An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation.

Background: Given the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmost importance.

Objective: Our aim is to propose and validate a HS segmentation model that is both training-free and highly generalizable.

Deep learning radiomics on shear wave elastography and b-mode ultrasound videos of diaphragm for weaning outcome prediction.

Purpose: We proposed an automatic method based on deep learning radiomics (DLR) on shear wave elastography (SWE) and B-mode ultrasound videos of diaphragm for two classification tasks, one for differentiation between the control and patient groups, and the other for weaning outcome prediction.

Materials And Methods: We included a total of 581 SWE and B-mode ultrasound videos, of which 466 were from the control group of 179 normal subjects, and 115 were from the patient group of 35 mechanically ventilated subjects in the intensive care unit (ICU). Among the patient group, 17 subjects successfully weaned and 18 failed.

Cognitive Symptoms of Post-COVID-19 Condition and Daily Functioning.

Importance: The frequent occurrence of cognitive symptoms in post-COVID-19 condition has been described, but the nature of these symptoms and their demographic and functional factors are not well characterized in generalizable populations.

Objective: To investigate the prevalence of self-reported cognitive symptoms in post-COVID-19 condition, in comparison with individuals with prior acute SARS-CoV-2 infection who did not develop post-COVID-19 condition, and their association with other individual features, including depressive symptoms and functional status.

Design, Setting, And Participants: Two waves of a 50-state nonprobability population-based internet survey conducted between December 22, 2022, and May 5, 2023.

WiFi-based non-contact human presence detection technology.

In the swiftly evolving landscape of Internet of Things (IoT) technology, the demand for adaptive non-contact sensing has seen a considerable surge. Traditional human perception technologies, such as vision-based approaches, often grapple with problems including lack of sensor versatility and sub-optimal accuracy. To address these issues, this paper introduces a novel, non-contact method for human presence perception, relying on WiFi.

Cross-modality interaction for few-shot multispectral object detection with semantic knowledge.

Multispectral object detection (MOD), which incorporates additional information from thermal images into object detection (OD) to robustly cope with complex illumination conditions, has garnered significant attention. However, existing MOD methods always demand a considerable amount of annotated data for training. Inspired by the concept of few-shot learning, we propose a novel task called few-shot multispectral object detection (FSMOD) that aims to accomplish MOD using only a few annotated data from each category.

Sd-net: a semi-supervised double-cooperative network for liver segmentation from computed tomography (CT) images.

Introduction: The automatic segmentation of the liver is a crucial step in obtaining quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This task is challenging due to the frequent presence of noise and sampling artifacts in computerized tomography (CT) images, as well as the complex background, variable shapes, and blurry boundaries of the liver. Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations.

Advances in objective assessment of ergonomics in endoscopic surgery: a review.

Background: Minimally invasive surgery, in particular endoscopic surgery, has revolutionized the benefits for patients, but poses greater challenges for surgeons in terms of ergonomics. Integrating ergonomic assessments and interventions into the multi-stage endoscopic procedure contributes to the surgeon's musculoskeletal health and the patient's intraoperative safety and postoperative recovery.

Objective: The purpose of this study was to overview the objective assessment techniques, tools and assessment settings involved in endoscopic procedures over the past decade and to identify the potential factors that induce differences in high workloads in endoscopic procedures and ultimately to design a framework for ergonomic assessment in endoscopic surgery.

Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications.

Background And Hypothesis: Substantive inquiry into the predictive power of eye movement (EM) features for clinical high-risk (CHR) conversion and their longitudinal trajectories is currently sparse. This study aimed to investigate the efficiency of machine learning predictive models relying on EM indices and examine the longitudinal alterations of these indices across the temporal continuum.

Study Design: EM assessments (fixation stability, free-viewing, and smooth pursuit tasks) were performed on 140 CHR and 98 healthy control participants at baseline, followed by a 1-year longitudinal observational study.

A scientometric analysis of fairness in health AI literature.

Artificial intelligence (AI) and machine learning are central components of today's medical environment. The fairness of AI, i.e.

TSCA-Net: Transformer based spatial-channel attention segmentation network for medical images.

Deep learning architectures based on convolutional neural network (CNN) and Transformer have achieved great success in medical image segmentation. Models based on the encoder-decoder framework like U-Net have been successfully employed in many realistic scenarios. However, due to the low contrast between object and background, various shapes and scales of objects, and complex background in medical images, it is difficult to locate targets and obtain better segmentation performance by extracting effective information from images.

Integrated Sensing and Secure Communication with XL-MIMO.

This paper studies extremely large-scale multiple-input multiple-output (XL-MIMO)-empowered integrated sensing and secure communication systems, where both the radar targets and the communication user are located within the near-field region of the transmitter. The radar targets, being untrusted entities, have the potential to intercept the confidential messages intended for the communication user. In this context, we investigate the near-field beam-focusing design, aiming to maximize the achievable secrecy rate for the communication user while satisfying the transmit beampattern gain requirements for the radar targets.

Static and dynamic diffuse wave inspection of delaminations in carbon fiber reinforced composites.

This paper aims to realize a self-reference delamination inspection in CFRPs with diffuse ultrasonic waves. The static experiments are first conducted on samples with a machined defect of 0.5 mm in height which simulates an opening state delamination.

"Are you otherwise fit and well?": Past medical history questions in UK paediatric consultations.

Unlabelled: Accurate diagnosis and treatment depend upon detailed knowledge of both the child's presenting symptoms and their past medical history. However, the process of soliciting past medical history has never been subject to systematic scrutiny in actual clinical practice.

Objective: To examine the function of the question "are you otherwise fit and well?" to elicit a child's general medical history in UK paediatric allergy outpatient consultations.

