A quantile integral linear model to quantify genetic effects on phenotypic variability.

Detecting genetic variants associated with the variance of complex traits, that is, variance quantitative trait loci (vQTLs), can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through extensive simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank (n = 375,791), QUAIL identified 11 vQTLs for body mass index (BMI) that have not been previously reported. Top vQTL findings showed substantial enrichment for interactions with physical activities and sedentary behavior. Furthermore, variance polygenic scores (vPGSs) based on QUAIL effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. Overall, QUAIL is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and vPGS levels. It addresses critical limitations in existing approaches and may have broad applications in future gene-environment interaction studies.

Among-species variation in six decades of changing migration timings explained through ecology, life-history and local migratory abundance.

Species exploiting seasonal environments must alter timings of key life-history events in response to large-scale climatic changes in order to maintain trophic synchrony with required resources. Yet, substantial among-species variation in long-term phenological changes has been observed. Advancing from simply describing such variation towards predicting future phenological responses requires studies that rigorously quantify and explain variation in the direction and magnitude of changing timings across diverse species in relation to key ecological and life-history variables. Accordingly, we fitted multi-quantile regressions to 59 years of multi-species data on spring and autumn bird migration timings through northern Scotland. We demonstrate substantial variation in changes in timings among 72 species, and tested whether such variation can be explained by species ecology, life-history and changes in local abundance. Consistent with predictions, species that advanced their migration timing in one or both seasons had more seasonally restricted diet types, fewer suitable breeding habitat types, shorter generation lengths and capability to produce multiple offspring broods per year. In contrast, species with less seasonally restricted diet types and that produce single annual offspring broods, showed no change. Meanwhile, contrary to prediction, long-distance and short-distance migrants advanced migration timings similarly. Changes in migration timing also varied with changes in local migratory abundance, such that species with increasing seasonal abundance apparently altered their migration timing, whilst species with decreasing abundance did not. Such patterns broadly concur with expectation given adaptive changes in migration timing. However, we demonstrate that similar patterns can be generated by numerical sampling given changing local abundances. Any apparent phenology-abundance relationships should, therefore, be carefully validated and interpreted. Overall, our results show that migrant bird species with differing ecologies and life-histories showed systematically differing phenological changes over six decades contextualised by large-scale environmental changes, potentially facilitating future predictions and altering temporal dynamics of seasonal species co-occurrences.

High-dimensional sparse vine copula regression with application to genomic prediction.

High-dimensional data sets are often available in genome-enabled predictions. Such data sets include nonlinear relationships with complex dependence structures. For such situations, vine copula-based (quantile) regression is an important tool. However, the current vine copula-based regression approaches do not scale up to high and ultra-high dimensions. To perform high-dimensional sparse vine copula-based regression, we propose 2 methods. First, we show their superiority regarding computational complexity over the existing methods. Second, we define relevant, irrelevant, and redundant explanatory variables for quantile regression. Then, we show our method's power in selecting relevant variables and prediction accuracy in high-dimensional sparse data sets via simulation studies. Next, we apply the proposed methods to the high-dimensional real data, aiming at the genomic prediction of maize traits. Some data processing and feature extraction steps for the real data are further discussed. Finally, we show the advantage of our methods over linear models and quantile regression forests in simulation studies and real data applications.

Penalized weighted smoothed quantile regression for high-dimensional longitudinal data.

Quantile regression, known as a robust alternative to linear regression, has been widely used in statistical modeling and inference. In this paper, we propose a penalized weighted convolution-type smoothed method for variable selection and robust parameter estimation of the quantile regression with high dimensional longitudinal data. The proposed method utilizes a twice-differentiable and smoothed loss function instead of the check function in quantile regression without penalty, and can select the important covariates consistently using the efficient gradient-based iterative algorithms when the dimension of covariates is larger than the sample size. Moreover, the proposed method can circumvent the influence of outliers in the response variable and/or the covariates. To incorporate the correlation within each subject and enhance the accuracy of the parameter estimation, a two-step weighted estimation method is also established. Furthermore, we prove the oracle properties of the proposed method under some regularity conditions. Finally, the performance of the proposed method is demonstrated by simulation studies and two real examples.

