Nonlinear Relationship Research Articles

Cerebrospinal Fluid CCL25 as a Biomarker for Alzheimer's Disease: Associations with Pathology, Neurodegeneration, and Cognitive Decline.

Neuroinflammation plays a crucial role in Alzheimer's disease (AD) pathogenesis. We investigated the relationship between cerebrospinal fluid (CSF) C-C chemokine ligand 25 (CCL25), an inflammatory regulator, and AD pathology and progression. We analyzed data on CSF CCL25, AD biomarkers (CSF β-amyloid [Aβ]42, phosphorylated tau [pTau]181, amyloid positron emission tomography [PET]), postmortem neuropathology, magnetic resonance imaging-based neurodegeneration, and cognitive function from 703 participants in the Alzheimer's Disease Neuroimaging Initiative cohort. We found that elevated CSF CCL25 levels were associated with cognitive impairment, abnormal Aβ and tau pathology, greater brain atrophy, and worse cognitive performance (all P < 0.05). Notably, CSF CCL25 exhibited nonlinear relationships with Aβ and tau pathology, reaching a plateau as AD pathology increased. CSF CCL25 showed acceptable diagnostic accuracy in distinguishing amyloid-positive/negative (A ±) and tau-positive/negative (T ±) participants (area under the curve [AUC] = 0.71-0.77) and autopsy-confirmed AD cases (AUC = 0.77), with optimal performance in differentiating A + T + from A-T- participants (AUC = 0.82-0.85 with age and sex adjustment). Longitudinally, higher baseline CSF CCL25 predicted accelerated amyloid accumulation, hippocampal atrophy, and cognitive decline. Mediation analyses revealed that CCL25 partially mediated associations between Aβ pathology and tau pathology (mediating effect: 54.5%), neurodegeneration (18.2%), and cognitive decline (7.4%). Among 37 CSF CCL and CXCL chemokines examined, 28 were associated with at least one AD-related outcome, with CCL25 demonstrating the strongest associations overall. These findings suggest that CSF CCL25 is involved in early AD pathological progression and may serve as an inflammatory biomarker for diagnosis and monitoring of disease progression in AD.

Nonlinear Relationship Between Vital Signs and Hospitalization/Emergency Department Visits Among Older Home Healthcare Patients and Critical Vital Sign Cutoff for Adverse Outcomes: Application of Generalized Additive Model.

Previous studies have focused on identifying risk factors for older adults receiving home healthcare services without considering vital signs. This may provide important information on deteriorating health conditions that may lead to hospitalization and/or emergency department (ED) visits. Thus, it is important to understand the relationship between vital signs and hospitalization and/or ED visits and critical vital sign points for mitigating the higher risks of hospitalization and/or ED visits. This secondary data analysis uses cross-sectional data from a large, urban home healthcare organization (n = 61,615). A generalized additive model was used to understand the nonlinear relationship between each vital sign and hospitalization and/or ED visits through three unadjusted and adjusted models, and to identify a critical vital sign point related to a higher risk of hospitalization and/or ED visits. A significant nonlinear relationship (effective degree of freedom >2.0) was found between systolic, diastolic blood pressure, heart rate, hospitalization, and/or ED visits. The critical inflection point for systolic blood pressure was 120.36 (SE 3.625, p < .001), diastolic blood pressure was 72.00 (SE 3.108, p < .001), and heart rate was 83.24 (SE 1.994, p = .052). Among all vital signs, the risk of hospitalization and/or ED visits sharply increased when an older adult's heart rate surpassed 83.24 bpm. Our findings reveal that vital signs may serve as a critical indicator of a patient's clinical condition, especially related to hospitalization and/or ED visit. Clinicians need to be cognizant of these critical thresholds for each vital sign and monitor any deviations from baseline to preempt adverse outcomes.

