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Stress forecasting is the cognitive process of anticipating and preparing to respond to future stress experiences based on one's own perceived future stress. Though it may seem intuitive that stress forecasting predicts stress, competing theories exist that indicate the relationship is not so inherent. In this paper we conducted two studies which both examine the relationships between stress forecasting and day stress, examining two different stress domains (appraisal and coping). Participants in Study 1 (n = 143 working adults) and Study 2 (n = 60 undergraduate students) completed 4 and 14 days, respectively, of ecological momentary assessment reports of stress appraisal forecasting, stress coping forecasting, stress appraisal reports, and stress coping reports, for a combined total of 5280 completed assessments. Exploratory analysis in Study 2 examined the importance of morning forecast discrepancy in relation to evening recalls of day stress. Results indicate that within domain (e.g., stress appraisal to appraisal reports), or domain specific, stress forecasting is predictive of day stress, and that across domains (e.g., stress appraisal to coping reports), or domain crossover, was consistent between stress appraisal forecasting and stress coping reports. Results suggest that magnitude of bias and day outlook (i.e., being either optimistic or pessimistic) about one's coping ability matters for day stress outcomes. Findings have implications for developing challenge‐based thinking interventions and further understanding cognitive processes for building stress management strategies. [ABSTRACT FROM AUTHOR]

Background: MiRNA is a class of non-coding single-stranded RNA molecules with a length of approximately 22 nucleotides encoded by endogenous genes, which can regulate the expression of other genes. Therefore, it is very important to predict the associations between miRNA and disease. Predecessors developed a new prediction method of drug-disease association, and it achieved good results.Methods: In this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. First, we integrate three associations into a heterogeneous network, such as the known miRNA-disease association, miRNA-miRNA similarities and disease-disease similarities, next we apply graph convolution network to learn the embedding of miRNA and disease. We use an attention mechanism to combine embedding from multiple convolution layers. Unobserved miRNA-disease associations are scored based on integrated embedding.Results: After fivefold cross-validations, the value of AUC is reached 0.9091, which is higher than other prediction methods and baseline methods.Conclusions: In this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. LAGCN has achieved good performance in predicting miRNA-disease associations, and it is superior to other association prediction methods and baseline methods. [ABSTRACT FROM AUTHOR]

This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative. [ABSTRACT FROM AUTHOR]

Objectives: Expound upon priorities for basic/translational science identified in a recent paper by a group of experts assigned by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine.Data Sources: Original paper, search of the literature.Study Selection: By several members of the original task force with specific expertise in basic/translational science.Data Extraction: None.Data Synthesis: None.Conclusions: In the first of a series of follow-up reports to the original paper, several members of the original task force with specific expertise provided a more in-depth analysis of the five identified priorities directly related to basic/translational science. This analysis expounds on what is known about the question and what was identified as priorities for ongoing research. It is hoped that this analysis will aid the development of future research initiatives. [ABSTRACT FROM AUTHOR]

Purpose: Physical education policies provide guidance and accountability to develop quality programs that increase physical literacy. The purpose of this study was to conduct a systematic review of physical education policy research to explore its effects on the school environment and programming as an essential component of physical education. Methods: Using the PRISMA guidelines of identify, screen, determine eligibility, and include, studies were extracted from four different databases, using search terms related to the essential physical education component of policy and environment. Of the 225 publications identified, 42 studies met the inclusion criteria for this investigation. Each paper was coded, and emergent themes were identified. Results: The policy research was predominantly descriptive and focused on: (a) minutes in physical education (83%), (b) moderate to vigorous physical activity (MVPA; 31%), (c) certified/qualified teachers (24%), (d) exemptions (17%), and (e) student-teacher ratio (12%). Emergent themes of adherence, policy strength, and implementation accountability were identified as influential physical education policy aspects. Conclusions: Policy research over the last 20 years was focused on the regulatory mandate of time. Policy research did not directly address disciplinary process variables of learning activities or outcomes of physical education. The effects of policy exemptions and class size were underrepresented. Themes may explain the lack of reporting student performance as the primary outcome. Further research is needed to examine the downstream effects of physical education policy and determine whether well-written policies increase the number of physically literate individuals. [ABSTRACT FROM AUTHOR]

