Hardware-enabled low latency rhythmic brain state tracking for brain stimulation applications.

Objective: Upcoming neuroscientific research will require bidirectional and context dependent interaction with nervous tissue. To facilitate the future neuroscientific discoveries we have created HarPULL, a genuinely real-time system for tracking oscillatory brain state.

Approach: The HarPULL technology ensures reliable, accurate and affordable real-time phase and amplitude tracking based on the state-space estimation framework operationalized by Kalman filtering.

Mine-DW-Fusion: BEV Multiscale-Enhanced Fusion Object-Detection Model for Underground Coal Mine Based on Dynamic Weight Adjustment.

Environmental perception is crucial for achieving autonomous driving of auxiliary haulage vehicles in underground coal mines. The complex underground environment and working conditions, such as dust pollution, uneven lighting, and sensor data abnormalities, pose challenges to multimodal fusion perception. These challenges include: (1) the lack of a reasonable and effective method for evaluating the reliability of different modality data; (2) the absence of in-depth fusion methods for different modality data that can handle sensor failures; and (3) the lack of a multimodal dataset for underground coal mines to support model training.

Mortality risk associated with general and central obesity in inflammatory bowel disease patients: a long-term prospective cohort study.

Aim: To comprehensively investigate the long-term risk of all-cause mortality associated with general and central obesity in patients with inflammatory bowel disease (IBD).

Methods: Overall, 5107 IBD patients [mean age 57.0 (SD: 8.

In Situ Plant Sensors: Toward Real-Time, High-Resolution Monitoring.

The field of plant sensing technologies is undergoing a transformative shift, driven by innovations in both flexible wearable devices and genetically encoded sensors (GESs). From this standpoint, we emphasize their potential in real-time, in situ monitoring of plant physiology and stress responses. Wearable sensors enable continuous detection of plant growth, microclimate, water transport, surface potential, and immune responses, offering unprecedented insight at the tissue level.

Design of function-regulating RNA via deep learning and AlphaFold 3.

RNAs are programmable macromolecules that play diverse regulatory roles in living organisms. However, the intricate structure-function relationships underlying their regulatory activities pose significant challenges for RNA design. Here, we introduce a computational framework that integrates deep learning and energy-based methods to enhance the sequence diversity of sgRNAs designs.

On the practical applicability of DM21 neural-network DFT functional for chemical calculations: Focus on geometry optimization.

Density functional theory is the workhorse of present-day quantum chemistry thanks to its good balance between calculation accuracy and speed. In recent years, several neural network-based exchange-correlation functionals have been developed, with DM21, developed by Google DeepMind, being the most recognizable among them. In this study, we focus on evaluating the efficiency of DM21 functional on the task of optimizing molecular geometries and investigate how the non-smooth behavior of neural network-predicted exchange-correlation energy and potential affects the final geometry precision.

Airborne-Platform-Assisted Transmission and Control Separation for Multiple Access in Integrated Satellite-Terrestrial Networks.

Currently, the primary random access protocol for satellite communications is Irregular Repetition Slotted ALOHA (IRSA). This protocol leverages interference cancellation and burst repetition based on probabilistic distributions, achieving up to 80% channel utilization in practical use. However, it faces three significant issues: (1) low channel utilization with smaller frame sizes; (2) drastic performance degradation under heavy load, where channel utilization can be lower than that of traditional Slotted ALOHA; and (3) even under optimal load and frame sizes, up to 20% of the valuable satellite channel resources are still wasted despite reaching up to 80% channel utilization.

Direction and modality of transcription changes caused by TAD boundary disruption in Slc29a3/Unc5b locus depends on tissue-specific epigenetic context.

Background: Topologically associating domains (TADs) are believed to play a role in the regulation of gene expression by constraining or guiding interactions between the regulatory elements. While the impact of TAD perturbations is typically studied in developmental genes with highly cell-type-specific expression patterns, this study examines genes with broad expression profiles separated by a strong insulator boundary. We focused on the mouse Slc29a3/Unc5b locus, which encompasses two distinct TADs containing ubiquitously expressed and essential for viability genes.

Motivations, positive experiences, and concept changes of medical students in Korea after participating in an experiential entrepreneurship course: a qualitative study.