Principal stratification for quantile causal effects under partial compliance.

Within the principal stratification framework in causal inference, the majority of the literature has focused on binary compliance with an intervention and modelling means. Yet in some research areas, compliance is partial, and research questions-and hence analyses-are concerned with causal effects on (possibly high) quantiles rather than on shifts in average outcomes. Modelling partial compliance is challenging because it can suffer from lack of identifiability. We develop an approach to estimate quantile causal effects within a principal stratification framework, where principal strata are defined by the bivariate vector of (partial) compliance to the two levels of a binary intervention. We propose a conditional copula approach to impute the missing potential compliance and estimate the principal quantile treatment effect surface at high quantiles, allowing the copula association parameter to vary with the covariates. A bootstrap procedure is used to estimate the parameter to account for inflation due to imputation of missing compliance. Moreover, we describe precise assumptions on which the proposed approach is based, and investigate the finite sample behavior of our method by a simulation study. The proposed approach is used to study the 90th principal quantile treatment effect of executive stay-at-home orders on mitigating the risk of COVID-19 transmission in the United States.

Multi-Factor Coral Disease Risk: A new product for early warning and management.

Ecological forecasts are becoming increasingly valuable tools for conservation and management. However, there are few examples of near-real-time forecasting systems that account for the wide range of ecological complexities. We developed a new coral disease ecological forecasting system that explores a suite of ecological relationships and their uncertainty and investigates how forecast skill changes with shorter lead times. The Multi-Factor Coral Disease Risk product introduced here uses a combination of ecological and marine environmental conditions to predict the risk of white syndromes and growth anomalies across reefs in the central and western Pacific and along the east coast of Australia and is available through the US National Oceanic and Atmospheric Administration Coral Reef Watch program. This product produces weekly forecasts for a moving window of 6 months at a resolution of ~5 km based on quantile regression forests. The forecasts show superior skill at predicting disease risk on withheld survey data from 2012 to 2020 compared with predecessor forecast systems, with the biggest improvements shown for predicting disease risk at mid- to high-disease levels. Most of the prediction uncertainty arises from model uncertainty, so prediction accuracy and precision do not improve substantially with shorter lead times. This result arises because many predictor variables cannot be accurately forecasted, which is a common challenge across ecosystems. Weekly forecasts and scenarios can be explored through an online decision support tool and data explorer, co-developed with end-user groups to improve use and understanding of ecological forecasts. The models provide near-real-time disease risk assessments and allow users to refine predictions and assess intervention scenarios. This work advances the field of ecological forecasting with real-world complexities and, in doing so, better supports near-term decision making for coral reef ecosystem managers and stakeholders. Secondarily, we identify clear needs and provide recommendations to further enhance our ability to forecast coral disease risk.

Smoothed quantile residual life regression analysis with application to the Korea HIV/AIDS cohort study.

BACKGROUND: The residual life of a patient with human immunodeficiency virus (HIV) is of major interest to patients and their physicians. While existing analyses of HIV patient survival focus mostly on data collected at baseline, residual life analysis allows for dynamic analysis based on additional data collected over a period of time. As survival times typically exhibit a right-skewed distribution, the median provides a more useful summary of the underlying distribution than the mean. In this paper, we propose an efficient inference procedure that fits a semiparametric quantile regression model assessing the effect of longitudinal biomarkers on the residual life of HIV patients until the development of dyslipidemia, a disease becoming more prevalent among those with HIV. METHODS: For estimation of model parameters, we propose an induced smoothing method that smooths nonsmooth estimating functions based on check functions. For variance estimation, we propose an efficient resampling-based estimator. The proposed estimators are theoretically justified. Simulation studies are used to evaluate their finite sample performances, including their prediction accuracy. We analyze the Korea HIV/AIDS cohort study data to examine the effects of CD4 (cluster of differentiation 4) cell count on the residual life of HIV patients to the onset of dyslipidemia. RESULTS: The proposed estimator is shown to be consistent and normally distributed asymptotically. Under various simulation settings, our estimates are approximately unbiased. Their variances estimates are close to the empirical variances and their computational efficiency is superior to that of the nonsmooth counterparts. Two measures of prediction performance indicate that our method adequately reflects the dynamic character of longitudinal biomarkers and residual life. The analysis of the Korea HIV/AIDS cohort study data shows that CD4 cell count is positively associated with residual life to the onset of dyslipidemia but the effect is not statistically significant. CONCLUSIONS: Our method enables direct prediction of residual lifetimes with a dynamic feature that accommodates data accumulated at different times. Our estimator significantly improves computational efficiency in variance estimation compared to the existing nonsmooth estimator. Analysis of the HIV/AIDS cohort study data reveals dynamic effects of CD4 cell count on the residual life to the onset of dyslipidemia.

