Synaptic Density Reductions in MSA: A Potential Biomarker Identified Through [F]SynVesT-1 PET Imaging.

Objective: The objective of this study was to delineate synaptic density alterations in multiple system atrophy (MSA) and explore its potential role as a biomarker for MSA diagnosis and disease severity monitoring using [F]SynVesT-1 positron emission tomography / computed tomography (PET CT).

Methods: In this prospective study, 60 patients with MSA (30 patients with MSA-parkinsonian [MSA-P] subtype and 30 patients with MSA-cerebellar [MSA-C] subtype), 30 patients with Parkinson's disease (PD), and 30 age-matched healthy controls (HCs) underwent [F]SynVesT-1 PET/CT for synaptic density assessment. Visual, voxel, and volumetric region of interest (VOI) analyses were used to elucidate synaptic density patterns in the MSA brain and establish diagnostic criteria.

Predicting circRNA-Disease Associations through Multisource Domain-Aware Embeddings and Feature Projection Networks.

Recent studies have highlighted the significant role of circular RNAs (circRNAs) in various diseases. Accurately predicting circRNA-disease associations is crucial for understanding their biological functions and disease mechanisms. This work introduces the MNDCDA method, designed to address the challenges posed by the limited number of known circRNA-disease associations and the high cost of biological experiments.

Gene function revealed at the moment of sitochastic gene silencing.

Gene expression is a dynamic and stochastic process characterized by transcriptional bursting followed by periods of silence. Single-cell RNA sequencing (scRNA-seq) is a powerful tool to measure transcriptional bursting and silencing at the individual cell level. In this study, we introduce the single-cell Stochastic Gene Silencing (scSGS) method, which leverages the natural variability in single-cell gene expression to decipher gene function.

Enhancing predictive accuracy for urinary tract infections post-pediatric pyeloplasty with explainable AI: an ensemble TabNet approach.

Ureteropelvic junction obstruction (UPJO) is a common pediatric condition often treated with pyeloplasty. Despite the surgical intervention, postoperative urinary tract infections (UTIs) occur in over 30% of cases within six months, adversely affecting recovery and increasing both clinical and economic burdens. Current prediction methods for postoperative UTIs rely on empirical judgment and limited clinical parameters, underscoring the need for a robust, multifactorial predictive model.

Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model.

Enhancement of security, personalization, and safety in advanced transportation systems depends on driver identification. In this context, this work suggests a new method to find drivers by means of a Random Forest model optimized using the osprey optimization algorithm (OOA) for feature selection and the salp swarm optimization (SSO) for hyperparameter tuning based on driving behavior. The proposed model achieves an accuracy of 92%, a precision of 91%, a recall of 93%, and an F1-score of 92%, significantly outperforming traditional machine learning models such as XGBoost, CatBoost, and Support Vector Machines.

[Statistical methods for extremely unbalanced data in genome-wide association study (2)].

Extremely unbalanced data refers to datasets with independent or dependent variables showing severe imbalances in proportions, which might lead to deviation of classical test statistics from theoretical distribution and difficulties in controlling type Ⅰ error. The increased availability of genome-wide resources from large population cohorts has highlighted the growing demand for efficient and accurate statistical methods for the process of extremely unbalanced data to improve the development of genetic statistical methods. This paper introduces two widely used correction methods in current genome-wide association study for extremely unbalanced data, i.

Computer-Aided Design of Self-Assembled Nanoparticles to Enhance Cancer Chemoimmunotherapy via Dual-Modulation Strategy.

The rational design of self-assembled compounds is crucial for the highly efficient development of carrier-free nanomedicines. Herein, based on computer-aided strategies, important physicochemical properties are identified to guide the rational design of self-assembled compounds. Then, the pharmacophore hybridization strategy is used to design self-assemble nanoparticles by preparing new chemical structures by combining pharmacophore groups of different bioactive compounds.

Development of an intelligent control system of high efficiency and online monitoring for hot air drying of peony flowers.

To enhance the drying quality of peony flowers, this study developed an integrated intelligent control and monitoring system. The system incorporates computer vision technology to enable real-time continuous monitoring and analysis of the total color change (ΔE) and shrinkage rate (SR) of the material. Additionally, by integrating drying time and temperature data, a hybrid neural network model combining convolutional neural networks, long short-term memory, and attention mechanisms (CNN-LSTM-Attention) was employed to accurately predict the moisture ratio (MR) of peony flowers.

Towards molecular structure discovery from cryo-ET density volumes via modelling auxiliary semantic prototypes.

Cryo-electron tomography (cryo-ET) is confronted with the intricate task of unveiling novel structures. General class discovery (GCD) seeks to identify new classes by learning a model that can pseudo-label unannotated (novel) instances solely using supervision from labeled (base) classes. While 2D GCD for image data has made strides, its 3D counterpart remains unexplored.

Computer-assisted enzyme cocktails enhance fermentation by overcoming toxic inhibitors from pretreatment processes.

Lignocellulosic biomass is the most abundant form of biomass available for fuel production, serving as the fourth leading energy source globally. However, inhibitors generated during pretreatment processes often hinder fermentation performance and conversion efficiency. In this study, we developed an enhanced computer-assisted enzyme cocktail strategy (ComEC 2.

Taming vision transformers for clinical laryngoscopy assessment.

Objective: Laryngoscopy, essential for diagnosing laryngeal cancer (LCA), faces challenges due to high inter-observer variability and the reliance on endoscopist expertise. Distinguishing precancerous from early-stage cancerous lesions is particularly challenging, even for experienced practitioners, given their similar appearances. This study aims to enhance laryngoscopic image analysis to improve early screening/detection of cancer or precancerous conditions.

Temporal multi-modal knowledge graph generation for link prediction.

Temporal Multi-Modal Knowledge Graphs (TMMKGs) can be regarded as a synthesis of Temporal Knowledge Graphs (TKGs) and Multi-Modal Knowledge Graphs (MMKGs), combining the characteristics of both. TMMKGs can effectively model dynamic real-world phenomena, particularly in scenarios involving multiple heterogeneous information sources and time series characteristics, such as e-commerce websites, scene recording data, and intelligent transportation systems. We propose a Temporal Multi-Modal Knowledge Graph Generation (TMMKGG) method that can automatically construct TMMKGs, aiming to reduce construction costs.

Multi-region nomogram for predicting central lymph node metastasis in papillary thyroid carcinoma using multimodal imaging: A multicenter study.

Background And Objective: Central lymph node metastasis (CLNM) is associated with high recurrence rate and low survival in patients with papillary thyroid carcinoma (PTC). However, there is no satisfactory model to predict CLNM in PTC. This study aimed to integrate PTC deep learning feature based on ultrasound (US) images, fat radiomics features based on computed tomography (CT) images and clinical characteristics to construct a multimodal and multi-region nomogram (MMRN) for predicting the CLNM in PTC.

AutoCOR: Autonomous condylar offset ratio calculator for post-operative total knee arthroplasty radiographs.

Background: This study aims to automate the measurement process of posterior condylar offset ratio (PCOR) and anterior condylar offset ratio (ACOR) to improve the Total Knee Arthroplasty (TKA) evaluation. Accurate calculation of PCOR and ACOR, performed manually by orthopedic surgeons, is crucial for assessing postoperative range of motion and implant positioning. Manual measurements, however, are time-consuming, prone to human error, and subject to variability.

Resource-Rational Virtual Bargaining for Moral Judgment: Toward a Probabilistic Cognitive Model.

Recent theoretical work has argued that moral psychology can be understood through the lens of "resource rational contractualism." The view posits that the best way of making a decision that affects other people is to get everyone together to negotiate under idealized conditions. The outcome of that negotiation is an arrangement (or "contract") that would lead to mutual benefit.

Inferring single-cell and spatial microRNA activity from transcriptomics data.

