Quantile Regression Model Research Articles

Nonlinear effect of urban noise pollution on depression of the elderly in China based on the Bayesian machine learning method

With the intensification of urbanisation and population ageing globally, it is crucial to understand the effect of urban noise pollution on the mental health of the ageing population. This research employed the Bayesian machine learning method to investigate the nonlinear effect of urban noise pollution on depression scores in the elderly (DSE) population in China. Based on individual survey data, the China Health and Retirement Longitudinal Study (CHARLS) data from 2011 to 2020, and corresponding temporally monitored annual urban equivalent sound pressure level (ESPL) data in China, propensity score matching (PSM) was used to identify the causal effect of urban ESPL on DSE. The quantile regression model (QRM), the Bayesian additive regression tree model (BARTM), and the Bayesian causal forest method (BCFM) were used to detect the nonlinear influencing mechanism of urban ESPL on DSE. The PSM analysis which can balance confounding influence revealed a significant disparity in depression scores between ageing individuals exposed to higher ESPL and those residing in more tranquil settings, suggesting a definitive causal relationship. Furthermore, the QRM results highlight a significantly increasing effect of ESPL on DSE at higher quantiles (0.4528 and 0.5709 at the 75th and 85th percentiles, respectively). The BARTM identifies a ‘U-shaped’ dose–response curve between ESPL and DSE, with low ESPL as beneficial, but exposure above 53.67 dB(A) representing noise pollution, especially past 55.85 dB(A), significantly increasing DSE. Specifically, when ESPL ranges from 53.67 to 55.68 dB(A) with other variables held constant, DSE increases by an average of 0.28 for every 1 dB(A) rise. Beyond 55.68 dB(A), the average DSE increase is 0.85 for each additional 1 dB(A) in ESPL. A notable spatial heterogeneity was observed in the local effects of annual urban ESPL on DSE. The BCFM results indicate that socio-economic exposure level can modulate the effect of ESPL on DSE.

Comparative lifespan and healthspan of nonhuman primate species common to biomedical research.

There is a critical need to generate age- and sex-specific survival curves to characterize chronological aging consistently across nonhuman primates (NHP) used in biomedical research. Accurate measures of chronological aging are essential for inferences into genetic, demographic, and physiological variables driving differences in NHP lifespan within and between species. Understanding NHP lifespans is relevant to public health because unraveling the demographic, molecular, and clinical bases of health across the life course in translationally relevant NHP species is fundamentally important to the study of human aging. Data from more than 110,000 captive individual NHP were contributed by 15 major research institutions to generate sex-specific Kaplan-Meier survival curves using uniform methods in 12 translational aging models: Callithrix jacchus (common marmoset), Chlorocebus aethiops sabaeus (vervet/African green), Macaca fascicularis (cynomolgus macaque), M. fuscata (Japanese macaque), M. mulatta (rhesus macaque), M. nemestrina (pigtail macaque), M. radiata (bonnet macaque), Pan troglodytes spp. (chimpanzee), Papio hamadryas spp. (baboon), Plecturocebus cupreus (coppery titi monkey), Saguinus oedipus (cotton-top tamarin), and Saimiri spp. (squirrel monkey). After employing strict inclusion criteria, primary analysis results are based on 12,269 NHP that survived to adulthood and died of natural/health-related causes. A secondary analysis was completed for 32,616 NHP that died of any cause. For the primary analyses, we report ages of 25th, 50th, 75th, and 85th percentiles of survival, maximum observed ages, rates of survivorship, and sex-based differences captured by quantile regression models and Kolmogorov-Smirnov tests. Our findings show a pattern of reduced male survival among catarrhines (African and Asian primates), especially macaques, but not platyrrhines (Central and South American primates). For many species, median lifespans were lower than previously reported. An important consideration is that these analyses may offer a better reflection of healthspan than lifespan. Captive NHP used in research are typically euthanized for humane welfare reasons before their natural end of life, often after diagnosis of their first major disease requiring long-term treatment with reduced quality of life (e.g., endometriosis, cancer, osteoarthritis). Supporting the idea that these data are capturing healthspan, for several species typical age at onset of chronic disease is similar to the median lifespan estimates. This data resource represents the most comprehensive characterization of sex-specific lifespan and age-at-death distributions for 12 biomedically relevant species, to date. The results clarify the relationships among NHP ages and will provide a valuable resource for the aging research community, improving human-NHP age equivalencies, informing investigators of the expected survival rates of NHP assigned to studies, providing a metric for comparisons in future studies, and contributing to our understanding of the factors that drive lifespan differences within and among species.

