Notes

A singular biliopancreatic duct endoscope blended eye coherence tomography with intraductal ultrasonography pertaining to checking out the bile air duct: the analytic examine inside a porcine product.
Nephrotic syndrome is a unique clinical disorder, which provides interesting teaching opportunities that connect physiological and pathological aspects to clinical practice. During the current COVID-19 outbreak, in-person teaching in our institution was not permitted, thus creating a unique challenge for clinical skills teaching. A case-based electronic learning (e-learning) activity was designed to replace the traditional in-person teaching of renal semiology. e-Learning activities have been shown to be effective for knowledge retention and increasing novice learners' performance. However, major knowledge gaps exist concerning the satisfaction of learners with e-learning activities as the sole form of teaching, specifically for undergraduate clinical skills education.

Our study aimed to prospectively assess undergraduate medical students' perceptions of and satisfaction with an e-learning activity teaching renal semiology.

All second-year medical students (novice learners) from the medical faculty of tow interest in the activity being the sole means of teaching.

Case-based e-learning activities might be better suited for advanced learners and could increase learners' satisfaction within a blended teaching instructional design. More research on students' satisfaction with e-learning activities in the field of clinical skills education should be done. In addition, more effort should be put into finding alternative teaching tools for clinical skills education in light of the ongoing COVID-19 pandemic and future health crises.
Case-based e-learning activities might be better suited for advanced learners and could increase learners' satisfaction within a blended teaching instructional design. More research on students' satisfaction with e-learning activities in the field of clinical skills education should be done. Epigenetic inhibitor In addition, more effort should be put into finding alternative teaching tools for clinical skills education in light of the ongoing COVID-19 pandemic and future health crises.
The COVID-19 pandemic has changed work life profoundly and concerns regarding the mental well-being of employees' have arisen. Organizations have made rapid digital advancements and have started to use new collaborative tools such as social media platforms overnight.

Our study aimed to investigate how professional social media communication has affected work engagement before and during the COVID-19 pandemic and the role of perceived social support, task resources, and psychological distress as predictors and moderators of work engagement.

Nationally representative longitudinal survey data were collected in 2019-2020, and 965 respondents participated in all 4 surveys. Measures included work engagement, perceived social support and task resources, and psychological distress. The data were analyzed using a hybrid linear regression model.

Work engagement remained stable and only decreased in autumn 2020. Within-person changes in social media communication at work, social support, task resources, and psychological distress were all associated with work engagement. The negative association between psychological distress and work engagement was stronger in autumn 2020 than before the COVID-19 outbreak.

The COVID-19 pandemic has exerted pressure on mental health at work. Fostering social support and task resources at work is important in maintaining work engagement. Social media communication could help maintain a supportive work environment.
The COVID-19 pandemic has exerted pressure on mental health at work. Fostering social support and task resources at work is important in maintaining work engagement. Social media communication could help maintain a supportive work environment.
The global adoption of teleconsultation has been expedited as a result of the COVID-19 pandemic. By allowing remote communication, teleconsultation may help limit the spread of the virus while maintaining the crucial patient-provider relationship.

The aim of this study is to evaluate the value of teleconsultation compared to in-person visits in the management of elective orthopedic and spinal procedures.

This was a prospective observational cohort study of 853 patients receiving orthopedic and spinal care at a private outpatient clinic in New Zealand. Patients were randomly divided into two groups (1) patients receiving telephone consultation remotely, and (2) patients receiving in-person office consultations at the outpatient clinic. All patients received telephone consultations for 4 weeks during the mandated COVID-19 lockdown, followed by 4 weeks of telephone or in-person consultation. Patient preference, satisfaction, and duration of visit were recorded. Comparisons of patient preference between gro likely to prefer it over traditional, in-person visits in the future. This increased preference, coupled with higher patient satisfaction scores and shorter duration of visits, suggests that teleconsultation has a role in orthopedic surgery, which may even extend beyond the COVID-19 pandemic.
Patients who use telephone consultations are more likely to prefer it over traditional, in-person visits in the future. This increased preference, coupled with higher patient satisfaction scores and shorter duration of visits, suggests that teleconsultation has a role in orthopedic surgery, which may even extend beyond the COVID-19 pandemic.
The COVID-19 pandemic resulted in a rapid shift from center-based rehabilitation to telerehabilitation for chronic respiratory disease and lung transplantation due to infection control precautions. Clinical experience with this delivery model on a large scale has not been described.

