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. In this sense, it is not only possible to achieve end-to-end prediction directly from the structure of MOFs to adsorption performance but also to ensure the accuracy of prediction. The comparison between Grand Canonical Monte Carlo (GCMC) simulation and prediction supports the performance and effectiveness of the proposed algorithm.

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. We also created a simulated ur5 robotic arm grasping system to reproduce the inferred activity sequence.

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. The multimodal data include the common flight status and visual information. The preprocessing is a new design that maps some flight data by nonlinear functions based on the general flight dynamics before these data are fed into the RL model. Extensive experiments under different crosswind conditions with a professional flight simulator demonstrate that the proposed method can effectively control a civil aircraft to take off under various crosswind conditions and achieve better performance than trials without visual information or preprocessing data.

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. Then, the initial pose estimation is obtained based on the motion estimation of the sparse image alignment, and the feature alignment is further performed to obtain the sub-pixel level feature correlation. Finally, more accurate poses and 3D landmarks are obtained by minimizing the re-projection errors of local map points and lines. The experimental results on EuRoC public datasets show that the proposed algorithm outperforms the Open Keyframe-based Visual-Inertial SLAM (OKVIS-mono) algorithm and Oriented FAST and Rotated BRIEF-SLAM (ORB-SLAM) algorithm, which demonstrates the accuracy and speed of the algorithm.

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. The CNN's input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns.

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. We hypothesized that MenSC could attenuate the inflammation in lipopolysaccharide (LPS)-induced ALI and promote the repair of damaged lung. ALI model was induced by LPS in C57 mice, and saline or MenSCs were administered via tail vein after four hours. The MenSCs were subsequently detected in the lungs by a live imaging system. The MenSCs not only improved pulmonary microvascular permeability and attenuated histopathological damage, but also mediated the downregulation of IL-1ß and the upregulation of IL-10 in bronchoalveolar lavage fluid (BALF) and the damaged lung. Immunohistochemistry revealed the increased expression of proliferating cell nuclear antigen (PCNA) and the reduced expression of caspase-3 indicating the beneficial effect of MenSCs. Keratinocyte growth factor (KGF) was also upregulated after MenSCs administrated. As shown using transwell co-culture, the MenSCs also could improve the viability of BEAS-2B cells and inhibit LPS-induced apoptosis. These findings suggest that MenSC-based therapies could be promising strategies for treating ALI.

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. First, we construct a fully equivalent digital-twin environment based on the real-world counterpart, equipped with visual, tactile force, and proprioception information, and then collect multimodal demonstration data using virtual reality (VR) devices. Next, we construct a skill knowledge base through multimodal skill parsing of demonstration data, resulting in primitive policy sequences for achieving 3C assembly tasks. Finally, we train primitive policies via a combination of curriculum learning, residual reinforcement learning, and domain randomization methods and transfer the learned skill from the digital-twin environment to the real-world environment. The experiments are conducted to verify the effectiveness of our proposed method.

[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. The expressions of Cox4, Atp5b, Ucp2,Pgc-1alpha, Nrf1, Tfam mRNA were assayed by using RT-PCR method. RESULT: Euodiae Fructus 4.2 g x kg(-1) could obviously increase ST3 and RCR of asthenia cold syndrome rats, and expressions of Cox4, Ucp2 Nrf1 mRNA. It could also increase expressions of Atp5b and Pgc-1alpha mRNA, but with no statistical significance. No obvious change was observed in Tfam mRNA expression. Euodiae Fructus 4.2 g x kg(-1) could significantly increase ST3 and RCR of asthenia cold syndrome rats and Pgc-1alpha mRNA and Nrf1 mRNA expressions, and significantly decrease P/O, with no obvious impact on Cox4, AtpSb, Ucp2, Tfam mRNA expressions. CONCLUSION: Euodiae Fructus can promote mitochondrial respiratory function and oxidative phosphorylation efficiency by improving Pgc-1alpha mRNA and Nrf1 mRNA expressions and regulating Cox4 and Atp5b mRNA in mitochondrial respiratory chain. It can also strengthen mitochondrial uncoupling respiration and add heat production by activating Ucp2 mRNA expression in liver.

