Foundation model of ECG diagnosis: Diagnostics and explanations of any form and rhythm on ECG.

We propose a knowledge-enhanced electrocardiogram (ECG) diagnosis foundation model (KED) that utilizes large language models to incorporate domain-specific knowledge of ECG signals. This model is trained on 800,000 ECGs from nearly 160,000 unique patients. Despite being trained on single-center data, KED demonstrates exceptional zero-shot diagnosis performance across various regions, including different locales in China, the United States, and other regions.

Ultrasonic wind vector measurement based on quadratic correlation two-frequency phase method.

A proposed method for measuring ultrasonic wind speed and direction utilizes the quadratic correlation phase method to address limited range issues and low measurement accuracy associated with the phase difference method when the signal-to-noise ratio is low. First, the proposed method uses quadratic correlation method combined with the four-step phase shift method to estimate the phase difference based on wind measuring structure of the opposing-type Array Structures. Then, the two-frequency phase difference method extends the measurement range of ultrasonic wind speed and direction.

Selective Pyk2 inhibition enhances bone restoration through SCARA5-mediated bone marrow remodeling in ovariectomized mice.

Understanding the intricate cellular interactions involved in bone restoration is crucial for developing effective strategies to promote bone healing and mitigate conditions such as osteoporosis and fractures. Here, we provide compelling evidence supporting the anabolic effects of a pharmacological Pyk2 inhibitor (Pyk2-Inh) in promoting bone restoration. In vitro, Pyk2 signaling inhibition markedly enhances alkaline phosphatase (ALP) activity, a hallmark of osteoblast differentiation, through activation of canonical Wnt/β-catenin signaling.

Marginal interaction test for detecting interactions between genetic marker sets and environment in genome-wide studies.

As human complex diseases are influenced by the interaction between genetics and the environment, identifying gene-environment interactions (G × E) is crucial for understanding disease mechanisms and predicting risk. Developing robust quantitative tools for G × E analysis can enhance the study of complex diseases. However, many existing methods that explore G × E focus on the interplay between an environmental factor and genetic variants, exclusively for common or rare variants.

Improvement of multiscale decomposition for space-based gravitational wave signal processing technology.

During the process of detecting gravitational waves in space, addressing noise issues caused by terrestrial vibrations, natural environmental changes, and the factors intrinsic to the detectors, this paper proposes a multiscale variational mode adaptive denoising algorithm based on momentum gradient descent. This algorithm integrates momentum factors and multiscale concepts into the variational mode algorithm to resolve the issue of multiple local optima encountered during operation, reduce oscillations in regions with large or unstable gradient changes, and improve convergence speed. Additionally, the algorithm combines the least mean squares algorithm to automatically adjust weights, thereby mitigating the impact of noise, addressing the issue of noise from multiple and random sources, effectively suppressing noise in the gravitational wave signal, and enhancing the quality and reliability of the gravitational wave signal.

Flowering in aged white tea: Recovering umami taste and amplifying of stale aroma.

Throughout the natural aging process from new to aged white tea, the flavor evolves into a 'stale flavor', despite the initial umami diminishes. The flowering process, inoculation of Eurotium cristatum to white tea, improves the flavor. The impact on sensory qualities and underlying chemical basis of flowering in aged white tea warrant investigation.

SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data.

Spatial barcoding-based transcriptomic (ST) data require deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER tackles platform effects between ST and scRNA-seq data, ensuring a linear relationship between them while addressing sparsity and spatial correlations in cell types across capture spots.

Confinement synthesis of few-layer MXene-cobalt@N-doped carbon and its application for electrochemical sensing.

Herein, the few-layer TiCT nanosheets loaded zeolitic imidazolate framework-67 nanoplates (TiCT-ZIF-67) with a unique structure has been synthesized by surfactant control method, and then is employed as the core of precursor. A thin layer of polydopamine as the shell of precursor covered TiCT-ZIF-67 forms a micro-nano reactor, leading to the confinement carbonization process. Consequently, a novel sensing material that few-layer TiCT nanosheets loaded Co nanoparticles coated N-doped carbon (TiCT-Co@NC) is obtained for the non-enzymatic determination of glucose.

Multi-gas pollutant detection based on sparrow search algorithm optimized ALSTM-FCN.

It is critical to identify and detect hazardous, flammable, explosive, and poisonous gases in the realms of industrial production and medical diagnostics. To detect and categorize a range of common hazardous gasses, we propose an attention-based Long Short term memory Full Convolutional network (ALSTM-FCN) in this paper. We adjust the network parameters of ALSTM-FCN using the Sparrow search algorithm (SSA) based on this, by comparison, SSA outperforms Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), Gray Wolf Optimization (GWO) Algorithm, Cuckoo Search (CS) Algorithm and other traditional optimization algorithms.

Multidepth quantitative analysis of liver cell viscoelastic properties: Fusion of nanoindentation and finite element modeling techniques.

Liver cells are the basic functional unit of the liver. However, repeated or sustained injury leads to structural disorders of liver lobules, proliferation of fibrous tissue and changes in structure, thus increasing scar tissue. Cellular fibrosis affects tissue stiffness, shear force, and other cellular mechanical forces.

Research on remote sensing ecological livability index based on Google Earth Engine: a case study from Urumqi-Changji-Shihezi urban cluster.

