Global multi-center and multi-modal magnetic resonance imaging study of obsessive-compulsive disorder: Harmonization and monitoring of protocols in healthy volunteers and phantoms.

OBJECTIVES: We describe the harmonized MRI acquisition and quality assessment of an ongoing global OCD study, with the aim to translate representative, well-powered neuroimaging findings in neuropsychiatric research to worldwide populations. METHODS: We report on T1-weighted structural MRI, resting-state functional MRI, and multi-shell diffusion-weighted imaging of 140 healthy participants (28 per site), two traveling controls, and regular phantom scans. RESULTS: Human image quality measures (IQMs) and outcome measures showed smaller within-site variation than between-site variation. Outcome measures were less variable than IQMs, especially for the traveling controls. Phantom IQMs were stable regarding geometry, SNR, and mean diffusivity, while fMRI fluctuation was more variable between sites. CONCLUSIONS: Variation in IQMs persists, even for an a priori harmonized data acquisition protocol, but after pre-processing they have less of an impact on the outcome measures. Continuous monitoring IQMs per site is valuable to detect potential artifacts and outliers. The inclusion of both cases and healthy participants at each site remains mandatory.

INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT) AS A TOOL FOR SUSTAINABLE TOURISM MANAGEMENT: A BIBLIOMETRIC ANALYSIS

The tourism sector has experienced a steady growth in the last decades, becoming one of the key sectors for the development of countries. However, the pandemic caused by COVID-19 brought about an unprecedented social, economic and health crisis that has forced a change in the way tourism is conducted. This article presents a comprehensive bibliometric analysis of different Information and Communication Technologies' uses in sustainable tourism management to study the literature and serve as a roadmap for future research in this field. © 2022 Universidad de Murcia. All rights reserved.

Online assessment vs Traditional assessment: perception of medical teachers in a tertiary level teaching hospital in South India

The use of e-assessment has increased in higher education over the last two decades, which means that medical teachers are required to work by adapting to the increasing usage of technology. Because of the automated marking and feedback, online tests are viewed as highly efficient, fast, and reliable. The online assessment was not used for formative/summative assessment except in fewer renowned institutions in our country. But it had increased recently in all educational setups because of the COVID-19 pandemic. This study aims to know the perception of preclinical faculty on the advantages and disadvantages of using online internal assessment when compared to the traditional method. A cross-sectional survey was done using Google form with standard and validated questionnaires with Likert scale scoring (1- strongly disagree, 2- disagree, 3- neutral, 4- agree, and 5- strongly agree) for preclinical medical faculty to assess their perception of online vs traditional assessment method. The result was analyzed by descriptive statistics. Out of 45 responses, only 50% were competent to handle the online assessment, but the other 50% were confident though not competent due to lack of training. 96% of faculty agreed that paper correction load is reduced in online aptitude tests. But nearly 40% agreed that aptitude tests can assess only the student's knowledge in the cognitive domain. In our study, we found that not all the faculty preferred to switch from the conventional method. However, they show their willingness to adopt a blended teaching and assessment method. Copyright © 2022 Sociedad Anatomica Espanola. All rights reserved.

The in vivo RNA structurome of the malaria parasite Plasmodium falciparum, a protozoan with an A/U-rich transcriptome.

Plasmodium falciparum, a protozoan parasite and causative agent of human malaria, has one of the most A/T-biased genomes sequenced to date. This may give the genome and the transcriptome unusual structural features. Recent progress in sequencing techniques has made it possible to study the secondary structures of RNA molecules at the transcriptomic level. Thus, in this study we produced the in vivo RNA structurome of a protozoan parasite with a highly A/U-biased transcriptome. We showed that it is possible to probe the secondary structures of P. falciparum RNA molecules in vivo using two different chemical probes, and obtained structures for more than half of all transcripts in the transcriptome. These showed greater stability (lower free energy) than the same structures modelled in silico, and structural features appeared to influence translation efficiency and RNA decay. Finally, we compared the P. falciparum RNA structurome with the predicted RNA structurome of an A/U-balanced species, P. knowlesi, finding a bias towards lower overall transcript stability and more hairpins and multi-stem loops in P. falciparum. This unusual protozoan RNA structurome will provide a basis for similar studies in other protozoans and also in other unusual genomes.