Analyzing quality of life after low anterior resection for rectal cancer.

Rectal cancer is one of the most common cancers worldwide that imposes high costs on patients and the healthcare system while also having a significant impact on the patient's quality of life (QoL). We aimed to assess the QoL of rectal cancer patients undergoing lower anterior resection (LAR) and evaluate potential confounding factors. In this cross-sectional study, we included patients undergoing neo-adjuvant chemotherapy followed by LAR, diverting ileostomy, and adjuvant chemotherapy. Six months after stoma reversal, QoL was assessed using a cancer-specific core questionnaire EORTC QLQ-C30 and also a colorectal cancer module EORTC QLQ-CR29. Linear and quantile regression models were used to examine associations of QoL and patients' demographical and clinical features. The study population comprised 210 patients with an average age of 61.9 ± 11.0 (range: 37-85) and 112 (53.3%) male patients. The higher tumor stage increases the QoL of patients by reducing diarrhea, loss of appetite, defecation problems, and stool frequency. Based on the linear regression analysis, a farther distance of the anastomosis from the anal verge (AV) was correlated with lower symptom scores and higher QoL. Also, at the 75th percentile cut-off of the QLQ-CR29 scores, a higher N stage of the tumor was correlated with higher QoL based on functional subscale (coefficient = 3.032, P = 0.016). QoL of patients after LAR for rectal cancer is significantly associated with the distance of the anastomosis site from the AV.

TRANSLATING DIRECTIONAL ACCURACY INTO PROFITABILITY: MACHINE LEARNING BASED PREDICTIVE MODELS FOR GLOBAL STOCK INDICES

Understanding the linkage between directional accuracy and return remains a negligibly explored domain in the context of stock market prediction. Maximum studies in the given domain are based on mathematical accuracy measures and directional accuracy measures. Merely the efficiency of directional prediction may not suffice, as themagnitude of directional change also assumes importance in stock markets. Without any doubt, profitability measures reign supreme in the case of the stock market. The current study attempts to investigate the association between Accuracy rate and returns for data sets based on seven stock indices across the globe. The outcome of the study relies on machine learning: ANN (Artificial Neural Networks) and SVM (Support Vector Machine) based predictive models, built using 196 data sets of variable duration. The results of the current research reveal a strong positive correlation between accuracy rate and returns. Moreover, significant variations in the extent of therelationship are also observed. Quantile regression models indicate a relatively stronger relationship between accuracy rate and return at the lower range of returns. Findings of the study carry value for academicians, researchers, and practitioners in the field of machine learning and stock market predictions.

Koreksi Bias Data Hujan Satelit Persiann Sebagai Alternatif Pos Stasiun Hujan dengan Pendekatan Quantile Mapping di Sub Das Brantas Hulu