Understanding the effects of sub-inhibitory antibiotic concentrations on the development of ß-lactamase resistance based on quantile regression analysis.

AIMS: Quantile regression is an alternate type of regression analysis that has been shown to have numerous advantages over standard linear regression. Unlike linear regression, which uses the mean to fit a linear model, quantile regression uses a data set's quantiles (or percentiles), which leads to a more comprehensive analysis of the data. However, while relatively common in other scientific fields such as economic and environmental modeling, it is infrequently used to understand biological and microbiological systems. METHODS AND RESULTS: We analyzed a set of bacterial growth rates using quantile regression analysis to better understand the effects of antibiotics on bacterial fitness. Using a bacterial model system containing 16 variant genotypes of the TEM ß-lactamase enzyme, we compared our quantile regression analysis to a previously published study that uses the Tukey's range test, or Tukey honestly significantly difference (HSD) test. We find that trends in the distribution of bacterial growth rate data, as viewed through the lens of quantile regression, can distinguish between novel genotypes and ones that have been clinically isolated from patients. Quantile regression also identified certain combinations of genotypes and antibiotics that resulted in bacterial populations growing faster as the antibiotic concentration increased-the opposite of what was expected. These analyses can provide new insights into the relationships between enzymatic efficacy and antibiotic concentration. CONCLUSIONS: Quantile regression analysis enhances our understanding of the impacts of sublethal antibiotic concentrations on enzymatic (TEM ß-lactamase) efficacy and bacterial fitness. We illustrate that quantile regression analysis can link patterns in growth rates with clinically relevant mutations and provides an understanding of how increasing sub-lethal antibiotic concentrations, like those found in our modern environment, can affect bacterial growth rates, and provide insight into the genetic basis for varied resistance.

Continental-scale evaluation of downy birch pollen production: Estimating the impacts of global change.

The high prevalence of hay fever in Europe has raised concerns about the implications of climate change-induced higher temperatures on pollen production. Our study focuses on downy birch pollen production across Europe by analyzing 456 catkins during 2019-2021 in 37 International Phenological Gardens (IPG) spanning a large geographic gradient. As IPGs rely on genetically identical plants, we were able to reduce the effects of genetic variability. We studied the potential association with masting behavior and three model specifications based on mean and quantile regression to assess the impact of meteorology (e.g., temperature and precipitation) and atmospheric gases (e.g., ozone (O3) and carbon-dioxide (CO2)) on pollen and catkin production, while controlling for tree age approximated by stem circumference. The results revealed a substantial geographic variability in mean pollen production, ranging from 1.9 to 2.5 million pollen grains per catkin. Regression analyses indicated that elevated average temperatures of the previous summer corresponded to increased pollen production, while higher O3 levels led to a reduction. Additionally, catkins number was positively influenced by preceding summer's temperature and precipitation but negatively by O3 levels. The investigation of quantile effects revealed that the impacts of mean temperature and O3 levels from the previous summer varied throughout the conditional response distribution. We found that temperature predominantly affected trees characterized by a high pollen production. We therefore suggest that birches modulate their physiological processes to optimize pollen production under varying temperature regimes. In turn, O3 levels negatively affected trees with pollen production levels exceeding the conditional median. We conclude that future temperature increase might exacerbate pollen production while other factors may modify (decrease in the case of O3 and amplify for precipitation) this effect. Our comprehensive study sheds light on potential impacts of climate change on downy birch pollen production, which is crucial for birch reproduction and human health.

