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Introduction: The opioid crisis is a pervasive public health threat in the U.S. Simulation modeling approaches that integrate a systems perspective are used to understand the complexity of this crisis and analyze what policy interventions can best address it. However, limitations in currently available data sources can hamper the quantification of these models.Methods: To understand and discuss data needs and challenges for opioid systems modeling, a meeting of federal partners, modeling teams, and data experts was held at the U.S. Food and Drug Administration in April 2019. This paper synthesizes the meeting discussions and interprets them in the context of ongoing simulation modeling work.Results: The current landscape of national-level quantitative data sources of potential use in opioid systems modeling is identified, and significant issues within data sources are discussed. Major recommendations on how to improve data sources are to: maintain close collaboration among modeling teams, enhance data collection to better fit modeling needs, focus on bridging the most crucial information gaps, engage in direct and regular interaction between modelers and data experts, and gain a clearer definition of policymakers' research questions and policy goals.Conclusions: This article provides an important step in identifying and discussing data challenges in opioid research generally and opioid systems modeling specifically. It also identifies opportunities for systems modelers and government agencies to improve opioid systems models. [ABSTRACT FROM AUTHOR]

Abstract: In Israel, the ‘Civic-National Volunteer Service for Youth At-risk’ was developed to help support their transition to adulthood, while making a contribution to society. The present paper examines life satisfaction and perceptions of the future among youth at-risk completing their volunteer service. Further, the paper explores how socio-demographic background, personal strengths (sense of mastery and self-esteem), and their experiences during service predict these outcomes. The participants were selected from a convenience sample, which included most of the participants in a cohort of national service for a project designed specifically for youth at-risk in a nonprofit organization that operates national service projects. The sample included 161 at-risk youth (93.2% females) who responded to structured questionnaires. Stepwise hierarchical regressions were conducted to examine predictor of each of the dependent variables. The findings revealed that experiences during volunteer service contributed significantly to prediction of their life satisfaction. Positive perceptions of the future, on the other hand, were predicted mainly by their personal assets of having a high school matriculation diploma and high self-esteem. In the discussion section, the paper addresses the theoretical and practical implications of these findings, with a strong emphasis on expanding this important support for normative transition to adulthood of at-risk youth. [Copyright &y& Elsevier]

This paper examines the paper of Haux et al. in this issue of this journal. It gives some background on specifics of the German health care system, which underlie the theses and prognoses proposed by Haux et al. In analogy to a forecast of the future of health informatics, which is now 10 years old, I then suggest that these attempts meet two types of challenges:•that of overestimating the positive effects of recent advances, which later are found not to scale up; and•that of blind spots with respect to unforeseen significant advances.The attempt to find indications of such in the projections of Haux et al. leads, among other, to the conclusion that the projections of direct linkages between patients and care providers may be overoptimistic. As to wheather the deviating opinions matter in the end, it is concluded that the technology advances may require less attention that the restructuring of the health care system required to take advantage of the advances of technology. [Copyright &y& Elsevier]

Adults experiencing homelessness are more likely to have an alcohol use disorder compared to adults in the general population. Although shelter-based treatments are common, completion rates tend to be poor, suggesting a need for more effective approaches that are tailored to this understudied and underserved population. One barrier to developing more effective treatments is the limited knowledge of the triggers of alcohol use among homeless adults. This paper describes the use of ecological momentary assessment (EMA) to identify predictors of "imminent drinking" (i.e., drinking within the next 4 h), among a sample of adults experiencing homelessness and receiving health services at a homeless shelter. A total of 78 mostly male (84.6%) adults experiencing homelessness (mean age = 46.6) who reported hazardous drinking completed up to five EMAs per day over 4 weeks (a total of 4557 completed EMAs). The study used machine learning techniques to create a drinking risk algorithm that predicted 82% of imminent drinking episodes within 4 h of the first drink of the day, and correctly identified 76% of nondrinking episodes. The algorithm included the following 7 predictors of imminent drinking: urge to drink, having alcohol easily available, feeling confident that alcohol would improve mood, feeling depressed, lower commitment to being alcohol free, not interacting with someone drinking alcohol, and being indoors. The research team used the results to develop intervention content (e.g., brief tailored messages) that will be delivered when imminent drinking is detected in an upcoming intervention phase. Specifically, we created three theoretically grounded message tracks focused on urge/craving, social/availability, and negative affect/mood, which are further tailored to a participant's current drinking goal (i.e., stay sober, drink less, no goal) to support positive change. To our knowledge, this is the first study to develop tailored intervention messages based on likelihood of imminent drinking, current drinking triggers, and drinking goals among adults experiencing homelessness. [ABSTRACT FROM AUTHOR]

Branch misprediction limits processor performance significantly, as the pipeline deepens and the instruction issued per cycle increases. Since the introduction of the two-level adaptive branch predictor, branch history has been a major input vector in branch prediction, together with the address of a branch instruction. Until now, the length of branch history has been statically fixed for all branch instructions, and the history length is usually selected in accordance with the size of branch prediction table. However, different branch instructions require different length histories to achieve high prediction accuracies. Therefore, to dynamically adjust to the optimal history length for each branch instruction, this paper presents “dynamic per-branch history length adjustment” policy, by tracking data dependencies of branches and identifying strongly correlated branches in branch history. Our method provides optimal history length for each branch instruction, resulting in substantial improvement in prediction accuracy. The proposed solution does not require any forms of prior-profilings, and it provides up to 6% improvement in prediction accuracy. Further, it even outperforms, in some applications, the prediction accuracy of optimally selected history length by prior-profilings. [Copyright &y& Elsevier]