Purpose When wearable and implantable devices first arose in the 1970s, they were rigid and clashed dramatically with our soft, pliable skin and organs. The past two decades have witnessed a major upheaval in these devices. Traditional electronics are six orders of magnitude stiffer than soft tissue. As a result, when rigid electronics are integrated with the human body, severe challenges in both mechanical and geometrical form mismatch occur. This mismatch creates an uneven contact at the interface of soft-tissue, leading to noisy and unreliable data gathering of the body’s vital signs. This paper aims to predict the role that discreet, seamless medical devices will play in personalized health care by discussing novel solutions for alleviating this interface mismatch and exploring the challenges in developing and commercializing such devices.Design methodology/approach Since the form factors of biology cannot be changed to match those of rigid devices, conformable devices that mimic the shape and mechanical properties of soft body tissue must be designed and fabricated. These conformable devices play the role of imperceptible medical interfaces. Such interfaces can help scientists and medical practitioners to gain further insights into the body by providing an accurate and reliable instrument that can conform closely to the target areas of interest for continuous, long-term monitoring of the human body, while improving user experience.Findings The authors have highlighted current attempts of mechanically adaptive devices for health care, and the authors forecast key aspects for the future of these conformable biomedical devices and the ways in which these devices will revolutionize how health care is administered or obtained.Originality/value The authors conclude this paper with the perspective on the challenges of implementing this technology for practical use, including device packaging, environmental life cycle, data privacy, industry partnership and collaboration. [ABSTRACT FROM AUTHOR]

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]

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi-outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real-world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction. [ABSTRACT FROM AUTHOR]

Background: Even though investigating predictors of intervention success (e.g Cognitive Training, CT) is gaining more and more interest in the light of an individualized medicine, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of CT and similar non-pharmacological interventions.Methods: We simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) × 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability.Results: Our results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges.Conclusion: Employing simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on CT and external predictors of CT success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain. [ABSTRACT FROM AUTHOR]

Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential outcomes approaches, often include processing steps before the final estimation step. The purposes of this paper are: (i) to overview the recent emergence of prediction underpinning steps in contemporary causal inference methods as a useful perspective on contemporary causal inference methods, and (ii) explore the role of machine learning (as one approach to 'best prediction') in causal inference. Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum likelihood estimation (TMLE). Machine learning has been used more for propensity scores and TMLE, and there is potential for increased use in G computation and estimation of IPTWs. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to describe and test novel computational driver models, predicting drivers' brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC).Background: Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving.Method: Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers' arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study.Results: The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study.Conclusion: Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data.Application: Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving. [ABSTRACT FROM AUTHOR]

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011–2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts. [ABSTRACT FROM PUBLISHER]

Background: Electronic medical records contain a variety of valuable medical information for patients. So, when we are able to recognize and extract risk factors for disease from EMRs of patients with cardiovascular disease (CVD), and are able to use them to predict CVD, we have the ability to automatically process clinical texts, resulting in an improved accuracy of supporting doctors for the clinical diagnosis of CVD. In the case where CVD is becoming more worldwide, predictive CVD based on EMRs has been studied by many researchers to address this important aspect of improving diagnostic efficiency.Methods: This paper proposes an Enhanced Character-level Deep Convolutional Neural Networks (EnDCNN) model for cardiovascular disease prediction.Results: On the manually annotated Chinese EMRs corpus, our risk factor identification extraction model achieved 0.9073 of F-score, our prediction model achieved 0.9516 of F-score, and the prediction result is better than the most previous methods.Conclusions: The character-level model based on text region embedding can well map risk factors and their labels as a unit into a vector, and downsampling plays a crucial role in improving the training efficiency of deep CNN. What's more, the shortcut connections with pre-activation used in our model architecture implements dimension-matching free in training. [ABSTRACT FROM AUTHOR]

Background: The accelerated growth of elderly population is creating a heavy burden to the healthcare system in many developed countries and regions. Electrocardiogram (ECG) analysis has been recognized as effective approach to cardiovascular disease diagnosis and widely utilized for monitoring personalized health conditions.Method: In this study, we present a novel approach to forecasting one-day-forward wellness conditions for community-dwelling elderly by analyzing single lead short ECG signals acquired from a station-based monitoring device. More specifically, exponentially weighted moving-average (EWMA) method is employed to eliminate the high-frequency noise from original signals at first. Then, Fisher-Yates normalization approach is used to adjust the self-evaluated wellness score distribution since the scores among different individuals are skewed. Finally, both deep learning-based and traditional machine learning-based methods are utilized for building wellness forecasting models.Results: The experiment results show that the deep learning-based methods achieve the best fitted forecasting performance, where the forecasting accuracy and F value are 93.21% and 91.98% respectively. The deep learning-based methods, with the merit of non-hand-crafted engineering, have superior wellness forecasting performance towards the competitive traditional machine learning-based methods.Conclusion: The developed approach in this paper is effective in wellness forecasting for community-dwelling elderly, which can provide insights in terms of implementing a cost-effective approach to informing healthcare provider about health conditions of elderly in advance and taking timely interventions to reduce the risk of malignant events. [ABSTRACT FROM AUTHOR]

