Explanatory Power Research Articles

Traditional or alternative wine packaging: a study of consumer choices and perceptions

ABSTRACT Sustainability concerns in the wine sector have resulted in alternative types of wine packaging to traditional glass packaging. This article examines the impact of cultural heritage and environmental awareness on consumer behaviour and purchasing decisions in relation to wine packaging. To investigate this, a wine tasting experiment was carried out on a sample of 211 people in the city of Pécs, in Hungary, to assess consumers’ perceptions, taste perceptions and purchase propensity for 7 types of wine packaging. In order to measure the effect of each type of packaging, the same wine was poured into the same types of packaging. The aim of the experiment was to identify consumers’ attitudes towards traditional packaging and their preferences for alternative options. The results were analysed using factor analysis to identify 2 factor groups for each pack, which were validated using Bartlett's Test and Chi-squared Test. The results show that packaging style and traditionality have a significant impact on consumer preferences, especially for alternatives such as bag-in-box or pouch, which are completely different from the traditional glass bottle. In the case of PET bottles, the packaging itself has a strong explanatory power, which may be explained by its similarity in form to the glass bottle. The novelty of this research lies in the fact that with the help of the we were able to measure the effect of different wine packaging, alongside the role of cultural traditions. Based on the results, it is proposed that wine industry stakeholders should raise consumer awareness of the benefits of environmentally friendly packaging alternatives and take measures to reduce the environmental impact of traditional glass bottles.

Unravelling national governments’ positions on EU rule of law enforcement: structural, semi-structural and ideological factors

ABSTRACT The Council’s enforcement inaction regarding the rule of law crisis has been endlessly criticised by scholars and commentators. However, there is currently no theoretically parsimonious explanation of why some national governments oppose enforcement whilst others are more inclined to support it. We address this question by considering structural (time of accession), semi-structural (contributions to, or receipts from, the EU budget and democratic quality) and circumstantial (partisanship, ideology, EU integration stance) conditions. We use Qualitative Comparative Analysis (QCA) to test the impact of these conditions on governments’ positions from 2016 to 2021. Findings show relatively stable preferences on enforcement within Member States, despite changes in the ideology of the ruling party. Time of accession, having a high quality of democracy and being a net contributor to the budget emerges as the most robust combination of conditions to explain strong pro-enforcement preferences. In contrast, governments of newer Member States that are net recipients of funds tend to be more reluctant to enforce the rule of law. While ideology lacks sufficient parsimonious explanatory power, it is particularly relevant for explaining extreme anti-enforcement reactions.

Factors Associated with the Patient Safety Activities among Home-Visit Caregiver: A Cross-Sectional Study.

Home-visit care should prioritize patient safety and promote positive safety awareness to maintain caregivers' commitment to providing safe care. This descriptive study aims to examine the relationship between home-visit caregivers' knowledge, attitudes, awareness of patient safety, and safety-related activities. The participants were 210 adult home-visit caregivers, aged 20 years or older, with more than six months of experience working at six long-term care centers in D Metropolitan City. Data were collected using a structured self-report questionnaire to measure participants' general characteristics and their patient safety knowledge, attitudes, awareness, and activities and analyzed using SPSS/WIN 28.0. This study demonstrated that patient safety activities were significantly positively correlated with patient safety knowledge (r=.63, p˂.001), patient safety attitude (r=.49, p ˂.001), and patient safety awareness (r=.69, p˂.001). The factors affecting patient safety activities were patient safety awareness and patient safety knowledge, with an explanatory power of 55%. To practice patient safety activities and maintain a desirable attitude, an educational strategy that promotes cognitive change, such as a systematic and continuous practice-oriented campaign, is needed. Furthermore, efforts to improve the working environment to extend the tenure of experienced caregivers are required.

Benefits of longitudinally linked national records of child maltreatment report and foster care.

