Diagnostic and prognostic perspectives of fabry disease via fiber evanescent wave spectroscopy advanced by machine learning.

Fabry disease (FD) is a rare disorder resulting from a genetic mutation characterized by the accumulation of sphingolipids in various cells throughout the human body, leading to progressive and irreversible organ damage, particularly in males. Genetically-determined deficiency or reduced activity of the enzyme (alpha - Galactosidase; α-Gal) leads to the accumulation of sphingolipids in the lysosomes of various cell types, including the heart, kidneys, skin, eyes, central nervous system, and digestive system, triggering damage, leading to the failure of vital organs, and resulting in progressive disability and premature death. FD diagnostics currently depend on costly and time-intensive genetic tests and enzymatic analysis, often leading to delayed or inaccurate diagnoses, which contribute to rapid disease progression.

EEG reveals key features of binocular color fusion and rivalry.

Differences in the brain sensitivity to color responses may cause significant differences in the latency and amplitude of the electroencephalographic (EEG) component. This paper investigated the electroencephalography features of binocular color fusion and binocular color rivalry when watching stereoscopic three-dimensional (3D) displays. EEG experiments were conducted on a conventional 3D display platform.

Reconstruction of Local Three-dimensional Temperature Field of Tumor Cells with Low-toxic Nanoscale Quantum-dot Thermometer and Cepstrum Spatial Localization Algorithm.

The optimal method for three-dimensional thermal imaging within cells involves collecting intracellular temperature responses while simultaneously obtaining corresponding 3D positional information. Current temperature measurement techniques based on the photothermal properties of quantum dots face several limitations, including high cytotoxicity and low fluorescence quantum yields. These issues affect the normal metabolic processes of tumor cells.

Person-Specific Analyses of Smartphone Use and Mental Health: An Intensive Longitudinal Study Over One Year.

Background: Contrary to popular concerns about the harmful effects of media use on mental health, research on this relationship is ambiguous, stalling advances in theory, interventions, and policy. Scientific explorations of the relationship between media and mental health have mostly found null or small associations, with the results often blamed on the use of cross-sectional study designs or imprecise measures of media use and mental health.

Objective: This exploratory empirical demonstration aimed to answer whether mental health effects are associated with media use experiences by (1) redirecting research investments to granular and intensive longitudinal recordings of digital experiences to build models of media use and mental health for single individuals over the course of one entire year, (2) using new metrics of fragmented media use to propose explanations of mental health effects that will advance person-specific theorizing in media psychology, and (3) identifying combinations of media behaviors and mental health symptoms that may be more useful for studying media effects than single measures of dosage and affect or assessments of clinical symptoms related to specific disorders.

A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach.

This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable of predicting wind speed and direction up to 30-minute ahead with 1-minute temporal resolution. The optimized architecture demonstrated robust predictive performance across all forecast horizons.

Estimating realized relatedness in free-ranging macaques by inferring identity-by-descent segments.

Biological relatedness is a key consideration in studies of behavior, population structure, and trait evolution. Except for parent-offspring dyads, pedigrees capture relatedness imperfectly. The number and length of identical-by-descent DNA segments (IBD) yield the most precise relatedness estimates.

The intersection of finTech adoption, HR competency potential, service innovation, and firm growth in the banking sectors using Entropy and TOPSIS.

The adoption of Financial Technology (FinTech), along with the enhancement of Human Resource (HR) competencies, service innovation, and firm growth, plays a crucial role in the development of the banking sector. Despite their importance, obtaining reliable results is often challenging due to the complex, high-dimensional correlations among various features that affect the industry. To address this issue, this research introduces a hybrid Multi-Criteria Decision-Making (MCDM) model that integrates the Entropy-Weighted Method (EWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).

Association between cardiometabolic index and overactive bladder in adult American women: A cross-sectional study.

Background: Overactive bladder (OAB) is a common disorder, particularly in women, and its symptoms, including urgency, frequency, and nocturia, can significantly affect quality of life. The cardiometabolic index (CMI) is a novel metabolic risk indicator that has been receiving more attention lately. This study investigated the association between CMI and OAB in adult women.

Integrating intuitionistic fuzzy and MCDM methods for sustainable energy management in smart factories.

Improving energy efficiency is crucial for smart factories that want to meet sustainability goals and operational excellence. This study introduces a novel decision-making framework to optimize energy efficiency in smart manufacturing environments, integrating Intuitionistic Fuzzy Sets (IFS) with Multi-Criteria Decision-Making (MCDM) techniques. The proposed approach addresses key challenges, including reducing carbon footprints, managing operating costs, and adhering to stringent environmental standards.

Assessing the global dynamics of Nipah infection under vaccination and treatment: A novel computational modeling approach.

In biology and life sciences, fractal theory and fractional calculus have significant applications in simulating and understanding complex problems. In this paper, a compartmental model employing Caputo-type fractional and fractal-fractional operators is presented to analyze Nipah virus (NiV) dynamics and transmission. Initially, the model includes nine nonlinear ordinary differential equations that consider viral concentration, flying fox, and human populations simultaneously.

Flexibility in choosing decision policies in gathering discrete evidence over time.