The rainfall data is usually obtained from meteorological stations, which many sill observed manually in Indonesia and can lead to an observation error. Remote sensing rainfall data observed by satellite offer interesting alternatives to overcome the limitation of existing meteorological stations in Indonesia. It offers accuracy, spatial coverage, timeliness and cost effectiveness. However it contains bias if compared to land based station. This research will evaluate rainfall data observed in PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) in Brantas Hulu Watershed. The rainfall data of 13 years from 12 stations is evaluated to obtain area averaged rainfall using Thiessen Polygon method. Three datasets from PERSIANN, PERSIANN CCS and PERSIANN CDR are compared statistically with the area averaged area for daily, monthly and yearly rainfall. Bias correction is performed using Quantile Mapping to match the cumulative distribution curve from the area averaged rainfall. Verification of bias correction is performed using 2 years data from 2019 to 2020. Finally, the extreme rainfall analysis is performed to test the corrected data for design rainfall calculations. The correlation of daily data shows poor agreement for all three datasets with correlation coefficient below 0,40. The monthly and yearly data gives better correlation with coefficient above 0,80. It shows that for time series event, the satellite data gives poor correlation with the area averaged rainfall. However, quantile mapping correction gives consistent cumulative distribution agreement for correction and model verification. The verification analysis shows an increase in coefficient correlation from 0.387 to 0.43 (daily) and 0.887 to 0.915 (monthly). In addition, there is a decrease in normalized root mean square error (NRMSE) from 31.03% to 16.91% (daily) and 46.03% to 14.03% (monthly). A decrease in normalized mean absolute error (NMAE) occurred from 16.12% to 9.74% (daily) and 31.67% to 10.07% (monthly). Beside that, the results of extreme rainfall analysis produce values that are very close to the rain station. So it is found that the PERSIANN CCS quantile mapping regression model is the best in improving data quality.

Exploring spatiotemporal Dynamics and influencing factors of cyber-fraud: A quantile regression approach in Xiaoshan District, China

This study utilizes data on cyber-fraud crimes from the Public Security Bureau in Xiaoshan District for the year 2021 as its case study. It examines the spatiotemporal distribution of various types of cyber-fraud and investigates the influencing factors from social and built environments. Additionally, quantile regression models are employed to analyze the variation in the number of cases across different quantiles of the influencing factors. The study reveals significant differences in individual characteristics among victims of different types of fraud. Cyber-fraud occurrences exhibit distinct temporal patterns across various temporal scales, with significant differences in the duration of crimes among different fraud types. Cyber-fraud demonstrates significant spatial clustering, mainly concentrated in residential areas. The results of quantile regression indicate that cyber fraud is influenced by both the built environment and social environment, with noticeable variations in the coefficient of influence across different quantiles of the independent variables.

A green revolution in the making: integrating environmental performance and green finance for China’s sustainable development

Green finance initiatives are encouraged in the world to attain green economic milestones. Therefore, it is the centered area of research for the plethora of researchers across the globe. Instead of this significant transition to green finance, the study analyzed the Chinese data to explore the relationship between Green Finance, Green Technology innovation, and Environmental Performance from 2003 to 2022 in the Chinese context. We preferred the CS-ARDL and panel Quantile regression models for exploring our hypothesis and to examine the relationship of these variables. Our results validated that both green finance and green technology innovation have positive effects in both the long-term and short-term on the environmental performance in numerous Chinese cities. The statistical results showed that Green Finance and Green technology innovation reduce the amount of CO2 emission. Hence our results confirmed that both Green Finance and green technology innovations are significantly contributing to the environmental quality of China as the selected cities considered for the study investigation are considerably more driven towards green initiatives. This research also discussed the potential for implementing policy interventions in the future to leverage the adoption of green technology and make a significant impact on the world’s environmental quality as China is the biggest emitter of CO2 emissions. The study places and puts forward valuable insights to the regulatory bodies that would likely help them in achieving sustainable milestones and would contribute to their long-term strategic landscape for the attainment of environmental objectives and heights.

The impact of corporate governance on firm performance: panel data evidence from S&P 500 Information Technology

This research is important for both the academic and business environments due to the extraordinary results obtained. Additionally, the significance of the study is also attributed to the addressed topic, which is intensively studied in the world of corporate finance. The primary aim of this research is to scrutinize a cohort of 66 information and technology (IT) companies, all of which are constituents of the American Standard and Poor’s 500 Index (S&P 500). The study period spans two decades, covering the years 2003–2022. To summarize the outcomes, the analytical framework incorporated linear models with both fixed (fe) and random effects (re), as well as quantile regression models. This study's key outcomes highlight that firm size, sales growth, current ratio, long-term debt to capital, free cash flow, asset turnover and receivable turnover, board meeting frequency, female board representation, chief executive officer age, audit committee independence, and the presence of compensation and nomination committees, alongside a pandemic indicator, positively impact firm performance. Conversely, firm age, dividend payout ratio, effective tax rate, board size, chief executive officer duality, and corporate social responsibility committee presence have negative effects on performance. Also, regarding quantile regressions, CEO duality significantly influences companies with high profitability rates, and companies with low to medium profitability rates are more strongly and negatively influenced by board size. The implications of the core policy in this research focusing on corporate governance will consider certain rules and guidelines regarding financial transparency and protecting shareholders' interests. Additionally, it will take into account the independence of the board of directors and the presence of its committees, as well as ethical leadership practices.

