Rice yield and quality in response to daytime and nighttime temperature increase - A meta-analysis perspective.

Increased heat stress during cropping season poses significant challenges to rice production, yet the complex stoichiometry between rice grain yield, quality and high daytime, nighttime temperature remains with gaps in current knowledge. We conducted a meta-analysis using a combined dataset of 1105 experiments for daytime temperature and 841 experiments for nighttime temperature from published literature to investigate the effects of high daytime temperature (HDT) and high nighttime temperatures (HNT) on rice yield and its various components (such as panicle number, spikelet number per panicle, seed set rate, grain weight) and grain quality traits (such as milling yield, chalkiness, amylose and protein contents). We established relationships between rice yield, its components, grain quality and the HDT/HNT, and studied phenotypic plasticity of the traits in response to HDT and HNT. Results showed that HNT had a more detrimental impact on rice yield and quality when compared with the HDT. The optimum daytime and nighttime temperatures for best rice yield were approximately 28 °C and 22 °C, respectively. Grain yield showed a decline by 7% and 6% for each 1 °C increase in HNT and HDT, respectively, when exceeded the optimum temperatures. Seed set rate (i.e., percent fertility) was the most sensitive trait to HDT and HNT and accounted for most of the yield losses. Both the HDT and HNT affected grain quality by increasing chalkiness and decreasing head rice percentage, which may affect marketability of the rice produced. Additionally, HNT was found to significantly impact nutritional quality (e.g., protein content) of rice grains. Our findings fill current knowledge gaps on estimations of rice yield losses and possible economic consequences under high temperatures and suggest that impacts on rice quality should also be considered for selection and breeding of high-temperature tolerant rice varieties in response to HDT and HNT.

Daily exposure to virtual nature reduces symptoms of anxiety in college students.

Exposure to natural environments offers an array of mental health benefits. Virtual reality provides simulated experiences of being in nature when outdoor access is limited. Previous studies on virtual nature have focused mainly on single "doses" of virtual nature. The effects of repeated exposure remain poorly understood. Motivated by this gap, we studied the influence of a daily virtual nature intervention on symptoms of anxiety, depression, and an underlying cause of poor mental health: rumination. Forty college students (58% non-Hispanic White, median age = 19) were recruited from two U.S. universities and randomly assigned to the intervention or control group. Over several weeks, anxious arousal (panic) and anxious apprehension (worry) decreased with virtual nature exposure. Participants identifying as women, past VR users, experienced with the outdoors, and engaged with the beauty in nature benefited particularly strongly from virtual nature. Virtual nature did not help symptoms of anhedonic depression or rumination. Further research is necessary to distinguish when and for whom virtual nature interventions impact mental health outcomes.

Efficacy of virtual reality assisted guided imagery (VRAGI) in a home setting for pain management in patients with advanced cancer: protocol for a randomised controlled trial.

INTRODUCTION: Patients with advanced cancer often experience high levels of debilitating pain and pain-related psychological distress. Although there is increasing evidence that non-pharmacological interventions are needed to manage their pain, pharmacologic modalities remain the preferred treatment . Guided imagery is a form of focused relaxation that helps create harmony between the mind and body and has been shown to significantly improve cancer pain. Our study presents Virtual Reality Assisted Guided Imagery (VRAGI) as a complementary treatment modality to manage chronic pain in patients with cancer. We will conduct a randomised controlled trial to test its impact on patients with advanced cancer in a home setting. METHODS AND ANALYSIS: We will recruit 80 patients from Prisma Health, a tertiary-level healthcare centre based in Greenville, South Carolina, USA. The prospective 2×2 randomised controlled trial will randomise participants into four groups: (1) VRAGI, (2) laptop-assisted guided imagery, (3) VR (no guided imagery) and (4) laptop (no guided imagery). Patients allocated to VR groups will be trained to use a head-mounted display that immerses them in 3D audio-video content. The non-VR group will use a laptop displaying 2D video content. We will collect measures before and during the 3-week intervention as well as 3 weeks after the intervention ends. Measures will include patient-reported outcomes of pain, anxiety, depression and fatigue in addition to opioid use. The primary objective of the current study is to assess the efficacy of VRAGI on pain in the home setting. The secondary objective is to assess the efficacy of VRAGI on opioid use, anxiety, depression and fatigue. ETHICS AND DISSEMINATION: This study was approved by the Prisma Health Institutional Review Board (#Pro00114598) in November 2021. All participants enrolled in the study will provide written informed consent. Dissemination will be through peer-reviewed publications and conference presentations. TRIAL REGISTRATION NUMBER: NCT05348174, clinicaltrials.gov.