The brain can remarkably adapt its decision-making process to suit the dynamic environment and diverse aims and demands. The brain's flexibility can be classified into three categories: flexibility in choosing solutions, decision policies, and actions. We employ two experiments to explore flexibility in decision policy: a visual object categorization task and an auditory object categorization task.

Metastatic Lung Lesion Changes in Follow-up Chest CT: The Advantage of Deep Learning Simultaneous Analysis of Prior and Current Scans With SimU-Net.

Purpose: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.

Materials And Methods: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient.

Adenine nucleotide translocator and ATP synthase cooperate in mediating the mitochondrial permeability transition.

The permeability transition (PT) is a permeability increase of the mitochondrial inner membrane causing mitochondrial swelling in response to matrix Ca. The PT is mediated by regulated channel(s), the PT pore(s) (PTP), which can be generated by at least two components, adenine nucleotide translocator (ANT) and ATP synthase. Whether these provide independent permeation pathways remains to be established.

Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.

Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.

AAPM Truth-based CT (TrueCT) reconstruction grand challenge.

Background: This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction.

Purpose: To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases.

Methods: Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions.

Translational nanorobotics breaking through biological membranes.

In the dynamic realm of translational nanorobotics, the endeavor to develop nanorobots carrying therapeutics in rational applications necessitates a profound understanding of the biological landscape of the human body and its complexity. Within this landscape, biological membranes stand as critical barriers to the successful delivery of therapeutic cargo to the target site. Their crossing is not only a challenge for nanorobotics but also a pivotal criterion for the clinical success of therapeutic-carrying nanorobots.

Accurate Estimation of Diffusion Coefficients and their Uncertainties from Computer Simulation.

Self-diffusion coefficients, *, are routinely estimated from molecular dynamics simulations by fitting a linear model to the observed mean squared displacements (MSDs) of mobile species. MSDs derived from simulations exhibit statistical noise that causes uncertainty in the resulting estimate of *. An optimal scheme for estimating * minimizes this uncertainty, i.

Lower bounds on trees and unicyclic graphs with respect to the misbalance rodeg index.

The Misbalance Rodeg () index stands out among the 148 discrete Adriatic indices demonstrating considerable predictive capabilities in evaluations carried out by the International Academy of Mathematical Chemistry. This index excels particularly in forecasting both the enthalpy and the standard enthalpy of vaporization for octane isomers. Despite its significant chemical applicability, the index has not been extensively explored in the literature.

Correlation Between Dynamic Contrast-Enhanced CT Imaging Signs and Differentiation Grade and Microvascular Invasion of Hepatocellular Carcinoma.

Objective: This study aimed to investigate how dynamic contrast-enhanced CT imaging signs correlate with the differentiation grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to assess their predictive value for MVI when combined with clinical characteristics.

Methods: We conducted a retrospective analysis of clinical data from 232 patients diagnosed with HCC at our hospital between 2021 and 2022. All patients underwent preoperative enhanced CT scans, laboratory tests, and postoperative pathological examinations.

An empirical study of LLaMA3 quantization: from LLMs to MLLMs.

The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3's capabilities when quantized to low bit-width.

Enhancing the Travel Experience for People with Visual Impairments through Multimodal Interaction: NaviGPT, A Real-Time AI-Driven Mobile Navigation System.

Assistive technologies for people with visual impairments (PVI) have made significant advancements, particularly with the integration of artificial intelligence (AI) and real-time sensor technologies. However, current solutions often require PVI to switch between multiple apps and tools for tasks like image recognition, navigation, and obstacle detection, which can hinder a seamless and efficient user experience. In this paper, we present NaviGPT, a high-fidelity prototype that integrates LiDAR-based obstacle detection, vibration feedback, and large language model (LLM) responses to provide a comprehensive and real-time navigation aid for PVI.

Real-time evaluation of an automated computer vision system to monitor pain behavior in older adults.

Regular use of standardized observational tools to assess nonverbal pain behaviors results in improved pain care for older adults with severe dementia. While frequent monitoring of pain behaviors in long-term care (LTC) is constrained by resource limitations, computer vision technology has the potential to mitigate these challenges. A computerized algorithm designed to assess pain behavior in older adults with and without dementia was recently developed and validated using video recordings.

Adjoint method-based Fourier neural operator surrogate solver for wavefront shaping in tunable metasurfaces.

We present a Fourier neural operator (FNO)-based surrogate solver for the efficient optimization of wavefronts in tunable metasurface controls. Existing methods, including the Gerchberg-Saxton algorithm and the adjoint optimization, are often computationally demanding due to their iterative processes, which require numerical simulations at each step. Our surrogate solver overcomes this limitation by providing highly accurate gradient estimations with respect to changes in tunable meta-atoms without the need for direct simulations.

Virseqimprover: an integrated pipeline for viral contig error correction, extension, and annotation.

Despite the recent surge of viral metagenomic studies, it remains a significant challenge to recover complete virus genomes from metagenomic data. The majority of viral contigs generated from de novo assembly programs are highly fragmented, presenting significant challenges to downstream analysis and inference. To address this issue, we have developed Virseqimprover, a computational pipeline that can extend assembled contigs to complete or nearly complete genomes while maintaining extension quality.