The Report and Placement Integrated Data System (RAPIDS) integrates two U.S. national data systems-NCANDS' child maltreatment report (CMR) records and AFCARS' foster care (FC) records-into a single longitudinal dataset spanning 2006-2021. This integration enables comprehensive child maltreatment analysis by linking the annual files from these previously separate systems. To explore benefits of RAPIDS data in understanding CMR outcomes. Children aged 0-10years with CMRs in 2018 (N=2,371,119). Using logistic regression, we modeled five outcomes: two current outcomes from 2018 index reports (substantiation and foster care entry) and three future outcomes within two years (re-report, substantiated re-report, and foster are entry). For each outcome, we compared models using only index report data without RAPIDS variables against models incorporating RAPIDS-enabled variables that capture longitudinal patterns across reports, placements, and siblings. RAPIDS data improved model performance across all outcomes, with greater gains for future outcomes. Overall model fit (Tjur's R2) increased for substantiation (11.75%→12.53%), FC entry (4.17%→6.38%), rereport (0.94%→4.99%), substantiated rereport (1.04%→3.31%), and future FC entry (0.85%→2.51%). Predictive performance also improved: at 80% sensitivity, specificity increased for substantiation (54%→56%), FC entry (52%→58%), rereport (27%→36%), substantiated rereport (33%→42%), and future FC entry (37%→49%). Additionally, RAPIDS data enabled analysis of a wider array of predictors and their associations with outcomes, fully utilizing national longitudinal CMR and FC records. RAPIDS data enhance explanatory power and predictive accuracy, enabling nationwide, longitudinal analysis of CMR and FC records and offering valuable insights into risk and protective factors.

Land Use and Its Impact on Phosphorus Adsorption Capacity in a Tropical Dryland Agricultural Frontier: Insights and Predictive Modeling

ABSTRACT Land use significantly impacts the physical-chemical properties of soil, particularly in tropical regions where the maximum phosphorus adsorption capacity (MPAC) plays a crucial role in agricultural sustainability. This study evaluates the influence of land uses (cropland, grassland, and forest) on MPAC in dryland soils of Sergipe, Alagoas, and Bahia (SEALBA) agricultural frontier in Northeast Brazil. Soil samples from nine sites were analyzed for their physical-chemical characteristics (potential of hydrogen (pH), available phosphorus (P), remaining P, soil organic matter (SOM), calcium and magnesium content, exchangeable aluminum, potential acidity and texture) in the 0–20 cm soil depth. MPAC was determined using two isotherm models, and three predictive models were applied. The results revealed that cropland soils exhibit the highest MPAC, contrary to expectations of lower values due to legacy P from P inputs. However, MPAC varied slightly across sites, indicating specific dependencies on soil physical-chemical properties and suggesting that land use may have a more significant influence over the long term. Random forest and decision tree models identified SOM and available P as the most significant predictors of MPAC, yet with these routine soil fertility indicators alone, the maximum explanatory power (R2) was 75% with a precision (RMSE) of 77 mg kg−1 using the random forest model. Consequently, the conversion of forest to cropland can increase MPAC, and predicting MPAC can be a valuable tool for guiding land use and assisting in the formulation of more sustainable agricultural and environmental policies, particularly in dryland agricultural frontiers.

Precision data-driven modeling of cortical dynamics reveals person-specific mechanisms underpinning brain electrophysiology

Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of individual differences. In this work, we present an algorithmic optimization framework that directly inverts and parameterizes brain-wide dynamical-systems models involving hundreds of interacting neural populations, from single-subject M/EEG time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions. We extensively validate the models' performance in forecasting future brain activity and predicting individual variability in key M/EEG metrics. Last, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We characterize subjects based upon model attractor topology and a dynamical-systems mechanism by which these topologies generate individual variation in the expression of alpha vs. beta rhythms. We trace these phenomena back to global variation in excitatory-inhibitory balance, highlighting the explanatory power of our framework to generate mechanistic insights.