Micro-environmental personal radio-frequency electromagnetic field exposures in Melbourne: A longitudinal trend analysis.

BACKGROUND: A knowledge gap exists regarding longitudinal assessment of personal radio-frequency electromagnetic field (RF-EMF) exposures globally. It is unclear how the change in telecommunication technology over the years translates to change in RF-EMF exposure. This study aims to evaluate longitudinal trends of micro-environmental personal RF-EMF exposures in Australia. METHODS: The study utilised baseline (2015-16) and follow-up (2022) data on personal RF-EMF exposure (88 MHz-6 GHz) measured across 18 micro-environments in Melbourne. Simultaneous quantile regression analysis was conducted to compare exposure data distribution percentiles, particularly median (P50), upper extreme value (P99) and overall exposure trends. RF-EMF exposures were compared across six exposure source types: mobile downlink, mobile uplink, broadcast, 5G-New Radio, Others and Total (of the aforementioned sources). Frequency-specific exposures measured at baseline and follow-up were compared. Total exposure across different groups of micro-environment types were also compared. RESULTS: For all micro-environmental data, total (median and P99) exposure levels did not significantly change at follow-up. Overall exposure trend of total exposure increased at follow-up. Mobile downlink contributed the highest exposure among all sources showing an increase in median exposure and overall exposure trend. Of seven micro-environment types, five of them showed total exposure levels (median and P99) and overall exposure trend increased at follow-up.

The Association between Education and Cognitive Performance Varies at Different Levels of Cognitive Performance: A Quantile Regression Approach.

INTRODUCTION: Educational differences in cognitive performance among older adults are well documented. Studies that explore this association typically estimate a single average effect of education on cognitive performance. We argue that the processes that contribute to the association between education and cognitive performance are unlikely to have equal effects at all levels of cognitive performance. In this study, we employ an analytical approach that enables us to go beyond averages to examine the association between education and five measures of global and domain-specific cognitive performance across the outcome distributions. METHODS: This cross-sectional study included 1,780 older adults aged 58-68 years from the Longitudinal Aging Study Amsterdam. Conditional quantile regression was used to examine variation across the outcome distribution. Cognitive outcomes included Mini-Mental State Examination (MMSE) score, crystallized intelligence, information processing speed, episodic memory, and a composite score of global cognitive performance. RESULTS: The results showed that the associations between education and different cognitive measures varied across the outcome distributions. Specifically, we found that education had a stronger association with crystallized intelligence, MMSE, and a composite cognitive performance measure in the lower tail of performance distributions. The associations between education and information processing speed and episodic memory were uniform across the outcome distributions. CONCLUSION: Larger associations between education and some domains of cognitive performance in the lower tail of the performance distributions imply that inequalities are primarily generated among individuals with lower performance rather than among average and high performers. Additionally, the varying associations across some of the outcome distributions indicate that estimating a single average effect through standard regression methods may overlook variations in cognitive performance between educational groups. Future studies should consider heterogeneity across the outcome distribution.

Quantile regression shrinkage and selection via the Lqsso.

Quantile regression has recently received a considerable attention due to its remarkable development in enriching the variety of regression models. Many efforts have been made to blend different penalty and loss function to extend or develop novel regression models that are unique from different perspectives. Bearing in mind that the lasso quantile regression model ignores the randomness of the realizations in the penalty part, we propose a new penalty for the quantile regression models. Similar to the adaptive lasso quantile regression model, the proposed model simultaneously does estimation and variable selection tasks. We call the new model 'lqsso-QR', standing for the least quantile shrinkage and selection operator quantile regression. In this article, we present a sufficient and necessary condition for the variable selection of the lasso quantile regression to enjoy the consistent property. We show that the lqsso-QR follows oracle properties under some mild conditions. From computational perspective, we apply an efficient algorithm, originally developed for the lasso quantile regression. Using simulation studies, we elaborate on the superiority of the proposed model compared with other lasso-type penalties, especially regarding relative prediction error. Also, an application of our method to a real-life data; the rat eye data, is reported.

