Chinese Herb and Formulas for Promoting Blood Circulation and Removing Blood Stasis and Antiplatelet Therapies

Atherothrombosis, which directly threatens people's health and lives, is the main cause of morbidity and mortality all over the world. Platelets play a key role in the development of acute coronary syndromes (ACSs) and contribute to cardiovascular events. Oral antiplatelet drugs are a milestone in the therapy of cardiovascular atherothrombotic diseases. In recent years, many reports have shown the possibility that “resistance” to oral anti-platelet drugs and many adverse reactions, such as serious bleeding risk, which provides an impetus for developing new anti-platelet drugs possesses highly efficiency and fewer adverse effects. Study on the blood stasis syndrome and promoting blood circulation and removing blood stasis is the most active field of research of integration of traditional and western medicine in China. Blood-stasis syndrome and platelet activation have close relationship, many Chinese herb and formulas for promoting blood circulation and removing blood stasis possess definite anti-platelet effect. This paper covers the progress of anti-platelet mechanism of Chinese herb and formulas for promoting blood circulation and removing blood stasis and is to be deeply discussed in further research

Poly[[(μ3-5,6-dicarboxy­bicyclo­[2.2.2]oct-7-ene-2,3-dicarboxyl­ato)(1,10-phenanthroline)copper(II)] monohydrate]

In the title compound, {[Cu(C12H10O8)(C12H8N2)]·H2O}n, the CuII ion is five-coordinated by two N atoms from one phenanthroline ligand and three O atoms from three different H2 L 2− anions (H4 L is bicyclo­[2.2.2]oct-7-ene-2,3,5,6-tetra­carboxylic acid) in a distorted square-pyramidal geometry. Each H2 L 2− ion bridges three CuII atoms to form a zigzag sheet parallel to the ab plane. The crystal structure is consolidated by O—H⋯O hydrogen bonds

Binarized Neural Architecture Search

Neural architecture search (NAS) can have a significant impact in computer vision by automatically designing optimal neural network architectures for various tasks. A variant, binarized neural architecture search (BNAS), with a search space of binarized convolutions, can produce extremely compressed models. Unfortunately, this area remains largely unexplored. BNAS is more challenging than NAS due to the learning inefficiency caused by optimization requirements and the huge architecture space. To address these issues, we introduce channel sampling and operation space reduction into a differentiable NAS to significantly reduce the cost of searching. This is accomplished through a performance-based strategy used to abandon less potential operations. Two optimization methods for binarized neural networks are used to validate the effectiveness of our BNAS. Extensive experiments demonstrate that the proposed BNAS achieves a performance comparable to NAS on both CIFAR and ImageNet databases. An accuracy of $96.53\%$ vs. $97.22\%$ is achieved on the CIFAR-10 dataset, but with a significantly compressed model, and a $40\%$ faster search than the state-of-the-art PC-DARTS

The microstructure and mechanical properties of friction stir welded Ti6Al4V titanium alloy under β transus temperature

Ti6Al4V titanium alloy is friction stir welded using a W-Re rotational tool. The effects of welding speed on the microstructure, tensile strength and fracture properties of weld are investigated. At the rotational velocity of 250 r/min, the peak temperature is lower than β transus temperature, and the weld nugget is made up of fine α phase and transformed β phase. The grain size of shoulder affected zone is bigger than that of weld nugget because of low thermal conductivity of Ti6Al4V titanium alloy. By increasing the welding speed, the grain size of weld nugget, the tensile strength and the ductility of weld all are decreased

C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak

The novel coronavirus disease (COVID-19) has crushed daily routines and is still rampaging through the world. Existing solution for nonpharmaceutical interventions usually needs to timely and precisely select a subset of residential urban areas for containment or even quarantine, where the spatial distribution of confirmed cases has been considered as a key criterion for the subset selection. While such containment measure has successfully stopped or slowed down the spread of COVID-19 in some countries, it is criticized for being inefficient or ineffective, as the statistics of confirmed cases are usually time-delayed and coarse-grained. To tackle the issues, we propose C-Watcher, a novel data-driven framework that aims at screening every neighborhood in a target city and predicting infection risks, prior to the spread of COVID-19 from epicenters to the city. In terms of design, C-Watcher collects large-scale long-term human mobility data from Baidu Maps, then characterizes every residential neighborhood in the city using a set of features based on urban mobility patterns. Furthermore, to transfer the firsthand knowledge (witted in epicenters) to the target city before local outbreaks, we adopt a novel adversarial encoder framework to learn "city-invariant" representations from the mobility-related features for precise early detection of high-risk neighborhoods, even before any confirmed cases known, in the target city. We carried out extensive experiments on C-Watcher using the real-data records in the early stage of COVID-19 outbreaks, where the results demonstrate the efficiency and effectiveness of C-Watcher for early detection of high-risk neighborhoods from a large number of cities.Comment: 11 pages, accepted by AAAI 2021, appendix is include

