Proteomic analysis of spinal dorsal horn in prior exercise protection against neuropathic pain.

Neuropathic pain (NP) is a complex and prevalent chronic pain condition that affects millions of individuals worldwide. Previous studies have shown that prior exercise protects against NP caused by nerve injury. However, the underlying mechanisms of this protective effect remain to be uncovered.

Scalable Synaptic Transistor Memory from Solution-Processed Carbon Nanotubes for High-Speed Neuromorphic Data Processing.

Neural networks as a core information processing technology in machine learning and artificial intelligence demand substantial computational resources to deal with the extensive multiply-accumulate operations. Neuromorphic computing is an emergent solution to address this problem, allowing the computation performed in memory arrays in parallel with high efficiencies conforming to the neural networks. Here, scalable synaptic transistor memories are developed from solution-sorted carbon nanotubes.

Weld seam object detection system based on the fusion of 2D images and 3D point clouds using interpretable neural networks.

This study introduces a novel approach that addresses the limitations of existing methods by integrating 2D image processing with 3D point cloud analysis, enhanced by interpretable neural networks. Unlike traditional methods that rely on either 2D or 3D data alone, our approach leverages the complementary strengths of both data types to improve detection accuracy in environments adversely affected by welding spatter and smoke. Our system employs an improved Faster R-CNN model with a ResNet50 backbone for 2D image analysis, coupled with an innovative orthogonal plane intersection line extraction algorithm for 3D point cloud processing.

Memristor-based adaptive neuromorphic perception in unstructured environments.

Efficient operation of control systems in robotics or autonomous driving targeting real-world navigation scenarios requires perception methods that allow them to understand and adapt to unstructured environments with good accuracy, adaptation, and generality, similar to humans. To address this need, we present a memristor-based differential neuromorphic computing, perceptual signal processing, and online adaptation method providing neuromorphic style adaptation to external sensory stimuli. The adaptation ability and generality of this method are confirmed in two application scenarios: object grasping and autonomous driving.

De novo transcriptome assembly database for 100 tissues from each of seven species of domestic herbivore.

Domesticated herbivores are an important agricultural resource that play a critical role in global food security, particularly as they can adapt to varied environments, including marginal lands. An understanding of the molecular basis of their biology would contribute to better management and sustainable production. Thus, we conducted transcriptome sequencing of 100 to 105 tissues from two females of each of seven species of herbivore (cattle, sheep, goats, sika deer, horses, donkeys, and rabbits) including two breeds of sheep.

Integrated Proteomics Analysis of Baseline Protein Expression in Pig Tissues.

The availability of an increasingly large amount of public proteomics data sets presents an opportunity for performing combined analyses to generate comprehensive organism-wide protein expression maps across different organisms and biological conditions. , a domestic pig, is a model organism relevant for food production and for human biomedical research. Here, we reanalyzed 14 public proteomics data sets from the PRIDE database coming from pig tissues to assess baseline (without any biological perturbation) protein abundance in 14 organs, encompassing a total of 20 healthy tissues from 128 samples.

CSANet: a lightweight channel and spatial attention neural network for grading diabetic retinopathy with optical coherence tomography angiography.

Background: Diabetic retinopathy (DR) is one of the most common eye diseases. Convolutional neural networks (CNNs) have proven to be a powerful tool for learning DR features; however, accurate DR grading remains challenging due to the small lesions in optical coherence tomography angiography (OCTA) images and the small number of samples.

Methods: In this article, we developed a novel deep-learning framework to achieve the fine-grained classification of DR; that is, the lightweight channel and spatial attention network (CSANet).

Image segmentation of mouse eye with optical coherence tomography based on Bayesian classification.

Objectives: Optical coherence tomography (OCT) is a new imaging technology that uses an optical analog of ultrasound imaging for biological tissues. Image segmentation plays an important role in dealing with quantitative analysis of medical images.

Methods: We have proposed a novel framework to deal with the low intensity problem, based on the labeled patches and Bayesian classification () model.

Expression Atlas update: insights from sequencing data at both bulk and single cell level.

Expression Atlas (www.ebi.ac.

SoyMD: a platform combining multi-omics data with various tools for soybean research and breeding.

Advanced multi-omics technologies offer much information that can uncover the regulatory mechanisms from genotype to phenotype. In soybean, numerous multi-omics databases have been published. Although they cover multiple omics, there are still limitations when it comes to the types and scales of omics datasets and analysis methods utilized.

Deep-learning segmentation method for optical coherence tomography angiography in ophthalmology.

Purpose: The optic disc and the macular are two major anatomical structures in the human eye. Optic discs are associated with the optic nerve. Macular mainly involves degeneration and impaired function of the macular region.

A new retinal OCT-angiography diabetic retinopathy dataset for segmentation and DR grading.

Purpose: Diabetic retinopathy (DR) is one of the most common diseases caused by diabetes and can lead to vision loss or even blindness. The wide-field optical coherence tomography (OCT) angiography is non-invasive imaging technology and convenient to diagnose DR.

Methods: A newly constructed Retinal OCT-Angiography Diabetic retinopathy (ROAD) dataset is utilized for segmentation and grading tasks.

Multiangle, self-powered sensor array for monitoring head impacts.

Mild concussions occur frequently and may come with long-term cognitive, affective, and physical sequelae. However, the diagnosis of mild concussions lacks objective assessment and portable monitoring techniques. Here, we propose a multiangle self-powered sensor array for real-time monitoring of head impact to further assist in clinical analysis and prevention of mild concussions.

Comparative transcriptome profiling reveals the multiple levels of crosstalk in phytohormone networks in Brassica napus.

Plant hormones are the intrinsic factors that control plant development. The integration of different phytohormone pathways in a complex network of synergistic, antagonistic and additive interactions has been elucidated in model plants. However, the systemic level of transcriptional responses to hormone crosstalk in Brassica napus is largely unknown.

AutoGMap: Learning to Map Large-Scale Sparse Graphs on Memristive Crossbars.

The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., social networks and knowledge graphs) on traditional computing architectures (CPU, GPU, or TPU).

Integrated View of Baseline Protein Expression in Human Tissues.

The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs.

The ProteomeXchange consortium at 10 years: 2023 update.

Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.

CottonMD: a multi-omics database for cotton biological study.

Cotton is an important economic crop, and many loci for important traits have been identified, but it remains challenging and time-consuming to identify candidate or causal genes/variants and clarify their roles in phenotype formation and regulation. Here, we first collected and integrated the multi-omics datasets including 25 genomes, transcriptomes in 76 tissue samples, epigenome data of five species and metabolome data of 768 metabolites from four tissues, and genetic variation, trait and transcriptome datasets from 4180 cotton accessions. Then, a cotton multi-omics database (CottonMD, http://yanglab.

Septin 9 methylation analysis of lymph node micrometastases for predicting relapse of colorectal cancer.

Background: Molecular markers for the detection of lymph node micrometastases of malignant tumors have been extensively investigated. However, epigenetic signatures have rarely been reported for identification of metastatic lymph nodes and disease relapse. Septin 9 is the most frequently reported hypermethylated gene in colorectal cancer (CRC).

The Role of Liquid Biopsy Analytes in Diagnosis, Treatment and Prognosis of Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common malignant tumors of the digestive tract worldwide and is a serious threat to human life and health. CRC occurs and develops in a multi-step, multi-stage, and multi-gene process, in which abnormal gene expression plays an important role. CRC is currently diagnosed endoscopy combined with tissue biopsy.