Health facilities readiness for standard precautions to infection prevention and control in Nepal: A secondary analysis of Nepal Health Facility Survey 2021.

Improvements in standard precaution related to infection prevention and control (IPC) at the national and local-level health facilities (HFs) are critical to ensuring patient's safety, preventing healthcare-associated infections (HAIs), mitigating Antimicrobial Resistance (AMR), protecting health workers, and improving trust in HFs. This study aimed to assess HF's readiness to implement standard precautions for IPC in Nepal. This study conducted a secondary analysis of the nationally-representative Nepal Health Facility Survey (NHFS) 2021 data and used the Service Availability and Readiness Assessment (SARA) Manual from the World Health Organization (WHO) to examine the HF's readiness to implement standard precautions for IPC. The readiness score for IPC was calculated for eight service delivery domains based on the availability of eight tracer items: guidelines for standard precautions, latex gloves, soap and running water or alcohol-based hand rub, single use of standard disposal or auto-disable syringes, disinfectant, safe final disposal of sharps, safe final disposal of infectious wastes, and appropriate storage of infectious waste. We used simple and multiple linear regression and quantile regression models to examine the association of HF's readiness with their characteristics. Results were presented as beta (β) coefficients and 95% confidence interval (95% CI). The overall readiness scores of all HFs, federal/provincial hospitals, local HFs, and private hospitals were 59.9±15.6, 67.1±14.4, 59.6±15.6, and 62.6±15.5, respectively. Across all eight health service delivery domains, the HFs' readiness for tuberculosis services was the lowest (57.8±20.0) and highest for delivery and newborn care services (67.1±15.6). The HFs performing quality assurance activities (β = 3.68; 95%CI: 1.84, 5.51), reviewing clients' opinions (β = 6.66; 95%CI: 2.54, 10.77), and HFs with a monthly meeting (β = 3.28; 95%CI: 1.08, 5.49) had higher readiness scores. The HFs from Bagmati, Gandaki, Lumbini, Karnali and Sudurpaschim had readiness scores higher by 7.80 (95%CI: 5.24, 10.36), 7.73 (95%CI: 4.83, 10.62), 4.76 (95%CI: 2.00, 7.52), 9.40 (95%CI: 6.11, 12.68), and 3.77 (95%CI: 0.81, 6.74) compared to Koshi. The readiness of HFs to implement standard precautions was higher in HFs with quality assurance activities, monthly HF meetings, and mechanisms for reviewing clients' opinions. Emphasizing quality assurance activities, implementing client feedback mechanisms, and promoting effective management practices in HFs with poor readiness can help to enhance IPC efforts.

Association of plasma metals with resting-state functional connectivity in ischemic stroke

