Lesion filling index predicts brain arteriovenous malformation obliteration after Gamma knife radiosurgery: a hemodynamic analysis.

Hemodynamics significantly influences the clinical outcomes of brain arteriovenous malformations (AVM). This study aimed to determine if the lesion filling index (LFI), obtained via quantitative digital subtraction angiography (QDSA), can predict complete complete obliteration after Gamma knife radiosurgery (GKRS). We retrospectively reviewed AVM patients who underwent GKRS and DSA exams from 2011 to 2021.

[Tooth Design of Ophthalmic Microsurgical Forceps Based on Minimum Slip Force].

This study proposed the use of the minimum slip force as an indicator to measure the clamping ability of microsurgical forceps. By analyzing the minimum slip forces of four typical tooth types of microsurgical forceps, the clamping capacity of each type was evaluated. Among the existing tooth forms, the staggered tooth type exhibited a relatively large minimum slip force.

Decreased Cortical Sulcus Depth in Parkinson's Disease with Excessive Daytime Sleepiness.

Introduction: Excessive daytime sleepiness (EDS), a prevalent non-motor symptom in Parkinson's disease (PD), significantly impacts the quality of life for PD patients and elevates the risks of injury. Our study is to investigate the altered cortical surface morphology characteristics in PD patients with EDS (PD-EDS).

Methods: Clinical data and magnetic resonance imaging were obtained from the Parkinson's Progression Marker Initiative database, comprising 36 PD-EDS and 98 PD patients without EDS (PD-nEDS).

Snapshot computational spectroscopy enabled by deep learning.

Spectroscopy is a technique that analyzes the interaction between matter and light as a function of wavelength. It is the most convenient method for obtaining qualitative and quantitative information about an unknown sample with reasonable accuracy. However, traditional spectroscopy is reliant on bulky and expensive spectrometers, while emerging applications of portable, low-cost and lightweight sensing and imaging necessitate the development of miniaturized spectrometers.

High-efficiency nonlinear frequency conversion enabled by optimizing the ferroelectric domain structure in -cut LNOI ridge waveguide.

Photonic devices based on ferroelectric domain engineering in thin film lithium niobate are key components for both classical and quantum information processing. Periodic poling of ridge waveguide can avoid the selective etching effect of lithium niobate, however, the fabrication of high-quality ferroelectric domain is still a challenge. In this work, we optimized the applied electric field distribution, and rectangular inverted domain structure was obtained in the ridge waveguide which is beneficial for efficient nonlinear frequency conversions.

Event-based adaptive fixed-time optimal control for saturated fault-tolerant nonlinear multiagent systems via reinforcement learning algorithm.

This article investigates the problem of adaptive fixed-time optimal consensus tracking control for nonlinear multiagent systems (MASs) affected by actuator faults and input saturation. To achieve optimal control, reinforcement learning (RL) algorithm which is implemented based on neural network (NN) is employed. Under the actor-critic structure, an innovative simple positive definite function is constructed to obtain the upper bound of the estimation error of the actor-critic NN updating law, which is crucial for analyzing fixed-time stabilization.

A layered Janus metastructure for multi-physical quantity detection based on the second harmonic wave.

In the field of nonlinear optics and physical quantity detection, the use of the second harmonic wave (SHW) generated in ferroelectric crystals is proposed to realize multi-physical quantity detection with the Janus property. In view of the single physical quantity detected by the current research and the single application scenario, this paper proposes a multi-functional and novel nonlinear Janus metastructure (NJMS), which exploits the SHW to achieve highly sensitive multi-physical quantity detection in the terahertz frequency range and shows Janus properties in both the forward and backward directions of the system. The NJMS is realized to detect refractive indices, thicknesses, and angles with different modes in the forward and backward directions.

GPTrans: A Biological Language Model-Based Approach for Predicting Disease-Associated Mutations in G Protein-Coupled Receptors.

Accurately predicting mutations in G protein-coupled receptors (GPCRs) is critical for advancing disease diagnosis and drug discovery. In response to this imperative, GPTrans has emerged as a highly accurate predictor of disease-related mutations in GPCRs. The core innovation of GPTrans resides in the design of a novel feature extraction network, that is capable of integrating features from both wildtype and mutant protein variant sites, utilizing multifeature connections within a transformer framework to ensure comprehensive feature extraction.

Phenolic multiple kinetics-dynamics and discrete crystallization thermodynamics in amorphous carbon nanostructures for electromagnetic wave absorption.

The lack of a chemical platform with high spatial dimensional diversity, coupled with the elusive multi-scale amorphous physics, significantly hinder advancements in amorphous electromagnetic wave absorption (EWA) materials. Herein, we present a synergistic engineering of phenolic multiple kinetic dynamics and discrete crystallization thermodynamics, to elucidate the origin of the dielectric properties in amorphous carbon and the cascade effect during EWA. Leveraging the scalability of phenolic synthesis, we design dozens of morphologies from the bottom up and combine with in-situ pyrolysis to establish a nanomaterial ecosystem of hundreds of amorphous carbon materials.

Contrastive message passing for robust graph neural networks with sparse labels.

Graph Neural Networks (GNNs) have achieved great success in semi-supervised learning. Existing GNNs typically aggregate the features via message passing with the aid of rich labels. However, real-world graphs have limited labels, and overfitting weakens the classification ability when labels are insufficient.

CrackNet: A Hybrid Model for Crack Segmentation with Dynamic Loss Function.

