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.

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.

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.

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.

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.

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.

Maternal proximity to Central Appalachia surface mining and birth outcomes.

Unlabelled: 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.

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.

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.

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.

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.

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.

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.

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.