Examining Antecedents of Factual Knowledge and Perceived Familiarity of COVID-19 Contact Tracing App: A Modified Cognitive Mediation Model.

This study employs a modified cognitive mediation model to investigate the role of health valuation and fear in shaping media attention, factual knowledge, and perceived familiarity with COVID-19 contact tracing apps. Data were collected from a national survey of 906 Singaporeans, and structural equation modeling was used to analyze the results. Findings indicated that both fear of COVID-19 and health valuation were positively associated with newspapers, television, and social media attention, with health valuation demonstrating a stronger association with each media type.

Responsibility to report symptoms: Pursuing symptom reports from children in pediatric encounters.

Objective: The paper focuses on cases in which children disconfirm a symptom previously reported by their parents and analyzes how doctors and parents continue asking questions to elicit symptom confirmation from children.

Methods: The study employs Conversation Analysis (CA) to examine 50 video recordings of Russian-language pediatric consultations with 4 pediatricians and approximately 50 children.

Results: The findings indicate two strategies used by participants to pursue symptom confirmation.

Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) has great potential for the implementation of high spectral-efficiency underwater acoustic (UWA) communications. However, general receivers consisting of the optimal maximum likelihood detection suffer from high computational load, which prohibits real-time data transmissions in underwater scenarios. In this paper, we propose a detection based on a vector approximate message passing (VAMP) algorithm for UWA OFDM-IM communications.

Is evoking fear effective? Exploratory findings from a randomised experiment on the impacts of health warning labels on sugar-sweetened beverages.

Objective: Health warning labels (HWL) have been suggested to be effective in reducing consumption of sugar-sweetened beverages (SSB). Yet, the efficacy and acceptability of SSB HWL of different formats (textual/pictorial) and severity remain unclear. This exploratory study aims to examine the extent and mechanism through which HWL of different formats and severity may affect responses towards the HWL and SSB consumption.

Player detection method based on scale attention and scale equalization algorithm.

Introduction: Object detection methods for team ball games players often struggle due to their reliance on dataset scale statistics, resulting in missed detections for players with smaller bounding boxes and reduced accuracy for larger bounding boxes.

Methods: This study introduces a two-fold approach to address these challenges. Firstly, a novel multi-scale attention mechanism is proposed, aiming to reduce reliance on scale statistics by utilizing a specially created SIoU (Similar to Intersection over Union) label that explicitly represents multi-scale features.

Modeling the influence of attitudes, trust, and beliefs on endoscopists' acceptance of artificial intelligence applications in medical practice.

Introduction: The potential for deployment of Artificial Intelligence (AI) technologies in various fields of medicine is vast, yet acceptance of AI amongst clinicians has been patchy. This research therefore examines the role of antecedents, namely trust, attitude, and beliefs in driving AI acceptance in clinical practice.

Methods: We utilized online surveys to gather data from clinicians in the field of gastroenterology.

AMSF: attention-based multi-view slice fusion for early diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible neurodegenerative disease with a high prevalence in the elderly population over 65 years of age. Intervention in the early stages of AD is of great significance to alleviate the symptoms. Recent advances in deep learning have shown extreme advantages in computer-aided diagnosis of AD.

rTMS concurrent with cognitive training rewires AD brain by enhancing GM-WM functional connectivity: a preliminary study.

Repetitive transcranial magnetic stimulation (rTMS) and cognitive training for patients with Alzheimer's disease (AD) can change functional connectivity (FC) within gray matter (GM). However, the role of white matter (WM) and changes of GM-WM FC under these therapies are still unclear. To clarify this problem, we applied 40 Hz rTMS over angular gyrus (AG) concurrent with cognitive training to 15 mild-moderate AD patients and analyzed the resting-state functional magnetic resonance imaging before and after treatment.

Exploring the general public's and experts' risk and benefit perceptions of cultured meat in Singapore: A mental models approach.

Despite the recent approval of cultured meat products in Singapore, the understanding of public perceptions towards this novel food technology remains limited. Utilizing attitude formation theory and the mental models approach, this study compares the mental models of the general public and experts regarding their risk and benefit perceptions of cultured meat. Through four online focus group discussions with 40 participants, we found convergences in the mental models of experts and the general public concerning perceived individual- and societal-level benefits of cultured meat (e.

Research on an Active Hydrogen Maser Digital Circuit Control System Based on FPGA.

A hydrogen maser is a high-precision time measurement instrument with high frequency stability and low frequency drift, which is widely used in satellite navigation, ground time keeping, frequency measurement, and other fields. An active hydrogen maser (AHM) is better than the current space passive hydrogen maser (PHM) in orbit in terms of its frequency stability and drift rate, but it has the disadvantages of large volume and weight. To further reduce the volume and weight of the circuit, this paper demonstrates a digital circuit control system based on a field-programmable gate array (FPGA).

Cultural tightness-looseness and normative social influence in eight Asian countries: Associations of individual and collective norms with vaccination intentions.

Rationale: Countries worldwide faced the same public health crisis that required promoting the same health behavior-vaccinations-during the COVID-19 pandemic. Thus, scholars have a unique opportunity to test behavioral change theories across countries with different cultural backgrounds.

Objective: Employing the extended theory of social normative behavior, this study examines the influence of individual and collective norms on COVID-19 vaccination intention across eight Asian countries.

Examining Theoretical Frameworks and Antecedents of Health Apps and Wearables Use: A Scoping Review.

The advancement of health apps and wearables has garnered substantial academic attention, particularly in examining why individuals decide to use or not use them. In response to the extensive body of research on this topic, we conducted a scoping review of 61 articles published from 2007 to 2022, aiming to examine the dominant theoretical frameworks and antecedents of health apps and wearables use. The findings demonstrated that the dominant theoretical frameworks within this domain were rooted in the human-computer interaction theories, notably the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology.