Deep Drug-Target Binding Affinity Prediction Base on Multiple Feature Extraction and Fusion.

Accurate drug-target binding affinity (DTA) prediction is crucial in drug discovery. Recently, deep learning methods for DTA prediction have made significant progress. However, there are still two challenges: (1) recent models always ignore the correlations in drug and target data in the drug/target representation process and (2) the interaction learning of drug-target pairs always is by simple concatenation, which is insufficient to explore their fusion.

Effects of perinatal nutrition supplementation and early weaning on serum biochemistry, metabolomics, and reproduction in yaks.

The transition period is a crucial stage in the reproductive cycle for dams and is linked closely with postpartum recovery, reproduction performance, and health. The confronting problem in the yak industry is that transition yaks under a conventional grazing feeding regime endure nutritional deficiency since this period is in late winter and early spring of the Qinghai-Tibet Plateau with the lack of grass on natural pasture. Therefore, this study aimed to investigate the effects of perinatal nutritional supplementation and early weaning on serum biochemistry, reproductive performance, and metabolomics in transition yaks.

[Construction of a muscle-specific synthetic promoter library and correlation analysis of the element composition and activity of highly active promoters].

The purpose of this study is to construct a muscle-specific synthetic promoter library, screen out muscle-specific promoters with high activity, analyze the relationship between element composition and activity of highly active promoters, and provide a theoretical basis for artificial synthesis of promoters. In this study, 19 promoter fragments derived from muscle-specific elements, conserved elements, and viral regulatory sequences were selected and randomLy connected to construct a muscle-specific synthetic promoter library. The luciferase plasmids pCMV-Luc and pSPs-Luc were constructed and transfected into the myoblast cell line C2C12.

An improved low-rank plus sparse unrolling network method for dynamic magnetic resonance imaging.

Background: Recent advances in deep learning have sparked new research interests in dynamic magnetic resonance imaging (MRI) reconstruction. However, existing deep learning-based approaches suffer from insufficient reconstruction efficiency and accuracy due to the lack of time correlation modeling during the reconstruction procedure.

Purpose: Inappropriate tensor processing steps and deep learning models may lead to not only a lack of modeling in the time dimension but also an increase in the overall size of the network.

Dietary Conversion from All-Concentrate to All-Roughage Alters Rumen Bacterial Community Composition and Function in Yak, Cattle-Yak, Tibetan Yellow Cattle and Yellow Cattle.

The experiment was to compare the effects of switching all-concentrate to all-roughage diets on rumen microflora and functional metabolism of yak, cattle-yak, Tibetan yellow cattle and yellow cattle living in different altitudes. A total of 24 yaks, cattle-yaks, Tibetan yellow cattle and yellow cattle with a similar weight and good body condition aged 3.5 years were selected and divided into four groups according to species.

In Situ Expression of Yak in Mammary Glands as a Treatment for Bovine -Induced Mastitis in Mice.

Since the development of dairy farming, bovine mastitis has been a problem plaguing the whole industry, which has led to a decrease in milk production, a reduction in dairy product quality, and an increase in costs. The use of antibiotics to treat mastitis can cause a series of problems, which can bring a series of harm to the animal itself, such as the development of bacterial resistance and dramatic changes in the gut flora. However, the in vivo and in vitro antibacterial activity of yak Interleukin-22 (IL-22) and its application in mastitis caused by have not been reported.

Distance guided generative adversarial network for explainable medical image classifications.

Despite the potential benefits of data augmentation for mitigating data insufficiency, traditional augmentation methods primarily rely on prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generate inter-domain samples with limited variety. These previous methods make limited contributions to describing the decision boundaries for binary classification.

Metabolic profiling of triptophenolide in rat plasma, urine, bile and faeces after intragastric administration.

Triptophenolide, a major diterpenoid extracted from Hook. f., has been reported to possess significant anti-tumour, anti-androgen and anti-inflammatory activities.

LUCMT: Learnable under-sampling and reconstructed network with cross multi-head attention transformer for accelerating MR image reconstruction.

Background And Objective: As a widely used technique for Magnetic Resonance Image (MRI) acceleration, compressed sensing MRI involves two main issues: designing an effective sampling strategy and reconstructing the image from significantly under-sampled K-space data. In this paper, an innovative approach is proposed to address these two challenges simultaneously.