This study explored the experiences of medical students enrolled in an elective course titled "Healthcare Innovation and Women's Ventures II" at Ewha Womans University College of Medicine. The research questions were as follows: First, what motivated medical students to participate in the experiential entrepreneurship course? Second, what experiences did the students have during the course? Third, what changes did the students undergo as a result of the course? Focus group interviews were conducted with six medical students who participated in the experiential entrepreneurship course from February 13 to 23, 2024. The analysis identified three domains, seven categories, and 17 subcategories.

Development of automatic organ segmentation based on positron-emission tomography analysis system using Swin UNETR in breast cancer patients in Korea.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region-of-interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning-based quantitative analysis method that enhances diagnostic and prognostic accuracy.

Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis.

Pre- to post-contrast medical image synthesis with outline-guide accelerate diffusion model.

Contrast agents are widely used in medical imaging, which enhance the contrast of lesion region and promote detection and treatment. Due to contrast agents injected into the human body may cause adverse reactions and potential damage to sensitive organs. Then, some generative models attempt to synthesize post-contrast images from pre-contrast images to avoid the use of contrast agents.

Missing data imputation of climate time series: A review.

Missing data in climate time series is a significant problem because it complicates the monitoring and prediction of climatic phenomena. The primary objective of this research document is to describe the most relevant imputation methods for missing data in the climate context over the last decade. Results reveal a superior concentration of documents on the use of imputation methods for climate time series in Asia and Europe, with notable examples from Malaysia, China, and Italy.

State shyness enhances recruitment of social processing regions while reducing communication of prefrontal regulatory regions.

State shyness is characterized by a swift and intense emotional response to social stressors, playing a crucial role in shaping the dynamic neural processes in social interactions. However, its underlying neural mechanisms remain unclear. Using a novel shyness-induction paradigm and functional magnetic resonance imaging (fMRI), we investigated brain activation and connectivity patterns associated with state shyness in 41 healthy adults (25 females; Mage = 21.

Leveraging federated learning and edge computing for pandemic-resilient healthcare.

The universal demand for the development and deployment of responsive medical infrastructure and damage control techniques, including the application of technology, is the foremost necessity that emerged immediately in the post-pandemic era. Numerous technologies, such as artificial intelligence (AI)-aided decision-making and the Internet of Things (IoT), have been rendered indispensable for such applications. Federated learning (FL) is a popular approach used to enhance AI-driven decision support systems and maintain decentralized learning.

Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion.

The most common types of kidneys and liver cancer are renal cell carcinoma (RCC) and hepatic cell carcinoma (HCC), respectively. Accurate grading of these carcinomas is essential for determining the most appropriate treatment strategies, including surgery or pharmacological interventions. Traditional deep learning methods often struggle with the intricate and complex patterns seen in histopathology images of RCC and HCC, leading to inaccuracies in classification.

Charm is a flexible pipeline to simulate chromosomal rearrangements on Hi-C-like data.

Identifying structural variants (SVs) remains a pivotal challenge within genomic studies. The recent advent of chromosome conformation capture (3C) techniques has emerged as a promising avenue for the accurate identification of SVs. However, development and validation of computational methods leveraging 3C data necessitate comprehensive datasets of well-characterized chromosomal rearrangements, which are presently lacking.

sEntIMeldCL: Enhancing explicit knowledge via Uniform-based Implicit Contrastive Mechanism for Aspect-Level Sentiment Analysis.

Aspect-level sentiment analysis (ALSA) is a fine-grained task which consists of aspect terms and sentiment polarities within sentences. Numerous research studies only focus on the syntactical dependencies between words, ignoring the impact of negations on sentiment polarity. Several pre-trained augmentation models play an essential role in solving this problem.

Degradation-Guided cross-consistent deep unfolding network for video restoration under diverse weathers.

Existing video restoration (VR) methods have made promising progress in improving the quality of videos degraded by adverse weather. However, these approaches only restore videos with one specific type of degradation and ignore the diversity of degradations in the real world, which limits their application in realistic scenes with diverse adverse weathers. To address the aforementioned issue, in this paper, we propose a Cross-consistent Deep Unfolding Network (CDUN) to adaptively restore frames corrupted by different degradations via the guidance of degradation features.