The impact of perceived usefulness, ease of use, trust, and usage attitude on the intention to maintain engagement in AR/VR sports: An exploration of the technology acceptance framework

This study examines how perceived usefulness, ease of use, trust, and usage attitude influence the intention to maintain engagement in AR/VR sports. Virtual Reality (VR) and Augmented Reality (AR) technologies have garnered significant attention in the sports domain in recent years, holding promise for substantial economic and social benefits in the future. However, factors influencing user acceptance and continued use of AR/VR remain unclear. To foster sustained adoption of AR/VR sports technologies and meet the growing demand for diverse forms of athletic activities, this study investigates the factors influencing Chinese sports university students' willingness to continue participating in VR/AR sports using the Technology Acceptance Model (TAM). Data were collected through a questionnaire survey among 283 Chinese sports major university students who had participated in VR/AR sports. Data processing and hypothesis testing were conducted using SPSS 26.0 and AMOS 24.0. The main findings of this study are as follows: First, perceived usefulness has a positive influence on usage attitudes. Second, perceived ease of use positively influences user attitudes. Third, trust positively influences usage attitudes. Fourth, usage attitudes have a positive influence on behavioral intentions. These findings expand the application and explanatory power of TAM in the field of AR/VR sports and offer practical implications for the industry. They can serve as a reference for developing marketing strategies to encourage continued consumer use of these services in the future.

Social jetlag decreases across the lifespan: A prospective big data analysis of objective sleep metrics.

Changes in social zeitgebers across the lifespan affect the interaction between biological and social clocks, potentially contributing to social jetlag. Extant literature suggests a reduction in social jetlag given declining social obligations occurring after retirement, but is limited to self-reported methods and cross-sectional designs. Leveraging longitudinal and ecologically valid data from consumer sleep technology, we analysed objective sleep data from 2439 users of the polysomnography-validated SleepScore mobile application, encompassing 500,415 total nights recorded. We examined the relationship between age as a continuous variable, age as a proxy for retirement status, and social jetlag. Additional linear models were employed to assess the effect of self-reported chronotype, average reported daily caffeine, alcohol and stress on social jetlag. There was a significant negative association between overall age and social jetlag (β = -0.64, t = -9.90, p < 0.001, effect size = 0.040), such that every 1-year increase in age corresponded with a 0.64-min reduction in social jetlag. The inclusion of self-reported chronotype, stress, caffeine and alcohol increased the explanatory power of our models slightly, but the effect of age remained consistent (β = -0.642, t = -8.91, p < 0.001, effect size = 0.046). Retirement-aged individuals exhibited nearly 50% less reduction in social jetlag than pre-retirement (30.6 ± 48.2 min versus post-retirement: 15.8 ± 41.6 min, p < 0.0001). While social jetlag after retirement was most pronounced for strong evening chronotypes (β = -0.41, t = -2.876, p = 0.004, effect size = -0.4276), pairwise comparisons revealed no statistically significant differences in the slopes between chronotypes (p > 0.05). Thus, jetlag decreases across the lifespan, and its reduction appears to be amplified post-retirement even after accounting for behavioural factors.

An analysis of knowledge, attitudes, practice and influencing factors for tuberculosis prevention and control among Hainan University students

ObjectiveTo assess the current status of knowledge, attitude, and practice pertaining to tuberculosis prevention among college students in Hainan Province, China, and to identify influential factors. The findings of this study are intended to provide valuable insights for the development and implementation of effective health education programs aimed at tuberculosis prevention and control.MethodsA convenient sampling method was employed to conduct a questionnaire-based survey among college students at a university in Hainan Province using the Wenjuanxing platform from January to February 2023. The survey evaluated students’ general information and their knowledge, attitude, and practice regarding tuberculosis prevention and control. The scores of correct knowledge, attitude, and practice were compared based on students’ demographic characteristics. Multiple linear regression analysis was used to determine the influencing factors of students’ knowledge, attitude, and practice related to tuberculosis prevention and control.ResultsA total of 280 questionnaires were distributed, of which 265 valid responses were collected, resulting in a valid response rate of 94.6%. The study found that medical students exhibited higher levels of correct knowledge, attitude, and practice compared to non-medical students (5.92 vs. 5.17, 3.17 vs. 2.57, 3.48 vs. 2.78, respectively). Moreover, students who had received tuberculosis education had higher correct scores compared to those who had not (5.92 vs. 5.31, 3.08 vs. 2.65, 3.31 vs. 2.93, respectively). Additionally, students in higher grades demonstrated higher scores in attitude. Three variables were found to influence students’ knowledge, attitude, and practice related to tuberculosis prevention and control, accounting for 33.4% of the explanatory power (F = 11.930; p &amp;lt; 0.001). Grade, major, and whether they had received tuberculosis education included.ConclusionThe study revealed a generally poor level of awareness among college students regarding tuberculosis prevention and control knowledge and the importance of regular physical exercise. Grade, major, and whether students had received tuberculosis education were identified as influencing factors. These factors should be prioritized in university tuberculosis education programs. Moreover, the implementation of physical education in schools is crucial in promoting students’ health.