A Human Mobility Data Driven Hybrid GNN plus RNN Based Model For Epidemic Prediction

Epidemic simulation traditionally serves as one of the important methods to forecast how an epidemic may spread among a population. However, there are two key limitations that restrict the scope of such methods. The first limitation is that the existing tools rely on different sets of static parameters (e.g., infection probability, recovering probability) for simulating an epidemic spread that may fail to capture the dynamic nature of population interactions that acts as a dominant factor in an epidemic spread scenario such as COVID-19 pandemic. To handle this challenge, we propose a machine learning based model that combines a Graph Convolutional Neural Network (GCN) and a Recurrent Neural Network (RNN). It integrates the ability of the GCN to capture spatial dependency in human interaction and the ability of the RNN to incorporate temporal effects of the virus spread. The second limitation is that these methods do not address the computation overhead problem when dealing with time-dynamic graphs. Training a GCN on a very large graph suffers from the communication overhead from different graph partitions and the computation overheads stemming from partitioning dynamic graphs. This limitation impacts the scalability of the existing systems. To solve this challenge, we partition the graph in a computationally less expensive manner by partitioning the graph using the min-cut principle. We conducted comprehensive large scale real-world human mobility data driven experiments. Our experimental result shows that the proposed machine learning based forecasting model achieves overall 84% classification accuracy with greater than 72% precision and 62% recall. Also, the proposed graph partitioning approach reduces computation time and commutation overhead by a significant margin.

Detection of recovery of covid-19 cases using machine learning

Introduction: Classification is one of the most important research and applications of machine learning techniques. Research in the area of human-machine interaction and machine learning contributed to the success of Chatbots. Objective: This research concentrates on some of the most important developments in machine learning classification research and the issues of Coronavirus Disease 2019 (COVID-19). Since December 2019, COVID-19 has been causing a massive health crisis all over the world resulted in 5,418,237 confirmed and 344,201 death COVID-19 cases to date (24.05.2020). Clinical experts say that COVID-19 patients to be diagnosed in early-stage to save their lives. Methods: This study attempted to detect COVID-19 patients who can recover from the disease, using machine learning techniques, so that suitable treatment can be given to the patients to save their lives. Support Vector Machines (SVM), Artificial Neural Network (ANN), Decision tree, K-Nearest Neighbors (KNN), Random Forest and Logistic Regression algorithms are used to evaluate the classification performance. Result and Conclusion: In this paper, a Chatbot was developed using the best algorithm evaluated to serve the society suffering from COVID-19. © IJCRR.

Formative Evaluation of a Home-Based Physical Activity Intervention for Adolescent Girls-The HERizon Project: A Randomised Controlled Trial.

BACKGROUND: This is a formative evaluation study of the HERizon Project, a home-based multi-component physical activity (PA) intervention for adolescent girls in the UK and Ireland. Although not intended, this study coincided with the initial COVID-19 lockdown restrictions. METHODS: A total of 42 female participants, aged 13 to 16 years old (mean = 14.2, SD = 1.1), were randomly allocated to: (i) the HERizon group (n = 22) or (ii) the wait-list control group (n = 20). Participants in the six-week HERizon group were asked to complete three PA sessions each week and engage in weekly behaviour change support video calls. The primary outcome measure was self-reported habitual PA. Secondary outcomes measures included cardiorespiratory fitness (20 m shuttle run), muscular strength (standing long jump), muscular endurance (push up test), and psychosocial outcomes (Perceived Competence Scale, Body Appreciation Scale, Self-Esteem Questionnaire, Behavioural Regulation in Exercise Questionnaire). Quantitative and qualitative process evaluation data were also collected. Outcome measures were assessed at baseline and after the six-week intervention. RESULTS: There was no significant change in habitual PA between groups (LMM group*time interaction: p = 0.767). The HERizon group had significantly increased cardiorespiratory fitness (p = 0.001), muscular endurance (p = 0.022), intrinsic motivation (p = 0.037), and body appreciation (p < 0.003) in comparison to the wait-list control group. All participants in the intervention group completed the intervention and compliance to the intervention was high (participants completed 18 ± 2 sessions). CONCLUSIONS: Although no change in PA was observed, HERizon resulted in improved physical fitness and psychosocial outcomes. These preliminary findings, alongside positive findings for feasibility and acceptability, highlight potential benefits from the home-based intervention, thus further investigation is warranted.