Myo-inositol supplementation in diet-induced obese lactating rats mitigates metabolic dysregulation and improves offspring health in early life.

Myo-inositol (MI) supplementation has emerged as a promising intervention to mitigate the malprogramming effects associated with adverse maternal conditions during the perinatal period. This study aimed to assess the effects of MI supplementation during lactation on metabolism in diet-induced obese rats and on early health outcomes in their offspring. Female Wistar rats were fed either a control (CON) or Western diet (WD) for one month before mating and during gestation and lactation.

Perinatal leptin effects on hypothalamic brain-derived neurotrophic factor and energy balance-related gene regulation.

Brain-derived neurotrophic factor (BDNF) and leptin are essential in neurodevelopment and central regulation of feeding and energy balance. We studied the metabolic imprinting effects of physiological leptin supplementation during suckling in the brain of 5-week-old mouse pups. Leptin-treated animals showed lower cumulative food intake and increased energy efficiency, which was related to higher lean mass.

Multi-agent systems powered by large language models: applications in swarm intelligence.

This work examines the integration of large language models (LLMs) into multi-agent simulations by replacing the hard-coded programs of agents with LLM-driven prompts. The proposed approach is showcased in the context of two examples of complex systems from the field of swarm intelligence: ant colony foraging and bird flocking. Central to this study is a toolchain that integrates LLMs with the NetLogo simulation platform, leveraging its Python extension to enable communication with GPT-4o via the OpenAI API.

Context-dependent PSIICOS: A novel framework for functional connectivity estimation accounting for task-related power leakage.

Functional connectivity (FC) analysis using non-invasive neuroimaging methods, such as MEG and EEG, is often confounded by artifacts from spatial leakage and task-related power modulations. To address these limitations, we present Context-Dependent PSIICOS (CD-PSIICOS), a novel framework that improves the estimation of FC by incorporating task-specific cortical power distributions into the projection operator applied to the vectorized sensor-space cross-spectrum. Unlike the original PSIICOS (Phase Shift Invariant Imaging of Coherent Sources) approach, designed to suppress spatial leakage from all the sources, CD-PSIICOS dynamically adjusts the projection based on the active source distribution, enabling more accurate suppression of spatial leakage while preserving true zero-phase interactions.

Design, Modeling, and Experimental Validation of a Bio-Inspired Rigid-Flexible Continuum Robot Driven by Flexible Shaft Tension-Torsion Synergy.

This paper presents a bio-inspired rigid-flexible continuum robot driven by flexible shaft tension-torsion synergy, tackling the trade-off between actuation complexity and flexibility in continuum robots. Inspired by the muscular arrangement of octopus arms, enabling versatile multi-degree-of-freedom (DoF) movements, the robot achieves 6-DoF motion and 1-DoF gripper opening and closing movement with only six flexible shafts, simplifying actuation while boosting dexterity. A comprehensive kinetostatic model, grounded in Cosserat rod theory, is developed; this model explicitly incorporates the coupling between the spinal rods and flexible shafts, the distributed gravitational effects of spacer disks, and friction within the guide tubes.

Mediterranean diet and obesity polygenic risk interaction on adiposity in European children: The IDEFICS/I.Family Study.

Background And Objectives: To examine whether changes in the Mediterranean Diet (MD) or any of its MD food groups modulate the genetic susceptibility to obesity in European youth, both in cross-sectional and longitudinal analyses.

Methods: For cross-sectional analysis, 1982 participants at baseline, 1649 in follow-up 1 (FU1) and 1907 in follow-up 2 (FU2), aged 2-16 years of the IDEFICS/I.Family studies were considered.

A Generative Artificial Intelligence Copilot for Biomedical Nanoengineering.

The recent success of large language models (LLMs) in performing natural language processing tasks has increased interest in applying generative artificial intelligence (AI) to scientific research. However, a common problem of LLMs is their tendency to produce inaccurate and sometimes "hallucinated" outputs. Here, we established a generative AI tool, NanoSafari, to automatically extract knowledge from the biomedical nanoscience literature and address scientific queries.