CONFIDENCE LEVEL OF A MOTION FEEDBACK SYSTEM FOR ANALYZING EXERCISE TRAINING EFFICACY

The increase in aged population is a global trend. Inculcating healthy behaviors such as regular exercises in the elderly has a significant impact on the financial and medical burden globally. Moreover, air pollution and the outbreak of the coronavirus disease 19 (COVID-19) pose a serious threat to public health. In order to improve the health conditions of the population, this study developed a motion feedback system named MoveV that can be used for several indoor training exercises. This system provides instant motion feedback by synchronizing exercise training videos on the website using a motion analysis algorithm that is applicable on smartphones, and a cloud database platform is used to record health behaviors. Feature extraction is performed based on force intensity, motion velocity, and exercise direction. The resultant accuracy of the motion feedback system was tested by a motion science expert and presented as the confidence level. For perfect movement, a confidence level of up to 90.5% was achieved, indicating that the MoveV system was able to record users’ exercise frequency and distinguish whether the user was performing well in the exercise movements. The proposed system is convenient and does not incur additional expenditure by purchasing any new device. Furthermore, it provides visual and voice feedback, companionship, and exercise motivation to the users, all of which are important factors when using online exercise platforms.

A META-ANALYSIS ON THE EFFECTIVENESS OF MALARIA COMMUNITY-BASED INTERVENTION

Introduction: The community-based intervention was proven as one of the best approaches in combatting malaria. It is widely used to reduce malaria cases worldwide. A multisectoral approach must be made to ensure the success of the intervention. Therefore, this study is aimed is to determine the effectiveness of community-based intervention for malaria based on recent findings. Methods: A search strategy using Pub Med and Cochrane database platform investigating the effectiveness of malaria community-based intervention. There are selected terms and words predefined to obtain a comprehensive search strategy that included text fields within records and Medical Subject Headings (MeSH terms). Results: Eight articles were selected for this study. This review identified six communitybased interventions for malaria and four outcome measures. Most of the studies included a low risk of bias in each criterion except for random sequence and allocation of concealment. The meta-analysis found that the overall incidence difference of the included studies was significant (0.05 CI 0.02, 0.08), while the subgroup analysis of Permethrin LLIN the I2 was reduced to 75%. However, the overall usage percentage difference in health education and community mobilization to improve intervention usage was not significant (0.28 CI -0.02, 0.58). Conclusion: The community-based intervention approach is practical to fight malaria thus, to reduced mortality due to malaria. An integrated approach and engagement with multiagency will ensure outstanding achievement in fighting malaria.

Extraction of Oleic Acid from Animals Oil and Its Anti-inflammatory Effect on Network Pharmacology

The purpose of this paper is to explore the possible mechanisms of anti-inflammatory and scar repair by Mongolian horse oil. We used TCM database and literature mining to collect active compounds of horse oil and used Swiss TargetPrediction and SuperPred server to find targets of compounds. Anti-inflammatory drug targets were collected through the CTD database. Go annotation of targets and KEGG pathway were enriched and analyzed through Metascape database platform. Molecular docking between active ingredients and targets was verified by AutoDock software. Metascape analysis revealed that the key candidate targets were significantly enriched in a number of pathways associated with inflammatory pathology. The results of molecular docking showed that oleic acid, a major component of animals oil, could influence the regulatory functions of TNF, NGF, IL6, IL1B, Jun, and CDK1. This suggests that animals oil can regulate the development of inflammation through its active ingredient, oleic acid, and can influence the expression of multiple signaling pathways, with theoretical endogenous interactions with TNF, NGF, IL6, IL1B, JUN, and CDK1 proteins.

FlywheelTools: Data Curation and Manipulation on the Flywheel Platform

The recent and growing focus on reproducibility in neuroimaging studies has led many major academic centers to use cloud-based imaging databases for storing, analyzing, and sharing complex imaging data. Flywheel is one such database platform that offers easily accessible, large-scale data management, along with a framework for reproducible analyses through containerized pipelines. The Brain Imaging Data Structure (BIDS) is the de facto standard for neuroimaging data, but curating neuroimaging data into BIDS can be a challenging and time-consuming task. In particular, standard solutions for BIDS curation are limited on Flywheel. To address these challenges, we developed “FlywheelTools,” a software toolbox for reproducible data curation and manipulation on Flywheel. FlywheelTools includes two elements: fw-heudiconv, for heuristic-driven curation of data into BIDS, and flaudit, which audits and inventories projects on Flywheel. Together, these tools accelerate reproducible neuroscience research on the widely used Flywheel platform.

An Intelligent Self-Service Vending System for Smart Retail

The traditional weighing and selling process of non-barcode items requires manual service, which not only consumes manpower and material resources but is also more prone to errors or omissions of data. This paper proposes an intelligent self-service vending system embedded with a single camera to detect multiple products in real-time performance without any labels, and the system realizes the integration of weighing, identification, and online settlement in the process of non-barcode items. The system includes a self-service vending device and a multi-device data management platform. The flexible configuration of the structure gives the system the possibility of identifying fruits from multiple angles. The height of the system can be adjusted to provide self-service for people of different heights; then, deep learning skill is applied implementing product detection, and real-time multi-object detection technology is utilized in the image-based checkout system. In addition, on the multi-device data management platform, the information docking between embedded devices, WeChat applets, Alipay, and the database platform can be implemented. We conducted experiments to verify the accuracy of the measurement. The experimental results demonstrate that the correlation coefficient R2 between the measured value of the weight and the actual value is 0.99, and the accuracy of non-barcode item prediction is 93.73%. In Yangpu District, Shanghai, a comprehensive application scenario experiment was also conducted, proving that our system can effectively deal with the challenges of various sales situations.