Cracks are a common form of damage in infrastructure, posing significant risks to both personal safety and property. Along with the development of deep learning, visual-based crack automatic detection has been widely studied. However, this task is still challenging due to complex crack topology, noisy backgrounds, unbalanced categories, etc.

Secure and Lightweight Cluster-Based User Authentication Protocol for IoMT Deployment.

Authentication is considered one of the most critical technologies for the next generation of the Internet of Medical Things (IoMT) due to its ability to significantly improve the security of sensors. However, higher frequency cyber-attacks and more intrusion methods significantly increase the security risks of IoMT sensor devices, resulting in more and more patients' privacy being threatened. Different from traditional IoT devices, sensors are generally considered to be based on low-cost hardware designs with limited storage resources; thus, authentication techniques for IoMT scenarios might not be applicable anymore.

PRITrans: A Transformer-Based Approach for the Prediction of the Effects of Missense Mutation on Protein-RNA Interactions.

Protein-RNA interactions are essential to many cellular functions, and missense mutations in RNA-binding proteins can disrupt these interactions, often leading to disease. To address this, we developed PRITrans, a specialized computational method aimed at predicting the effects of missense mutations on protein-RNA interactions, which is vital for understanding disease mechanisms and advancing molecular biology research. PRITrans is a novel deep learning model designed to predict the effects of missense mutations on protein-RNA interactions, which employs a Transformer architecture enhanced with multiscale convolution modules for comprehensive feature extraction.

The Notable Role of Telomere Length Maintenance in Complex Diseases.

Telomere length function serves as a critical biomarker for biological aging and overall health. Its maintenance is linked to cancer, neurodegenerative conditions, and reproductive health. This review mainly examines genetic variations and environmental influences on telomere dynamics, highlighting key regulatory genes and mechanisms.

Information Propagation in Hypergraph-Based Social Networks.

Social networks, functioning as core platforms for modern information dissemination, manifest distinctive user clustering behaviors and state transition mechanisms, thereby presenting new challenges to traditional information propagation models. Based on hypergraph theory, this paper augments the traditional SEIR model by introducing a novel hypernetwork information dissemination SSEIR model specifically designed for online social networks. This model accurately represents complex, multi-user, high-order interactions.

Non-Contact Cross-Person Activity Recognition by Deep Metric Ensemble Learning.

In elderly monitoring or indoor intrusion detection, the recognition of human activity is a key task. Owing to several strengths of Wi-Fi-based devices, including their non-contact and privacy protection, these devices have been widely applied in the area of smart homes. By the deep learning technique, numerous Wi-Fi-based activity recognition methods can realize satisfied recognitions, however, these methods may fail to recognize the activities of an unknown person without the learning process.

Dual-modal overcoming of physical barriers for improved photodynamic cancer therapy via soft organosilica nanocapsules.

Amidst the burgeoning field of cancer nanomedicine, dense extracellular matrices and anomalous vascular structures in the tumor microenvironment (TME) present substantial physical barriers to effective therapeutic delivery. These physical barriers hinder the optimal bioavailability of nanomedicine. Here, we propose a pioneering dual-modal strategy for overcoming physical barriers via soft organosilica nanocapsules (SMONs).

Alkynyl Ligands Templated Assemblies of Silver Nanoclusters with Exceptional Electrochemiluminescence Activity for Pancreatic Cancer Specific tsRNAs Measurement.

Proper manipulation of the ligand complex on the motifs of metal nanoclusters (MNCs) to form an ordered self-assembly is an effective approach to enhance the electrochemiluminescence (ECL) emission of MNCs. We report a facile approach for the preparation of self-assembled AgNCs (AgNCs) induced by alkynyl ligands with enhanced ECL and stability. The formation of these AgNCs was simultaneously driven by the diverse coordination modes of alkynyl ligands with Ag and intercluster interactions, for which it was found that the para-substituted alkynyl ligands exhibited apparently irregular nanoparticles, while the monosubstituted counterparts were present in the form of ribbons.

Magnetic Field/Ultrasound-Responsive FeO Microbubbles for Targeted Mechanical/Catalytic Removal of Bacterial Biofilms.

Conventional antibiotics are limited by drug resistance, poor penetration, and inadequate targeting in the treatment of bacterial biofilm-associated infections. Microbubble-based ultrasound (US)-responsive drug delivery systems can disrupt biofilm structures and enhance antibiotic penetration through cavitation effects. However, currently developed US-responsive microbubbles still depend on antibiotics and lack targeting capability.

Bio-Inspired P-type TeSeO Synaptic Transistor Based on Multispectral Sensing for Neuromorphic Visual Multilevel Nociceptor.

The development of neuromorphic color vision has significant research implications in the fields of machine vision and artificial intelligence. By mimicking the processing mechanisms of energy-efficient biological visual systems, it offers a unique potential for real-time color environment perception and dynamic adaptability. This paper reports on a multispectral color sensing synaptic device based on a novel p-type TeSeO transistor, applied to a neuromorphic visual multilevel nociceptor.

Highly efficient ferroelectric capacitor reservoir computing through the study of its nonlinear polarization dynamics.

In this work, we aim to unveil the general correlations between the performance of a physical reservoir computing (RC) system and the inherent nonlinear dynamics of the adopted device. Taking the metal-ferroelectric-metal (MFM) capacitor, one of the most popular candidate devices for compute-in-memory (CIM) technology, as the computational platform, we construct a nonlinear dynamical model of polarization in the ferroelectric layer. We then design the physical RC utilizing a single and/or an array of MFM capacitors by analyzing the model's stability and feasible dynamical cases.