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Japanese encephalitis virus (JEV) is a major cause of neurological disability in Asia and causes thousands of severe encephalitis cases and deaths each year. Although Japanese encephalitis (JE) is a WHO reportable disease, cases and deaths are significantly underreported and the true burden of the disease is not well understood in most endemic countries. Here, we first conducted a spatial analysis of the risk factors associated with JE to identify the areas suitable for sustained JEV transmission and the size of the population living in at-risk areas. We then estimated the force of infection (FOI) for JE-endemic countries from age-specific incidence data. Estimates of the susceptible population size and the current FOI were then used to estimate the JE burden from 2010 to 2019, as well as the impact of vaccination. Overall, 1,543.1 million (range: 1,292.6-2,019.9 million) people were estimated to live in areas suitable for endemic JEV transmission, which represents only 37.7% (range: 31.6-53.5%) of the over four billion people living in countries with endemic JEV transmission. Based on the baseline number of people at risk of infection, there were an estimated 56,847 (95% CI: 18,003-184,525) JE cases and 20,642 (95% CI: 2,252-77,204) deaths in 2019. Estimated incidence declined from 81,258 (95% CI: 25,437-273,640) cases and 29,520 (95% CI: 3,334-112,498) deaths in 2010, largely due to increases in vaccination coverage which have prevented an estimated 314,793 (95% CI: 94,566-1,049,645) cases and 114,946 (95% CI: 11,421-431,224) deaths over the past decade. India had the largest estimated JE burden in 2019, followed by Bangladesh and China. From 2010-2019, we estimate that vaccination had the largest absolute impact in China, with 204,734 (95% CI: 74,419-664,871) cases and 74,893 (95% CI: 8,989-286,239) deaths prevented, while Taiwan (91.2%) and Malaysia (80.1%) had the largest percent reductions in JE burden due to vaccination. Our estimates of the size of at-risk populations and current JE incidence highlight countries where increasing vaccination coverage could have the largest impact on reducing their JE burden.

Technological developments in recent decades have increased young people's engagement with screen-based technologies (screen time), and a reduction in young people's contact with nature (green time) has been observed concurrently. This combination of high screen time and low green time may affect mental health and well-being. The aim of this systematic scoping review was to collate evidence assessing associations between screen time, green time, and psychological outcomes (including mental health, cognitive functioning, and academic achievement) for young children (<5 years), schoolchildren (5-11 years), early adolescents (12-14 years), and older adolescents (15-18 years). Original quantitative studies were identified in four databases (PubMed, PsycInfo, Scopus, Embase), resulting in 186 eligible studies. A third of included studies were undertaken in Europe and almost as many in the United States. The majority of studies were cross-sectional (62%). In general, high levels of screen time appeared to be associated with unfavourable psychological outcomes while green time appeared to be associated with favourable psychological outcomes. The ways screen time and green time were conceptualised and measured were highly heterogeneous, limiting the ability to synthesise the literature. The preponderance of cross-sectional studies with broadly similar findings, despite heterogeneous exposure measures, suggested results were not artefacts. However, additional high-quality longitudinal studies and randomised controlled trials are needed to make a compelling case for causal relationships. Different developmental stages appeared to shape which exposures and outcomes were salient. Young people from low socioeconomic backgrounds may be disproportionately affected by high screen time and low green time. Future research should distinguish between passive and interactive screen activities, and incidental versus purposive exposure to nature. Few studies considered screen time and green time together, and possible reciprocal psychological effects. However, there is preliminary evidence that green time could buffer consequences of high screen time, therefore nature may be an under-utilised public health resource for youth psychological well-being in a high-tech era.

Understanding and predicting extreme events and their anomalous statistics in complex nonlinear systems are a grand challenge in climate, material, and neuroscience as well as for engineering design. Recent laboratory experiments in weakly turbulent shallow water reveal a remarkable transition from Gaussian to anomalous behavior as surface waves cross an abrupt depth change (ADC). Downstream of the ADC, probability density functions of surface displacement exhibit strong positive skewness accompanied by an elevated level of extreme events. Here, we develop a statistical dynamical model to explain and quantitatively predict the above anomalous statistical behavior as experimental control parameters are varied. The first step is to use incoming and outgoing truncated Korteweg–de Vries (TKdV) equations matched in time at the ADC. The TKdV equation is a Hamiltonian system, which induces incoming and outgoing statistical Gibbs invariant measures. The statistical matching of the known nearly Gaussian incoming Gibbs state at the ADC completely determines the predicted anomalous outgoing Gibbs state, which can be calculated by a simple sampling algorithm verified by direct numerical simulations, and successfully captures key features of the experiment. There is even an analytic formula for the anomalous outgoing skewness. The strategy here should be useful for predicting extreme anomalous statistical behavior in other dispersive media.