BackgroundMetal exposure has long been considered a significant risk factor for ischemic stroke. However, existing data on the effects of metal exposure on brain function in ischemic stroke are limited. Therefore, this study aimed to explore the correlation between exposure to various metals and changes in resting-state functional connectivity (rs-FC) in ischemic stroke patients. MethodsThis study included 28 acute ischemic stroke patients with hemiplegia and 28 matched healthy controls (HCs). All participants underwent T1-weighted MRI and 3.0 T resting-state functional magnetic resonance imaging (fMRI). After MRI acquisition, the rs-FC between 137 cortical and subcortical regions was extracted and preprocessed. Plasma levels of 19 metals were measured using inductively coupled plasma mass spectrometry (ICP-MS). The Bayesian kernel machine regression (BKMR) model and the weighted quantile sum regression (WQS) model were used to assess the overall effect of metal mixture exposure. The severity of neurological deficits in each acute ischemic stroke patient was evaluated using the National Institutes of Health Stroke Scale (NIHSS). Additionally, the associations between exposure to various metals and modifications in brain functional connectivity were determined using Pearson or Spearman correlation analysis. ResultsBilateral brain connectivity was significantly decreased compared to controls and was associated with neurological impairment in ischemic stroke. In patients with ischemic stroke, the plasma concentrations of Cr (p < 0.001), Cu (p = 0.004), As (p = 0.010), Cs (p = 0.046), Rb (p = 0.041), and Sb (p = 0.001) were significantly higher than those in the HCs, whereas the plasma Tl concentrations (p = 0.022) were significantly lower. The results of the BKMR and WQS models showed that combined exposure to metal mixtures was linked to a higher risk of ischemic stroke. Cr was positively correlated with the rs-FC between the left Rolandic_Oper and the left Supp_Motor_Area (r = 0.414, p = 0.029), while negatively correlated with the rs-FC between the right Parietal_Inf and the left supramarginal (r = −0.398, p = 0.037). Cu was negatively correlated with the rs-FC between the left paracentral lobule and the left thalamus (r = −0.409, p = 0.031). Tl was positively correlated with the rs-FC between the right Parietal_Inf and the left supramarginal cortex (r = 0.590, p = 0.001). A negative correlation was observed between Cs and rs-FC between the right Cingulate_Mid and left Occipital_Sup (r = −0.429, p = 0.024). Sb was negatively correlated with the rs-FC between the left Parietal_Inf and the right SupraMarginal (r = −0.384, p = 0.044), the right Parietal_Inf and the left SupraMarginal (r = −0.583, p = 0.001), and the left SupraMarginal and the right SupraMarginal (r = −0.377, p = 0.048). ConclusionPlasma levels of Cr, Cu, Tl, Cs, and Sb were associated with altered rs-FC in brain regions related to motor control, sensory integration, executive function, language processing, and emotional regulation in ischemic stroke patients with basal ganglia infarction.

Probabilistic ductile deformation limit state prediction of monolithic exterior shear keys based on quantile regression machine learning techniques

Shear strength and ductile deformation capacity of monolithic exterior shear keys play important roles on the seismic behavior of highway bridges. Improving the ductility of the shear key through reasonable design could effectively restrict superstructure displacement and minimize damage to pier columns. However, most existing studies primarily focus on evaluating the shear strength and damage mechanisms of shear keys, with limited attention given to analytical models for assessing ductile deformation capacity. To this end, this study proposes a method that combines quantile regression and machine learning techniques to predict the ductile deformation limit states of shear keys. The proposed numerical modeling method for shear keys is validated using the experimental data on lateral load-displacement curves. On this basis, Latin hypercubic sampling and joint simulation are utilized to establish the ductile deformation limit state database. The ductile deformation limit states of shear keys are characterized by their lateral displacements in three distinct damage states. The main influencing factors of each limit states are investigated by correlation analysis. Three quantile regression machine learning models are trained and constructed to conduct point and interval prediction for the ductile deformation limit states. All three models, particularly the Gaussian process quantile regression model, are proven to demonstrate exceptional prediction accuracy and uncertainty quantification. Experimental data validation confirms that these models outperform the prediction accuracy of existing simplified models.

Association between organophosphate esters individual and mixed exposure with the risk of hyperlipidemia and serum lipid levels among adults in Wuhan, China.

