Intraoperative sensitization in trigeminal region caused by postherpetic neuralgia: a case report.

Background: Interventional therapy of trigeminal neuropathic pain has been well documented; however, intraoperative monitoring and management of pain hypersensitivity remains barely reported, which may pose a great challenge for pain physicians as well as anesthesiologists.

Case Presentation: A 77-year-old Han Chinese male, who suffered from severe craniofacial postherpetic neuralgia, underwent pulsed radiofrequency of trigeminal ganglion in the authors' department twice. The authors successfully placed a radiofrequency needle through the foramen ovale during the first procedure with local anesthesia and intravenous sedation (dexmedetomidine).

HCTTI: High-Performance Heterogeneous Computing Toolkit for Tissue Image Stain Normalization.

Whole slide imaging (WSI) has transformed diagnostic medicine, particularly in the field of cancer diagnosis and treatment. The use of deep learning algorithms for predicting WSIs has opened up new avenues for advanced medical diagnostics. Additionally, stain normalization can reduce the color and intensity variations present in WSI from different hospitals.

HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023.

The Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is essential for assessing vegetation's photosynthetic efficiency and ecosystem energy balance. While the MODIS FPAR product provides valuable global data, its reliability is compromised by noise, particularly under poor observation conditions like cloud cover. To solve this problem, we developed the Spatio-Temporal Information Composition Algorithm (STICA), which enhances MODIS FPAR by integrating quality control, spatio-temporal correlations, and original FPAR values, resulting in the High-Quality FPAR (HiQ-FPAR) product.

Deep learning methods for improving the accuracy and efficiency of pathological image analysis.

This study presents a novel integration of two advanced deep learning models, U-Net and EfficientNetV2, to achieve high-precision segmentation and rapid classification of pathological images. A key innovation is the development of a new heatmap generation algorithm, which leverages meticulous image preprocessing, data enhancement strategies, ensemble learning, attention mechanisms, and deep feature fusion techniques. This algorithm not only produces highly accurate and interpretatively rich heatmaps but also significantly improves the accuracy and efficiency of pathological image analysis.

Enhancing catalytic activity in MoC nanodots via nitrogen doping and graphene integration for efficient hydrogen evolution under alkaline conditions.

Due to its exceptional electronic properties and catalytic activity, MoC has garnered significant attention for its application in electrocatalysis, particularly for the hydrogen evolution reaction (HER). However, several critical challenges continue to impede its widespread use, especially under strongly alkaline conditions. A primary obstacle is the enhancement of its intrinsic activity through further modification strategies, which remains a key limitation for its broader utilization.

An effective feature selection approach based on hybrid Grey Wolf Optimizer and Genetic Algorithm for hyperspectral image.

Feature selection (FS) is a critical step in hyperspectral image (HSI) classification, essential for reducing data dimensionality while preserving classification accuracy. However, FS for HSIs remains an NP-hard challenge, as existing swarm intelligence and evolutionary algorithms (SIEAs) often suffer from limited exploration capabilities or susceptibility to local optima, particularly in high-dimensional scenarios. To address these challenges, we propose GWOGA, a novel hybrid algorithm that combines Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA), aiming to achieve an effective balance between exploration and exploitation.

Static and dynamic connectivity structure of white-matter functional networks across the adult lifespan.

Aging of the human brain involves intricate biological processes, resulting in complex changes in structure and function. While the effects of aging on gray matter (GM) connectivity are extensively studied, white matter (WM) functional changes have received comparatively less attention. This study examines age-related WM functional dynamics using resting-state fMRI across the adult lifespan.

Research on bearing fault diagnosis based on a multimodal method.

As an essential component of mechanical systems, bearing fault diagnosis is crucial to ensure the safe operation of the equipment. However, vibration data from bearings often exhibit non-stationary and nonlinear features, which complicates fault diagnosis. To address this challenge, this paper introduces a novel multi-scale time-frequency and statistical features fusion model (MTSF-FM).

Chinese herbal medicine recognition network based on knowledge distillation and cross-attention.