Bayesian modeling of quantiles of body mass index among under-five children in Ethiopia.

BACKGROUND: Body Mass Index (BMI) is a measurement of nutritional status, which is a vital pre-condition for good health. The prevalence of childhood malnutrition and the potential long-term health risks associated with obesity in Ethiopia have recently increased globally. The main objective of this study was to investigate the factors associated with the quantiles of under-five children's BMI in Ethiopia. METHODS: Data on 5,323 children, aged between 0-59 months from March 21, 2019, to June 28, 2019, were obtained from the Ethiopian Mini Demographic Health Survey (EMDHS, 2019), based on the standards set by the World Health Organization. The study used a Bayesian quantile regression model to investigate the association of factors with the quantiles of under-five children's body mass index. Markov Chain Monte Carlo (MCMC) with Gibbs sampling was used to estimate the country-specific marginal posterior distribution estimates of model parameters, using the Brq R package. RESULTS: Out of a total of 5323 children included in this study, 5.09% were underweight (less than 12.92 BMI), 10.05% were overweight (BMI: 17.06 - 18.27), and 5.02% were obese (greater than or equal to 18.27 BMI) children's. The result of the Bayesian quantile regression model, including marginal posterior credible intervals (CIs), showed that for the prediction of the 0.05 quantile of BMI, the current age of children [ ß = -0.007, 95% CI :(-0.01, -0.004)], the region Afar [ ß = - 0.32, 95% CI: (-0.57, -0.08)] and Somalia[ ß = -0.72, 95% CI: (-0.96, -0.49)] were negatively associated with body mass index while maternal age [ ß = 0.01, 95% CI: (0.005, 0.02)], mothers primary education [ ß = 0.19, 95% CI: (0.08, 0.29)], secondary and above [ ß = 0.44, 95% CI: (0.29, 0.58)], and family follows protestant [ ß = 0.22, 95% CI: (0.07, 0.37)] were positively associated with body mass index. In the prediction of the 0.95 (or 0.85?) quantile of BMI, in the upper quantile, still breastfeeding [ ß = -0.25, 95% CI: (-0.41, -0.10)], being female [ ß = -0.13, 95% CI: (-0.23, -0.03)] were negatively related while wealth index [ ß = 0.436, 95% CI: (0.25, 0.62)] was positively associated with under-five children's BMI. CONCLUSIONS: In conclusion, the research findings indicate that the percentage of lower and higher BMI for under-five children in Ethiopia is high. Factors such as the current age of children, sex of children, maternal age, religion of the family, region and wealth index were found to have a significant impact on the BMI of under-five children both at lower and upper quantile levels. Thus, these findings highlight the need for administrators and policymakers to devise and implement strategies aimed at enhancing the normal or healthy weight status among under-five children in Ethiopia.

Age at death during the Covid-19 lockdown in French metropolitan regions: a non parametric quantile regression approach.

BACKGROUND: Lockdowns have been implemented to limit the number of hospitalisations and deaths during the first wave of 2019 coronavirus disease. These measures may have affected differently death characteristics, such age and sex. France was one of the hardest hit countries in Europe with a decreasing east-west gradient in excess mortality. This study aimed at describing the evolution of age at death quantiles during the lockdown in spring 2020 (17 March-11 May 2020) in the French metropolitan regions focusing on 3 representatives of the epidemic variations in the country: Bretagne, Ile-de-France (IDF) and Bourgogne-Franche-Comté (BFC). METHODS: Data were extracted from the French public mortality database from 1 January 2011 to 31 August 2020. The age distribution of mortality observed during the lockdown period (based on each decile, plus quantiles 1, 5, 95 and 99) was compared with the expected one using Bayesian non-parametric quantile regression. RESULTS: During the lockdown, 5457, 5917 and 22 346 deaths were reported in Bretagne, BFC and IDF, respectively. An excess mortality from + 3% in Bretagne to + 102% in IDF was observed during lockdown compared to the 3 previous years. Lockdown led to an important increase in the first quantiles of age at death, irrespective of the region, while the increase was more gradual for older age groups. It corresponded to fewer young people, mainly males, dying during the lockdown, with an increase in the age at death in the first quantile of about 7 years across regions. In females, a less significant shift in the first quantiles and a greater heterogeneity between regions were shown. A greater shift was observed in eastern region and IDF, which may also represent excess mortality among the elderly. CONCLUSIONS: This study focused on the innovative outcome of the age distribution at death. It shows the first quantiles of age at death increased differentially according to sex during the lockdown period, overall shift seems to depend on prior epidemic intensity before lockdown and complements studies on excess mortality during lockdowns.