While Web-based interventions have been shown to assist a wide range of patients successfully in managing their illness, few studies have examined the relative contribution of different Web-based components to improve outcomes. Further efficacy trials are needed to test the effects of Web support when offered as a part of routine care. Our aim was to compare in regular care the effects of (1) an Internet-based patient provider communication service (IPPC), (2) WebChoice, a Web-based illness management system for breast cancer patients (IPPC included), and (3) usual care on symptom distress, anxiety, depression, (primary outcomes), and self-efficacy (secondary outcome). This study reports preliminary findings from 6 months' follow-up data in a 12-month trial. We recruited 167 patients recently diagnosed with breast cancer and undergoing treatment from three Norwegian hospitals. The nurse-administered IPPC allowed patients to send secure e-messages to and receive e-messages from health care personnel at the hospital where they were treated. In addition to the IPPC, WebChoice contains components for symptom monitoring, tailored information and self-management support, a diary, and communication with other patients. A total of 20 care providers (11 nurses, 6 physicians, and 3 social workers) were trained to answer questions from patients. Outcomes were measured with questionnaires at study entry and at study months 2, 4, and 6. Linear mixed models for repeated measures were fitted to compare effects on outcomes over time. Patients were randomly assigned to the WebChoice group (n=64), the IPPC group (n=45), or the usual care group (n=58). Response rates to questionnaires were 73.7% (123/167) at 2 months, 65.9 (110/167) at 4 months, and 62.3% (104/167) at 6 months. Attrition was similar in all study groups. Among those with access to WebChoice, 64% (41/64) logged on more than once and 39% (25/64) sent e-messages to care providers. In the IPPC group, 40% (18/45) sent e-messages. Linear mixed models analyses revealed that the WebChoice group reported significantly lower symptom distress (mean difference 0.16, 95% CI 0.06-0.25, P=.001), anxiety (mean difference 0.79, 95% CI 0.09-1.49, P=.03), and depression (mean difference 0.79, 95% CI 0.09-1.49, P=.03) compared with the usual care group. The IPPC group reported significant lower depression scores compared with the usual care group (mean difference 0.69, 95% CI 0.05-1.32, P=.03), but no differences were observed for symptom distress or anxiety. No significant differences in self-efficacy were found among the study groups. In spite of practice variations and moderate use of the interventions, our results suggest that offering Web support as part of regular care can be a powerful tool to help patients manage their illness. Our finding that a nurse-administered IPPC alone can significantly reduce depression is particularly promising. However, the multicomponent intervention WebChoice had additional positive effects. Clinicaltrials.gov:NCT00971009; http://clinicaltrials.gov/show/NCT00971009 (Archived by WebCite at http://www.webcitation.org/6USKezP0Y).

Both the growth in hedge funds and the changing nature of the merger and acquisition business have affected the process of risk arbitrage and the techniques used to participate in the business. 'Risk Arbitrage' goes to great lengths to reflect these changes with case studies on new mergers, and more.

A key step in the analysis of circadian data is to make an accurate estimate of the underlying period. There are many different techniques and algorithms for determining period, all with different assumptions and with differing levels of complexity. Choosing which algorithm, which implementation and which measures of accuracy to use can offer many pitfalls, especially for the non-expert. We have developed the BioDare system, an online service allowing data-sharing (including public dissemination), data-processing and analysis. Circadian experiments are the main focus of BioDare hence performing period analysis is a major feature of the system. Six methods have been incorporated into BioDare: Enright and Lomb-Scargle periodograms, FFT-NLLS, mFourfit, MESA and Spectrum Resampling. Here we review those six techniques, explain the principles behind each algorithm and evaluate their performance. In order to quantify the methods' accuracy, we examine the algorithms against artificial mathematical test signals and model-generated mRNA data. Our re-implementation of each method in Java allows meaningful comparisons of the computational complexity and computing time associated with each algorithm. Finally, we provide guidelines on which algorithms are most appropriate for which data types, and recommendations on experimental design to extract optimal data for analysis.