Sparse Spike Feature Learning to Recognize Traceable Interictal Epileptiform Spikes.

Interictal epileptiform spikes (spikes) and epileptogenic focus are strongly correlated. However, partial spikes are insensitive to epileptogenic focus, which restricts epilepsy neurosurgery. Therefore, identifying spike subtypes that are strongly associated with epileptogenic focus (traceable spikes) could facilitate their use as reliable signal sources for accurately tracing epileptogenic focus.

Advancing prognostic precision in gastric cancer with an immunoinflammatory index.

Gastric cancer remains a major global health challenge with high morbidity and mortality rates. Recent advancements in immunology and inflammation research have highlighted the crucial roles that these biological processes play in tumor progression and patient outcomes. This has sparked new interest in developing prognostic biomarkers that integrate these two key biological processes.

Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual Graph Contrastive Learning.

Recent advances in spatial transcriptomics have enabled simultaneous preservation of high-throughput gene expression profiles and the spatial context, enabling high-resolution exploration of distinct regional characterization in tissue. To effectively understand the underlying biological mechanisms within tissue microenvironments, there is a requisite for methods that can accurately capture external spatial heterogeneity and interpret internal gene regulation from spatial transcriptomics data. However, current methods for region identification often lack the simultaneous characterizing of spatial structure and gene regulation, thereby limiting the ability of spatial dissection and gene interpretation.

Machine learning prediction of fundamental sewage sludge biochar properties based on sludge characteristics and pyrolysis conditions.

Sewage sludge biochar (SSBC) has significant potential for resource recovery from sewage sludge (SS) and has been widely studied and applied across various fields. However, the variability in SSBC properties, resulting from the diverse nature of SS and its intricate interaction with pyrolysis conditions, presents notable challenges to its practical use. This research employed machine learning techniques to predict fundamental SSBC properties, including elemental content, proximate compositions, surface area, and yield, which are essential for assessing the applicability of SSBC.

Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm.

Despite the implementation of numerous interventions to enhance urban traffic safety, the estimation of the risk of traffic crashes resulting in life-threatening and economic costs remains a significant challenge. In light of the above, an online inference method for traffic crash risk based on the self-developed TAR-DETR and WOA-SA-SVM methods is proposed. The method's robust data inference capabilities can be applied to autonomous mobile robots and vehicle systems, enabling real-time road condition prediction, continuous risk monitoring, and timely roadside assistance.

ABA-activated low-nanomolar Ca-CPK signalling controls root cap cycle plasticity and stress adaptation.

Abscisic acid (ABA) regulates plant stress adaptation, growth and reproduction. Despite extensive ABA-Ca signalling links, imaging ABA-induced increases in Ca concentration has been challenging, except in guard cells. Here we visualize ABA-triggered [Ca] dynamics in diverse organs and cell types of Arabidopsis thaliana using a genetically encoded Ca ratiometric sensor with a low-nanomolar Ca-binding affinity and a large dynamic range.

An improved face attributes editing method based on DDIM.

The main advantage of DDIM is that it guarantees the quality of the generated images while increasing the efficiency of the generation by modifying the sampling strategy in the diffusion process. DiffusionRig, which addresses the problem of maintaining identity consistency by learning the person-specific facial prior in a tiny personalized dataset, is a successful representation of the DDIM strategy. Based on DiffusionRig, in this article, we propose an improved face attributes editing method based on DDIM to improve naturalness and accuracy of editing results in complex face attribute editing tasks and the generalization ability.