Nonlinear association between AST/ALT ratio and 28-day all-cause mortality following ICU admission in critically ill cirrhotic patients: a retrospective cohort study

BackgroundThe AST/ALT ratio is a biochemical marker associated with poor clinical outcomes in various patients, but its role in severe cirrhosis is unclear. This study investigated the relationship between the AST/ALT ratio and mortality in the intensive care unit (ICU) patients with cirrhosis.MethodsThis retrospective cohort study analyzed 2,090 liver cirrhosis patients from the MIMIC-IV database, focusing on their first ICU admission between 2008 and 2019. The AST/ALT ratio, measured within 24 h of admission, was the exposure variable, and the main outcome was 28-day mortality. A multivariable logistic regression model evaluated the link between the AST/ALT ratio and mortality. Nonlinear relationships were explored using smooth curve fitting and saturation effect analyses. Stratified analyses and interaction tests were also performed based on demographic and clinical characteristics.ResultsThe study involved 2,090 critically ill liver cirrhosis patients, averaging 59.1 years old, with 65% male and a 28-day post-ICU admission mortality rate of 29%. The AST/ALT ratio was linked to mortality risk (adjusted odds ratio (OR) 1.1, 95% confidence interval (CI) 1.0–1.2; p = 0.015), showing a nonlinear pattern with a critical point at 3.6. Below this, each unit increase raised mortality risk by 40% (adjusted OR 1.4, 95% CI 1.2–1.6, p < 0.001), but the effect plateaued beyond this level (adjusted OR 1.0, 95% CI 0.8–1.1, p = 0.600). Subgroup analyses confirmed the consistent association, with interaction P values over 0.05.ConclusionsThe AST/ALT ratio is an independent predictor of 28-day mortality in critically ill cirrhotic patients, with a nonlinear relationship (risk increases up to a ratio of ~ 3.6, then plateaus). This marker could enhance ICU risk stratification and inform clinical decision-making.

Does budget proposal evaluation intensity curb budgetary slack? Examining the role of myopic loss aversion under experimental in multiperiod setting

Purpose This study use myopic loss aversion theory to investigate the causal relationship between budget proposal evaluation intensity and budgetary slack under agency problems. In particular, this study aims to explore the amount of optimum evaluation intensity to generate slight slack. Design/methodology/approach This research used a 2 × 3 between-subjects laboratory experiment with a multiperiod setting and was conducted on 112 accounting students. Information asymmetry manipulates into two levels (high vs low), whereas the budget proposal evaluation intensity manipulates into three levels (high vs low vs undetermined). Findings High budget proposal evaluation intensity played a more crucial role than low or undetermined ones. The result shows that there was a nonlinear relationship between budget proposal evaluation intensity and slack. The decrease in slack did not always accompany an increase in evaluation intensity. Therefore, it did not need increased evaluation intensity but proportional and optimum evaluation intensity to create slight slack. Originality/value This study provides new empirical evidence regarding the nonlinear relationship in budget evaluation. An excessive evaluation intensity can trigger fear immunity, thereby increasing budgetary slack. This study has also strived to reduce an overlapping concept of increased evaluation intensity and optimum evaluation intensity to create slight slack by applying the right basic concept during the experiment.