Out-of-pocket expenditures associated with double disease burden in Pakistan: a quantile regression analysis.

BACKGROUND: Pakistan is currently experiencing a double burden of disease. Families with members having both communicable and noncommunicable diseases are at a greater risk of impoverishment due to enormous out-of-pocket payments. This study examines the percentile distribution of the determinants of the out-of-pocket expenditure on the double disease burden. METHOD: The study extracted a sample of 6,775 households with at least one member experiencing both communicable and noncommunicable diseases from the Household Integrated Economic Survey 2018-19. The dataset is cross-sectional and nationally representative. Quantile regression was used to analyze the association of various socioeconomic factors with the OOP expenditure associated with double disease burden. RESULTS: Overall, 28.5% of households had double disease in 2018-19. The households with uneducated heads, male heads, outpatient healthcare, patients availing public sector healthcare services, and rural and older members showed a significant association with the prevalence of double disease. The out-of-pocket expenditure was higher for depression, liver and kidney disease, hepatitis, and pneumonia in the upper percentiles. The quantile regression results showed that an increased number of communicable and noncommunicable diseases was associated with higher monthly OOP expenditure in the lower percentiles (10th percentile, coefficient 312, 95% CI: 92-532), and OOP expenditure was less pronounced among the higher percentiles (75th percentile, coefficient 155, 95% CI: 30-270). The households with older members were associated with higher OOP expenditure at higher tails (50th and 75th percentiles) compared to lower (10th and 25th percentiles). Family size was associated with higher OOPE at lower percentiles than higher ones. CONCLUSION: The coexistence of communicable and noncommunicable diseases is associated with excessive private healthcare costs in Pakistan. The results call for addressing the variations in financial costs associated with double diseases.

Inspecting the "health poverty trap" mechanism: self-reinforcing effect and endogenous force.

INTRODUCTION: The term "health poverty trap" describes a vicious cycle in which developing countries or regions become trapped in low levels of health and poverty during the process of modernization. Although significant progress has been made in alleviating poverty in China, there is still a need to further enhance the living conditions of its impoverished population. METHODS: This research utilizes the data of the three national representative panel surveys from 2014 to 2020. The primary objective is to gain a better understanding of the intricate relationship between health and poverty. To examine the self-reinforcing effects of the cumulative cycle between health and poverty, we employ unconditional quantile regression analysis. RESULT: The low-income group exhibits lower overall health status compared to the average level. Economic constraints partially hinder the ability of low-income individuals to access healthcare resources, thereby reinforcing the cyclical relationship between health and poverty. Additionally, the unique psychological and behavioral preferences of individuals in health poverty act as indirect factors that further strengthen this cycle. Health poverty individuals can generate endogenous force to escape the "health poverty trap" by enhancing their confidence levels and digital literacy. CONCLUSIONS: The research examines the coexistence of health gradients and economic inequality among Chinese residents. Additionally, the study explores the endogenous force mechanism of escaping the health poverty trap from psychological and behavioral perspectives. This research also offers insights into optimizing government poverty alleviation programs to effectively address this issue.

Distributional conformal prediction.

We propose a robust method for constructing conditionally valid prediction intervals based on models for conditional distributions such as quantile and distribution regression. Our approach can be applied to important prediction problems, including cross-sectional prediction, k-step-ahead forecasts, synthetic controls and counterfactual prediction, and individual treatment effects prediction. Our method exploits the probability integral transform and relies on permuting estimated ranks. Unlike regression residuals, ranks are independent of the predictors, allowing us to construct conditionally valid prediction intervals under heteroskedasticity. We establish approximate conditional validity under consistent estimation and provide approximate unconditional validity under model misspecification, under overfitting, and with time series data. We also propose a simple "shape" adjustment of our baseline method that yields optimal prediction intervals.

Association between serum cytokines and timeframe for conversion from clinical high-risk to psychosis.

AIM: Although many studies have explored the link between inflammatory markers and psychosis, there is a paucity of research investigating the temporal progression in individuals at clinical high-risk (CHR) who eventually develop full psychosis. To address this gap, we investigated the correlation between serum cytokine levels and Timeframe for Conversion to Psychosis (TCP) in individuals with CHR. METHODS: We enrolled 53 individuals with CHR who completed a 5-year follow-up with a confirmed conversion to psychosis. Granulocyte macrophage-colony stimulating factor (GM-CSF), interleukin (IL)-1ß, 2, 6, 8, 10, tumor necrosis factor-α (TNF-α), and vascular endothelial growth factor (VEGF) levels were measured at baseline and 1-year. Correlation and quantile regression analyses were performed. RESULTS: The median TCP duration was 14 months. A significantly shorter TCP was associated with higher levels of TNF-α (P = 0.022) and VEGF (P = 0.016). A negative correlation was observed between TCP and TNF-α level (P = 0.006) and VEGF level (P = 0.04). Quantile regression indicated negative associations between TCP and GM-CSF levels below the 0.5 quantile, IL-10 levels below the 0.3 quantile, IL-2 levels below the 0.25 quantile, IL-6 levels between the 0.65 and 0.75 quantiles, TNF-α levels below the 0.8 quantile, and VEGF levels below the 0.7 quantile. A mixed linear effects model identified significant time effects for IL-10 and IL-2, and significant group effects for changes in IL-2 and TNF-α. CONCLUSIONS: Our findings underscore that a more pronounced baseline inflammatory state is associated with faster progression of psychosis in individuals with CHR. This highlights the importance of considering individual inflammatory profiles during early intervention and of tailoring preventive measures for risk profiles.

Parent-child interaction, appetite self-regulation, and BMIz in Chinese preschoolers: a mediation analysis.

OBJECTIVES: Research on parent-child interaction (PCI) and its impact on children's weight status is a thriving study area. However, their potential pathways have not been established. This study investigated the association between PCI and children's body-mass index z score (BMIz) examining the role of appetite self-regulation (ASR) as a mediator. STUDY DESIGN: Mediation analysis. METHODS: We included children from 33 kindergartens in Wuhan with parents' consent, measuring children's height and weight, and calculating BMIz. To assess the PCI quality, we utilized the Brigance Parent-Child Interactions Scale. Additionally, children's ASR was tested by satiety responsiveness (SR) and food responsiveness (FR) using the Children's Eating Behavior Questionnaire. Quantile regression was employed to examine the PCI-BMIz association, while mediation analysis was conducted to explore ASR's mediating effect on the relationship between PCI and BMIz. RESULTS: Of 3973 children (53.88% boys) included in the analysis, the mean BMIz was 0.24 ± 1.13. The results revealed that children with poorer PCI quality have higher BMIz across all selected BMIz percentiles, except for the 5th percentile. Furthermore, these associations were significant across most percentiles, whether for boys or girls. Mediation analysis suggested that these associations were partially mediated by children's ASR (indFR = -0.026, PFR < 0.001; indSR = -0.058, PSR < 0.001), with stronger effects observed among boys. CONCLUSION: The variation in how strongly BMIz was linked to PCI across different percentiles suggests that children with poorer PCI have higher BMIz. The link is partially mediated through children's ASR. It's important to pay attention to the PCI quality in children with higher BMIz levels, especially in boys.

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data.

Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.