Effects of transcranial alternating current stimulation on Spike train correlation in two-compartment model neurons.

Correlated spiking has been widely found in large population of neurons and been linked to neural coding. Transcranial alternating current stimulation (tACS) is a promising non-invasive brain stimulation technique that can modulate the spiking activity of neurons. Despite its growing application, the tACS effects on the temporal correlation between spike trains are still not fully understood.

The accuracy of surface imaging in detecting secondary patient motion caused by pitch and roll corrections in pelvic radiotherapy.

Background And Objective: Correction of rotational setup errors by tilting the treatment couch improves target dose accuracy and prevents healthy tissue overdosage in external beam radiotherapy. However, couch tilts may cause secondary patient motion. This study aimed to quantify the secondary motion caused by pitch and roll corrections and to evaluate the feasibility of surface imaging for detecting the secondary motion in pelvic radiotherapy.

Research status of small molecule inhibitors, probes, and degraders of NSDs: a comprehensive review.

The nuclear receptor binding SET domain (NSD) family of histone methyltransferases, which comprised NSD1, NSD2, and NSD3. They play a pivotal role in catalyzing mono- and dimethylation of histone H3 at lysine 36 (H3K36me1/2), a modification critical for maintaining chromatin structure and transcriptional fidelity. Dysregulation of NSD enzymes, often through overexpression, mutation, or chromosomal translocation, has been implicated in a broad spectrum of malignancies and various diseases.

Advanced multimodal for Segmentation and Classification of breast cancer with DenseNet and Optimal Attention approach.

Background And Objective: The early detection of breast cancer plays a critical role in improving survival rates and facilitating precise medical interventions. Therefore, the automated identification of breast abnormalities becomes paramount, significantly enhancing the prospects of successful treatment outcomes. To address this imperative, our research leverages multiple modalities such as MRI, CT, and mammography to detect and screen for breast cancer.

Long sequence temporal knowledge tracing for student performance prediction via integrating LSTM and informer.

Knowledge tracing can reveal students' level of knowledge in relation to their learning performance. Recently, plenty of machine learning algorithms have been proposed to exploit to implement knowledge tracing and have achieved promising outcomes. However, most of the previous approaches were unable to cope with long sequence time-series prediction, which is more valuable than short sequence prediction that is extensively utilized in current knowledge-tracing studies.

Multi-scale model of neural entrainment by transcranial alternating current stimulation in realistic cortical anatomy.

Transcranial alternating current stimulation (tACS) enables non-invasive modulation of brain activity, holding promise for cognitive research and clinical applications. However, it remains unclear how the spiking activity of cortical neurons is modulated by specific electric field (E-field) distributions. Here, we use a multi-scale computational framework that integrates an anatomically accurate head model with morphologically realistic neuron models to simulate the responses of layer 5 pyramidal cells (L5 PCs) to the E-fields generated by conventional M1-SO tACS.

Discovery of Potential GPRC5D Inhibitors through Virtual Screening and Molecular Dynamics Simulations.

G protein-coupled receptor family C, group 5, member D (GPRC5D), a member of the G protein-coupled receptor (GPCR) family, has recently emerged as a promising target for immunotherapy in hematologic malignancies, particularly multiple myeloma. However, no systematic virtual screening studies have been conducted to identify small-molecule inhibitors targeting GPRC5D. To address this gap, a multistep computational screening strategy is developed that integrates Protein-Ligand Affinity prediction NETwork (PLANET), a GPU-accelerated version of AutoDock Vina (Vina-GPU), molecular mechanics/generalized born surface area (MM/GBSA), and an online tool for Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) property prediction (admetSAR 3.

MACML: Marrying attention and convolution-based meta-learning method for few-shot IoT intrusion detection.

The widespread deployment of Internet of Things (IoT) devices has made them prime targets for cyberattacks. Existing intrusion detection systems (IDSs) heavily rely on large-scale labeled datasets, which limits their effectiveness in detecting novel attacks under few-shot scenarios. To address this challenge, we propose a meta-learning-based intrusion detection method called MACML (Marrying Attention and Convolution-based Meta-Learning).