After the diagnosis of a disease, one major objective is to predict cumulative probabilities of events such as clinical relapse or death from the individual information collected up to a prediction time, usually including biomarker repeated measurements. Several competing estimators have been proposed, mainly from two approaches: joint modelling and landmarking. These approaches differ by the information used, the model assumptions and the complexity of the computational procedures. This paper aims to review the two approaches, precisely define the derived estimators of dynamic predictions and compare their performances notably in case of misspecification. The ultimate goal is to provide key elements for the use of individual dynamic predictions in clinical practice. Prediction of two competing causes of prostate cancer progression from the history of prostate-specific antigen is used as a motivated example. We formally define the quantity to estimate and its estimators, propose techniques to assess the uncertainty around predictions and validate them. We then conduct an in-depth simulation study compare the estimators in terms of prediction error, discriminatory power, efficiency and robustness to model assumptions. We show that prediction tools should be handled with care, in particular by properly specifying models and estimators. [ABSTRACT FROM AUTHOR]

Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors. Simulation studies show that the proposed method performs well for practical settings and is more robust for model misspecification than the likelihood-based approach. A case study is also provided for illustration. [ABSTRACT FROM AUTHOR]

Accompanying the ageing of contemporary ageing societies is an increase in age associated morbidity, with dementia having an important impact. Mental frailty in later life is a source of fear for many and a major policy concern to all those concerned with health and welfare services. This introduction to the special issue on 'Ageing, dementia and the social mind' situates the selected papers within the context of debates about dementia and its social relations. In particular it draws attention to the importance of the social imaginary of the fourth age and what this means for the issue of personhood, care, social representations of dementia and its social contextualisation. The papers illuminating these themes draw on a variety of disciplines and approaches; from the social sciences to the humanities and from the theoretical to the empirical in order to help orientate future researchers to the complexities of dementia and the social and cultural matrix in which it exists. This paper provides an introduction to the potential for a more extended sociology of dementia; one which could combine the insights from medical sociology with the concerns of social gerontology. [ABSTRACT FROM AUTHOR]

In the last 50 years Thailand has achieved advanced demographic and health transitions. Many infectious diseases are controlled and infant and maternal mortality rates are among the lowest in the region. Within such a low mortality setting, however, substantial variations in health persist, with education being a major driver. This paper assesses the ongoing mortality transition in Thailand, examining relationships between risk factors and mortality outcomes among a large nationwide cohort of distance-learning Open University students, as well as examining the differential mortality benefit enjoyed by this educated group when compared with the general Thai population. The cohort comprised 87,151 participants, who in 2005 completed a questionnaire collecting detailed information on socio-demographics, health behaviours and health outcomes. Participants were aged 15-87 at baseline and lived in all regions of the country. Cohort members' citizen identification numbers were matched with official death registration records to identify mortality among participants. A total of 1401 deaths were identified up to November 2016. Results show cohort study participants experiencing mortality at approximately one-third of the rate of the general population in the same age and sex groups. The gap between the mortality rates in the two groups widened with increased age. Differential risk factor prevalence among the two populations, particularly lower overweight and obesity prevalence and lower cigarette smoking rates in the cohort, can explain some of this variation. The largely unmeasurable effect of aspiration for life improvement through distance education while embedded in their communities is shown by this study to have a powerful effect on mortality risk. With overall education levels, including higher education, rising in Thailand this growing group of educated aspirational Thais may represent future trends in Thai mortality. Identifying the drivers and characteristics of this mortality variation can help inform policies to provide health services and to help reduce mortality in the whole population. [ABSTRACT FROM AUTHOR]

In order to address the flight delays and risks associated with the forecasted increase in air traffic, there is a need to increase the capacity of air traffic management systems. This should be based on objective measurements of traffic situation complexity. In current air traffic complexity research, no simple means is available to integrate airspace and traffic flow characteristics. In this paper, we propose a new approach for the measurement of air traffic situation complexity. This approach considers the effects of both airspace and traffic flow and objectively quantifies air traffic situation complexity. Considering the aircraft, waypoints, and airways as nodes, and the complexity relationships among these nodes as edges, a dynamic weighted network is constructed. Air traffic situation complexity is defined as the sum of the weights of all edges in the network, and the relationships of complexity with some commonly used indices are statistically analyzed. The results indicate that the new complexity index is more accurate than traffic count and reflects the number of trajectory changes as well as the high-risk situations. Additionally, analysis of potential applications reveals that this new index contributes to achieving complexity-based management, which represents an efficient method for increasing airspace system capacity. [ABSTRACT FROM AUTHOR]