Barriers to transition to resource-oriented sanitation in rural Ethiopia.

Recycling excreta resources through resource-oriented toilet systems (ROTS) holds transformative potential, yet adoption remains limited, especially where benefits could be high. This study aims to understand constraints hindering the adoption of ROTS in one such area in Ethiopia. Based on a survey among 476 households comprising 2393 individuals, we examine the plans to use ROTS and willingness to pay for ROTS and apply structural equation modelling to analyze the drivers of these two outcomes while comparing the explanative power of the extended technology acceptance model, extended theory of planned behaviour, and their combined model. While 40% of households expressed a plan to use ROTS and 20% reported willingness to pay for a subsidized ROTS with a biogas unit, merely 7% revealed both the plan to use and sufficient willingness to pay, highlighting the need to target both these complementary outcomes concurrently. The theory of planned behaviour showed the best explanative power, also revealing that these two outcomes are influenced by partly distinct setsof factors. Findings imply that common efforts to ease objective constraints through subsidies, which incentivize willingness to pay, are necessary but not sufficient for facilitating the adoption of ROTS. To simultaneously enhance intentions to use, it is also recommended to target psychosocial drivers, such as perceived behavioural control and perceived community support, through awareness creation, behaviour change activities, and community engagement techniques.

Understanding price momentum, market fluctuations, and crashes: insights from the extended Samuelson model

Although momentum strategies result in abnormal profitability, thereby challenging the efficient market hypothesis (EMH), concerns persist regarding their reliability due to their significant volatility and susceptibility to substantial losses. In this study, we investigate the limitations of these strategies and propose a solution. Our literature review reveals that the volatile profits are due to statistical analyses that assume the persistence of past patterns, leading to unreliable results in out-of-sample scenarios when underlying mechanisms evolve. Statistical analysis, the predominant method in financial economics, often proves inadequate in explaining market fluctuations and predicting crashes. To overcome these limitations, a paradigm shift towards dynamic approaches is essential. Drawing inspiration from three groundbreaking economists, we introduce the extended Samuelson model (ESM), a dynamic model that connects price changes to market participant actions. This paradigm transition uncovers several significant findings. First, timely signals indicate momentum initiations, cessations, and reversals, validated using S&P 500 data from 1999 to 2023. Second, ESM predicts the 1987 Black Monday crash weeks in advance, offering a new perspective on its underlying cause. Third, we classify sequential stock price data into eight distinct market states, including their thresholds for transitions, laying the groundwork for market trend predictions and risk assessments. Fourth, the ESM is shown to be a compelling alternative to EMH, offering potent explanatory and predictive power based on a single, realistic assumption. Our findings suggest that ESM has the potential to provide policymakers with proactive tools, enabling financial institutions to enhance their risk assessment and management strategies.

Toward a Strategic Culture-Enabled Theory of Black Sea Trilateral Cooperation

This article explores the dynamics of Türkiye-Azerbaijan-Georgia (TAG) trilateral cooperation by integrating strategic culture into the theoretical and empirical analysis of regional alignments. The study begins with an introduction to the TAG trilateral framework, situating it within contemporary regional security concerns. A comprehensive literature review follows, surveying dominant neorealist and positivist approaches to alliance formation, with particular attention to their gradual incorporation of ideational and cultural factors. Building on this foundation, the author develops a theoretical framework centered on strategic culture, emphasizing its utility in complementing materialist explanations by incorporating historical narratives, elite perceptions, and shared norms. The case study of TAG trilateral cooperation in the Black Sea region illustrates the explanatory power of this approach, highlighting how nonmaterial factors sustain and expand the alignment beyond energy cooperation to include military coordination, security dialogues, and infrastructure projects. By combining theoretical depth with empirical analysis, this article contributes to the literature on strategic culture and offers a nuanced understanding of how material and nonmaterial factors interact to shape regional cooperation.