Toxicologic studies reported that organophosphate esters (OPEs) may disrupt lipid metabolism, thus affecting serum lipid levels. However, epidemiological evidence regarding the association between OPEs and the risk of hyperlipidemia (HPL) as well as serum lipid levels is scarce. In the present study, our aim was to investigate the impact of individual and mixed OPE exposure on HPL. A total of 1981 Chinese adults were involved based on a cross-sectional design. Overall, we found a positive association between bis(1,3-dichloro-2-propyl) phosphate (BDCIPP) and the risk of HPL. Bis(1-chloro-2-propyl) phosphate (BCIPHIPP) showed a positive association with total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C). BDCIPP, diphenyl phosphate (DPHP), di-ocresyl phosphate and di-p-cresyl phosphate (Docp&Dpcp), and 4-hydroxyphenyl-diphenyl phosphate (4-OH-DPHP) exhibited a negative association with high-density lipoprotein cholesterol (HDL-C). In stratified analyses, BDCIPP and BCIPHIPP were significantly correlated with the increased risk of HPL in the age ≤ 45 group. Bis(2-butoxyethyl) phosphate (BBOEP) was in relationship with an elevated risk of HPL in the subgroup of BMI < 24kg/m2. BDCIPP was also positively associated with HPL in men. Quantile-based g computation (qgcomp) and generalized weighted quantile sum regression (gWQS) models demonstrated a negative association between OPEs mixed exposure and HDL-c in the total population, as well as a positive effect of them on HPL in the subgroup of age ≤ 45years, which is consistent with the individual analyses. Furthermore, joint effect analyses revealed that participants with detected BDCIPP urinary levels and unhealthy lifestyles had the highest risk of HPL. Our findings offer evidence supporting the correlation between exposure to OPE and the risk of HPL, necessitating further prospective studies for validation.

Homogeneity Pursuit in the Functional-Coefficient Quantile Regression Model for Panel Data with Censored Data

Abstract Homogeneity identification of panel data models has been popular in the literature in recent years. Most of the existing works only focus on the complete data case. This paper considers a functional-coefficient quantile regression model for panel data with homogeneity when its response variables are subject to censoring. In particular, we consider a more general censoring framework, i.e. different types of censoring are allowed to occur in the model simultaneously. For this, a “three-stage” method is proposed, which includes the preliminary estimation of subject-specific function coefficients based on data augmentation, the identification of group structure over subjects by clustering, and post-grouping estimation of function coefficients. Simulation studies considering the left-, right-, and double-censored data, are carried out to verify the finite-sample properties of the proposed method. Simulation results show that our method gives comparable performance to the complete data case. The application to the bank stock data further illustrates the practical advantages of this method.

Modeling and Monitoring CO2 Emissions in G20 Countries: A Comparative Analysis of Multiple Statistical Models

The emission of carbon dioxide (CO2) is considered one of the main factors responsible for one of the greatest challenges faced by the world today: climate change. On the other hand, with the increase in energy demand due to the increase in population and industrialization, the emission of CO2 has increased rapidly in the past few decades. However, the world’s leaders, including the United Nations, are now taking serious action on how to minimize the emission of CO2 into the atmosphere. Towards this end, accurate modeling and monitoring of historical CO2 can help in the development of rational policies. This study aims to analyze the carbon emitted by the Group Twenty (G20) countries for the period 1971–2021. The datasets include CO2 emissions, nonrenewable energy (NREN), renewable energy (REN), Gross Domestic Product (GDP), and Urbanization (URB). Various regression-based models, including multiple linear regression models, quantile regression models, and panel data models with different variants, were used to quantify the influence of independent variables on the response variable. In this study, CO2 is a response variable, and the other variables are covariates. The ultimate objective was to choose the best model among the competing models. It is noted that the USA, Canada, and Australia produced the highest amount of CO2 consistently for the entire duration; however, in the last decade (2011–2021) it has decreased to 12.63–17.95 metric tons per capita as compared to the duration of 1971–1980 (14.33–22.16 metric tons per capita). In contrast, CO2 emissions have increased in Saudi Arabia and China recently. For modeling purposes, the duration of the data has been divided into two independent, equal parts: 1971–1995 and 1996–2021. The panel fixed effect model (PFEM) and panel mixed effect model (PMEM) outperformed the other competing models using model selection and model prediction criteria. Different models provide different insights into the relationship between CO2 emissions and independent variables. In the later duration, all models show that REN has negative impacts on CO2 emissions, except the quantile regression model with tau = 0.25. In contrast, NREN has strong positive impacts on CO2 emissions. URB has significantly negative impacts on CO2 emissions globally. The findings of this study hold the potential to provide valuable information to policymakers on carbon emissions and monitoring globally. In addition, results can help in addressing some of the sustainable development goals of the United Nation Development Programme.

The Healthy Eating Index 2020 components contributed unequally to systemic inflammatory biomarkers: A large national cross-sectional study.