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. In this work, we proposed and developed a graph transformer, combined with convolution parallel networks, called GC-Trans. The model can accurately and efficiently predict the adsorption performance of MOFs under the single- and binary-component adsorption conditions using only the features of the crystal diagram as inputs. By extracting and fusing local and global feature information, the model has stronger expression and generalization abilities. Thus, we used it to screen the ARC-MOF database and analyze the MOF structures that meet the target requirements. Additionally, to demonstrate the transferability of the model, we applied transfer learning methods to predict the CO2/CH4 separations and CH4 uptake, both of which showed good predictive performance.

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. METHODS: Epigenetic alterations of hepatocellular carcinoma (HCC) mediated by MenSCs were examined by immunofluorescence, ELISA, and RT-PCR assays. The suppressive impact of MenSCs on HCC was investigated in vitro using CCK8, apoptosis, wound healing, and invasion assays and in vivo using a xenograft mice model. MeDIP-seq, hMeDIP-seq, and RNA-seq were used to identify the genome-wide pattern of DNA methylation and hydroxymethylation in HCC cells after MenSC therapy. RESULTS: We show that HCC cells display distinct genome-wide alterations in DNA hydroxymethylation and methylation after MenSC therapy. MenSCs exert an inhibitory effect on HCC growth via regulating 5-hmC and 5-mC abundance in the regulatory regions of oncogenic pathways including PI3K/AKT and MAPK signaling, especially in enhancers and promoters. FOXO3 expression is rescued via reversal of 5-hmC and 5-mC levels in its enhancers and contributes to the activation of downstream apoptosis. Inactivation of the MAPK pathway further disrupts c-myc-mediated epithelial-mesenchymal transitions (EMT). Additionally, chemotherapy resistance-associated genes including ID4 and HMGA1 are suppressed via amending 5-hmC and 5-mC abundance at their regulatory regions. HMGA1 and BYSL might be potential targets for gene-modified MSC therapy. CONCLUSIONS: Our results confirm that MSCs could regulate the epigenetic mechanism of HCC cells and provide a novel concept for a modified MSC strategy or combination therapy with chemotherapeutics based on epigenetics.

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. Recent work has highlighted that SFB used microbial adhesion-triggered endocytosis (MATE) to transfer SFB antigenic proteins into small intestinal epithelial cells (SI ECs) and modulate host immune homeostasis. However, which components of SFB are involved in this immune response process remains unclear. Here, we examined the roles of SFB flagellins in Th17 cells induction using various techniques, including ELISA, ELISPOT, and RNA-seq in vitro and in vivo. The results show that the immune function of SFB flagellins is similar to SFB, i.e., induces the appearance of CD4+ T helper cells that produce IL-17 and IL-22 (Th17 cells) in the SI LP. Furthermore, treatment of mice with SFB flagellins lead to a significant increase in the expression of genes associated with the IL-17 signaling pathway, such as IL-6, IL-1ß, TNF-α, IL-17A, IL-17F, and IL-22. In addition, SFB flagellins have an intimate relationship with intestinal epithelial cells, influencing the expression of epithelial cell-specific genes such as Nos2, Duox2, Duoxa2, SAA3, Tat, and Lcn2. Thus, we propose that SFB flagellins play a significant role in the involvement of SFB in the induction of intestinal Th17 cells.

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

Extracellular ß-amyloid (Aß) 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. Although MenSCs have exhibited therapeutic efficacy in some diseases, their effects on AD remain elusive. In the present study, we showed that intracerebral transplantation of MenSCs dramatically improved the spatial learning and memory of APP/PS1 mice. In addition, MenSCs significantly ameliorated amyloid plaques and reduced tau hyperphosphorylation in APP/PS1 mice. Remarkably, we also found that intracerebral transplantation of MenSCs markedly increased several Aß degrading enzymes and modulated a panel of proinflammatory cytokines associated with an altered microglial phenotype, suggesting an Aß degrading and anti-inflammatory impact of MenSCs in the brains of APP/PS1 mice. In conclusion, these findings suggest that MenSCs are a promising therapeutic candidate for AD.