The aim of this study is to describe usage and satisfaction of providers and lung transplant (LTx) candidates and recipients and functional outcomes following the broad implementation of telerehabilitation with remote patient monitoring during the first wave of the COVID-19 pandemic.

This study was a program evaluation of providers, LTx candidates, and early LTx recipients who used a web-based, remote monitoring app for at least four weeks between March 16 and September 1, 2020, to participate in telerehabilitation. Within-subjects analysis was performed for physical activity, Self-efficacy For Exercise (SEE) scale score, aerobic and resistance exercise volumes, 6-minute walk test results, and Short Physical Performance Batte COVID-19 pandemic.
We were able to provide telerehabilitation despite challenges around exercise equipment. This early experience will inform the development of a robust and equitable telerehabilitation model beyond the COVID-19 pandemic.
Obesity is a major public health problem globally and in Europe. The prevalence of childhood obesity is also soaring. Several parameters of the living environment are contributing to this increase, such as the density of fast food retailers, and thus, preventive health policies against childhood obesity must focus on the environment to which children are exposed. Currently, there are no systems in place to objectively measure the effect of living environment parameters on obesogenic behaviors and obesity. The H2020 project "BigO Big Data Against Childhood Obesity" aims to tackle childhood obesity by creating new sources of evidence based on big data.

This paper introduces the Obesity Prevention dashboard (OPdashboard), implemented in the context of BigO, which offers an interactive data platform for the exploration of objective obesity-related behaviors and local environments based on the data recorded using the BigO mHealth (mobile health) app.

The OPdashboard, which can be accessed on the web, allows hildhood obesity and inform the design of regional interventions both for prevention and treatment.
Our analyses serve as an initial investigation using the OPdashboard. Additional factors must be incorporated in order to optimize its use and obtain a clearer understanding of the results. The unique big data that are available through the OPdashboard can lead to the implementation of models that are able to predict population behavior. The OPdashboard can be considered as a tool that will increase our understanding of the underlying factors in childhood obesity and inform the design of regional interventions both for prevention and treatment.Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing (HU), yet their ability to simultaneously generalize various spectral variabilities (SVs) and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various SVs. Inspired by the powerful learning ability of deep learning (DL), we attempt to develop a general DL approach for HU, by fully considering the properties of endmembers extracted from the hyperspectral imagery, called endmember-guided unmixing network (EGU-Net). Beyond the alone autoencoder-like architecture, EGU-Net is a two-stream Siamese deep network, which learns an additional network from the pure or nearly pure endmembers to correct the weights of another unmixing network by sharing network parameters and adding spectrally meaningful constraints (e.g., nonnegativity and sum-to-one) toward a more accurate and interpretable unmixing solution. Furthermore, the resulting general framework is not only limited to pixelwise spectral unmixing but also applicable to spatial information modeling with convolutional operators for spatial-spectral unmixing. Experimental results conducted on three different datasets with the ground truth of abundance maps corresponding to each material demonstrate the effectiveness and superiority of the EGU-Net over state-of-the-art unmixing algorithms. The codes will be available from the website https//github.com/danfenghong/IEEE_TNNLS_EGU-Net.Deep neural networks have achieved breakthrough improvement in various application fields. Nevertheless, they usually suffer from a time-consuming training process because of the complicated structures of neural networks with a huge number of parameters. As an alternative, a fast and efficient discriminative broad learning system (BLS) is proposed, which takes the advantages of flat structure and incremental learning. The BLS has achieved outstanding performance in classification and regression problems. However, the previous studies ignored the reason why the BLS can generalize well. In this article, we focus on the interpretation from the viewpoint of the frequency domain. We discover the existence of the frequency principle in BLS, i.e., the BLS preferentially captures low-frequency components quickly and then fits the high frequencies during the incremental process of adding feature nodes and enhancement nodes. The frequency principle may be of great inspiration for expanding the application of BLS.This article presents a visual navigation and landing control paradigm for an unmanned aerial vehicle (UAV) to land on a moving autonomous surface vehicle (ASV). Therein, an adaptive learning navigation rule with a multilayer nested guidance is designed to pinpoint the position of the ASV and to guide and control the UAV to fulfill horizontal tracking and vertical descending in a narrow landing region of the ASV by means of merely relative position feedback. To ensure the feasibility of the proposed control law, asymptotical stability conditions are derived based on Lyapunov stability theory. Landing experimental results are reported for a UAV-ASV system consisting of an M-100 UAV and a self-developed three-meters-long HUSTER-30 ASV on a lake to substantiate the efficacy of the proposed landing control method.
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