Temperature change and male infertility prevalence: an ecological study

BackgroundAlthough the effects of climate change on human health are widely recognized, its potential role in male infertility prevalence has not been thoroughly examined. This study seeks to explore the association between changes in ambient temperature and the prevalence of male infertility.MethodsThis ecological study encompassed 174 countries and regions. We utilized data from 2000 to 2019 on the age-standardized prevalence rate (ASPR) of male infertility and ambient temperature to assess their potential association. The analysis accounted for several covariates, including the Sociodemographic Index (SDI), continent, smoking prevalence, alcohol consumption per capita (APC), nitrogen dioxide (NO₂), and ozone (O₃). Annual temperature values were derived by averaging monthly temperatures, and the deviance percentage of temperature (DPT) was computed based on the 20-year mean temperature. To examine spatial and nonlinear relationships between temperature and male infertility ASPR, we applied the geographic detector approach and Restricted Cubic Spline (RCS) curves. Furthermore, linear mixed-effects models were employed to quantify the association between DPT and male infertility ASPR, and adjusted models were subsequently used to forecast changes in ASPR under projected temperature scenarios for 2020–2030.ResultsFrom 2000 to 2019, a spatial association was identified between temperature and the ASPR of male infertility. Additionally, a U-shaped correlation emerged, indicating the lowest ASPR at 15.7 °C. Higher DPT were linked to elevated male infertility ASPR, with an adjusted β estimate of 38.770 (95% CI: 8.392, 69.162). Projections suggest that ongoing temperature increases may continue to drive up male infertility ASPR.ConclusionTemperature change may be associated with an increased male infertility prevalence.

Inverted U-shaped relationship between HbA1c and diabetic retinopathy in diabetic patients: a cross-sectional study

BackgroundDiabetic retinopathy (DR) is a leading cause of blindness among adults with diabetes. Glycated hemoglobin A1C (HbA1C) is a critical biomarker for long-term glycemic control and has been closely associated with the risk of developing DR. However, the relationship between HbA1C and DR remains complex and multifaceted, with limited research exploring the nonlinear aspects of this association. This study aims to investigate the nonlinear relationship between HbA1C and DR, providing insights into their association and informing clinical interventions.ObjectiveMany studies have indicated that HbA1C is positively correlated with DR. However, although elevated HbA1C is common in patients with DR, its relationship with DR remains controversial. Our study aimed to investigate the nonlinear relationship between HbA1c and DR, thereby accurately elucidating their association and providing a basis for clinical interventions.MethodsThis study is the second analysis based on a cross-sectional studv. A total of 2,001 patients with type 2 Diabetes Mellitus (T2DM) visited the diabetic clinic in the Internal Medicine outpatient departments of two hospitals in southern Taiwan between April 2002 and November 2004 were included in this analysis. Demographic and clinical data were collected, and HbA1c levels were measured. The association between HbA1c and DR was analyzed using multivariate logistic regression, adjusting for potential confounders, and the potential nonlinear correlation was explored with a smooth curve fitting approach.ResultsThe fully-adjusted model showed that HbA1c positively correlated with DR (OR:1.13, 95%CI: 1.05–1.22). However, an inverted U-shaped association between them was observed by applying the smooth curve fitted method. The inflection point of HbA1c (9.4%) was calculated by utilizing the two-piecewise logistic regression model. In the subgroup analysis, the inverted U-shaped nonlinear correlation between HbA1c and DR was also found in age, sex and BMI.ConclusionsHbA1C and DR have an inverted U-shaped relationship, with a peak at an HbA1C of 9.4% in the early phase of DR. After this peak, HbA1C decreases as DR increases. These results have crucial implications for DR patients. The findings also offer insights for public health policy, highlighting the necessity of regular screening and intervention for diabetic patients. Future research should further explore the mechanisms linking HbA1c to DR and consider individualized management strategies for different populations to effectively mitigate the burden of DR.

Association between Neutrophil Percentage-to-Albumin ratio and anemia risk: a population-based study

Anemia remains a significant global health challenge, driven by complex inflammatory mechanisms. This study investigated the association between neutrophil percentage-to-albumin ratio (NPAR) and anemia risk, utilizing data from 24,938 participants in the National Health and Nutrition Examination Survey (2005–2018). Multivariable logistic regression analysis revealed a significant association between NPAR and anemia risk (OR = 1.16; 95% CI 1.13–1.18, p < 0.0001). Two-piecewise regression analysis identified a nonlinear relationship with a threshold at NPAR 11.96: below this threshold, an inverse association was observed (OR = 0.88; 95% CI 0.79–0.98, p = 0.0249), while above it, a positive association was evident (OR = 1.21; 95% CI 1.18–1.25, p < 0.0001). Subgroup analyses demonstrated stronger associations in males, non-Hispanic Whites, diabetic patients, and individuals who were married or living with a partner. These findings highlight the potential of NPAR as a novel biomarker for assessing anemia risk in clinical practice.