The U-Chang-Shi (Urumqi-Changji-Shihezi) urban cluster, located at the heart of Xinjiang, boasts abundant natural resources. Over the past two decades, rapid urbanization, industrialization, and climate change have significantly threatened the region's ecological livability. To comprehensively, scientifically, and objectively assess the ecological livability of this area, this study leverages the Google Earth Engine (GEE) platform and multi-source remote sensing data to develop a comprehensive evaluation metric: the Remote Sensing Ecological Livability Index (RSELI).

A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification.

As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and automated system for detecting electrocardiogram (ECG) abnormalities is becoming increasingly essential. Scholars have developed numerous methods of arrhythmia classification using machine learning or deep learning. However, the issue of low classification rates of individual classes in inter-patient heartbeat classification remains a challenge.

Improved U-net network asphalt pavement crack detection method.

Road crack detection is one of the important parts of road safety detection. Aiming at the problems such as weak segmentation effect of basic U-Net on pavement crack, insufficient precision of crack contour segmentation, difficult to identify narrow crack and low segmentation accuracy, this paper proposes an improved U-net network pavement crack segmentation method. VGG16 and Up_Conv (Upsampling Convolution) modules are introduced as backbone network and feature enhancement network respectively, and the more abstract features in the image are extracted by using the Block depth separable convolution blocks, and the multi-scale features are captured and enhanced by higher level semantic information to improve the recognition accuracy of narrow cracks in the road surface.

Biological evaluation of novel phosphorylated pullulan-based calcium hydroxide formulations as direct pulp capping materials: An in vivo study on a rat model.

Aim: Calcium hydroxide (CH) has been considered as a direct pulp capping materials (DPC) for the last decades despite having some limitations. Phosphorylate pullulan (PPL) incorporated with CH (CHPPL) is a novel biomaterial that was introduced as a promising DPC material. Thus, the aim of the study was to evaluate the inflammatory response and mineralized tissue formation (MTF) ability of PPL-based CH formulations on rat molars after DPC.

Effect of Simulated Dental Pulpal Pressure Using Fetal Bovine Serum for the Bonding Performance of Contemporary Adhesive to Dentin.

This study evaluated the effect of simulated pulpal pressure (SPP) conditions and storage time on contemporary adhesive systems' microtensile bond strength (µTBS) to dentin. Extracted human molars were prepared and randomly divided into four groups according to the adhesives: Clearfil Megabond 2 (CSE), Beautibond Xtreme Universal (BXU), G2-Bond (G2B), and Scotchbond Universal Plus (SBP). Each adhesive group was further divided following the SPP conditions: control with no simulation (SPP-CTR), SPP with distilled water (SPP-DTW), and SPP with fetal bovine serum (SPP-FBS).

Cross-Modal Supervised Human Body Pose Recognition Techniques for Through-Wall Radar.

Through-wall radar human body pose recognition technology has broad applications in both military and civilian sectors. Identifying the current pose of targets behind walls and predicting subsequent pose changes are significant challenges. Conventional methods typically utilize radar information along with machine learning algorithms such as SVM and random forests to aid in recognition.

RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION.

Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression.

A New Network Structure for Speech Emotion Recognition Research.

Deep learning promotes the breakthrough of emotion recognition in many fields, especially speech emotion recognition (SER). As an important part of speech emotion recognition, the most relevant acoustic feature extraction has always attracted the attention of existing researchers. Aiming at the problem that the emotional information contained in the current speech signals is distributed dispersedly and cannot comprehensively integrate local and global information, this paper presents a network model based on a gated recurrent unit (GRU) and multi-head attention.

Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis.

Background: Long-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting.

microRNA-mRNA Analysis Reveals Tissue-Specific Regulation of microRNA in Mangrove Clam ().

is an important benthic animal in the mangrove, serving as an indicator organism for coastal environmental pollution. This study aimed to investigate the tissue-specific expression of miRNAs and their regulatory roles in predicted targets in . Through miRNA sequencing and co-expression network analysis, we extensively studied the miRNA expression in three tissues: gills, hepatopancreas, and muscle.

Preparation and adsorption properties of magnetic chitosan/sludge biochar composites for removal of Cu ions.

The magnetic chitosan/sludge biochar composite adsorbent was prepared using chitosan, FeO, and sludge biochar as raw materials. The composite adsorbent was able to achieve rapid solid-liquid separation under an applied magnetic field. The morphology and microstructure of the composite adsorbent were characterized by FTIR, XRD, SEM, VSM, and BET analysis.

Centrosome clustering control in osteoclasts through CCR5-mediated signaling.

Osteoclasts uniquely resorb calcified bone matrices. To exert their function, mature osteoclasts maintain the cellular polarity and directional vesicle trafficking to and from the resorbing bone surface. However, the regulatory mechanisms and pathophysiological relevance of these processes remain largely unexplored.

Ultrasound-guided pericapsular nerve group (PENG) block for early analgesia in elderly patients with hip fractures: a single-center prospective randomized controlled study.

Background: The aim of this study was to compare the efficacy of ultrasound-guided PENG (pericapsular nerve group) block and drug therapy with intravenous flurbiprofen for early analgesia in elderly patients with hip fractures after hospitalization.

Methods: This is a single-center, observer-blinded, prospective, randomized, controlled trial. A total of 41 elderly patients (aged 60 or older) with hip fractures were enrolled in the current study.