Associations Between Surface Mining Airsheds and Birth Outcomes in Central Appalachia at Multiple Spatial Scales.

A considerable body of research exists outlining ecological impacts of surface coal mining, but less work has explicitly focused on human health, and few studies have examined potential links between health and surface coal mining at fine spatial scales. In particular, relationships between individual birth outcomes and exposure to air contaminants from coal mining activities has received little attention. Central Appalachia (portions of Virginia, West Virginia, Kentucky, and Tennessee, USA), our study area, has a history of resource extraction, and epidemiologic research notes that the region experiences a greater level of adverse health outcomes compared to the rest of the country that are not fully explained by socioeconomic and behavioral factors. The purpose of this study is to examine associations between surface mining and birth outcomes at four spatial scales: individual, Census tract, county, and across county-sized grid cells. Notably, this study is among the first to examine these associations at the individual scale, providing a more direct measure of exposure and outcome. Airsheds were constructed for surface mines using an atmospheric trajectory model. We then implemented linear (birthweight) and logistic (preterm birth [PTB]) regression models to examine associations between airsheds and birth outcomes, which were geocoded to home address for individual analyses and then aggregated for areal unit analyses, while controlling for a number of demographic variables. This study found that surface mining airsheds are significantly associated with PTB and decreased birthweight at all four spatial scales, suggesting that surface coal mining activities impact birth outcomes via airborne contaminants.

Effects of an arm-support exoskeleton on perceived work intensity and musculoskeletal discomfort: An 18-month field study in automotive assembly.

BACKGROUND: Exoskeleton (EXO) technologies are a promising ergonomic intervention to reduce the risk of work-related musculoskeletal disorders, with efficacy supported by laboratory- and field-based studies. However, there is a lack of field-based evidence on long-term effects of EXO use on physical demands. METHODS: A longitudinal, controlled research design was used to examine the effects of arm-support exoskeleton (ASE) use on perceived physical demands during overhead work at nine automotive manufacturing facilities. Data were collected at five milestones (baseline and at 1, 6, 12, and 18 months) using questionnaires. Linear mixed models were used to understand the effects of ASE use on perceived work intensity and musculoskeletal discomfort (MSD). Analyses were based on a total of 41 participants in the EXO group and 83 in a control group. RESULTS: Across facilities, perceived work intensity and MSD scores did not differ significantly between the EXO and control groups. In some facilities, however, neck and shoulder MSD scores in the EXO group decreased over time. Wrist MSD scores in the EXO group in some facilities remained unchanged, while those scores increased in the control group over time. Upper arm and low back MSD scores were comparable between the experimental groups. CONCLUSION: Longitudinal effects of ASE use on perceived physical demands were not found, though some suggestive results were evident. This lack of consistent findings is discussed, particularly supporting the need for systematic and evidence-based ASE implementation approaches in the field that can guide the optimal selection of a job for ASE use.

Maternal proximity to Central Appalachia surface mining and birth outcomes.