Exploring Principal Component Analysis for Enhanced Insights into Physical and Operational Characteristics of Palladium-Based Membrane Composites: Advancing Hydrogen (H2) Energy Potential to Revolutionize the Energy Sector

In this study, we used Principal Component Analysis (PCA) to evaluate the physical and operational properties of palladium (Pd)-based membrane composites, focusing on variables like temperature, differential pressure (ΔP), thickness, hydrogen (H2) permeability, and H2 flux. The analysis revealed that the first two principal components explained 53.16% of the total variance, indicating moderate explanatory power. Interdependencies were observed among temperature, thickness, H2 flux, and H2 permeability, while ΔP functioned independently. This study found similarities among membranes, such as eco-friendly chitosan-based membranes, which performed comparably to conventional options like Pd–PSS and Pd–Cu/αAl2O3. Overall, PCA proved to be an invaluable tool for uncovering hidden patterns, optimizing experimental processes, and deepening the understanding of Pd-based membranes. The findings underscore PCA’s potential to enhance material performance and promote sustainable alternatives, with practical benefits for advancing hydrogen separation technologies. This study illustrates how data-driven approaches can refine material analysis and drive innovation in membrane design.

Spatio-temporal Evolution Patterns and Spatial Differentiation Attribution of Ecological Quality in Guiyang from 1991 to 2020

Exploring the trend of long time-series ecological quality evolution and spatial differentiation of influencing factors in Guiyang is of great significance for realizing regional ecological protection and high-quality development strategies. Based on the 7-period Landsat remote sensing images from 1991 to 2020, the remote sensing ecological index （RSEI） of Guiyang from 1991 to 2020 was calculated using the GEE remote sensing big data platform, and a geodetector, Hurst index, and coefficient of variation with different random sampling quantities and classification strategies were used to analyze the evolutionary pattern of ecological quality, change trends, and spatial differentiation influencing factors. The results showed that ① The ecological quality of Guiyang in the past 30 years was mainly medium and good, showing a wave-like pattern of change, with the highest and lowest values occurring in 2020 （mean value 0.58） and 2010 （mean value 0.47）, respectively, and the overall trend of the RSEI in the past ten years was good, with a spatial trend of decreasing distribution from the northeast to the southwest. ② The domain of values of the coefficient of variation for the RSEI in Guiyang was in the range of （0-2）, of which 46.61% of the area was in high fluctuation change, and the overall volatility of the RSEI was large； the mean value of the Hurst index was 0.59, and the area greater than 0.5 accounted for 73.98%, most of which showed weak persistence, and the future trend of change was the same as that of the past 30 years. ③ Different classification methods and random samples affect the q-value results of the geodetector, but the trend of the size ordering of the explanatory power of different factors was generally consistent. Land use, nighttime lighting index, slope, and population density indicators had stronger explanatory power for RSEI spatial differentiation； factor interaction detection was two-factor enhancement and nonlinear enhancement； and the interaction between land use and other factors was most favorable for explaining RSEI spatial differentiation.

Japan’s super-aging society and the need for the dementia prevention -CKPT to detect decline before dementia onset and it’s application-

