Predicting drug and target interaction with dilated reparameterize convolution.

Predicting drug-target interaction (DTI) stands as a pivotal and formidable challenge in pharmaceutical research. Many existing deep learning methods only learn the high-dimensional representation of ligands and targets on a small scale. However, it is difficult for the model to obtain the potential law of combining pockets or multiple binding sites on a large scale.

Digital-Twin-Assisted Skill Learning for 3C Assembly Tasks.

The utilization of robots in computer, communication, and consumer electronics (3C) assembly has the potential to significantly reduce labor costs and enhance assembly efficiency. However, many typical scenarios in 3C assembly, such as the assembly of flexible printed circuits (FPCs), involve complex manipulations with long-horizon steps and high-precision requirements that cannot be effectively accomplished through manual programming or conventional skill-learning methods. To address this challenge, this article proposes a learning-based framework for the acquisition of complex 3C assembly skills assisted by a multimodal digital-twin environment.

Graph Transformer with Convolution Parallel Networks for Predicting Single and Binary Component Adsorption Performance of Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) are considered one of the most important materials for carbon capture and storage (CCS) due to the advantages of porosity, multifunction, diverse structure, and controllable chemical composition. With the continuous development of artificial intelligence (AI) technology, more and more machine learning models are used to identify MOFs with high performance within a massive search space. However, current works have yet to form a model that uses graph-structured data only, which can predict the adsorption properties of single and binary components.

An XGBoost Algorithm Based on Molecular Structure and Molecular Specificity Parameters for Predicting Gas Adsorption.

In this paper, an improved Extreme Gradient Boosting (XGBoost) algorithm based on the Graph Isomorphic Network (GIN) for predicting the adsorption performance of metal-organic frameworks (MOFs) is developed. It is shown that the graph isomorphic layer of this algorithm can directly learn the feature representation of materials from the connection of atoms in MOFs. Then, XGBoost can be used to predict the adsorption performance of MOFs based on feature representation.

Real-Time Learning and Recognition of Assembly Activities Based on Virtual Reality Demonstration.

Teaching robots to learn through human demonstrations is a natural and direct method, and virtual reality technology is an effective way to achieve fast and realistic demonstrations. In this paper, we construct a virtual reality demonstration system that uses virtual reality equipment for assembly activities demonstration, and using the motion data of the virtual demonstration system, the human demonstration is deduced into an activity sequence that can be performed by the robot. Through experimentation, the virtual reality demonstration system in this paper can achieve a 95% correct rate of activity recognition.

Learning to Have a Civil Aircraft Take Off under Crosswind Conditions by Reinforcement Learning with Multimodal Data and Preprocessing Data.

Autopilot technology in the field of aviation has developed over many years. However, it is difficult for an autopilot system to autonomously operate a civil aircraft under bad weather conditions. In this paper, we present a reinforcement learning (RL) algorithm using multimodal data and preprocessing data to have a civil aircraft take off autonomously under crosswind conditions.

Induction of Intestinal Th17 Cells by Flagellins From Segmented Filamentous Bacteria.

T-helper-17 (Th17) cells are a subset of CD4+ T cells that can produce the cytokine interleukin (IL)-17 and play vital roles in protecting the host from bacterial and fungal infections, especially at the mucosal surface. These are abundant in the small intestinal lamina propria (SILP) and their differentiation are associated with the colonization of the intestinal flora. Segmented filamentous bacteria (SFB) drew the attention of researchers due to their unique ability to drive the accumulation of Th17 cells in the SI LP of mice.

Fast and Robust Monocular Visua-Inertial Odometry Using Points and Lines.

When the camera moves quickly and the image is blurred or the texture in the scene is missing, the Simultaneous Localization and Mapping (SLAM) algorithm based on point feature experiences difficulty tracking enough effective feature points, and the positioning accuracy and robustness are poor, and even may not work properly. For this problem, we propose a monocular visual odometry algorithm based on the point and line features and combining IMU measurement data. Based on this, an environmental-feature map with geometric information is constructed, and the IMU measurement data is incorporated to provide prior and scale information for the visual localization algorithm.

Genome-wide DNA methylation and hydroxymethylation analysis reveal human menstrual blood-derived stem cells inhibit hepatocellular carcinoma growth through oncogenic pathway suppression via regulating 5-hmC in enhancer elements.

Background: Epigenetic alteration is an important indicator of crosstalk between cancer cells and surrounding microenvironment components including mesenchymal stem cells (MSC). Human menstrual blood-derived stem cells (MenSCs) are novel source of MSCs which exert suppressive effects on cancers via multiple components of microenvironmental paracrine signaling. However, whether MenSCs play a crucial role in the epigenetic regulation of cancer cells remains unknown.

Transplantation of Human Menstrual Blood-Derived Mesenchymal Stem Cells Alleviates Alzheimer's Disease-Like Pathology in APP/PS1 Transgenic Mice.

Extracellular β-amyloid (Aβ) plaques and neurofibrillary tangles (NFTs) are the pathological hallmarks of Alzheimer's disease (AD). Mesenchymal stem cells (MSCs) have shown therapeutic efficacy in many neurodegenerative diseases, including AD. Human menstrual blood-derived stem cells (MenSCs) are a novel source of MSCs advantageous for their higher proliferation rate and because they are easy to obtain without ethical concerns.

Transplantation of Menstrual Blood-Derived Mesenchymal Stem Cells Promotes the Repair of LPS-Induced Acute Lung Injury.

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are associated with high morbidity and mortality. Menstrual blood-derived stem cells (MenSCs) have been shown to be good therapeutic tools in diseases such as ovarian failure and cardiac fibrosis. However, relevant studies of MenSCs in ALI have not yet proceeded.

Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network.

The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features.

[Effect of euodiae fructus on hepatic energy metabolism-related mechanisms of reserpine-induced asthenia cold syndrome rats].

Objective: To discuss the effect of Euodiae Fructus on hepatic energy metabolism-related mechanisms of mitochondria of hepatic tissues of asthenia cold syndrome rats.

Method: Rats were subcutaneously injected with Reserpine to establish the model. After the oral administration with Euodiae Fructus for 12 d, the oxygen electrode method was adopted to determine the respiration efficiency.