In order to reduce the number of parameters in the Chinese herbal medicine recognition model while maintaining accuracy, this paper takes 20 classes of Chinese herbs as the research object and proposes a recognition network based on knowledge distillation and cross-attention - ShuffleCANet (ShuffleNet and Cross-Attention). Firstly, transfer learning was used for experiments on 20 classic networks, and DenseNet and RegNet were selected as dual teacher models. Then, considering the parameter count and recognition accuracy, ShuffleNet was determined as the student model, and a new cross-attention mechanism was proposed.

Functional connectome gradient of prefrontal cortex as biomarkers of high risk for internet gaming disorder.

Adolescents and young adults are considered a high-risk group for internet gaming disorder (IGD). Early screening for high-risk individuals with IGD and exploring the underlying neural mechanisms is an effective strategy to reduce the harm of IGD. We recruited 219 non-internet gaming addicted college students and evaluated them with magnetic resonance imaging, followed by a two-year longitudinal follow-up.

Lightweight Deep Learning Model, ConvNeXt-U: An Improved U-Net Network for Extracting Cropland in Complex Landscapes from Gaofen-2 Images.

Extracting fragmented cropland is essential for effective cropland management and sustainable agricultural development. However, extracting fragmented cropland presents significant challenges due to its irregular and blurred boundaries, as well as the diversity in crop types and distribution. Deep learning methods are widely used for land cover classification.

Elephant-inspired tapered cable-driven hyper-redundant manipulator: design and performance analysis.

The Cable-Driven Hyper-redundant Manipulator (CDHM), distinguished by its high flexibility and adjustable stiffness, is extensively utilized in confined and obstacle-rich environments such as aerospace and nuclear facilities. This paper introduces a novel CDHM inspired by the trunk of elephants, which changes the arm structure from cylindrical to conical. This alteration diminishes the arm's self-weight, reduces the moment arm of gravity, decreases the volume of the end joint, narrows the stroke of the driving cables, and boosts the maximum joint speed of the manipulator.

Identifying Protein-Nucleotide Binding Residues via Grouped Multi-task Learning and Pre-trained Protein Language Models.

The accurate identification of protein-nucleotide binding residues is crucial for protein function annotation and drug discovery. Numerous computational methods have been proposed to predict these binding residues, achieving remarkable performance. However, due to the limited availability and high variability of nucleotides, predicting binding residues for diverse nucleotides remains a significant challenge.

Lightweight multidimensional feature enhancement algorithm LPS-YOLO for UAV remote sensing target detection.

Detecting small targets in UAV remote sensing images is challenging for traditional lightweight methods due to difficulty in feature extraction and high background interference. We propose LPS-YOLO, which improves small target feature extraction while reducing computational complexity by replacing the Conv backbone with SPDConv to retain fine-grained features. LPS-YOLO introduces the SKAPP module for better feature fusion and incorporates the E-BiFPN and OFTP structures to efficiently preserve and transfer backbone information.

Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI.

Mass spectrometry imaging (MSI) provides valuable insights into metabolic heterogeneity by capturing in situ molecular profiles within organisms. One challenge of MSI heterogeneity analysis is performing an objective segmentation to differentiate the biological tissue into distinct regions with unique characteristics. However, current methods struggle due to the insufficient incorporation of biological context and high computational demand.

A mutual inclusion mechanism for precise boundary segmentation in medical images.

Introduction: Accurate image segmentation is crucial in medical imaging for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods often fall short in integrating global and local features in a meaningful way, failing to give sufficient attention to abnormal regions and boundary details in medical images. These limitations hinder the effectiveness of segmentation techniques in clinical settings.

Multi-view knowledge representation learning for personalized news recommendation.

In the rapidly evolving field of personalized news recommendation, capturing and effectively utilizing user interests remains a significant challenge due to the vast diversity and dynamic nature of user interactions with news content. Existing recommendation models often fail to fully integrate candidate news items into user interest modeling, which can result in suboptimal recommendation accuracy and relevance. This limitation stems from their insufficient ability to jointly consider user history and the characteristics of candidate news items in the modeling process.

Experimental demonstration of 8190-km long-haul semiconductor-laser chaos synchronization induced by digital optical communication signal.