Aloin promotes cell apoptosis by targeting HMGB1-TLR4-ERK axis in human melanoma cells

Aloin (ALO) is the major anthraquinone glycoside purified from the Aloe species. It is well known for its anti-tumor effects. However, the protective effects of ALO in melanoma cancer and underlying molecular mechanism remain unclear. High-mobility group protein B1 (HMGB1) is an intracellular protein, which has closely association with cell survival, proliferation and metastasis in various cancers. In this study, we explored the effect of ALO on cell survival and apoptosis by targeting HMGB1 signal pathway. We confirmed that ALO exerts a strong effect on promoting cell apoptosis of melanoma cells in vitro. Furthermore, HMGB1 release was significantly inhibited in melanoma cancer cells treated with ALO. Knockdown of HMGB1 could enhance melanoma cell death that is induced by ALO treatment. Moreover, HMGB1 facilitated ALO mediated melanoma cell apoptosis by binding to its receptor, Toll-like receptor 4 and activating extracellular regulated protein kinases (ERK) signal pathway. Altogether, our study demonstrated that ALO plays an important role in promoting apoptosis of melanoma cells by inhibiting HMGB1 release and activation of downstream ERK signal pathway

Loss of Fructose-1,6-Bisphosphatase Induces Glycolysis and Promotes Apoptosis Resistance of Cancer Stem-Like Cells: An Important Role in Hexavalent Chromium-Induced Carcinogenesis

Hexavalent chromium (Cr(VI)) compounds are confirmed human carcinogens for lung cancer. Our previous studies has demonstrated that chronic exposure of human bronchial epithelial BEAS-2B cells to low dose of Cr(VI) causes malignant cell transformation. The acquisition of cancer stem cell-like properties is involved in the initiation of cancers. The present study has observed that a small population of cancer stem-like cells (BEAS-2B-Cr-CSC) exists in the Cr(VI)-transformed cells (BEAS-2B-Cr). Those BEAS-2B-Cr-CSC exhibit extremely reduced capability of generating reactive oxygen species (ROS) and apoptosis resistance. BEAS-2B-Cr-CSC are metabolic inactive as evidenced by reductions in oxygen consumption, glucose uptake, ATP production, and lactate production. Most importantly, BEAS-2B-Cr-CSC are more tumorigenic with high levels of cell self-renewal genes, Notch1 and p21. Further study has found that fructose-1,6-bisphosphatase (FBP1), an rate-limiting enzyme driving glyconeogenesis, was lost in BEAS-2B-Cr-CSC. Forced expression of FBP1 in BEAS-2B-Cr-CSC restored ROS generation, resulting in increased apoptosis, leading to inhibition of tumorigenesis. In summary, the present study suggests that loss of FBP1 is a critical event in tumorigenesis of Cr(VI)-transformed cells

Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence

With the rapid development of electronic science and technology, the research on wearable devices is constantly updated, but for now, it is not comprehensive for wearable devices to recognize and analyze the movement of specific sports. Based on this, this paper improves wearable devices of table tennis sport, and realizes the pattern recognition and evaluation of table tennis players' motor skills through artificial intelligence. Firstly, a device is designed to collect the movement information of table tennis players and the actual movement data is processed. Secondly, a sliding window is made to divide the collected motion data into a characteristic database of six table tennis benchmark movements. Thirdly, motion features were constructed based on feature engineering, and motor skills were identified for different models after dimensionality reduction. Finally, the hierarchical evaluation system of motor skills is established with the loss functions of different evaluation indexes. The results show that in the recognition of table tennis players' motor skills, the feature-based BP neural network proposed in this paper has higher recognition accuracy and stronger generalization ability than the traditional convolutional neural network.Comment: 34pages, 16figure

FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing

Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval. However, existing scene graph parsers that convert image captions into scene graphs often suffer from two types of errors. First, the generated scene graphs fail to capture the true semantics of the captions or the corresponding images, resulting in a lack of faithfulness. Second, the generated scene graphs have high inconsistency, with the same semantics represented by different annotations. To address these challenges, we propose a novel dataset, which involves re-annotating the captions in Visual Genome (VG) using a new intermediate representation called FACTUAL-MR. FACTUAL-MR can be directly converted into faithful and consistent scene graph annotations. Our experimental results clearly demonstrate that the parser trained on our dataset outperforms existing approaches in terms of faithfulness and consistency. This improvement leads to a significant performance boost in both image caption evaluation and zero-shot image retrieval tasks. Furthermore, we introduce a novel metric for measuring scene graph similarity, which, when combined with the improved scene graph parser, achieves state-of-the-art (SOTA) results on multiple benchmark datasets for the aforementioned tasks. The code and dataset are available at https://github.com/zhuang-li/FACTUAL .Comment: 9 pages, ACL 2023 (findings