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Abstract Integrated hydrologic models can simulate coupled surface and subsurface processes but are computationally expensive to run at high resolutions over large domains. Here we develop a novel deep learning model to emulate subsurface flows simulated by the integrated ParFlow‐CLM model across the contiguous US. We compare convolutional neural networks like ResNet and UNet run autoregressively against our novel architecture called the Forced SpatioTemporal RNN (FSTR). The FSTR model incorporates separate encoding of initial conditions, static parameters, and meteorological forcings, which are fused in a recurrent loop to produce spatiotemporal predictions of groundwater. We evaluate the model architectures on their ability to reproduce 4D pressure heads, water table depths, and surface soil moisture over the contiguous US at 1 km resolution and daily time steps over the course of a full water year. The FSTR model shows superior performance to the baseline models, producing stable simulations that capture both seasonal and event‐scale dynamics across a wide array of hydroclimatic regimes. The emulators provide over 1,000× speedup compared to the original physical model, which will enable new capabilities like uncertainty quantification and data assimilation for integrated hydrologic modeling that were not previously possible. Our results demonstrate the promise of using specialized deep learning architectures like FSTR for emulating complex process‐based models without sacrificing fidelity.

Abstract Contrast-rebalanced dichoptic movies have been shown to be an effective binocular treatment for amblyopia in the laboratory. Yet, at-home therapy is a more practical approach. In a randomized clinical trial, we compared dichoptic movies, streamed at-home on a handheld 3D-enabled game console, versus patching as amblyopia treatment. Sixty-five amblyopic children (3–7 years; 20/32–125) were randomly assigned to one of two parallel arms, binocular treatment (3 movies/week) or patching (14 h/week). The primary outcome, change in best corrected visual acuity (BCVA) at the 2-week visit was completed by 28 and 30, respectively. After the primary outcome, both groups of children had the option to complete up to 6 weeks of binocular treatment. At the 2-week primary outcome visit, BCVA had improved in the movie (0.07 ± 0.02 logMAR; p

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Despite the global expansion in women's access to political leadership in recent decades, female parliamentarians remain a distinct minority in most national legislatures. Previous studies have linked variations in women's descriptive representation to international and domestic security threats, such as interstate war and transnational terrorist campaigns. However, existing research has thus far overlooked how the turbulence, unrest, and violence often associated with the election process itself may produce gendered electoral outcomes. I argue that violent contention in the period immediately preceding elections introduces gendered distortions to the political recruitment process that determines the gender composition of legislatures. The cumulative effect of these distortions is a reduction in the proportion of legislative seats subsequently held by women. To evaluate my hypothesis, I analyze data from 620 nominally competitive legislative elections in 128 countries between 1990 and 2012. Consistent with expectations, the results suggest that higher levels of election-related violence are associated with lower rates of women's descriptive representation. [ABSTRACT FROM AUTHOR]

Concerns about threats to politicians have been growing. But what is the public's view of abuse and intimidation directed at MPs? Sarah Shair-Rosenfield, Robert Johns, Reed M. Wood, and Graeme A.M. Davies report on new research that found some surprisingly upbeat results.Professor of Politics at the University of York.Professor of Politics at the University of Southampton.Professor of International Relations at the University of Essex.Professor of Politics at the University of York. [Extracted from the article]

Music listening, an inherently emotional activity, is commonly used as a form of emotion regulation (ER). Although research suggests that some strategies of listening to music to regulate emotion are linked with psychological well-being and symptoms of affective disorders, the mechanisms underlying these relationships have not been thoroughly explored. The present study examined whether associations between music as ER and symptoms of depression and anxiety were mediated by individuals' use of other adaptive and maladaptive ER strategies. A nationwide online sample of US adults (n = 146) completed measures assessing their use of music as ER, other adaptive and maladaptive ER strategies, and symptoms of depression and anxiety. The results indicated that the overall use of music as ER was not directly associated with symptoms of depression and anxiety, whereas using music as discharge (venting negative emotions by listening to mood-congruent music) was positively associated with such symptoms. Mediational analyses revealed that adaptive ER mediated the relationship between overall use of music as ER and symptoms of depression and anxiety, whereas maladaptive ER mediated the relationships between discharge and symptoms. These findings suggest that people use music as ER alongside their other ER efforts, which in turn are linked with symptoms of depression and anxiety. Furthermore, although the overall use of music to regulate emotion may be beneficial for well-being via its association with adaptive ER, a more maladaptive pattern when using music for emotional discharge suggests that music plays a more nuanced role in ER and mental health. [ABSTRACT FROM AUTHOR]