Maternal residency in Central Appalachia counties with coal production has been previously associated with increased rates of low birth weight (LBW). To refine the relationship between surface mining and birth outcomes, this study employs finer spatiotemporal estimates of exposure. METHODS: We developed characterizations of annual surface mining boundaries in Central Appalachia between 1986 and 2015 using Landsat data. Maternal address on birth records was geocoded and assigned amount of surface mining within a 5 km radius of residence (street-level). Births were also assigned the amount of surface mining within residential ZIP code tabulation area (ZCTA). Associations between exposure to active mining during gestation year and birth weight, LBW, preterm birth (PTB), and term low birth weight (tLBW) were determined, adjusting for outcome rates before active mining and available covariates. RESULTS: The percent of land actively mined within a 5 km buffer of residence (or ZCTA) was negatively associated with birth weight (5 km: ß = -14.07 g; 95% confidence interval [CI] = -19.35, -8.79, P = 1.79 × 10-7; ZCTA: ß = -9.93 g; 95% CI = -12.54, -7.33, P = 7.94 × 10-14). We also found positive associations between PTB and active mining within 5 km (odds ratio [OR] = 1.06; 95% CI = 1.03, 1.09, P = 1.43 × 10-4) and within ZCTA (OR = 1.04; 95% CI = 1.03, 1.06, P = 9.21 × 10-8). Positive relationships were also found between amount of active mining within 5 km or ZIP code of residence and LBW and tLBW outcomes. CONCLUSIONS: Maternal residency near active surface mining during gestation may increase risk of PTB and LBW.

Bayesian auxiliary variable model for birth records data with qualitative and quantitative responses.

Many applications involve data with qualitative and quantitative responses. When there is an association between the two responses, a joint model will produce improved results than fitting them separately. In this paper, a Bayesian method is proposed to jointly model such data. The joint model links the qualitative and quantitative responses and can assess their dependency strength via a latent variable. The posterior distributions of parameters are obtained through an efficient MCMC sampling algorithm. The simulation is conducted to show that the proposed method improves the prediction capacity for both responses. Further, the proposed joint model is applied to the birth records data acquired by the Virginia Department of Health for studying the mutual dependence between preterm birth of infants and their birth weights.

Multi-level modeling with nonlinear movement metrics to classify self-injurious behaviors in autism spectrum disorder.

Self-injurious behavior (SIB) is among the most dangerous concerns in autism spectrum disorder (ASD), often requiring detailed and tedious management methods. Sensor-based behavioral monitoring could address the limitations of these methods, though the complex problem of classifying variable behavior should be addressed first. We aimed to address this need by developing a group-level model accounting for individual variability and potential nonlinear trends in SIB, as a secondary analysis of existing data. Ten participants with ASD and SIB engaged in free play while wearing accelerometers. Movement data were collected from > 200 episodes and 18 different types of SIB. Frequency domain and linear movement variability measures of acceleration signals were extracted to capture differences in behaviors, and metrics of nonlinear movement variability were used to quantify the complexity of SIB. The multi-level logistic regression model, comprising of 12 principal components, explained > 65% of the variance, and classified SIB with > 75% accuracy. Our findings imply that frequency-domain and movement variability metrics can effectively predict SIB. Our modeling approach yielded superior accuracy than commonly used classifiers (~ 75 vs. ~ 64% accuracy) and had superior performance compared to prior reports (~ 75 vs. ~ 69% accuracy) This work provides an approach to generating an accurate and interpretable group-level model for SIB identification, and further supports the feasibility of developing a real-time SIB monitoring system.

A novel method for measuring asymmetry in kinematic and kinetic variables: The normalized symmetry index.

Gait and movement asymmetries are important variables for assessing locomotor mechanics in humans and other animals and as a predictor of injury risk and success of clinical interventions. The four indices used most often to assess symmetry are not well designed for different variable types, perform poorly when presented with cases of high asymmetry or when variables are of low magnitude, and are easily influenced by small variation in the signal. The purpose of the present study was to test the performance of these indices on previously unpublished data on ACL-R patients and to propose a new index to resolve some of these limitations. The performance of four currently used indices and a new index-the Normalized Symmetry Index (NSI), which is scaled to the range of variables being tested across multiple trials-were compared using force and angular data on participants who had undergone anterior cruciate ligament reconstruction and healthy controls. The NSI performed well compared to all other indices with all variables and had the additional benefit of returning values that range from 0% (full symmetry) to ±100% (full asymmetry). Therefore, the NSI can serve as a universal index for assessing asymmetry in humans, nonhuman animal models, and in a clinical context for assessing risk for injury and clinical outcomes.