Background: The total world population was about 7.8 billion in 2020, but is expected to exceed 10.1 billion in 2060. The population of people aged 65 and over is also expected to rapidly increase from about 700 million to about 1.8 billion. This clearly indicates that the number of dementia patients, the biggest risk factor for which is aging, is rapidly increasing, and this is a challenge for humanity. In Japan, which has the world’s highest aging rate, the number of dementia patients is estimated to be about 6.3 million in 2020 and reach 11.54 million in 2060, and the burden on the public in relation to dementia care is a problem. For this reason, the government established the Dementia Policy Promotion Guidelines in 2019, and is promoting policies based on two pillars: coexistence with people with dementia and prevention of dementia. Objective: To detect subtle changes before the onset of dementia without using expensive equipment or invasive methods in order to prevent dementia. Methods: To achieve this goal, we devised a neuropsychological test called CKPT (Color Kanji Pick-out Test) and established evidence for it. We then established diagnostic criteria using large-scale data and are moving towards practical use. Results: It has been about a year since we started dementia prevention activities using CKPT, and we will introduce how it works in annal CKPT Reports. Since the CKPT can be easily translated into other languages, we have been preparing an English version. We have prepared the test questions and the explanatory Power Point presentation to be used when administering the test. Conclusion: I hope that someone will be interested in an English version of CKPT.

Exploring the altitude differentiation and influencing factors of PM2.5 and O3: a case study of the Fenwei Plain, China

Altitude differentiation has a substantial effect on the synergistic control of PM2.5 and O3 pollution. This study targets the Fenwei Plain, which is affected by mountain range blockage, divided into different altitude scales, and employs the methods of correlation analysis and geographical detector to explore the spatiotemporal heterogeneity of PM2.5 and O3 between different altitude zones and to identify the key controlling factors of pollutants between different altitude areas. The results showed that PM2.5 showed a significant decreasing trend from 2014 to 2023, whereas O3 exhibited an opposite trend. The concentrations of both pollutants decreased with increasing altitude, particularly for PM2.5, which showed significant altitudinal differentiation under the influence of topography. PM2.5 was negatively correlated with gross domestic product (GDP) and precipitation, and positively correlated with SO2. In contrast, the correlation of O3 with these factors was opposite to that of PM2.5. For spatial differentiation, NO2 and SO2 were the main factors influencing the spatial differentiation of PM2.5 and O3 at different altitudes. The explanatory power of the spatial divergence of PM2.5 and O3 was greatly increased by the interactions between the two precursors and between the precursors and meteorological factors. Furthermore, the explanatory power of the PM2.5 dominant factor increased with elevation, while the explanatory power of the O3 dominant factor was relatively high across low, middle, and high altitudes. This study serves as a guide for reducing air pollution in the Fenwei Plain and offers a novel perspective for the study of PM2.5 and O3 influenced by terrain.

Spatiotemporal Variation and Driving Factors of Carbon Sequestration Rate in Terrestrial Ecosystems of Ningxia, China

This study investigates the spatiotemporal variation characteristics and influencing factors of an ecosystem’s carbon sequestration rate (CSR) in the Ningxia region from 2001 to 2023, providing scientific evidence for assessing the regional carbon sequestration capacity and formulating carbon neutrality policies. Based on ground observation data and multimodal datasets, the optimal machine learning model (EXT) was used to invert a 30 m high-resolution vegetation and soil carbon density dataset for Ningxia from 2000 to 2023. Annual variation analysis and geographical detector methods were employed to assess the spatiotemporal distribution characteristics of the CSR from 2001 to 2023 and identify the primary influencing factors. The results show that from 2001 to 2023, the CSR of the Ningxia ecosystem exhibits a spatial distribution pattern characterized by higher values in the south and lower values in the north, with a mean value of 21.95 gC·m−2, and an overall fluctuating increasing trend, with an annual growth rate of 0.53 gC·m−2 a−1. Significant differences in the CSR exist across different ecological regions. In terms of land use types, the ranking of carbon sequestration capacity is forest &gt; farmland &gt; grassland &gt; barren, while the ranking of the carbon sequestration enhancement capacity is farmland &gt; forest &gt; grassland &gt; barren. Among land use change types, the carbon sequestration enhancement capacity significantly increased when grassland was converted to forest or shrubland, farmland to forest–grassland, and bare land to forest–grassland, with increases of 42.9%, 9.2%, and 34.6%, respectively. The NDVI is the primary driver of CSR spatiotemporal variation, while the interaction between the Enhanced Vegetation Index (EVI) and soil bulk density has a more significant explanatory power for CSR spatial differentiation. This study shows that ecological restoration projects, such as the conversion of cropland to forest (or grassland) and protective farmland measures, play a significant role in enhancing the carbon sequestration capacity in Ningxia.

