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During the COVID-19 crisis, digital technologies have become a major route for accessing remote care. Therefore, the need to ensure that these tools are safe and effective has never been greater. We raise five calls to action to ensure the safety, availability and long-term sustainability of theseDuring the COVID-19 crisis, digital technologies have become a major route for accessing remote care. Therefore, the need to ensure that these tools are safe and effective has never been greater. We raise five calls to action to ensure the safety, availability and long-term sustainability of these technologies (1) Due diligence Remove harmful health apps from app stores; (2) Data insights Use relevant health data insights from high-quality digital tools to inform the greater response to COVID-19; (3) Freely available resources Make high-quality digital health tools available without charge, where possible and for as long as possible, especially to those who are most vulnerable; (4) Digital transitioning Transform conventional offline mental health services to make them digitally available; and (5) Population self-management Encourage governments and insurers to work with developers to look at how digital health management could be subsidised or funded. We believe this should be carried out at the population level, rather than at a prescription level.Background Routinely recorded primary care data have been used for many years by sentinel networks for surveillance. More recently, real world data have been used for a wider range of research projects with the anticipation they could be used to support rapid, lower cost clinical trials. Much larger numbers of general practices are required to deliver effective surveillance and in-pandemic trials, given the partial national lockdown has resulted in falling community disease incidence. Objective To describe the rapid design and development of the Oxford Royal College of General Practitioners Clinical Informatics Digital (ORCHID) Hub, and its first two platforms. The Surveillance Platform will provide extended primary care surveillance, while the Trials Platform will be a streamlined clinical trials platform integrated into routine primary care practice. Methods We will apply the FAIR (Findable, Accessible, Interoperable and Reusable) metadata principles to a new, integrated digital health hub that will extract data, through a professionally-led national primary care network and the application of FAIR principles. The secure data hub will host routinely collected general practice data linked to other key healthcare repositories for clinical trials and support enhanced surveillance in-situ, without always needing large volume data extracts. ORCHID will support rapid data extraction, analysis and dissemination with the aim of improving future research and development in general practice to positively impact upon patient care.We introduce a new deep detail network architecture with grouped multiscale dilated convolutions to sharpen images contain multiband spectral information. Specifically, our end-to-end network directly fuses low-resolution multispectral and panchromatic inputs to produce high-resolution multispectral results, which is the same goal of the pansharpening in remote sensing. The proposed network architecture is designed by utilizing our domain knowledge and considering the two aims of the pansharpening spectral and spatial preservations. For spectral preservation, the up-sampled multispectral images are directly added to the output for lossless spectral information propagation. For spatial preservation, we train the proposed network in the high-frequency domain instead of the commonly used image domain. Different from conventional network structures, we remove pooling and batch normalization layers to preserve spatial information and improve generalization to new satellites, respectively. To effectively and efficiently obtain multiscale contextual features at a fine-grained level, we propose a grouped multiscale dilated network structure to enlarge the receptive fields for each network layer. This structure allows the network to capture multiscale representations without increasing the parameter burden and network complexity. These representations are finally utilized to reconstruct the residual images which contain spatial details of PAN. Our trained network is able to generalize different satellite images without the need for parameter tuning. Moreover, our model is a general framework, which can be directly used for other kinds of multiband spectral image sharpening, e.g., hyperspectral image sharpening. Experiments show that our model performs favorably against compared methods in terms of both qualitative and quantitative qualities.Many time-varying problems have been solved using the zeroing neural network proposed by Zhang et al. In this article, nine types of time-varying problems, namely time-varying nonlinear equation system, time-varying linear equation system, time-varying convex nonlinear optimization under linear equalities, unconstrained time-varying convex nonlinear optimization, time-varying convex quadratic programming under linear equalities, unconstrained time-varying convex quadratic programming, time-varying nonlinear inequality system, time-varying linear inequality system, and time-varying division, are investigated to better understand the essence of zeroing neutral network. Discrete-form time-varying problems are studied by considering the nature of unknown future and the requirement of real-time computation for time-varying problems. A unified model is proposed in the frame of zeroing neural network to uniformly solve these time-varying problems on the basis of their connections and a newly developed discretization formula. Theoretical analyses and numerical experiments, including the tracking control of PUMA560 robot manipulator, verify the effectiveness and precision of the proposed unified model.Involvement of dopamine in regulating exploration during decision-making has long been hypothesized, but direct causal evidence in humans is still lacking. Here, we use a combination of computational modeling, pharmacological intervention and functional magnetic resonance imaging to address this issue. Streptozotocin chemical structure Thirty-one healthy male participants performed a restless four-armed bandit task in a within-subjects design under three drug conditions 150 mg of the dopamine precursor L-dopa, 2 mg of the D2 receptor antagonist haloperidol, and placebo. Choices were best explained by an extension of an established Bayesian learning model accounting for perseveration, directed exploration and random exploration. Modeling revealed attenuated directed exploration under L-dopa, while neural signatures of exploration, exploitation and prediction error were unaffected. Instead, L-dopa attenuated neural representations of overall uncertainty in insula and dorsal anterior cingulate cortex. Our results highlight the computational role of these regions in exploration and suggest that dopamine modulates how this circuit tracks accumulating uncertainty during decision-making.
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