Cell Viability, Drug Screening, and Mechanism Study Under Mild Phototherapy Integrated Chemotherapy (PIC).

Phototherapy integrated chemotherapy (PIC) offers a promising cancer treatment strategy as single chemotherapy approaches are less effective due to tumor heterogeneity and drug resistance, and phototherapy often involves harmful radiation, and the potential of mild phototherapy to enhance chemotherapy remains underexplored. Cancer cells, as key drivers of tumor metastasis, are crucial targets, but current methods for visualizing their response to theranostics are slow. Here, nondestructive real-time impedance spectroscopy to monitor cancer cell response, identifying an optimal light exposure time of 60 min and defined cell viability and resistibility through impedance measurements, achieving high correlation (92.

Decoding the spatial pattern of PM pollution from the perspective of socioeconomic factors and regional connectivity.

Research on the spatial pattern of PM pollution has achieved progress recently, but there are still shortcomings in the effects of macro socioeconomic factors and regional connectivity of PM emissions. To address these issues, our study followed an analytical framework integrating empirical orthogonal function, Morlet wavelet analysis, time series decomposition, back propagation neural network, geographical detector and social network analysis. This framework was applied to a dataset embodying PM, meteorology and socioeconomics over seven years (2015-2021) across 279 cities of mainland China.

A new design of wind power prediction method based on multi-interaction optimization informer model.

The accurate prediction of wind power is imperative for maintaining grid stability. In order to address the limitations of traditional neural network algorithms, the Informer model is employed for wind power prediction, delivering higher accuracy. However, due to insufficient exploration of dynamic coupling among multi-source features and inadequate data health status perception, both prediction accuracy and computational efficiency deteriorate under complex working conditions.

Major depressive disorder detection via temporal-frequency-spatial transformer with sub-domain knowledge alignment using EEG.

Major Depressive Disorder (MDD) is a common mental illness that seriously jeopardizes the physical and mental health of patients. Accurate detection of MDD is crucial for treatment. Currently, there are significant differences in the EEG signals of each MDD patient, leading to lower accuracy of cross-subject MDD detection.

WGGLFA: Wavelet-Guided Global-Local Feature Aggregation Network for Facial Expression Recognition.

Facial expression plays an important role in human-computer interaction and affective computing. However, existing expression recognition methods cannot effectively capture multi-scale structural details contained in facial expressions, leading to a decline in recognition accuracy. Inspired by the multi-scale processing mechanism of the biological visual system, this paper proposes a wavelet-guided global-local feature aggregation network (WGGLFA) for facial expression recognition (FER).

Effective virtual screening strategy toward JAK3 covalent inhibitors: combining multi‑conformational consensus calculation with covalent docking.

Accumulating studies have demonstrated that the overactivation of Janus kinase 3 (JAK3) is closely associated with various inflammatory diseases, establishing it as a potential drug target for the treatment of autoimmune and inflammatory disorders. However, the high homology among kinase structures results in poor selectivity for existing JAK3 inhibitors. The approval of the JAK3 covalent inhibitor ritlecitinib has positioned the development of covalent inhibitors as an effective strategy for enhancing JAK3 selectivity.

DANet: spatial gene expression prediction from H&E histology images through dynamic alignment.

Predicting spatial gene expression from Hematoxylin and Eosin histology images offers a promising approach to significantly reduce the time and cost associated with gene expression sequencing, thereby facilitating a deeper understanding of tissue architecture and disease mechanisms. Achieving accurate gene expression prediction requires the extraction of highly refined features from pathological images; however, existing methods often struggle to effectively capture fine-grained local details and model gene-gene correlations. Moreover, in bimodal contrastive learning, dynamically and efficiently aligning heterogeneous modalities remains a critical challenge.

Discovery and Characterization of Novel FGFR1 V561M Inhibitors via Virtual Screening and Molecular Dynamics Simulations.

The FGFR1 V561M mutation significantly reduces the efficacy of current FGFR1 inhibitors, creating an urgent need for targeted second-generation therapies. In this study, we developed a comprehensive virtual screening protocol that combines energy-based screening and machine learning techniques, leading to the identification of a novel compound, . This compound exhibited potent inhibitory activity against FGFR1 V561M with an IC of 90.