The association of Healthy Eating Index 2020 (HEI-2020), especially its components with systemic inflammatory biomarkers, has not been examined. The aim of this study was to investigate the relationship between HEI-2020 and its components with systemic inflammatory biomarkers, and to provide a dietary pattern suggestion to combat systemic inflammation. Participants aged 20 years and older with complete information on two reliable dietary recalls, blood cell counts and demographic characteristics, were recruited from six NHANES cycles from 2007 to 2018. Weighted general linear methods showed that HEI-2020 was negatively associated with systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI). Weighted quantile regression (WQS) models and quantile g-computation (QGC) models supported the negative association between the mixed components and systemic inflammation. High intakes of whole fruits, whole grains, greens and beans, and seafood and plant proteins, along with a low intake of added sugars, were associated with reduced systemic inflammation. In contrast, the scores of sodium, dairy, total protein, and refined grains showed no significant effect. Briefly, our study provides an anti-inflammatory dietary pattern suggestion based on the 13 components of HEI-2020 and Dietary Guidelines for Americans.

Defining childhood hypertension: is it too complicated? An evaluation of the potential impact of different approaches in an Australian paediatric population.

Current American Academy of Pediatrics (AAP) and European Society of Hypertension (ESH) thresholds defining hypertension in children use blood pressure (BP) normalised to age, sex and height. However, scare data exists regarding the relative importance of these variables to accurately model the 95th quantile of BP. We hypothesised that height alone may fit the population data equally well compared to more complex definitions. We also compare the potential impact of various thresholds for defining hypertension in an Australian population. Longitudinal data from the Raine Study were used, with 2248 participants contributing 7479 valid BP values across the 3/5/10/14/17-year study visits. BP was measured after 5 min rest, ≥3 times at each visit, using a Dinamap device. Quantile regression was used to predict the 95th percentile of BP, with nonlinear modelling of covariates through restricted cubic spline terms. At a single visit, 6-16% of young children exceeded the ESH threshold and 12-23% the AAP threshold. The transition to fixed thresholds (≥13 years AAP, ≥16 years ESH), increased the number of males (AAP only) and reduced the number of females considered hypertensive. A quantile regression model constructed with Raine Study data using height-only as the explanatory variable better predicted BP than the respective model using age-only (or a combination of the two). There may be large differences in the prevalence of hypertension according to AAP and ESH criteria, with a marked sex-discrepancy emerging from the point of fixed threshold application in adolescence. It may not be necessary to normalise BP by both age and height, the latter being a better predictor of childhood BP. Simpler methods may be preferable in clinical practice but require validation against clinical outcomes.

The Effect of High-Standard Farmland Construction Policy on Grain Harvest Losses in China

The United Nations included reducing harvest losses as a Sustainable Development Goal in 2015, sparking heightened research and policymaker interest in reducing losses to ensure food security. High-standard farmland construction plays a crucial role in ensuring food security. Few studies have combined high-standard farmland construction with grain harvest losses. Drawing on the data from the 2022 Chinese Post-Harvest Loss Survey (CPHLS 2022), the study utilizes OLS (ordinary least square) and quantile regression models to explore the impact of high-standard farmland construction on grain harvest losses. Empirical results show that high-standard farmland construction can significantly reduce grain harvest losses. The research conclusions are still valid after passing a series of robustness tests. The heterogeneity analysis shows that high-standard farmland construction significantly impacts on grain harvest losses for farmers in major grain-producing areas, plain areas, and eastern regions. Mechanism analysis reveals that high-standard farmland construction mainly reduces grain harvest losses by expanding operational scale and enhancing mechanization application. Based on research findings, the Chinese government should formulate a targeted high-standard farmland construction policy, optimize the agricultural machinery operating environment, and promote appropriate operational scale to ensure national food security.