Association between cardiometabolic index and cardiovascular disease: evidence From the NHANES 2007–2018

BackgroundAlthough the cardiometabolic index (CMI) has gained recognition as a new tool for evaluating metabolic health, the relationship between CMI and cardiovascular disease (CVD) remains unclear. This research sought to explore the potential association between CMI and CVD.MethodsParticipants from the 2007–2018 National Health and Nutrition Examination Survey (NHANES) were selected. Multivariable logistic regression analyses and smooth curve fitting were utilized to investigate this relationship, along with subgroup evaluations and interaction analyses.ResultsThis study included 12,837 subjects and the prevalence of CVD was 11.83%. After full adjustment, participants presenting with an increase of one unit in Ln-transformed CMI associated a 15% higher odds of CVD prevalence (OR = 1.15, 95% CI: 1.05–1.26). In the fully adjusted model, individuals falling into the highest CMI quartile (Quartile 4) demonstrated substantially 35% higher odds than those in the lowest CMI quartile (Quartile 1) (OR = 1.35, 95% CI: 1.11–1.66). In addition, there was no nonlinear relationship between CMI and CVD in our selected sample. This positive association was not greatly influenced by any of the stratifications.ConclusionsAmong US adults, having higher CMI levels is substantially associated with higher odds of CVD prevalence. This finding suggests that regular monitoring of CMI levels could enable physicians to initiate early interventions, potentially slowing the progression of CVD. However, in order to corroborate our findings, further prospective investigations are still required.

Insulin resistance assessed by estimated glucose disposal rate and the risk of abdominal aortic calcification: findings from a nationwide cohort study

PurposeThe estimated glucose disposal rate (eGDR) serves as a straightforward and noninvasive indicator of insulin resistance (IR). This study aims to explore the association between eGDR and the risk of abdominal aortic calcification (AAC).MethodsWe utilized data from adult participants (≥40 years old, n=3006) from the 2013–2014 National Health and Nutrition Examination Survey (NHANES) database. AAC was measured by dual-energy X-ray absorptiometry and quantified using the Kauppila score. Severe AAC (SAAC) was defined as an AAC score &amp;gt; 6. Logistic regression, restricted cubic spline (RCS), and subgroup analysis were used to analyze the relationship between eGDR and SAAC risk.ResultsIn fully adjusted models, eGDR was found to be negatively associated with SAAC (OR=0.86, 95%CI:0.79–0.94, P&amp;lt;0.001). Compared to participants in the lowest eGDR quantile, those in the highest quantile exhibited a lower risk of SAAC (OR=0.47, 95%CI:0.25–0.91, P=0.026). The RCS analysis indicates a nonlinear relationship between eGDR and SAAC risk, with a turning point at 7.05 mg/kg/min. Subgroup analysis showed that the association between eGDR and SAAC risk was more significant in women.ConclusionsThe degree of IR assessed by eGDR is associated with SAAC risk. The eGDR shows promise as an epidemiological tool for evaluating the influence of IR on AAC.

Association Analysis of Benzo[a]pyrene Concentration Using an Association Rule Algorithm

Benzo[a]pyrene is an important indicator of polycyclic aromatic hydrocarbons pollution that exhibits complex atmospheric dynamics influenced by meteorological factors and suspended particulate matter (SPM). Herein, the factors influencing B(a)P concentration were elucidated by analyzing the monthly environmental data for Kyoto, Japan, from 2001 to 2021 using an improved association rule algorithm. Results revealed that B(a)P concentrations were 1.3–3 times higher in cold seasons than in warm seasons and SPM concentrations were lower in cold seasons. The clustering performance was enhanced by optimizing the K-means method using the sum of squared error. The efficiency and reliability of the traditional Apriori algorithm were enhanced by restructuring its candidate itemset generation process, specifically by (1) generating C2 exclusively from frequent itemset L₁ to avoid redundant database scans and (2) implementing the iterative pruning of nonfrequent subsets during Lk → Ck+1 transitions, adding the lift parameter, and eliminating invalid rules. Strong association rules revealed that B(a)P concentrations ≤ 0.185 ng/m3 were associated with specific meteorological conditions, including humidity ≤ 58%, wind speed ≥ 2 m/s, temperature ≥ 12.3 °C, and pressure ≤ 1009.2 hPa. Among these, changes in pressure had the most substantial impact on the confidence of the association rules, followed by humidity, wind speed, and temperature. Under the influence of high SPM concentrations, favorable meteorological conditions further accelerated pollutant dispersion. B(a)P concentration increased with increasing pressure, decreasing temperature, and decreasing wind speed. Principal component analysis confirmed the robustness and accuracy of our optimized association rule approach in quantifying complex, nonlinear relationships, while providing granular, interpretable insights beyond the traditional methods.

Dependence of premature ventricular complexes on heart rate-it's not that simple.

Frequent premature ventricular complexes (PVCs) can lead to adverse health conditions such as cardiomyopathy. The linear correlation between PVC frequency and heart rate (as positive, negative, or neutral) on a 24-hour Holter recording has been proposed as a way to classify patients and guide treatment with beta-blockers. Our objective was to evaluate the robustness of this classification to measurement methodology, different 24-hour periods, and nonlinear dependencies of PVCs on heart rate. We analyzed 82 multi-day Holter recordings (1-7 days) collected from 48 patients with frequent PVCs (burden 1%-44%). For each record, linear correlation between PVC frequency and heart rate was computed for different 24-hour periods and using different length intervals to determine PVC frequency. Using a 1-hour interval, the correlation between PVC frequency and heart rate was consistently positive, negative, or neutral on different days in only 36.6% of patients. Using shorter time intervals, the correlation was consistent in 56.1% of patients. Shorter time intervals revealed nonlinear and piecewise linear relationships between PVC frequency and heart rate in many patients. The variability of the correlation between PVC frequency and heart rate across different 24-hour periods and interval durations suggests that the relationship is neither strictly linear nor stationary. A better understanding of the mechanism driving the PVCs, combined with computational and biological models that represent these mechanisms, may provide insight into the observed nonlinear behavior and guide more robust classification strategies. Linear correlation as a tool to classify patients with frequent PVCs should be used with caution. It is sensitive to the specific 24-hour period analyzed and the methodology used to segment the data. More sophisticated classification approaches that can capture nonlinear and time-varying dependencies should be developed and considered in clinical practice.

The integration of green finance and logistics: implications for green industry transformation

Green finance (GF), as a critical policy tool for supporting green development, plays a pivotal role in directing capital flows toward low-carbon and environmentally friendly industries. This provides essential support for the green transformation (GT) of the logistics industry (LI). However, limited research exists on the regional GT of the LI from the perspective of GF and LI integration. To address this gap, this study develops an evaluation index system for the integration of GF and the LI, as well as a separate system for assessing the GT of the LI. Using panel data from 30 Chinese provinces spanning 2010 to 2022, the study employs the Super-SBM-Undesirable model and the coupling coordination degree model for evaluation and analysis. Additionally, a threshold effect model is applied to examine the nonlinear relationship between GF-LI integration and the GT of the LI, alongside a heterogeneity analysis to explore regional disparities. The findings reveal three key insights: (1) In recent years, the integration of GF and the LI in China has significantly improved, advancing from a mildly imbalanced stage to moderate and high levels of coordination. However, significant regional disparities in the level of integration persist. (2) The integration of GF and the LI has a significant positive effect on the GT of the regional LI, characterized by an S-shaped relationship. (3) The impact of GF-LI integration on the GT of the LI exhibits notable regional heterogeneity. Compared with existing research results, this study expands the application scope of GF, focusing on the GT of the LI, a fundamental service industry, and further verifies the universality of GF in promoting low-carbon devel-opment in different industries. In addition, current research on the GT of the LI mainly focuses on technological innovation, environmental regulation, and supply chain optimization, while there is less attention paid to how GF can promote the GT of the LI through industrial integration.

Weight on Bit and Hook Load Prediction in Drilling Based on the GA-RF

Abstract Weight on bit (WOB) and hook load are important parameters in the drilling process, and the accurate application of WOB and hook load can improve drilling efficiency. The traditional mathematical model needs to consider many relevant factors, the model is difficult to establish, and the prediction accuracy of the model is poor. In order to overcome these problems, this study established a genetic algorithm-optimized random forest (GA-RF) prediction model for WOB and hook load based on the real-time drilling data of a block in the Sichuan Basin, China. First, the data are filtered and denoised, and the correlation analysis of drilling parameters is carried out. It is found that the linear correlation between the WOB and other drilling parameters was not high, while the linear correlation between the hook load and the standpipe pressure is up to 0.82. Then, a random forest (RF) optimized by a genetic algorithm (GA) is established according to the data. The prediction model transmits the number of decision trees and the maximum tree depth of GA to the RF. The RF model is used to deal with the nonlinear relationship of drilling parameters to WOB and hook load, and is compared with other models. The research results indicate that the GA-RF algorithm outperforms other algorithms in predicting WOB and hook load, with an R2 as high as 0.976 for predicting WOB and up to 0.998 for predicting hook load. A comparison of various prediction models reveals that the GA-RF model demonstrates high predictive accuracy for both WOB and hook load.

The Effectiveness of Lean Management Practices on Organizational Performance: A Meta-Analysis of Empirical Studies

Lean management is a collection of management practices developed to advance competence and profitability by getting rid of waste. Lean's basic principle is to reduce and eliminate waste and non-value-adding operations in order to increase the profitability and competitiveness of the firm. The purpose of the research is to determine if lean management practices have a positive impact on organizational performance. This paper presents a hybrid approach to solving this problem. The association between lean techniques and business effectiveness was investigated using a meta-analysis. The results show that lean methods positively affect operational and non-operational efficiency. Understanding the nonlinear relationship between Lean Management Practices and business success may be aided by the broad perspective provided by this article's last section.

Effects of Sweep Ratio on High-pressure Membrane Dehumidifiers

Abstract This study investigates the performance of high-pressure membrane dehumidifiers (MDs) over a wide range of sweep ratios and feed mass flow rates to emulate the operating conditions of the high-pressure section of an air cycle machine (ACM). A previously validated numerical model [1] and experimental data were used to assess dehumidification efficiency, which exhibited asymptotic behavior with increasing sweep ratio. However, discrepancies between the model and experimental results are shown to emerge at higher feed mass flow rates. These discrepancies are likely due to compressible flow effects, which the model does not capture, coupled with flow choking on the sweep side. Additionally, the study reveals that while dehumidification efficiency tends to approach an asymptote, the dew point continues to decrease significantly with increasing sweep ratios and lower feed mass flow rates, underscoring the nonlinear relationship between dew point and humidity ratio. This behavior indicates that system designers must consider not only efficiency metrics but also dew point reductions in optimizing ACM performance. These findings highlight the need for both component-level and system-level considerations in ACM design incorporating MDs, particularly the use of additional membrane capacity, e.g., parallel membrane operation, to achieve lower dew points and improved overall system performance.

Triglyceride-glucose index as an independent predictor of mortality in patients with chronic respiratory diseases

ObjectiveThe consequences of chronic pulmonary illness are known to exacerbate in individuals with metabolic syndrome and insulin resistance. However, the relationship between triglyceride-glucose (TyG) index, a reliable alternative biomarker of metabolic dysfunction, and chronic respiratory diseases (CRDs) are inconclusive.Research design and methodsOur research involved a total of 7,819 adult individuals diagnosed with CRDs who participated in the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2018. To assess the correlation between the TyG index and survival rates, we employed multivariable weighted Cox regression analysis, smoothing curve fitting, survival curve analysis and subgroup analysis to investigate the relationship.ResultsHigher TyG index among CRDs shown a substantial positive correlation with all-cause mortality after controlling for relevant confounders. The restricted cubic spline analysis showed a nonlinear relationship between the TyG score and all-cause mortality in CRDs. Patients with higher TyG indexes had a greater risk of all-cause mortality according to Kaplan-Meier survival curves.ConclusionThe clinical relevance of the TyG index in predicting the life expectancy of individuals with CRDs is highlighted by our research. The TyG index can serve as a substitute biomarker for monitoring the wellbeing of the individuals with CRDs.

Projecting Daily Maximum Temperature Using an Enhanced Hybrid Downscaling Approach in Fujian Province, China

The rise in global temperatures and increased extreme weather events, such as heatwaves, underscore the need for accurate regional projections of daily maximum temperature (Tmax) to inform effective adaptation strategies. This study develops the CNN-BMA-QDM model, which integrates convolutional neural networks (CNNs), Bayesian model averaging (BMA), and quantile delta mapping (QDM) to downscale and project Tmax under future climate scenarios. The CNN-BMA-QDM model stands out for its ability to capture nonlinear relationships between Tmax and atmospheric circulation factors, reduce model uncertainty, and correct bias, thus improving simulation accuracy. The CNN-BMA-QDM model is applied to Fujian Province, China, using three CMIP6 GCMs and four shared socioeconomic pathways (SSPs) to project Tmax from 2015 to 2100. The results show that CNN-BMA-QDM outperforms CNN-BMA, CNNs, and other downscaling methods (e.g., RF, BPNN, SVM, LS-SVM, and SDSM), particularly in simulating extreme value at the 99% and 95% percentiles. Projections of Tmax indicate consistent warming trends across all SSP scenarios, with spatially averaged warming rates of 0.0077 °C/year for SSP126, 0.0269 °C/year for SSP245, 0.0412 °C/year for SSP370, and 0.0526 °C/year for SSP585. Coastal areas experience the most significant warming, with an increase of 4.62–5.73 °C under SSP585 by 2071–2100, while inland regions show a smaller rise of 3.64–3.67 °C. Monthly projections indicate that December sees the largest increase (5.30 °C under SSP585 by 2071–2100), while July experiences the smallest (2.40 °C). On a seasonal scale, winter experiences the highest warming, reaching 4.88 °C under SSP585, whereas summer shows a more modest rise of 3.10 °C. Notably, the greatest discrepancy in Tmax rise between the south and north occurs during the summer. These findings emphasize the importance of developing tailored adaptation strategies based on spatial and seasonal variations. The results provide valuable insights for policymakers and contribute to the advancement of regional climate projection research.

The Association Between Obesity and Wet Overactive Bladder: Results from 2005-2020 National Health and Nutrition Examination Survey.

The association between obesity and wet overactive bladder (wet OAB) was few, and was also inconsistent and controversial in sex differences. This study aimed to ascertain the specific association between obesity and wet OAB for females and males using five obesity measures. This cross-sectional study included 29,041 participants from the 2005-2020 National Health and Nutrition Examination Survey database. The outcome was the risk of wet OAB. Five obesity measures were exposure using quintile, including body mass index (BMI), waist-to-stature ratio (WSR), body fat percentage (BFP), fat mass, and lean mass. The associations were elucidated using weighted logistic regression models and restricted cubic spline analysis. The elevated BMI, WSR, BFP, and fat mass were positively associated with the risk of wet OAB in both sexes, which was evident in females. Interestingly, lean mass was adversely associated with the risk of wet OAB in both sexes. In restricted cubic spline (RCS) analysis about BMI, WSR, BFP, and fat mass, monotonically increasing nonlinear associations were found in males, while positive linear associations were found in females. Besides, negative linear and nonlinear relationships were shown between lean mass and the risk of wet OAB in females and males, respectively. Obesity was positively associated with the risk of wet OAB in both sexes. Controlling BMI, WSR, BFP, fat mass, and strengthening lean mass may help prevent the development of wet OAB.

The combined multilayer perceptron and logistic regression (MLP-LR) method better predicted the spread of Hyphantria cunea (Lepidoptera: Erebidae).

Hyphantria cunea (Lepidoptera: Erebidae) is one of the pests that pose a serious threat to forest and agronomic crops in China. Its spread is influenced by various factors, including environmental factors and anthropogenic factors, and the available data on pest spread and the influencing factor has nonlinear relationship. Additionally, the collection of pest data is often constrained, resulting in small datasets, a lack of long-term time series data, and issues such as missing data and anomalies. Traditional model-driven approaches have limitations in handling nonlinear relationships and high-dimensional data, while data-driven methods often lack interpretability and are prone to overfitting, ultimately leading to insufficient prediction accuracy. Therefore, this paper proposes the MLP-LR method, which combines logistic regression (LR) with a multilayer perceptron (MLP) to overcome these limitations. The model also used the Bayesian adaptive lasso method to select important influencing factors, that further improved the prediction accuracy. Based on H. cunea occurrence data in China, the current study demonstrated the stability and accuracy of the MLP-LR model on small datasets. The results showed that compared to traditional LR models and MLP independently, MLP-LR performs better in predicting the spread of H. cunea, effectively addressing the shortcomings of traditional methods. This study provides a new tool and perspective for forecasting and early warning of H. cunea outbreaks, offering important references for future research and its applications in the field.

Association between eosinophil number and overweight status: a nonlinear, bidirectional study

BackgroundThe relationship between eosinophil number and overweight status (or obesity) remains a subject of debate. While animal studies suggest a negative correlation between the two, most clinical studies indicate a positive correlation. Therefore, we hypothesize that a nonlinear relationship may exist between eosinophil number and overweight status. This study aims to investigate the association between eosinophil number and overweight status (as well as related indicators) using data from the National Health and Nutrition Examination Survey (NHANES).MethodsWe utilized data from NHANES 1999–2018, where eosinophil number was obtained from laboratory tests. Overweight status was defined as a body mass index (BMI) ≥ 25. We then applied weighted logistic regression/linear regression, subgroup analysis, and restricted cubic splines (RCS) analysis to investigate the association between eosinophil number and overweight status (as well as related indicators).ResultsA total of 77,217 individuals were included in this study, with 38,106 individuals in the non-overweight group (BMI < 25) and 39,111 individuals in the overweight group (overweight and obesity, BMI ≥ 25). The logistic regression analysis revealed a significant association between eosinophil number and overweight status (OR: 2.38, 95% CI: 1.81–3.12, P < 0.001). Additionally, eosinophil number was significantly positively correlated with obesity/BMI/triglycerides and negatively correlated with High-Density Lipoprotein (HDL). Finally, the nonlinear regression results indicated an inverted U-shaped relationship between eosinophil number and overweight status/obesity/BMI.ConclusionOur study demonstrates an inverted U-shaped relationship between eosinophil number and overweight status/obesity/BMI. Eosinophil number is also significantly associated with HDL and triglycerides. These findings suggest that eosinophils may play a role in overweight (or obesity) and provide valuable insights for exploring the underlying immune mechanisms of overweight status.