Motion Artifact Correction in Deep-Tissue Three-Photon Fluorescence Microscopy Using Adaptive Optical Flow Learning With Transformer.

Three-photon fluorescence microscopy (3PFM) enables high-resolution volumetric imaging in deep tissues but is often hindered by motion artifacts in dynamic physiological environments. Existing solutions, including surgical fixation and conventional image registration algorithms, frequently fail under intense and nonuniform motions, particularly in low-texture or highly deformed regions. To overcome these problems, we propose StabiFormer, a transformer-based optical flow learning network designed for robust motion correction.

A novel hybrid extreme learning machine-based diagnosis model for sensor node faults in aquaculture.

Sensor nodes in a wireless sensor network are influenced by the surrounding environment while monitoring data, which can lead to faults and data biases, resulting in erroneous decisions and losses. Identifying and classifying fault types is a challenge that still need to be addressed. The inertia weight ω and learning factor c were optimized to enhance the optimization ability of the particle swarm.

LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving.

The accurate detection of small objects remains a critical challenge in autonomous driving systems, where improving detection performance typically comes at the cost of increased model complexity, conflicting with the lightweight requirements of edge deployment. To address this dilemma, this paper proposes LEAD-YOLO (Lightweight Efficient Autonomous Driving YOLO), an enhanced network architecture based on YOLOv11n that achieves superior small object detection while maintaining computational efficiency. The proposed framework incorporates three innovative components: First, the Backbone integrates a lightweight Convolutional Gated Transformer (CGF) module, which employs normalized gating mechanisms with residual connections, and a Dilated Feature Fusion (DFF) structure that enables progressive multi-scale context modeling through dilated convolutions.

Principles, Applications, and Future Evolution of Agricultural Nondestructive Testing Based on Microwaves.

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control.

Anomaly Detection of Acoustic Signals in Ultra-High Voltage Converter Valves Based on the FAVAE-AS.

The converter valve is the core component of the ultra-high voltage direct current (UHVDC) transmission system, and its fault detection is very important to ensure the safe and stable operation of the transmission system. However, the voiceprint signals collected by converter stations under complex operating conditions are often affected by background noise, spikes, and nonlinear interference. Traditional methods make it difficult to achieve high-precision detection due to the lack of feature extraction ability and poor noise robustness.

Sensor Fault Detection and Reliable Control of Singular Stochastic Systems with Time-Varying Delays.

In unmanned systems, especially in large-scale and complex ones, sensor and communication failures occur from time to time and are hard to avoid. Therefore, this paper studies the fault detection problem of a class of unknown nonlinear singular uncertain time-varying delay Markov jump systems (UNSUTVDMJSs). Firstly, the corresponding sliding mode controller (SMC) is designed by using the equivalent control principle, and the unknown nonlinearity is equivalently replaced by changing the system input.

A Lightweight Intrusion Detection System with Dynamic Feature Fusion Federated Learning for Vehicular Network Security.

The rapid integration of complex sensors and electronic control units (ECUs) in autonomous vehicles significantly increases cybersecurity risks in vehicular networks. Although the Controller Area Network (CAN) is efficient, it lacks inherent security mechanisms and is vulnerable to various network attacks. The traditional Intrusion Detection System (IDS) makes it difficult to effectively deal with the dynamics and complexity of emerging threats.

Self-Supervised Visual Tracking via Image Synthesis and Domain Adversarial Learning.

With the widespread use of sensors in applications such as autonomous driving and intelligent security, stable and efficient target tracking from diverse sensor data has become increasingly important. Self-supervised visual tracking has attracted increasing attention due to its potential to eliminate reliance on costly manual annotations; however, existing methods often train on incomplete object representations, resulting in inaccurate localization during inference. In addition, current methods typically struggle when applied to deep networks.

Research Progress in the Detection of Mycotoxins in Cereals and Their Products by Vibrational Spectroscopy.

Grains and their derivatives play a crucial role as staple foods for the global population. Identifying grains in the food chain that are free from mycotoxin contamination is essential. Researchers have explored various traditional detection methods to address this concern.