Measurement(s) streamflow • groundwater • evaporation • evapotranspiration • total water storage Technology Type(s) integrated hydrological model Factor Type(s) temporal interval • geographic location Sample Characteristic - Environment river Sample Characteristic - Location Colorado River Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16632454

Dilhara Karunaratne,1 Nisal Karunaratne,2 Jade Wilmot,3 Tim Vincent,2 Juliet Wright,2 Nadia Mahmood,4 Alice Tang,5 Amir H Sam,5 Malcolm Reed,2 David Howlett1 1Eastbourne District General Hospital, Eastbourne, BN21 2UD, UK; 2Brighton and Sussex Medical School, Brighton, BN1 9PX, UK; 3Warwick Medical School, Coventry, CV4 7HL, UK; 4Royal Sussex County Hospital, Brighton, BN2 5BE, UK; 5Imperial College London, Charing Cross Campus, London, W6 8RP, UKCorrespondence: Dilhara KarunaratneEastbourne District General Hospital, King’s Drive, Eastbourne, BN21 2UD, UKTel +44 300 151 4500Fax +44 1323 43579Email diliekaru@yahoo.co.ukAbstract: This paper describes the development and use of the bespoke digital learning resource CAPSULE (Clinical and Professional Studies Unique Learning Environment) which was launched UK wide in May 2020 to facilitate the delivery of core learning content for UK medical students during the COVID-19 pandemic. CAPSULE is a digital learning resource comprising case-based scenarios and multiple-choice questions, encompassing all undergraduate medical specialities and supported by a pan-speciality editorial board. Following the COVID-19 pandemic lockdown and loss of face-to-face learning opportunities, CAPSULE was made available to all UK medical schools in May 2020. Following a global content review and edit and UK wide rollout, over 41,000 medical students and 3200 faculty registered as users. Approximately 1.5 million cases were completed in the first 12 months of use by up to 4500 distinct monthly users. Feedback from both students and faculty has been highly positive. CAPSULE continues to be used within UK medical schools and has allowed an entire cohort of medical students to access core curriculum content and progress their studies during the COVID-19 pandemic. Future directions may include further integration into UK medical school curricula, enhancement of platform functionality and potential expansion on an international scale.Keywords: CAPSULE, COVID-19, digital learning platform, UK medical school

Patients may use crowdfunding to solicit donations, typically from multiple small donors using internet-based means, to offset the financial toxicity of cancer care. To describe crowdfunding campaigns by gynecologic cancer patients and to compare campaign characteristics and needs expressed between patients with cervical, uterine, and ovarian cancer. We queried the public crowdfunding forum GoFundMe.com for "cervical cancer," "uterine cancer," and "ovarian cancer." The first 200 consecutive posts for each cancer type fundraising within the United States were analyzed. Data on campaign goals and needs expressed were manually extracted. Descriptive statistics and bivariate analyses were performed. Among the 600 fundraising pages, the median campaign goal was $10,000 [IQR $5000–$23,000]. Campaigns raised a median of 28.6% of their goal with only 8.7% of campaigns reaching their goal after a median of 54 days online. On average, ovarian cancer campaigns had higher monetary goals, more donors, and larger donation amounts than cervical cancer campaigns and raised more money than both cervical and uterine cancer campaigns. Campaigns were fundraising to support medical costs (80–85%) followed by lost wages (36–56%) or living expenses (27–41%). Cervical cancer campaigns reported need for non-medical costs more frequently than uterine or ovarian cancer campaigns. States without Medicaid expansions (31% of the national population) were over-represented among cervical cancer and uterine cancer, but not ovarian cancer campaigns. Crowdfunding pages reveal patients fundraising for out-of-pocket costs in the thousands of dollars and a wide range of unmet financial needs based on cancer type. • Patients crowdfunding for gynecologic cancer care had a median campaign goal of $10,000 and raised a median of $2800. • Campaigns raised a median of 28.6% of their goal. After a median of 54 days online, only 8.7% had reached their goal. • 82% of campaigns fundraised for medical expenses, while 32% fundraised for non-medical living expenses. • 32% of campaigns fundraised to offset lost wages, which was more common among cervical cancer campaigns. • States without an implemented Medicaid expansion were over-represented among cervical and uterine cancer campaigns. [ABSTRACT FROM AUTHOR]