The distributed model intercomparison project (DMIP) was formulated as a broad comparison of many distributed models amongst themselves and to a lumped model used for operational river forecasting in the US. DMIP was intended to provide guidance on research and implementation directions for the US National Weather Service as well as to address unresolved questions on the variability of rainfall and its effect on basin response. Twelve groups participated, including groups from Canada, China, Denmark, New Zealand, and the US. Numerous data sets including seven years of concurrent radar-rainfall and streamflow data were provided to participants through web access. Detailed modeling instructions specified calibration and verification periods and modeling points. Participating models were run in ‘simulation’ mode without a forecast component. DMIP proved to be a successful endeavour, providing the hydrologic research and forecasting communities with a wealth of results. This paper presents the background and motivations for DMIP and describes the major project elements. [Copyright &y& Elsevier]

Aging is associated with a number of physiologic and functional declines that can contribute to increased disability, frailty, and falls. Contributing factors are the loss of muscle mass and strength as age increases, a phenomenon called sarcopenia. Sarcopenia can result or be exacerbated by certain chronic conditions, and can also increase the burden of chronic disease. Current research has demonstrated that strength-training exercises have the ability to combat weakness and frailty and their debilitating consequences. Done regularly (e.g., 2 to 3 days per week), these exercises build muscle strength and muscle mass and preserve bone density, independence, and vitality with age. In addition, strength training also has the ability to reduce the risk of osteoporosis and the signs and symptoms of numerous chronic diseases such as heart disease, arthritis, and type 2 diabetes, while also improving sleep and reducing depression. This paper reviews the current research on strength training and older adults, evaluating exercise protocols in a variety of populations. It is clear that a variety of strength-training prescriptions from highly controlled laboratory-based to minimally supervised home-based programs have the ability to elicit meaningful health benefits in older adults. The key challenges as this field of exercise science moves forward are to best identify the most appropriate strength-training recommendations for older adults and to greatly increase the access to safe and effective programs in a variety of settings. [ABSTRACT FROM AUTHOR]

Applying information and communication technology (ICT) to a given medical domain is not merely adding a new technique. When applied to a medical domain, ICT has the potential to radically change processes in that domain. In their paper ‘Health care in the information society: a prognosis for the year 2013’ Haux and co-workers provide us with a set of predictions based on progress in three main areas: (1) patient-centred recording and use of medical data for collaborative care; (2) process-integrated decision support; and (3) comprehensive use of patient data for research and health care reporting. We complement their theses by predicting some of the feedback mechanisms that will develop as ICT is shaping health care. Feedback is return to the input of a part of the output of a mechanism; this part of the input constituting information that reports discrepancies between intended and actual operation and leads to a self-correcting action that can be utilised. We discuss feedback in the areas Haux identifies: the patient record, decision support, and the use of data for research and health care reporting. For each of these domains, we will discuss the output that serves as input, the discrepancies discovered, and the self-correction that will or should follow. [Copyright &y& Elsevier]

In this paper we argue that predictive processing (PP) theory cannot account for the phenomenon of affect-biased attention - prioritized attention to stimuli that are affectively salient because of their associations with reward or punishment. Specifically, the PP hypothesis that selective attention can be analyzed in terms of the optimization of precision expectations cannot accommodate affect-biased attention; affectively salient stimuli can capture our attention even when precision expectations are low. We review the prospects of three recent attempts to accommodate affect with tools internal to PP theory: Miller and Clark's (2018) embodied inference; Seth's (2013) interoceptive inference; and Joffily and Coricelli's (2013) rate of change of free energy. In each case we argue that the account does not resolve the challenge from affect-biased attention. For this reason, we conclude that prediction error minimization is not sufficient to explain all mental phenomena, contrary to the claim that the PP framework provides a unified theory of all mental phenomena or the brain's cognitive functioning. Nevertheless, we suggest that empirical investigation of the interaction between affective salience and precision expectations should prove helpful in understanding the limits of PP theory, and may provide new directions for the application of a Bayesian perspective to perception. [ABSTRACT FROM AUTHOR]

Patients with advanced cancer are burdened physically and psychologically, so there is an urgent need to pay more attention to their health-related quality of life (HRQOL). With an expected clinical endpoint prediction, over-treatment can be effectively eliminated by the means of palliative care at the right time. This paper develops a deep learning based approach for cancer clinical endpoint prediction based on patient's electronic health records (EHR). Due to the pervasive existence of categorical information in EHR, it brings unavoidably obstacles to the effective numerical learning algorithms. To address this issue, we propose a novel cross-field categorical attributes embedding (CCAE) model to learn a vectorized representation for cancer patients in attribute-level by orders, in which the strong semantic coupling among categorical variables are well exploited. By transforming the order-dependency modeling into a sequence learning task in an ingenious way, recurrent neural network is adopted to capture the semantic relevance among multi-order representations. Experimental results from the SEER-Medicare EHR dataset have illustrated that the proposed model can achieve competitive prediction performance compared with other baselines. [ABSTRACT FROM AUTHOR]

Forensic anthropology is an increasingly developing discipline born about a century ago in the United States with the objective to contribute the knowledge of bone biology and physical anthropology to the emerging needs of the court of law. The development of research in biological and forensic anthropology has made rapid progress worldwide in the past few years, however, in most countries--with the exception of the United States--forensic anthropology work is still considered within the duties of the forensic pathologist. This paper attempts to summarise the history and development of forensic anthropology in Thailand by providing information on past and current research and practice that can help forensic practitioners to apply existing methods in forensic cases and mass disasters. It is hoped that the lessons learned from the tsunami catastrophe and the emerging need for positive identification in medicolegal settings will lead to rapid advances in education, training and professional engagement of anthropologists from the forensic departments and the law enforcement agencies in Thailand. [ABSTRACT FROM AUTHOR]