Background: In the past 20 years, the trends of ischemic heart disease (IHD) mortality in China have been described in divergent claims. This research analyzes mortality trends for IHD by using the data from 102 continuous Disease Surveillance Points (DSP) from 1991 to 2009.Method: The 102 continuous DSP covered 7.3 million people during the period 1991-2000, and then were expanded to a population of 52 million in the same areas for 2004-2009. The data were adjusted by using garbage code redistribution and underreporting rate, mapped from international classification of diseases ICD-9 to ICD-10. The mortality rates for IHD were further adjusted by the crude death proportion multiplied by the total number of deaths in the mortality envelope, which was calculated by using logrt = a + bt. Age-standard death rates (ASDRs) were computed using China's 2010 census population structure. Trend in IHD was calculated from ASDRs by using a joinpoint regression model.Results: The IHD ASDRs increased in total in regions with an average annual percentage change (AAPC) 4.96%, especially for the Southwest (AAPC = 7.97%) and Northeast areas (AAPC = 7.10%), and for male and female subjects (with 5% AAPC) as well. In rural areas, the year 2000 was a cut-off point for mortality rate with annual percentage change increasing from 3.52% in 1991-2000 to 9.02% in 2000-2009, which was much higher than in urban areas (AAPC = 1.05%). And the proportion of deaths increased in older adults, and more male deaths occurred before age 60 compared to female deaths.Conclusion: By observing a wide range of areas across China from 1991 to 2009, this paper concludes that the ASDR trend for IHD increased. These trends reflect changes in the Chinese standard of living and lifestyle with diets higher in fat, higher blood lipids and increased body weight. [ABSTRACT FROM AUTHOR]

Home hemodialysis (HD) was first introduced in the 1960s with a rapid increase in its use due to inability of dialysis units to accommodate patient demand. A sharp decline was subsequently seen with expanding outpatient dialysis facilities and changes in reimbursement policies. In the last decade, with emerging reports of benefits with home HD and more user-friendly equipment, there has been resurgence in home HD. However, home HD remains underutilized with considerable variations between and within countries. This paper will review the history of home HD, elaborate on its established benefits, identify some of the barriers in uptake of this modality and expand on potential strategies to overcome these barriers. [ABSTRACT FROM AUTHOR]

To forecast future trends in diabetes prevalence, morbidity, and costs in the United States, the Institute for Alternative Futures has updated its diabetes forecasting model and extended its projections to 2030 for the nation, all states, and several metropolitan areas. This paper describes the methodology and data sources for these diabetes forecasts and discusses key implications. In short, diabetes will remain a major health crisis in America, in spite of medical advances and prevention efforts. The prevalence of diabetes (type 2 diabetes and type 1 diabetes) will increase by 54% to more than 54.9 million Americans between 2015 and 2030; annual deaths attributed to diabetes will climb by 38% to 385,800; and total annual medical and societal costs related to diabetes will increase 53% to more than $622 billion by 2030. Improvements in management reducing the annual incidence of morbidities and premature deaths related to diabetes over this time period will result in diabetes patients living longer, but requiring many years of comprehensive management of multiple chronic diseases, resulting in dramatically increased costs. Aggressive population health measures, including increased availability of diabetes prevention programs, could help millions of adults prevent or delay the progression to type 2 diabetes, thereby helping turn around these dire projections. [ABSTRACT FROM AUTHOR]

Background: Norway has achieved a noteworthy reduction in smoking prevalence over the past forty years. In 2015, 13% of Norwegians aged 13-74 smoked daily and a further 9% smoked occasionally. One of the objectives of the Norwegian 2013-16 national strategy for tobacco control is to achieve a reduction in the daily smoking prevalence to<10% by 2016. This paper aims to estimate how long it will take for Norway to achieve the 10% smoking prevalence. Methods: A dynamic forecasting model using smoking prevalence data from national survey data on the prevalence of current, former and never smokers in the Norwegian population from 1985 to 2012 was used to estimate future smoking prevalence in the Norwegian population based on a continuation of current patterns in smoking cessation and initiation rates. Results: The result suggests that Norway's smoking prevalence among men and women will continue to decline if current trends of smoking initiation and cessation continue. Our model predicts, based on figures for 1985-2012, that the prevalence of current daily and occasional smoking among men and women will not fall below the 10% mark until 2029 for men and 2026 for women, if current trends continue. Conclusion: Smoking is likely to remain an important public health issue in Norway for many years. New strategies are needed to accelerate the decline in smoking in Norway. [ABSTRACT FROM AUTHOR]

This editorial is a commentary on the review paper by Ramirez Varela et al. entitled "Mapping the historical development of physical activity and health research: a structured literature review and citation network analysis." This editorial highlights the significance and implications of this review, with a particular focus on future research and policy directions. [ABSTRACT FROM AUTHOR]

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]

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]