Predictive crystallography at scale: mapping, validating, and learning from 1000 crystal energy landscapes.

Computational crystal structure prediction (CSP) is an increasingly powerful technique in materials discovery, due to its ability to reveal trends and permit insight across the possibility space of crystal structures of a candidate molecule, beyond simply the observed structure(s). In this work, we demonstrate the reliability and scalability of CSP methods for small, rigid organic molecules by performing in-depth CSP investigations for over 1000 such compounds, the largest survey of its kind to-date. We show that this highly-efficient force-field-based CSP approach is superbly predictive, locating 99.4% of observed experimental structures, and ranking a large majority of these (74%) as among the most stable possible structures (to within uncertainty due to thermal effects). We present two examples of insights such large predicted datasets can permit, examining the space group preferences of organic molecular crystals and rationalising empirical rules concerning the spontaneous resolution of chiral molecules. Finally, we exploit this large and diverse dataset for developing transferable machine-learned energy potentials for the organic solid state, training a neural network lattice energy correction to force field energies that offers substantial improvements to the already impressive energy rankings, and a MACE equivariant message-passing neural network for crystal structure re-optimisation. We conclude that the excellent performance and reliability of the CSP workflow enables the creation of very large datasets of broad utility and explanatory power in materials design.

Domain-Specific Prediction of Clinical Progression in Parkinson's Disease Using the Mosaic Approach.

Due to the highly individualized clinical manifestation of Parkinson's disease (PD), personalized patient care may require domain-specific assessment of neurological disability. Evidence from magnetic resonance imaging (MRI) studies has proposed that heterogenous clinical manifestation corresponds to heterogeneous cortical disease burden, suggesting customized, high-resolution assessment of cortical pathology as a candidate biomarker for domain-specific assessment. Herein, we investigate the potential of the recently proposed Mosaic Approach (MAP), a normative framework for quantifying individual cortical disease burden with respect to a population-representative cohort, in predicting domain-specific clinical progression. Using MRI and clinical data from 135 recently diagnosed PD patients from the Parkinson's Progression Markers Initiative, we first defined an extremity-specific motor score. We then identified cortical regions corresponding to "extremity functions" and restricted MAP, respectively, and contrasted the explanatory power of the extremity-specific MAP to unrestricted MAP. As control conditions, domain-related but less specific general motor function and nondomain-specific cognitive scores were considered. We also tested the predictive power of the restricted MAP in predicting disease progression over 1 and 3 years using support vector machines. The restricted, extremity-specific MAP yielded higher explanatory power for extremity-specific motor function at baseline as opposed to the unrestricted, whole-brain MAP. On the contrary, for general motor function, the unrestricted, whole-brain MAP yielded higher power. No associations were found for cognitive function. The extremity-specific MAP predicted extremity-specific motor progression over 1 and 3 years above chance level. The MAP framework allows for domain-specific prediction of customized PD disease progression, which can inform machine learning, thereby contributing to personalized PD patient care.

Impacts of Weather Anomalies and Climate on Plant Disease.

Predicting the effects of climate change on plant disease is critical for protecting ecosystems and food production. Here, we show how disease pressure responds to short-term weather, historical climate and weather anomalies by compiling a global database (4339 plant-disease populations) of disease prevalence in both agricultural and wild plant systems. We hypothesised that weather and climate would play a larger role in disease in wild versus agricultural plant populations, which the results supported. In wild systems, disease prevalence peaked when the temperature was 2.7°C warmer than the historical average for the same time of year. We also found evidence of a negative interactive effect between weather anomalies and climate in wild systems, consistent with the idea that climate maladaptation can be an important driver of disease outbreaks. Temperature and precipitation had relatively little explanatory power in agricultural systems, though we observed a significant positive effect of current temperature. These results indicate that disease pressure in wild plants is sensitive to nonlinear effects of weather, weather anomalies and their interaction with historical climate. In contrast, warmer temperatures drove risks for agricultural plant disease outbreaks within the temperature range examined regardless of historical climate, suggesting vulnerability to ongoing climate change.