Sporting a virtual future: exploring sports and virtual reality patents using deep learning-based analysis.

We investigate the convergence of sports and emerging technologies from the Fourth Industrial Revolution, with a focus on virtual reality (VR) applications. Using patent big data, we introduce SportsBERT, a bidirectional encoder representation from transformers (BERT)-based algorithm tailored for enhanced natural language processing in sports-related knowledge-based documents. Through topic modeling, we extract key themes and clusters from sports-related VR patents, providing insights into the knowledge structure and technological trends in VR applications for sports.

W-band frequency selective digital metasurface using active learning-based binary optimization.

The W-band is essential for applications like high-resolution imaging and advanced monitoring systems, but high-frequency signal attenuation leads to poor signal-to-noise ratios, posing challenges for compact and multi-channel systems. This necessitates distinct frequency selective surfaces (FSS) on a single substrate, a complex task due to inherent substrate resonance modes. In this study, we use a digital metasurface platform to design W-band FSS on a glass substrate, optimized through binary optimization assisted by active learning.

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study.

Obesity is a global public health concern, often co-occurring in patients with severe mental illnesses. The impact of psychotropic drugs-induced weight gain is augmenting the disease burden and healthcare expenditure. However, predictors of psychotropic drug-induced weight gain and the efficacy of anti-obesity drugs remain underexplored.

Cardiac fibroblast BAG3 regulates TGFBR2 signaling and fibrosis in dilated cardiomyopathy.

Loss of Bcl2-associated athanogene 3 (BAG3) is associated with dilated cardiomyopathy (DCM). BAG3 regulates sarcomere protein turnover in cardiomyocytes; however, the function of BAG3 in other cardiac cell types is understudied. In this study, we used an isogenic pair of BAG3-knockout and wild-type human induced pluripotent stem cells (hiPSCs) to interrogate the role of BAG3 in hiPSC-derived cardiac fibroblasts (CFs).

An engineered human cardiac tissue model reveals contributions of systemic lupus erythematosus autoantibodies to myocardial injury.

Systemic lupus erythematosus (SLE) is a heterogenous autoimmune disease that affects multiple organs, including the heart. The mechanisms of myocardial injury in SLE remain poorly understood. In this study, we engineered human cardiac tissues and cultured them with IgG from patients with SLE, with and without myocardial involvement.

Modeling the Effects of Protracted Cosmic Radiation in a Human Organ-on-Chip Platform.

Galactic cosmic radiation (GCR) is one of the most serious risks posed to astronauts during missions to the Moon and Mars. Experimental models capable of recapitulating human physiology are critical to understanding the effects of radiation on human organs and developing radioprotective measures against space travel exposures. The effects of systemic radiation are studied using a multi-organ-on-a-chip (multi-OoC) platform containing engineered tissue models of human bone marrow (site of hematopoiesis and acute radiation damage), cardiac muscle (site of chronic radiation damage) and liver (site of metabolism), linked by vascular circulation with an endothelial barrier separating individual tissue chambers from the vascular perfusate.

Macrophages enhance contractile force in iPSC-derived human engineered cardiac tissue.

Resident cardiac macrophages are critical mediators of cardiac function. Despite their known importance to cardiac electrophysiology and tissue maintenance, there are currently no stem-cell-derived models of human engineered cardiac tissues (hECTs) that include resident macrophages. In this study, we made an induced pluripotent stem cell (iPSC)-derived hECT model with a resident population of macrophages (iM0) to better recapitulate the native myocardium and characterized their impact on tissue function.

BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs.

Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. We first validate BeatProfiler's accuracy, robustness, and speed by benchmarking against existing tools with a fixed dataset.

An engineered human cardiac tissue model reveals contributions of systemic lupus erythematosus autoantibodies to myocardial injury.

Systemic lupus erythematosus (SLE) is a highly heterogenous autoimmune disease that affects multiple organs, including the heart. The mechanisms by which myocardial injury develops in SLE, however, remain poorly understood. Here we engineered human cardiac tissues and cultured them with IgG fractions containing autoantibodies from SLE patients with and without myocardial involvement.

Soccer's AI transformation: deep learning's analysis of soccer's pandemic research evolution.

Introduction: This paper aims to identify and compare changes in trends and research interests in soccer articles from before and during the COVID-19 pandemic.

Methods: We compared research interests and trends in soccer-related journal articles published before COVID-19 (2018-2020) and during the COVID-19 pandemic (2021-2022) using Bidirectional Encoder Representations from Transformers (BERT) topic modeling.

Results: In both periods, we categorized the social sciences into psychology, sociology, business, and technology, with some interdisciplinary research topics identified, and we identified changes during the COVID-19 pandemic period, including a new approach to home advantage.

Modeling and countering the effects of cosmic radiation using bioengineered human tissues.

Cosmic radiation is the most serious risk that will be encountered during the planned missions to the Moon and Mars. There is a compelling need to understand the effects, safety thresholds, and mechanisms of radiation damage in human tissues, in order to develop measures for radiation protection during extended space travel. As animal models fail to recapitulate the molecular changes in astronauts, engineered human tissues and "organs-on-chips" are valuable tools for studying effects of radiation in vitro.

Ultrathin Metal Film on Graphene for Percolation-Threshold-Limited Thermal Emissivity Control.