The activity of miRNA varies across different cell populations and systems, as part of the mechanisms that distinguish cell types and roles in living organisms and in human health and disease. Typically, miRNA regulation drives changes in the composition and levels of protein-coding RNA and of lncRNA, with targets being down-regulated when miRNAs are active. The term "miRNA activity" is used to refer to this transcriptional effect of miRNAs.

Presence of EGF ligand restricts the binding ability of EgB4 nanobody to EGFR extracellular domain.

EgB4 is a nanobody that could facilitate the development of drug-nanobody conjugates or drug delivery in cancer treatment due to its specific binding ability to the EGFR transmembrane protein. More significantly, EgB4 does not hamper the EGFR function and associates with EGFR in both the presence and absence of an EGF ligand. However, the difference in EgB4-EGFR interaction with and without EGF ligand is not clear.

A novel approach for target deconvolution from phenotype-based screening using knowledge graph.

Deconvoluting drug targets is crucial in modern drug development, yet both traditional and artificial intelligence (AI)-driven methods face challenges in terms of completeness, accuracy, and efficiency. Identifying drug targets, especially within complex systems such as the p53 pathway, remains a formidable task. The regulation of this pathway by myriad stress signals and regulatory elements adds layers of complexity to the discovery of effective p53 pathway activators.

Incomplete human reference genomes can drive false sex biases and expose patient-identifying information in metagenomic data.

As next-generation sequencing technologies produce deeper genome coverages at lower costs, there is a critical need for reliable computational host DNA removal in metagenomic data. We find that insufficient host filtration using prior human genome references can introduce false sex biases and inadvertently permit flow-through of host-specific DNA during bioinformatic analyses, which could be exploited for individual identification. To address these issues, we introduce and benchmark three host filtration methods of varying throughput, with concomitant applications across low biomass samples such as skin and high microbial biomass datasets including fecal samples.

Advanced IoT-integrated parking systems with automated license plate recognition and payment management.

Urban parking management is a growing challenge with increasing vehicle numbers and limited parking space. Traditional methods often fail during peak hours, leading to inefficiencies, unauthorized usage, and revenue losses. For instance, a parking lot designed for 300 vehicles often exceeds 90% occupancy during peak times, creating congestion and billing inaccuracies.

Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique.

Cardio Vascular Disease (CVD) is one of the leading causes of mortality and it is estimated that 1 in 4 deaths happens due to it. The disease prevalence rate becomes higher since there is an inadequate system/model for predicting CVD at an earliest. Diabetic Retinopathy (DR) is a kind of eye disease was associated with increasing risk factors for all-causes of CVD events.

Particulate matter 2.5 (PM) - associated cognitive impairment and morbidity in humans and animal models: a systematic review.

Particulate matter with an aerodynamic diameter of less than 2.5 µm (PM) is one of the criteria air pollutants that (1) serve as an essential carrier of airborne toxicants arising from combustion-related events including emissions from industries, automobiles, and wildfires and (2) play an important role in transient to long-lasting cognitive dysfunction as well as several other neurological disorders. A systematic review was conducted to address differences in study design and various biochemical and molecular markers employed to elucidate neurological disorders in PM -exposed humans and animal models.

Real-time isolation of physiological tremor using recursive singular spectrum analysis and random vector functional link for surgical robotics.

Hand-held robotic instruments enhance precision in microsurgery by mitigating physiological tremor in real time. Current tremor filtering algorithms in these instruments often employ nonlinear phase prefilters to isolate the tremor signal. However, these filters introduce phase distortion in the filtered tremor, compromising accuracy.

Swarm-initialized adaptive controller with beetle antenna searching of wearable lower limb exoskeleton for sit-to-stand and walking motions.

In recent years, exoskeleton robots have attracted great interest from researchers in the area of robotics due to their ability to assist human functionality improvement. A wearable lower limb exoskeleton is aimed at supporting the limb functionality rehabilitation process and to assist physical therapists. Development of a stable and robust control system for multi-joint rehabilitation robots is a challenging task due to their non-linear dynamic systems.