Using Gait Variability to Predict Inter-individual Differences in Learning Rate of a Novel Obstacle Course.

This study aimed to determine whether inter-individual differences in learning rate of a novel motor task could be predicted by movement variability exhibited in a related baseline task, and determine which variability measures best discriminate individual differences in learning rate. Thirty-two participants were asked to repeatedly complete an obstacle course until achieving success in a dual-task paradigm. Their baseline gait kinematics during self-paced level walking were used to calculate stride-to-stride variability in stride characteristics, joint angle trajectories, and inter-joint coordination. The gait variability measures were reduced to functional attributes through principal component analysis and used as predictors in multiple linear regression models. The models were used to predict the number of trials needed by each individual to complete the obstacle course successfully. Frontal plane coordination variability of the hip-knee and knee-ankle joint couples in both stance and swing phases of baseline gait were the strongest predictors, and explained 62% of the variance in learning rate. These results show that gait variability measures can be used to predict short-term differences in function between individuals. Future research examining statistical persistence in gait time series that can capture the temporal dimension of gait pattern variability, may further improve learning performance prediction.

Setting development goals using stochastic dynamical system models.

The Millennium Development Goals (MDG) programme was an ambitious attempt to encourage a globalised solution to important but often-overlooked development problems. The programme led to wide-ranging development but it has also been criticised for unrealistic and arbitrary targets. In this paper, we show how country-specific development targets can be set using stochastic, dynamical system models built from historical data. In particular, we show that the MDG target of two-thirds reduction of child mortality from 1990 levels was infeasible for most countries, especially in sub-Saharan Africa. At the same time, the MDG targets were not ambitious enough for fast-developing countries such as Brazil and China. We suggest that model-based setting of country-specific targets is essential for the success of global development programmes such as the Sustainable Development Goals (SDG). This approach should provide clear, quantifiable targets for policymakers.

Understanding Democracy and Development Traps Using a Data-Driven Approach.

Methods from machine learning and data science are becoming increasingly important in the social sciences, providing powerful new ways of identifying statistical relationships in large data sets. However, these relationships do not necessarily offer an understanding of the processes underlying the data. To address this problem, we have developed a method for fitting nonlinear dynamical systems models to data related to social change. Here, we use this method to investigate how countries become trapped at low levels of socioeconomic development. We identify two types of traps. The first is a democracy trap, where countries with low levels of economic growth and/or citizen education fail to develop democracy. The second trap is in terms of cultural values, where countries with low levels of democracy and/or life expectancy fail to develop emancipative values. We show that many key developing countries, including India and Egypt, lie near the border of these development traps, and we investigate the time taken for these nations to transition toward higher democracy and socioeconomic well-being.

The dynamics of democracy, development and cultural values.

Over the past decades many countries have experienced rapid changes in their economies, their democratic institutions and the values of their citizens. Comprehensive data measuring these changes across very different countries has recently become openly available. Between country similarities suggest common underlying dynamics in how countries develop in terms of economy, democracy and cultural values. We apply a novel Bayesian dynamical systems approach to identify the model which best captures the complex, mainly non-linear dynamics that underlie these changes. We show that the level of Human Development Index (HDI) in a country drives first democracy and then higher emancipation of citizens. This change occurs once the countries pass a certain threshold in HDI. The data also suggests that there is a limit to the growth of wealth, set by higher emancipation. Having reached a high level of democracy and emancipation, societies tend towards equilibrium that does not support further economic growth. Our findings give strong empirical evidence against a popular political science theory, known as the Human Development Sequence. Contrary to this theory, we find that implementation of human-rights and democratisation precede increases in emancipative values.

Bayesian dynamical systems modelling in the social sciences.

Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach.