Free radical-scavenging activity and flavonoid contents of Polygonum orientale leaf, stem, and seed extracts

The present study was designed to explore the total flavonoid and taxifolin contents and the radical-scavenging activity of 50% ethanol extracts of Polygonum orientale leaves, stems, and seeds by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. The extract with higher total flavonoid content has higher radical scavenging activity. Taxifolin (IC50 = 2.83 μmol/L) has antioxidant activity stronger than that of rutin (IC50 = 3.08 μmol/L). The free radical-scavenging potentials of chloroform, ethyl acetate, water, ethanol, and methanol extracts of Polygonum orientale seeds were also investigated. The free radical-scavenging abilities of various extracts were determined as: methanol > ethanol > water > ethyl acetate > chloroform

The Influence of Higher Education on China’s Macro-Economy

Education contributes to economic growth. China’s economy has shifted from high growth to higher-quality development, creating a new demand to reform and develop higher education. This paper first measures the composite index of higher education in China from 2009 to 2019 by combining the Cobb-Douglas production function and Denison’s economic growth factors analysis method. Results showed that higher education in China has an increasing contribution rate to economic growth from 2009 to 2019 but is still lagging behind developed countries. Second, it analyzes the new trends of higher education development in developed countries, compares the development models of higher education in different countries, and reveals the problems in China’s higher education. Finally, it examines the impact of higher education on China’s macro-economy from the human capital theory and the economies of scale theory. Furthermore, it proposes strategies for the higher-quality development of higher education and provides a reference for adjusting and optimizing the development model of higher education

Improving the Performance of Modular Production in the Apparel Assembly: A Mathematical Programming Approach

We construct the mathematical models to find the optimal allocation of the module’s capacity (module members) to different assembly operations in a module for given garment assembly tasks in a modular production system. The objectives of the models are minimizing the holding cost for work in process (WIP) inventories in the module and the total deviation of the WIP inventories from their corresponding target values in the module during a specific time interval. The solutions of the models can be used as reference to achieve better allocation of the module members to different operations in a module to fulfill the given garment assembly tasks

CIM-based Data-sharing Scheme for Online Calculation of Theoretical Line Loss

AbstractThis paper presents a new CIM-based data-sharing scheme for online calculation of theoretical line loss. The proposed method can read data from other applications which are being used in electric power company, such as electrical SCADA, Power Distribution Network GIS, DMIS, and so on. Moreover, the calculation model, which is used in theoretical calculation of line loss, is formed automatically. Users no longer need to manually input the structure data and operation data of grid. The operation data are updated from the DMIS and the SCADA continually, and the structure data are changing according to GIS and SCADA. Main electric wiring diagrams are also consistent with GIS and SCADA. Compared with conventional approaches, the proposed implementation can cut down the requirement of time and energy that line loss management must spend in maintaining the original data of calculation

carat: An R Package for Covariate-Adaptive Randomization in Clinical Trials

Covariate-adaptive randomization is gaining popularity in clinical trials because they enable the generation of balanced allocations with respect to covariates. Over the past decade, substantial progress has been made in both new innovative randomization procedures and the theoretical properties of associated inferences. However, these results are scattered across the literature, and a single tool kit does not exist for use by clinical trial practitioners and researchers to conduct and evaluate these methods. The R package carat is proposed to address this need. It facilitates a broad range of covariate-adaptive randomization and testing procedures, such as the most common and classical methods, and also reflects recent developments in the field. The package contains comprehensive evaluation and comparison tools for use in both randomization procedures and tests. This enables power analysis to be conducted to assist the planning of a covariate-adaptive clinical trial. The package also implements a command-line interface to allow for an interactive allocation procedure, which is typically the case in real-world applications. In this paper, the features and functionalities of carat are presented

Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth Estimation

Monocular depth estimation (MDE) in the self-supervised scenario has emerged as a promising method as it refrains from the requirement of ground truth depth. Despite continuous efforts, MDE is still sensitive to scale changes especially when all the training samples are from one single camera. Meanwhile, it deteriorates further since camera movement results in heavy coupling between the predicted depth and the scale change. In this paper, we present a scale-invariant approach for self-supervised MDE, in which scale-sensitive features (SSFs) are detached away while scale-invariant features (SIFs) are boosted further. To be specific, a simple but effective data augmentation by imitating the camera zooming process is proposed to detach SSFs, making the model robust to scale changes. Besides, a dynamic cross-attention module is designed to boost SIFs by fusing multi-scale cross-attention features adaptively. Extensive experiments on the KITTI dataset demonstrate that the detaching and boosting strategies are mutually complementary in MDE and our approach achieves new State-of-The-Art performance against existing works from 0.097 to 0.090 w.r.t absolute relative error. The code will be made public soon.Comment: Accepted by IEEE Robotics and Automation Letters (RAL

Diagnostic utility of LunX mRNA in peripheral blood and pleural fluid in patients with primary non-small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>Progress in lung cancer is hampered by the lack of clinically useful diagnostic markers. The goal of this study was to provide a detailed evaluation of lung cancer tumor markers indicative of molecular abnormalities and to assess their diagnostic utility in non-small cell lung cancer (NSCLC) patients.</p> <p>Methods</p> <p>Quantitative real-time RT-PCR was used to determine <it>LunX, CK19, CEA, VEGF-C </it>and <it>hnRNP A2/B1 </it>mRNA levels in peripheral blood and pleural fluid from NSCLC patients, compared with those from patients with other epithelial cancer (esophagus cancer and breast cancer), benign lung disease (pneumonia and tuberculo pleurisy) and from healthy volunteers.</p> <p>Results</p> <p>In peripheral blood <it>LunX </it>mRNA was detectable in 75.0% (33/44) of patients with NSCLC, but not in patients with other epithelial cancer (0/28), benign lung disease (0/10) or in healthy volunteers (0/15). In contrast, all other genetic markers were detected in patients with either NSCLC, other epithelia cancer or benign lung disease, and in healthy volunteers. The expression level and positive rate of <it>LunX </it>mRNA in peripheral blood correlated with the pathologic stage of NSCLC (P < 0.001 and P = 0.010 respectively). Furthermore, <it>LunX </it>mRNA was detected in 92.9% (13/14) of malignant pleural fluid samples and was the only marker whose expression level was significantly different between malignant and benign pleural fluid (P < 0.001). Additionally, expression of <it>LunX </it>mRNA in the peripheral blood of NSCLC patients decreased shortly after clinical treatment (P = 0.005).</p> <p>Conclusion</p> <p>Of several commonly used genetic markers, <it>LunX </it>mRNA is the most specific gene marker for lung cancer and has potential diagnostic utility when measured in the peripheral blood and pleural fluid of NSCLC patients.</p

CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model

Supervised crowd counting relies heavily on costly manual labeling, which is difficult and expensive, especially in dense scenes. To alleviate the problem, we propose a novel unsupervised framework for crowd counting, named CrowdCLIP. The core idea is built on two observations: 1) the recent contrastive pre-trained vision-language model (CLIP) has presented impressive performance on various downstream tasks; 2) there is a natural mapping between crowd patches and count text. To the best of our knowledge, CrowdCLIP is the first to investigate the vision language knowledge to solve the counting problem. Specifically, in the training stage, we exploit the multi-modal ranking loss by constructing ranking text prompts to match the size-sorted crowd patches to guide the image encoder learning. In the testing stage, to deal with the diversity of image patches, we propose a simple yet effective progressive filtering strategy to first select the highly potential crowd patches and then map them into the language space with various counting intervals. Extensive experiments on five challenging datasets demonstrate that the proposed CrowdCLIP achieves superior performance compared to previous unsupervised state-of-the-art counting methods. Notably, CrowdCLIP even surpasses some popular fully-supervised methods under the cross-dataset setting. The source code will be available at https://github.com/dk-liang/CrowdCLIP.Comment: Accepted by CVPR 202