Notes

Nanoporous Nickel-Molybdenum Oxide with an Oxygen Openings pertaining to Electrocatalytic Nitrogen Fixation below Background Situations.
ffected the spread of COVID-19 information.

Different types of pandemic-related information have varying dissemination power. To effectively disseminate information and prevent the spread of COVID-19, we should identify the factors that affect this dissemination. We should then disseminate the types of information the public is most concerned about, use information to educate people to improve their health literacy, and improve public opinion and information governance.
Different types of pandemic-related information have varying dissemination power. Bcl-2 inhibitor To effectively disseminate information and prevent the spread of COVID-19, we should identify the factors that affect this dissemination. We should then disseminate the types of information the public is most concerned about, use information to educate people to improve their health literacy, and improve public opinion and information governance.
Based on ethical and methodological arguments, numerous calls have been made to increase end-users' involvement in serious game (SG) development. Involving end-users is considered a way to give them power and control over an educational software designed for them. It can also help identify areas for improvement in SG design and improve its efficacy on targeted learning outcomes. However, no recognized guidelines or framework exists to guide end-users' involvement in SG development.

To describe how end-users are involved in the development of SGs for healthcare professions education.

We examined the literature presenting the development of 45 SGs that had reached the stage of efficacy evaluation in randomized trials. One author performed data extraction using an ad hoc form based on a design and development framework for SGs. Data were then coded and synthesized on the basis of similarities. The coding scheme was refined iteratively with the involvement of a second author. Results are presented using frequencies and percentages.

End-users' involvement was mentioned in the development of 21/45 SGs. The number of end-users involved ranged from 12 to 36. End-users were often involved in answering specific concerns that arose during the SG design (n = 6) or in testing a prototype (n = 12). In many cases, researchers solicited input from end-users regarding the goals to reach (n = 10) or the functional esthetics of the SGs (n = 7). Most researchers used self-reported questionnaires (n = 7).

Researchers mention end-users' involvement, which is also poorly described, in the development of less than half of the SGs identified. This represents significant limitations to evaluating the impact of their involvement on SG efficacy and in making recommendations.
[This corrects the article DOI 10.2196/26145.].Not identified as being exposed or infected, the group of asymptomatic and presymptomatic patients has become the key source of infectious hosts for the COVID-19 pandemic, triggering the re-emergence of outbreaks. Acknowledging the impacts of movement of unidentified patients and the limited testing capacity on understanding the spread of the virus, an augmented Susceptible-Exposed-Infectious-Confirmed-Recovered (SEICR) model integrating intercity migration data and testing capacity is developed to probe into the number of unidentified COVID-19 infected patients. This model allows evaluation of the effectiveness of active interventions, and more accurate prediction of the pandemic progression in a country, region or city. A pseudo-coevolutionary algorithm is adopted in the model fitting to provide an effective estimation of high-dimensional unknown parameter sets using a limited amount of historical data. The model is applied to 175 regions in Australia, Canada, Italy, Japan, Spain, the UK and USA to estimate the number of unconfirmed cases using limited historical data. Results showed that the actual number of infected cases could be 4.309 times as many as the official confirmed number. By implementing mass COVID-19 testing, the number of infected cases could be reduced by about 50%.Estimating and surveillance volumes of patients are of great importance for public health and resource allocation. In many situations, the change of these volumes is correlated with many factors, e.g., seasonal environmental variables, medicine sales, and patient medical claims. It is often of interest to predict patient volumes and to that end, discovering causalities can improve the prediction accuracy. Correlations do not imply causations and they can be spurious, which in turn may entail deterioration of prediction performance if the prediction is based on them. By contrast, in this paper, we propose an approach for prediction based on causalities discovered by Gaussian processes. Our interest is in estimating volumes of patients that suffer from allergy and where the model and the results are highly interpretable. In selecting features, instead of only using correlation, we take causal information into account. Specifically, we adopt the Gaussian processes-based convergent cross mapping framework for causal discovery which is proven to be more reliable than the Granger causality when time series are coupled. Moreover, we introduce a novel method for selecting the history or look-back length of features from the perspective of a dynamical system in a principled manner. The quasi-periodicities that commonly exist in observations of volumes of patients and environment variables can readily be accommodated. Further, the proposed method performs well even in cases when the data are scarce. Also, the approach can be modified without much difficulty to forecast other types of patient volumes. We validate the method with synthetic and real-world datasets.Diseases can show different courses of progression even when patients share the same risk factors. Recent studies have revealed that the use of trajectories, the order in which diseases manifest throughout life, can be predictive of the course of progression. In this study, we propose a novel computational method for learning disease trajectories from EHR data. The proposed method consists of three parts first, we propose an algorithm for extracting trajectories from EHR data; second, three criteria for filtering trajectories; and third, a likelihood function for assessing the risk of developing a set of outcomes given a trajectory set. We applied our methods to extract a set of disease trajectories from Mayo Clinic EHR data and evaluated it internally based on log-likelihood, which can be interpreted as the trajectories' ability to explain the observed (partial) disease progressions. We then externally evaluated the trajectories on EHR data from an independent health system, M Health Fairview. The proposed algorithm extracted a comprehensive set of disease trajectories that can explain the observed outcomes substantially better than competing methods and the proposed filtering criteria selected a small subset of disease trajectories that are highly interpretable and suffered only a minimal (relative 5%) loss of the ability to explain disease progression in both the internal and external validation.
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