FlywheelTools: Data Curation and Manipulation on the Flywheel Platform

ABSTRACTThe recent and growing focus on reproducibility in neuroimaging studies has led many major academic centers to use cloud-based imaging databases for storing, analyzing, and sharing complex imaging data. Flywheel is one such database platform that offers easily accessible, large-scale data management, along with a framework for reproducible analyses through containerized pipelines. The Brain Imaging Data Structure (BIDS) is a data storage specification for neuroimaging data, but curating neuroimaging data into BIDS can be a challenging and time-consuming task. In particular, standard solutions for BIDS curation are not designed for use on cloud-based systems such as Flywheel. To address these challenges, we developed “FlywheelTools”, a software toolbox for reproducible data curation and manipulation on Flywheel. FlywheelTools includes two elements: fw-heudiconv, for heuristic-driven curation of data into BIDS, and flaudit, which audits and inventories projects on Flywheel. Together, these tools accelerate reproducible neuroscience research on the widely used Flywheel platform.

EcoBank: A flexible database platform for sharing ecological data

Environmental crisis challenges the human race harder than ever before. Ecologists have produced a massive amount of data to cope with the crisis. Accordingly, many national scale ecological database systems have been developed worldwide to manage and analyse these datasets. However, in Korea, ecological datasets produced by different research institutes for different purposes have not been integrated or serviced due to poorly designed information infrastructure. To address this obstacle, we present EcoBank (www.nie-ecobank.kr), an open, web-based ecological database platform designed to play an important role in ecosystem analysis, not only in Korea, but also worldwide. The architecture of EcoBank comprises core technologies of WebGIS, Application Programming Interface (API), responsive web and open-source software (OSS). Comprehensive ecological datasets from three different sources, including the National Institute of Ecology (NIE) in Korea, related national and international platforms and repositories, enter the three conceptual modules in EcoBank: data management, analysis and service. Diverse potential stakeholders of EcoBank can be classified into three groups: researchers, policy-makers and public users. EcoBank aims to expand its horizons through mutual communication amongst these stakeholders. We opened and launched the EcoBank service in December 2019 and have now begun to broaden its network by linking it to other data platforms and repositories over the globe to find possible solutions to ecological issues in Korea.

Microbiome Search Engine 2: a Platform for Taxonomic and Functional Search of Global Microbiomes on the Whole-Microbiome Level

ABSTRACT Metagenomic data sets from diverse environments have been growing rapidly. To ensure accessibility and reusability, tools that quickly and informatively correlate new microbiomes with existing ones are in demand. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes in the global metagenome data space based on the taxonomic or functional similarity of a whole microbiome to those in the database. MSE 2 consists of (i) a well-organized and regularly updated microbiome database that currently contains over 250,000 metagenomic shotgun and 16S rRNA gene amplicon samples associated with unified metadata collected from 798 studies, (ii) an enhanced search engine that enables real-time and fast (<0.5 s per query) searches against the entire database for best-matched microbiomes using overall taxonomic or functional profiles, and (iii) a Web-based graphical user interface for user-friendly searching, data browsing, and tutoring. MSE 2 is freely accessible via http://mse.ac.cn. For standalone searches of customized microbiome databases, the kernel of the MSE 2 search engine is provided at GitHub (https://github.com/qibebt-bioinfo/meta-storms). IMPORTANCE A search-based strategy is useful for large-scale mining of microbiome data sets, such as a bird’s-eye view of the microbiome data space and disease diagnosis via microbiome big data. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes against the existing microbiome data sets on the basis of their similarity in taxonomic structure or functional profile. Key improvements include database extension, data compatibility, a search engine kernel, and a user interface. The new ability to search the microbiome space via functional similarity greatly expands the scope of search-based mining of the microbiome big data.

Hot topics and frontier evolution of research on entrepreneurial intentions——Visual analysis based on the core collection of Web of science database

Since the Chinese slogan “Mass Entrepreneurship, Mass Innovation” was put forward, entrepreneurship has become a hot topic of research in academic circles and even people from all walks of life. Entrepreneurial Intentions is the best indicator to effectively predict entrepreneurial behavior. In order to understand the research status and trends of entrepreneurship intentions, based on the bibliometric method, this paper uses citespace software to visually analyze 1,038 core collections published in the Web of science database platform from January 1, 1997 to December 31, 2020. The results show that in the past 20 years, the research on entrepreneurial intentions has mainly focused on scholars from foreign universities. The distribution of research institutions is relatively concentrated, but the distribution of journals is relatively loose. Antecedent variables have become the focus of research. Future research should focus on outcome variables.