Translucent Au/graphene hybrid films are shown to be effective in reducing thermal emission from the underlying surfaces when the deposition thickness of Au is close to the percolation threshold. The critical Au deposition thickness for an abrupt change in emissivity is reduced from 15 nm (Si substrate) to a percolation-threshold-limited thickness of 8.5 nm (graphene/Si substrate) because of the chemical inertness of graphene leading to the deposited Au atoms forming a thin, crystalline layer.

Engineered cardiac tissue model of restrictive cardiomyopathy for drug discovery.

Restrictive cardiomyopathy (RCM) is defined as increased myocardial stiffness and impaired diastolic relaxation leading to elevated ventricular filling pressures. Human variants in filamin C (FLNC) are linked to a variety of cardiomyopathies, and in this study, we investigate an in-frame deletion (c.7416_7418delGAA, p.

A Surface Conformal Laser-Assisted Alloying Reaction for 3D-Printable Solid/Liquid Biphasic Conductors.

Recently, electronics research has made major advances toward a new platform technology facilitating form factor-free devices. 3D printing techniques have attracted significant attention in the context of fabricating arbitrarily shaped circuits. Herein, a 3D-printable metallic ink comprising multidimensional eutectic gallium indium (EGaIn)/Ag hierarchical particles is proposed to fabricate arbitrarily designable solid/liquid biphasic conductors that can be inherently self-healed/chip bonded and do not suffer from liquid flood out due to their liquid and solid nature, respectively.

LDA-based topic modeling for COVID-19-related sports research trends.

Introduction: The COVID-19 pandemic could generate a turning point for introducing a new system for sports participation and business. The purpose of this study is to explore trends and topic structures of COVID-19-related sports research by analyzing the relevant literature.

Methods: Sports studies related to COVID-19 were collected in searching international academic databases.

High-index-contrast photonic structures: a versatile platform for photon manipulation.

In optics, the refractive index of a material and its spatial distribution determine the characteristics of light propagation. Therefore, exploring both low- and high-index materials/structures is an important consideration in this regard. Hollow cavities, which are defined as low-index bases, exhibit a variety of unusual or even unexplored optical characteristics and are used in numerous functionalities including diffraction gratings, localised optical antennas and low-loss resonators.

Saliency guided data augmentation strategy for maximally utilizing an object's visual information.

Among the various types of data augmentation strategies, the mixup-based approach has been particularly studied. However, in existing mixup-based approaches, object loss and label mismatching can occur if random patches are utilized when constructing augmented images, and additionally, patches that do not contain objects might be included, which degrades performance. In this paper, we propose a novel augmentation method that mixes patches in a non-overlapping manner after they are extracted from the salient regions in an image.

Efficient learning representation of noise-reduced foam effects with convolutional denoising networks.

This study proposes a neural network framework for modeling the foam effects found in liquid simulation without noise. The position and advection of the foam particles are calculated using the existing screen projection method, and the noise problem that occurs in this process is prevented by using the neural network. A significant problem in the screen projection approach is the noise generated in the projection map during the projecting of momentum onto the discretized screen space.

milliPillar: A Platform for the Generation and Real-Time Assessment of Human Engineered Cardiac Tissues.

Engineered cardiac tissues derived from human induced pluripotent stem cells (iPSCs) are increasingly used for drug discovery, pharmacology and in models of development and disease. While there are numerous platforms to engineer cardiac tissues, they often require expensive and nonconventional equipment and utilize complex video-processing algorithms. As a result, only specialized academic laboratories have been able to harness this technology.

Machine Learning Techniques to Classify Healthy and Diseased Cardiomyocytes by Contractility Profile.

Cardiomyocytes derived from human induced pluripotent stem (iPS) cells enable the study of cardiac physiology and the developmental testing of new therapeutic drugs in a human setting. In parallel, machine learning methods are being applied to biomedical science in unprecedented ways. Machine learning has been used to distinguish healthy from diseased cardiomyocytes using calcium (Ca) transient signals.

Cooling Metals via Gap Plasmon Resonance.

We report highly emissive and radiatively cooled metallic surfaces that sustain multiple and high-amplitude gap plasmon cavity modes within the principal thermal radiation spectrum at room temperature (i.e., 8-13 μm).

Identification of Cathepsin D as a Plasma Biomarker for Alzheimer's Disease.

Although Alzheimer's disease (AD) is the most common neurodegenerative disease, there are still no drugs available to treat or prevent AD effectively. Here, we examined changes in levels of selected proteins implicated in the pathogenesis of AD using plasma samples of control subjects and patients with cognition impairment. To precisely categorize the disease, fifty-six participants were examined with clinical cognitive tests, amyloid positron emission tomography (PET) scan, and white matter hyperintensities scored by magnetic resonance imaging.

Facilitating the Development of Deep Learning Models with Visual Analytics for Electronic Health Records.

Electronic health record (EHR) data are widely used to perform early diagnoses and create treatment plans, which are key areas of research. We aimed to increase the efficiency of iteratively applying data-intensive technology and verifying the results for complex and big EHR data. We used a system entailing sequence mining, interpretable deep learning models, and visualization on data extracted from the MIMIC-IIIdatabase for a group of patients diagnosed with heart disease.