DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information.

Bacteriophages are the viruses that infect bacterial cells. They are the most diverse biological entities on earth and play important roles in microbiome. According to the phage lifestyle, phages can be divided into the virulent phages and the temperate phages.

scLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) enables the exploration of biological heterogeneity among different cell types within tissues at a resolution. Inferring cell types within tissues is foundational for downstream research. Most existing methods for cell type inference based on scRNA-seq data primarily utilize highly variable genes (HVGs) with higher expression levels as clustering features, overlooking the contribution of HVGs with lower expression levels.

Researching the CNN Collaborative Inference Mechanism for Heterogeneous Edge Devices.

Convolutional Neural Networks (CNNs) have been widely applied in various edge computing devices based on intelligent sensors. However, due to the high computational demands of CNN tasks, the limited computing resources of edge intelligent terminal devices, and significant architectural differences among these devices, it is challenging for edge devices to independently execute inference tasks locally. Collaborative inference among edge terminal devices can effectively utilize idle computing and storage resources and optimize latency characteristics, thus significantly addressing the challenges posed by the computational intensity of CNNs.

SVDF: enhancing structural variation detect from long-read sequencing via automatic filtering strategies.

Structural variation (SV) is an important form of genomic variation that influences gene function and expression by altering the structure of the genome. Although long-read data have been proven to better characterize SVs, SVs detected from noisy long-read data still include a considerable portion of false-positive calls. To accurately detect SVs in long-read data, we present SVDF, a method that employs a learning-based noise filtering strategy and an SV signature-adaptive clustering algorithm, for effectively reducing the likelihood of false-positive events.

Counteractive effects of predator invasion and habitat destruction on predator-prey systems.

Alien species invasion and habitat destruction are among the primary threats to native animal communities, particularly for native predator-prey systems. However, when predator invasion and habitat destruction co-occur, it remains unclear whether their respective threats to native systems compensate each other or accumulate, as well as how these effects respond to the different characteristics of predator invasion and habitat destruction. In this study, we developed a spatially explicit simulation model with one prey species and one predator species and exposed it to invasive predators and habitat destruction with different properties.

Enhanced Interfacial Electron Transfer in Photocatalyst-Natural Enzyme Coupled Artificial Photosynthesis System: Tuning Strategies and Molecular Simulations.

Laccase is capable of catalyzing a vast array of reactions, but its low redox potential limits its potential applications. The use of photocatalytic materials offers a solution to this problem by converting absorbed visible light into electrons to facilitate enzyme catalysis. Herein, MIL-53(Fe) and NH-MIL-53(Fe) serve as both light absorbers and enzyme immobilization carriers, and laccase is employed for solar-driven chemical conversion.

Tripartite evolutionary game and simulation analysis of agricultural non-point source pollution control.

Agricultural non-point source pollution control (ANSPC) is a complex, long-term and dynamic environmental protection process. In order to motivate multiple subjects to participate in ANSPC, this paper constructs a tripartite evolutionary game model of local government, village collectives and farmers, which explores the strategic choices and influencing factors of different subjects through simulation analysis. The results indicate that: There are five stable strategy points in the ANSPC game system, which can be divided into four stages based on subject interactions.

Mechanical Behavior of 3D-Printed Thickness Gradient Honeycomb Structures.

In order to obtain a lightweight, high-strength, and customizable cellular structure to meet the needs of modern production and life, the mechanical properties of four thickness gradient honeycomb structures were studied. In this paper, four types of honeycomb structure specimens with the same porosity and different Poisson's ratios were designed and manufactured by using SLA 3D-printing technology, including the honeycomb, square honeycomb, quasi-square honeycomb, and re-entrant honeycomb structures. Based on the plane compression mechanical properties and failure mode analysis of these specimens, the thickness gradient is applied to the honeycomb structure, and four structural forms of the thickness gradient honeycomb structure are formed.

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints.

Hierarchical multimodal self-attention-based graph neural network for DTI prediction.