Hazardous wildfire smoke events can alter dawn soundscapes in dry forests of central and eastern Washington, United States

As global wildfire activity increases, wildlife are facing greater exposure to hazardous smoke pollution – with unknown consequences for biodiversity. Research on the effects of smoke on wild animals is extremely limited, in part due to the inherent logistical challenges of observing how animals respond to smoke in real time. Passive acoustic monitoring may be a powerful tool to safely and effectively monitor biodiversity before, during, and after major smoke events. In this study, we used data collected from a large-scale network of bioacoustic recorders at 92 sites in central and eastern Washington state during August–September, 2019–2020 to investigate the effect of wildfire smoke on dawn soundscapes and, by extension, acoustically active wildlife. We used acoustic indices to document and characterize changes in soundscapes related to smoke exposure, including the Acoustic Complexity Index (ACI), Bioacoustic Index (BI), and Normalized Difference Soundscape Index (NDSI). Higher values of these indices likely indicate higher levels of biodiversity in our study area. We hypothesized that wildfire smoke would reduce bird vocalizations, leading to declines in ACI, BI, and NDSI at dawn, when birds are most active. We used linear and quantile regression models to test for an effect of daily exposure to fine particulate matter (PM2.5), a marker of wildfire smoke, on the mean daily values and the upper 90th percentile of each index at dawn. We also conducted a before-during-after analysis of a particularly hazardous smoke event that impacted our study area on September 12–14, 2020. We did not observe linear effects of daily PM2.5 on average or peak daily values of acoustic indices; however, we did observe a significant reduction in ACI and BI during the three-day smoke event in 2020 and in the two weeks following this air pollution episode. Our results indicate that, on average, ACI and BI were reduced by 2.7 % and 15.9 % during and 1.5 % and 11.0 % afterward, respectively. These findings add further evidence that wildfire smoke alters soundscapes, likely due to changes in the presence, abundance, or behavior of acoustically active animals. Furthermore, our study demonstrates that wildfire smoke may have delayed and/or cumulative effects on acoustically active wildlife. Our study highlights the potential for passive acoustic monitoring to document wildlife responses to smoke pollution and identify potentially relevant exposure periods.

Relationship between admission blood urea nitrogen levels and postoperative length of stay in patients with hip fracture: A retrospective study.

To investigate the relationship between admission blood urea nitrogen (BUN) levels and postoperative length of stay (LOS) in hip fracture (HF) patients. This retrospective study retrieved related data from the MIMIC-IV database, of which the laboratory variables were taken preoperatively. The patients were divided into 4 groups according to the BUN quartile levels. After exploring the nonlinear relationship between BUN and LOS by generalized additive model, their connection was further analyzed using the generalized linear models, quantile regression models, and interaction analysis. Receiver operating characteristic curve analysis and decision curve analysis were performed to evaluate its value in predicting first intensive care unit admission and in-hospital mortality. Totally 1274 patients with HF were enrolled in the study. There was a nonlinear relationship between BUN and LOS (P < .05). Besides, BUN was an independent predictor for LOS after adjusting different covariates in 3 models (P < .05). Age served as a significant interactor in this relationship (P < .05). Moreover, receiver operating characteristic curve and decision curve analysis revealed the predictive value of BUN for intensive care unit admission and in-hospital mortality in HF. Admission BUN level as a cost-effective and easy-to-collect biomarker is significantly related to LOS in patients with HF. It helps clinicians to identify potential high-risk populations and take effective preventions before surgery to reduce postoperative LOS.

A censored quantile transformation model for Alzheimer’s Disease data with multiple functional covariates

Abstract Alzheimer’s disease (AD) is a progressive disease that starts from mild cognitive impairment and may eventually lead to irreversible memory loss. It is imperative to explore the risk factors associated with the conversion time to AD that is usually right-censored. Classical statistical models like mean regression and Cox models fail to quantify the impact of risk factors across different quantiles of a response distribution, and previous research has primarily focused on modelling a single functional covariate, possibly overlooking the interdependence among multiple functional covariates and other crucial features of the distribution. To address these issues, this paper proposes a multivariate functional censored quantile regression model based on dynamic power transformations, which relaxes the global linear assumption and provides more robustness and flexibility. Uniform consistency and weak convergence of the quantile process are established. Simulation studies suggest that the proposed method outperforms the existing approaches. Real data analysis shows the importance of both left and right hippocampal radial distance curves for predicting the conversion time to AD at different quantile levels.