Methods: A novel MRI reconstruction method, termed as LUCMT, is implemented by integrating a learnable under-sampling strategy with a reconstruction network based on the Cross Multi-head Attention Transformer.

An Automatic Coronary Microvascular Dysfunction Classification Method Based on Hybrid ECG Features and Expert Features.

Objective: In recent years, the early diagnosis and treatment of coronary microvascular dysfunction (CMD) have become crucial for preventing coronary heart disease. This paper aims to develop a computer-assisted autonomous diagnosis method for CMD by using ECG features and expert features.

Approach: Clinical electrocardiogram (ECG), myocardial contrast echocardiography (MCE), and coronary angiography (CAG) are used in our method.

Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss.

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification.

Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features.

Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed.

Time-gated single-pixel imaging of Cherenkov emission from a medical linear accelerator.

Cherenkov imaging is an ideal tool for real-time in vivo verification of a radiation therapy dose. Given that radiation is pulsed from a medical linear accelerator (LINAC) together with weak Cherenkov emissions, time-gated high-sensitivity imaging is required for robust measurements. Instead of using an expensive camera system with limited efficiency of detection in each pixel, a single-pixel imaging (SPI) approach that maintains promising sensitivity over the entire spectral band could be used to provide a low-cost and viable alternative.

scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene-gene interactions.

Background: Single-cell clustering has played an important role in exploring the molecular mechanisms about cell differentiation and human diseases. Due to highly-stochastic transcriptomics data, accurate detection of cell types is still challenged, especially for RNA-sequencing data from human beings. In this case, deep neural networks have been increasingly employed to mine cell type specific patterns and have outperformed statistic approaches in cell clustering.

Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extraction.

With the widespread adoption of electronic health records, the amount of stored medical data has been increasing. Clinical data, often in the form of semi-structured or unstructured electronic medical records (EMRs), contains rich patient information. However, due to the use of natural language by physicians when composing these records, the effectiveness of traditional methods such as dictionaries, rule matching, and machine learning in the extraction of information from these unstructured texts falls short of clinical standards.

Incongruence between confirmed and suspected clinical cases of Japanese encephalitis virus infection.

Background: Japanese encephalitis (JE) is a notifiable infectious disease in China. Information on every case of JE is reported to the superior health administration department. However, reported cases include both laboratory-confirmed and clinically diagnosed cases.

Transcriptome profiling in rumen, reticulum, omasum, and abomasum tissues during the developmental transition of pre-ruminant to the ruminant in yaks.

The development of the four stomachs of yak is closely related to its health and performance, however the underlying molecular mechanisms are largely unknown. Here, we systematically analyzed mRNAs of four stomachs in five growth time points [0 day, 20 days, 60 days, 15 months and 3 years (adult)] of yaks. Overall, the expression patterns of DEmRNAs were unique at 0 d, similar at 20 d and 60 d, and similar at 15 m and adult in four stomachs.

SSA Net: Small Scale-Aware Enhancement Network for Human Pose Estimation.

In the field of human pose estimation, heatmap-based methods have emerged as the dominant approach, and numerous studies have achieved remarkable performance based on this technique. However, the inherent drawbacks of heatmaps lead to serious performance degradation in methods based on heatmaps for smaller-scale persons. While some researchers have attempted to tackle this issue by improving the performance of small-scale persons, their efforts have been hampered by the continued reliance on heatmap-based methods.

An efficient ECG denoising method by fusing ECA-Net and CycleGAN.

For wearable electrocardiogram (ECG) acquisition, it was easy to infer motion artifices and other noises. In this paper, a novel end-to-end ECG denoising method was proposed, which was implemented by fusing the Efficient Channel Attention (ECA-Net) and the cycle consistent generative adversarial network (CycleGAN) method. The proposed denoising model was optimized by using the ECA-Net method to highlight the key features and introducing a new loss function to further extract the global and local ECG features.

Erratum: [Corrigendum] Interpretation of HbA1c lies at the intersection of analytical methodology, clinical biochemistry and hematology (Review).

[This corrects the article DOI: 10.3892/etm.2022.