Common-signal-induced synchronization of semiconductor lasers have promising applications in physical-layer secure transmission with high speed and compatibility with the current fiber communication. Here, we propose an ultra-long-distance laser synchronization scheme by utilizing random digital optical communication signal as the common drive signal. By utilizing the long-haul optical coherent communication techniques, high-fidelity fiber transmission of the digital drive can be achieved and thus ultra-long-distance synchronization is expected.

Enhanced particle swarm optimization with chaotic search for offshore micro-energy systems.

As the global energy landscape shifts and sustainability becomes crucial, the offshore oil and gas sector confronts significant challenges and opportunities. This paper addresses the issues of energy efficiency and environmental impact of optimizing offshore micro-energy systems (OMIES) by proposing a multi-objective optimization model that integrates chaotic local search and particle swarm optimization (PSO). The model aims to achieve optimal scheduling of the energy system by comprehensively considering operational costs, carbon emissions, energy utilization efficiency, and energy fluctuation risks.

Large-scale acceleration algorithms for a deep convective physical parameterization scheme on GPU.

Early warning of geological hazards requires monitoring extreme weather conditions, such as heavy rainfall. Atmospheric circulation models are used for weather forecasting and climate simulation. As a critical physical process in atmospheric circulation models, the Zhang-McFarlane (ZM) deep convective physical parameterization scheme involves computationally intensive calculations that significantly impact the overall operational efficiency of the model.

MMSG-DTA: A Multimodal, Multiscale Model Based on Sequence and Graph Modalities for Drug-Target Affinity Prediction.

Drug-Target Affinity (DTA) prediction is a cornerstone of drug discovery and development, providing critical insights into the intricate interactions between candidate drugs and their biological targets. Despite its importance, existing methodologies often face significant limitations in capturing comprehensive global features from molecular graphs, which are essential for accurately characterizing drug properties. Furthermore, protein feature extraction is predominantly restricted to 1D amino acid sequences, which fail to adequately represent the spatial structures and complex functional regions of proteins.

DCA-YOLOv8: A Novel Framework Combined with AICI Loss Function for Coronary Artery Stenosis Detection.

Coronary artery stenosis detection remains a challenging task due to the complex vascular structure, poor quality of imaging pictures, poor vessel contouring caused by breathing artifacts and stenotic lesions that often appear in a small region of the image. In order to improve the accuracy and efficiency of detection, a new deep-learning technique based on a coronary artery stenosis detection framework (DCA-YOLOv8) is proposed in this paper. The framework consists of a histogram equalization and canny edge detection preprocessing (HEC) enhancement module, a double coordinate attention (DCA) feature extraction module and an output module that combines a newly designed loss function, named adaptive inner-CIoU (AICI).

Integration of gene expression and DNA methylation data using MLA-GNN for liver cancer biomarker mining.

The early symptoms of hepatocellular carcinoma patients are often subtle and easily overlooked. By the time patients exhibit noticeable symptoms, the disease has typically progressed to middle or late stages, missing optimal treatment opportunities. Therefore, discovering biomarkers is essential for elucidating their functions for the early diagnosis and prevention.

MIC: Breast Cancer Multi-label Diagnostic Framework Based on Multi-modal Fusion Interaction.

The automated diagnosis of low-resolution and difficult-to-recognize breast ultrasound images through multi-modal fusion holds significant clinical value. However, prevailing fusion methods predominantly rely on image modalities, neglecting the textual pathology information, and only benign and malignant diagnosis of breast tumors is not satisfying for clinical applications. Consequently, this paper proposes a novel multi-modal fusion interactive diagnostic framework, termed the MIC framework, to achieve the multi-label classification of breast cancer, namely benign-malignant classification and breast imaging reporting and data system (BI-RADS) 3, 4a, 4b, 4c, and 5 gradings.

Machine learning Nomogram for Predicting endometrial lesions after tamoxifen therapy in breast Cancer patients.

Objective Endometrial lesions are a frequent complication following breast cancer, and current diagnostic tools have limitations. This study aims to develop a machine learning-based nomogram model for predicting the early detection of endometrial lesions in patients. The model is designed to assess risk and facilitate individualized treatment strategies for premenopausal breast cancer patients.