Integrated hydrologic models can simulate coupled surface and subsurface processes but are computationally expensive to run at high resolutions over large domains. Here we develop a novel deep learning model to emulate subsurface flows simulated by the integrated ParFlow‐CLM model across the contiguous US. We compare convolutional neural networks like ResNet and UNet run autoregressively against our novel architecture called the Forced SpatioTemporal RNN (FSTR). The FSTR model incorporates separate encoding of initial conditions, static parameters, and meteorological forcings, which are fused in a recurrent loop to produce spatiotemporal predictions of groundwater. We evaluate the model architectures on their ability to reproduce 4D pressure heads, water table depths, and surface soil moisture over the contiguous US at 1 km resolution and daily time steps over the course of a full water year. The FSTR model shows superior performance to the baseline models, producing stable simulations that capture both seasonal and event‐scale dynamics across a wide array of hydroclimatic regimes. The emulators provide over 1,000× speedup compared to the original physical model, which will enable new capabilities like uncertainty quantification and data assimilation for integrated hydrologic modeling that were not previously possible. Our results demonstrate the promise of using specialized deep learning architectures like FSTR for emulating complex process‐based models without sacrificing fidelity. Plain Language Summary: Computational models are important for understanding and predicting terrestrial hydrology, but our most physically detailed models can be time‐consuming and expensive to run over large regions. In this study, we trained deep learning models to emulate a complex hydrology model that simulates groundwater flow over the contiguous US. We developed a new model architecture called FSTR that captures spatiotemporal patterns better than standard deep learning models. FSTR is over 1,000 times faster than ParFlow, the original hydrologic model. This enables new possibilities like forecasting groundwater changes and estimating uncertainties. Our results show that specialized deep learning architectures can accurately emulate complex hydrologic models while drastically reducing computation time. Key Points: Deep learning models can emulate a complex integrated hydrology model that simulates groundwater over the United StatesWe developed a new model architecture that is more robust over longer simulation periods than off‐the‐shelf neural networksDeep‐learning based emulators of complex models enable new applications such as real‐time forecasting and estimating uncertainties [ABSTRACT FROM AUTHOR]

Research indicates that receiving esteem support has multiple benefits, however, less is known about the factors that influence the production of esteem support messages of varying quality. We examine how characteristics of potential recipients shape esteem support message production, specifically, how recipients' effort to help themselves may alter emotions and motivations of potential esteem support providers, affecting the quality of esteem support messages produced. A between-subjects experiment tested predictions derived from the attribution-emotion-action model (AEAM) and the cognitive-emotional theory of esteem support messages (CETESM). Results are consistent with a process of serial mediation where recipient effort causes effort attributions in potential support providers, which increase sympathy, which increases helping motivation, leading to more high-quality content in esteem support messages. Anger, although negatively associated with effort attribution, did not appear to play a mediating role in esteem support message production within the job search context. Theoretic and pragmatic implications are discussed. [ABSTRACT FROM AUTHOR]