Drug-target interactions (DTIs) are a key part of drug development process and their accurate and efficient prediction can significantly boost development efficiency and reduce development time. Recent years have witnessed the rapid advancement of deep learning, resulting in an abundance of deep learning-based models for DTI prediction. However, most of these models used a single representation of drugs and proteins, making it difficult to comprehensively represent their characteristics.

LGC-DBP: the method of DNA-binding protein identification based on PSSM and deep learning.

The recognition of DNA Binding Proteins (DBPs) plays a crucial role in understanding biological functions such as replication, transcription, and repair. Although current sequence-based methods have shown some effectiveness, they often fail to fully utilize the potential of deep learning in capturing complex patterns. This study introduces a novel model, LGC-DBP, which integrates Long Short-Term Memory (LSTM), Gated Inception Convolution, and Improved Channel Attention mechanisms to enhance the prediction of DBPs.

scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering.

Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering.

The prognostic impact of pathogenic stromal cell-associated genes in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) stands as the most prevalent subtype among lung cancers. Interactions between stromal and cancer cells influence tumor growth, invasion, and metastasis. However, the regulatory mechanisms of stromal cells in the lung adenocarcinoma tumor microenvironment remain unclear.

The landscape of programmed cell death-related lncRNAs in Alzheimer's disease and Parkinson's disease.

This study delivers a thorough analysis of long non-coding RNAs (lncRNAs) in regulating programmed cell death (PCD), vital for neurodegenerative diseases like Alzheimer's disease (AD) and Parkinson's disease (PD). We propose a new framework PCDLnc, and identified 20 significant lncRNAs, including HEIH, SNHG15, and SNHG5, associated with PCD gene sets, which were known for roles in proliferation and apoptosis in neurodegenerative diseases. By using GREAT software, we identified regulatory functions of top lncRNAs in different neurodegenerative diseases.

Fine-Grained Cross-Modal Semantic Consistency in Natural Conservation Image Data from a Multi-Task Perspective.

Fine-grained representation is fundamental to species classification based on deep learning, and in this context, cross-modal contrastive learning is an effective method. The diversity of species coupled with the inherent contextual ambiguity of natural language poses a primary challenge in the cross-modal representation alignment of conservation area image data. Integrating cross-modal retrieval tasks with generation tasks contributes to cross-modal representation alignment based on contextual understanding.

CapsNet-TIS: Predicting translation initiation site based on multi-feature fusion and improved capsule network.

Genes are the basic units of protein synthesis in organisms, and accurately identifying the translation initiation site (TIS) of genes is crucial for understanding the regulation, transcription, and translation processes of genes. However, the existing models cannot adequately extract the feature information in TIS sequences, and they also inadequately capture the complex hierarchical relationships among features. Therefore, a novel predictor named CapsNet-TIS is proposed in this paper.

Characterization and epidemiologic analysis of mycoplasmal pneumonia of sheep in Qinghai Province.

Mycoplasmal pneumonia in sheep and goats usually result covert but huge economic losses in the sheep and goat industry. The disease is prevalent in various countries in Africa and Asia. Clinical manifestations in affected animals include anorexia, fever, and respiratory symptoms such as dyspnea, polypnea, cough, and nasal discharge.

HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies.

Background: The identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects.

Low illumination fog noise image denoising method based on ACE-GPM.

The Perona-Malik (P-M) model exhibits deficiencies such as noise amplification, new noise introduction, and significant gradient effects when processing noisy images. To address these issues, this paper proposes an image-denoising algorithm, ACE-GPM, which integrates an Automatic Color Equalization (ACE) algorithm with a gradient-adjusted P-M model. Initially, the ACE algorithm is employed to enhance the contrast of low-light images obscured by fog and noise.

Transcriptomic insights unveil the crucial roles of cytochromes, NADH, and pili in Ag(I) reduction by Geobacter sulfurreducens.

Silver (Ag) is a pivotal transition metal with applications in multiple industries, necessitating efficient recovery techniques. Despite various proposed methods for silver recovery from wastewaters, challenges persist especially for low concentrations. In this context, bioreduction by bacteria like Geobacter sulfurreducens, offers a promising approach by converting Ag(I) to Ag nanoparticles.

CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data.

In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales.