Generalized Additive Models For Location, Scale And Shape Research Articles

Ranking performances of Olympic-style weightlifters adjusted for body mass on the same scale for both sexes: A novel approach

ABSTRACT Males typically outperform females in Olympic-style weightlifting, as judged by total weight lifted, and similarly heavier athletes outperform lighter athletes. Current methods can standardise weightlifting performances across body mass to compare different body mass classes, but they do not address the sex gap. This study introduces a novel approach that puts male and female performances on a single unified scale, across the range of body mass, allowing mixed sex teams to be compared. The method uses standardised residuals or “z-scores” derived from models of performance regressed on sex and body mass fitted by GAMLSS (generalised additive models for location, scale and shape). Reference data come from the International Weightlifting Federation (IWF) world championships and Olympics 2017–2023. To ensure the validity of the model for ranking team performances in the future, results from 2000 to 2023 were compared and shown to be stable over time. A web application is available to aid athletes, coaches and health professionals to compare performances and monitor progress over time.

Development and validation of an ultrasound-based estimated fetal weight reference for Chinese twin pregnancy: a retrospective cohort study

BackgroundFetal growth monitoring is important for twin pregnancies. However, there has been no clinically validated tool for monitoring fetal growth of twin pregnancies in China. This study aims to develop and validate a chorionicity-specific growth chart of ultrasound estimated fetal weight (EFW) for Chinese twin pregnancies.MethodsThis retrospective cohort study included all twin pregnancies who delivered two live fetuses with gestational age ≥ 34 weeks without severe obstetric complications at a tertiary hospital from January 2007 to March 2021. The participants were divided into a development set (delivered in or before December 2017) and a validation set (delivered in or after January 2018). Chorionicity-specific growth charts were created using the generalized additive models for location, scale, and shape (GAMLSS) based on the development set. The fetuses from the validation set were classified into three groups based on the last EFW: small-for-gestational-age (SGA) indicated by both the newly established twin charts and the Hadlock singleton chart currently used for twin pregnancies in China, suspected SGA indicated by only the singleton chart, and no SGA indicated by either chart. The incidence of neonatal outcomes among the three groups was then compared accordingly, including intensive care unit (NICU) stay length, respiratory diseases, and neurological disorders.ResultsThe development set included 883 twin pregnancies and a total of 6374 EFW measurements between 16 and 38 weeks of gestation, and the validation set included 801 twin pregnancies and 7630 EFW measurements. In the development set, monochorionic diamniotic (MCDA) twins had a significantly lower EFW compared to dichorionic diamniotic (DCDA) twins beginning at 26 weeks, with the difference gradually increasing thereafter, supporting the establishment of chorionicity-specific growth charts. Of the 1,602 twin neonates in the validation set, 103 (6.4%) were classified into the SGA group, 164 (10.2%) into the suspected SGA group, and 1335 (83.3%) into the no SGA group. The incidence of respiratory diseases and neurological disorders was comparable between the suspected SGA group and the no SGA group, but apparently higher in the SGA group. Meanwhile, NICU stay lengths were consistently longer for twins in the SGA group compared to the no SGA group (difference: 0.57, 95% CI: 0.31–0.83), with no significant differences observed between the suspected SGA and no SGA groups.ConclusionsThe fetal growth trajectories differed by chorionicity, with a lower EFW for MCDA twins beginning at 26 weeks. The establishment of chorionicity-specific growth chart could reduce overdiagnosis of SGA and improve fetal growth monitoring of twin pregnancies.

Rice Responses to the Stem Borer Diatraea saccharalis (Lepidoptera: Crambidae) by Infrared-Thermal Imaging: Implications for Field Management

Diatraea saccharalis (Fabricius) is a major pest of rice crops, and its early detection—before any visible plant damage occurs—is crucial to prevent yield losses and establish effective, rational control methods. This study aimed to model the infrared-thermal responses of rice cultivars to D. saccharalis infestation levels. Between 2019 and 2020, two experiments were conducted in a controlled environment using the cultivars IR 40 and BR IRGA 409, previously identified as having different resistance reactions. Rice plants grown in pots were manually infested with first-instar larvae of D. saccharalis, ranging from 0 to 10 caterpillars per plant, with the plants maintained in cages covered with voile fabric throughout the trial. After 30 days of infestation, the number of live and dead caterpillars, the number of damaged and healthy stems, and the dry mass of the aerial parts were evaluated. A generalized linear mixed model was applied to the data obtained from leaf temperature as a function of infestation level throughout the infestation period, using the F-test to detect significant differences between cultivars. Generalized Additive Models for Location, Scale, and Shape (GAMLSS) were fitted to the variables related to resistance. It was observed that leaf surface temperature is related to the level of infestation and could be used to detect susceptibility in IR 40. In both cultivars, leaf temperatures were higher within the first 15 days post-infestation.

Canadian adult reference data for body composition, trabecular bone score and advanced hip analysis using DXA

DXA-derived reference data for visceral adipose tissue (VAT) and advanced hip analysis (AHA) parameters spanning the entire adult lifespan are limited. The purpose of this study was to develop age-, site- and sex-specific reference data for dual X-ray absorptiometry (DXA) -derived body composition, trabecular bone score (TBS) and advanced hip analysis (AHA) parameters across the adult lifespan. Adults (N = 908; female: 561 and male: 347) from Calgary and the surrounding area over the age of 20 years participated in this study. Participants received DXA scans of their hip (total hip [TH] and femoral neck [FN]), lumbar spine [LS], forearm [33 % site] and total body (iDXA, GE Lunar, GE Healthcare). Areal bone mineral density (aBMD, g/cm2) was captured at all sites, and body composition variables, including lean mass, fat mass and percent fat, were analyzed from the total body scan. VAT mass was assessed from total body DXA scans. Advanced hip analysis (AHA) was performed on hip scans and trabecular bone score (TBS) on the LS scans to assess bone quality. Site- and sex-specific centile curves and tables were generated using the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) method. Clinicians and researchers can use these Canadian reference data as a tool to assess body composition, TBS and AHA parameters across the adult lifespan.

Item response theory-based continuous test norming.

In norm-referenced psychological testing, an individual's performance is expressed in relation to a reference population using a standardized score, like an intelligence quotient score. The reference population can depend on a continuous variable, like age. Current continuous norming methods transform the raw score into an age-dependent standardized score. Such methods have the shortcoming to solely rely on the raw test scores, ignoring valuable information from individual item responses. Instead of modeling the raw test scores, we propose modeling the item scores with a Bayesian two-parameter logistic (2PL) item response theory model with age-dependent mean and variance of the latent trait distribution, 2PL-norm for short. Norms are then derived using the estimated latent trait score and the age-dependent distribution parameters. Simulations show that 2PL-norms are overall more accurate than those from the most popular raw score-based norming methods cNORM and generalized additive models for location, scale, and shape (GAMLSS). Furthermore, the credible intervals of 2PL-norm exhibit clearly superior coverage over the confidence intervals of the raw score-based methods. The only issue of 2PL-norm is its slightly lower performance at the tails of the norms. Among the raw score-based norming methods, GAMLSS outperforms cNORM. For empirical practice this suggests the use of 2PL-norm, if the model assumptions hold. If not, or the interest is solely in the point estimates of the extreme trait positions, GAMLSS-based norming is a better alternative. The use of the 2PL-norm is illustrated and compared with GAMLSS and cNORM using empirical data, and code is provided, so that users can readily apply 2PL-norm to their normative data. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparison of fetal growth patterns from Western India with Intergrowth-21st.

To generate longitudinal fetal growth data in an Indian population and compare it with Intergrowth-21st. Fetal biometry data was collected in a prospective longitudinal observational study (REVAMP: Research Exploring Various Aspects and Mechanisms in Preeclampsia) from 2017 to 2022. Fetal crown-rump length (CRL) was measured at 11-14 weeks gestation, and biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL) at 18-22 and 32-35 weeks, and converted into Z-scores using the Intergrowth standard. Generalized Additive Models for Location, Scale and Shape (GAMLSS) models were used to construct fetal growth centile curves compared against Intergrowth centiles. Out of 1096 singleton pregnancies in REVAMP, this analysis included 655 'healthy' pregnancies (uncomplicated by pre-eclampsia, diabetes, pre-term delivery or low birth weight) and a sub-set of 106 'low-risk' pregnancies defined using Intergrowth criteria. The 'healthy' study subjects showed lower mean CRL Z-score [-0.45 SD (95% CI:-0.54,-0.37)] at 11-14 weeks, and BPD Z-score [-1.2 SD (-1.28,-1.11) and -1.17 SD (-1.23,-1.1)] at 18-22 and 32-35 weeks respectively. Mean HC Z-score was comparable to the Intergrowth standard at 18-22 weeks [-0.08 SD (-0.16, 0.02)] but smaller at 32-35 weeks [-0.25 SD (-0.32,-0.19)]. Mean AC Z-score was lower at 18-22 weeks [-0.32 SD (-0.41,-0.23)] but comparable at 32-35 weeks [0.004 SD (-0.07, 0.07)]. FL was comparable to or larger than the Intergrowth standard at both time points [0.05 SD (-0.05, 0.14); 0.82 SD (0.75, 0.89), respectively]. These findings were similar, though measurements were slightly larger, in the 'low-risk' sample. This data from healthy and low-risk pregnant women in urban western India indicates that some fetal dimensions and growth trajectories differ significantly from the Intergrowth-21st. Our data suggest the need for a larger representative study to define a population-specific fetal growth reference for India, for identification of fetal growth restriction.

Establishing Neuron-Specific Enolase Reference Intervals: A Comparative Analysis of Partitioned Approach- and Gender-Based Continuous Age- and Season-Related Models.

Background/Objectives: Static reference intervals (RIs) fail to capture the dynamic changes in bioanalytes. This study aimed to develop gender-based continuous age- and season-related RIs for neuron-specific enolase (NSE) using real-world data and to compare them with partitioned RIs. Methods: The NSE results from 4097 individuals were included after rigorous screening. Partitioned RIs were determined using the Hoffmann method. Generalized additive models for location, scale and shape (GAMLSS) were selected to develop continuous RIs. Results: The partitioned RIs are as follows: <16.4 µg/L for males aged ≥19 years; <14.47 µg/L for females aged 19-49 years; and <17.25 µg/L for females aged ≥50 years. For continuous RIs, NSE levels in males remain stable with age, while in females, NSE levels evidently increase around the age of 50. Although less impactful than age, seasonal changes still affect NSE levels. Dynamic changes and continuous RIs for NSE are visualized in this study. Conclusions: We developed gender-based continuous age- and season-integrated RIs for NSE in North China, highlighting the variation in NSE levels in females with age and season. Compared to static RIs, continuous RIs are more responsive to NSE, potentially enhancing the precision and individualization of health assessments.

Propagation from Meteorological to Hydrological Drought considering Nonstationarity in the Luanhe River Basin: Spatiotemporal Pattern and Driving Factors

Abstract Under the joint effects of climate change and human activities, the assumption of the stationarity of hydrological series has been overturned, which is of great significance in the field of hydrology. However, the current research on drought propagation is generally based on the assumption of sequence stationarity, in which related results may be biased. To this end, the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) model was used to construct a time-varying drought index. The seasonal propagation characteristics from meteorological to hydrological drought were examined based on conditional probability. The dynamic change of propagation characteristics was explored by utilizing the moving window, and the driving factors were revealed by using the variable importance in projection. The Luanhe River basin, which has a fragile ecological environment, was selected as a case study. The results indicated that 1) using a time-varying drought index was more reasonable than using a stationary assumption, and the latter relatively easily underestimated the propagation time, especially in summer; 2) drought propagation in summer and autumn was faster than that in spring and winter, and the propagation time showed a significant downward trend; 3) the trigger threshold (absolute value) of meteorological drought in spring was significantly higher than that in other seasons, and hydrological drought was more likely to be triggered by meteorological drought in autumn and winter; and 4) the precipitation P, decreasing runoff R, and increasing evaporation E were the main factors affecting the seasonal propagation characteristics. The findings of this study are of great significance for water resource management and further understanding of drought propagation mechanisms.

Fetal body composition reference charts and sexual dimorphism using magnetic resonance imaging

BackgroundThe American Academy of Pediatrics advises that the nutrition of preterm infants should target a body composition similar to that of a fetus in utero. Still, reference charts for intrauterine body composition are missing. Moreover, data on sexual differences in intrauterine body composition during pregnancy are limited. ObjectiveThe objective of this study was to create reference charts for intrauterine body composition from 30 to 36+6 weeks post-conception and to evaluate the differences between sexes. MethodsIn this single-center retrospective study, data of 197 normal developing fetuses in late gestation was acquired at 3T magnetic resonance imaging (MRI) scans, including True Fast Imaging with Steady State Free Precession and T1-weighted 2-point Dixon sequences covering the entire fetus, were included. Deep Convolutional Neural Networks were utilized to automatically segment the fetal body and subcutaneous adipose tissue. The fetus's body mass (BM), fat signal fraction (FSF), fat mass (FM), FM percentage (FM%), fat-free mass (FFM), and FFM percentage (FFM%) were calculated. Using the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) method, reference charts were created, and sexual dimorphism was examined using analysis of covariance (ANCOVA). A P-value below 0.05 is deemed significant. ResultsThroughout late gestation, BM, FSF, FM, FM%, and FFM increased, while the FFM% decreased. Reference charts and gestational age and sex-specific percentiles are provided. Males exhibited significantly higher BM (7.2%; 95% confidence interval [95% CI], 1.9-12.4%), FFM (8.5%; 95% CI, 3.7- 13.4%), and FFM% (1.2%; 95% CI, 0.6-1.7%) and lower FSF (-3.6%; 95% CI, -5.6-[-1.8]%) and FM% (-1.2%; 95% CI, -1.7-[-0.6]%), (P<0.001) compared with females, with no significant difference in FM between sexes (P=0.126). ConclusionsMRI-derived intrauterine body composition growth charts are valuable for tracking growth in preterm infants. This study demonstrated that sexual differences in body composition are already present in the intrauterine phase.

Machine learning to predict morphology, topography and mechanical properties of sustainable gelatin-based electrospun scaffolds

Electrospinning is an outstanding manufacturing technique for producing nano-micro-scaled fibrous scaffolds comparable to biological tissues. However, the solvents used are normally hazardous for the health and the environment, which compromises the sustainability of the process and the industrial scaling. This novel study compares different machine learning models to predict how green solvents affect the morphology, topography and mechanical properties of gelatin-based scaffolds. Gelatin-based scaffolds were produced with different concentrations of distillate water (dH2O), acetic acid (HAc) and dimethyl sulfoxide (DMSO). 2214 observations, 12 machine learning approaches, including Generalised Linear Models, Generalised Additive Models, Generalised Additive Models for Location, Scale and Shape (GAMLSS), Decision Trees, Random Forest, Support Vector Machine and Artificial Neural Network, and a total of 72 models were developed to predict diameter of the fibres, inter-fibre separation, roughness, ultimate tensile strength, Young’s modulus and strain at break. The best GAMLSS models improved the performance of R2 with respect to the popular regression models by 6.868%, and the MAPE was improved by 21.16%. HAc highly influenced the morphology and topography; however, the importance of DMSO was higher in the mechanical properties. The addition of the morphological properties as covariates in the topographic and mechanical models enhanced their understanding.

Wildfire Loss Modeling: A Flexible Semiparametric Approach

Wildfires have emerged as one of the most devastating natural disasters worldwide, which, in addition to a loss of life, results in billions of dollars in annual losses. This study aims to identify factors that contribute to the frequency and severity of wildfire losses. We propose a semiparametric modeling approach to analyze wildfire property losses in the United States from 1995 to 2020. Our methodology incorporates flexible generalized additive models for location, scale, and shape (GAMLSS), which integrate climate, socioeconomic, and geographical variables into the modeling process. Through empirical analysis, we demonstrate the critical role of spatial effects in modeling wildfire-related property losses. Additionally, we identify key variables, including house index, state population, wildfire duration, and climate index, that significantly contribute to explaining the average property loss. These findings offer valuable insights for enhancing catastrophe risk management for insurance companies and government agencies. Furthermore, the proposed approach can be easily applied to diverse regions grappling with spatially varying catastrophe risks.

Integration of an electronic hand hygiene auditing system with electronic health records using machine learning to predict hospital-acquired infection in a health care setting

Hospital-acquired infections (HAIs) increase morbidity, mortality, and healthcare costs. Effective hand hygiene (HH) is crucial for prevention, but achieving high compliance remains challenge. This study explores using machine learning to integrate an electronic HH auditing system with electronic health records to predict HAIs. A retrospective cohort study was conducted at a Brazilian hospital during 2017-2020. HH compliance was recorded electronically, and patient data were collected from electronic health records. The primary outcomes were HAIs per CDC/NHSN surveillance definitions. Machine learning algorithms, balanced with Random Over Sampling Examples (ROSE), were utilized for predictive modeling, including generalized linear models (GLM); generalized additive models for location, scale, and shape (GAMLSS); random forest; support vector machine; and extreme gradient boosting (XGboost). 125 of 6,253 patients (2%) developed HAIs and 920,489 HH opportunities (49.3% compliance) were analyzed. A direct correlation between HH compliance and HAIs was observed. The GLM algorithm with ROSE demonstrated superior performance, with 84.2% sensitivity, 82.9% specificity, and a 93% AUC. Integrating electronic HH auditing systems with electronic health records and using machine learning models can enhance infection control surveillance and predict patient outcomes. Further research is needed to validate these findings and integrate them into clinical practice.

Improved identification and monitoring of meteorological, agricultural, and hydrological droughts using the modified nonstationary drought indices in the Yellow River Basin of China

Non-stationarity in hydro-meteorological series complicates drought monitoring and assessment. Our study aimed to better identify and monitor meteorological, agricultural and hydrological droughts in the Yellow River Basin (YRB) of China. We constructed nonstationary standardized precipitation index (NSPI), nonstationary standardized precipitation evapotranspiration index (NSPEI), nonstationary standardized soil moisture index (NSSMI) and nonstationary standardized runoff index (NSRI) based on the Generalized Additive Models for Location, Scale, and Shape (GAMLSS). We also investigated the performance of NSPI/NSPEI/NSSMI/NSRI by comparing them with historical drought events. Our results revealed that: (1) The probability distribution function (PDF), smoothing function (SF), and degrees of freedom (DF) significantly influenced the fitting effect of the GAMLSS model. The PDF emerged as the most critical factor in determining fitting accuracy, while SF and DF played crucial roles in preventing overfitting and accurately depicting data variations. (2) Stationary GAMLSS models were primarily suited for time series with lower-scales (1- and 3-month scales), whereas nonstationary models exhibited better suitability for time series with higher-scale (6- and 12-month scales). (3) NSPI and NSPEI demonstrated heightened sensitivity in identifying meteorological droughts, with NSPEI showing greater precision in determining drought intensity and duration. However, compared to SSMI, while NSSMI aligned more closely with historical agricultural drought events, it exhibited limited responsiveness to mild and moderate drought. (4) NSRI indicated that hydrological droughts in the tributaries of the YRB were significantly influenced by meteorological droughts, and the intensity of hydrological drought was stronger than that in the mainstream of the YRB. In conclusion, the nonstationary drought indices could better identify drought events in the period of climate change, and provide valuable information for drought management in the climate change environment.

Low-level HIV-1 viremia affects T-cell activation and senescence in long-term treated adults in the INSTI era

BackgroundAround 10% of people with HIV (PWH) exhibit a low-level viremia (LLV) under antiretroviral therapy (ART). However, its origin and clinical significance are largely unknown, particularly at viremias between 50 and 200 copies/mL and under modern ART based on integrase strand transfer inhibitors (INSTIs). Our aim was to characterize their poor immune response against HIV in comparison to individuals with suppressed viremia (SV) and non-HIV controls (NHC).MethodsTransversal observational study in 81 matched participants: 27 PWH with LLV, 27 PWH with SV, and 27 NHC. Activation (CD25, HLA-DR, and CD38) and senescence [CD57, PD1, and HAVCR2 (TIM3)] were characterized in peripheral T-cell subsets by spectral flow cytometry. 45 soluble biomarkers of systemic inflammation were evaluated by immunoassays. Differences in cell frequencies and plasma biomarkers among groups were evaluated by a generalized additive model for location, scale, and shape (GAMLSS) and generalized linear model (GLM) respectively, adjusted by age, sex at birth, and ART regimen.ResultsThe median age was 53 years and 77.8% were male. Compared to NHC, PWH showed a lower CD4+/CD8+ ratio and increased activation, senescence, and inflammation, highlighting IL-13 in LLV. In addition, LLV showed a downtrend in the frequency of CD8+ naive and effector memory (EM) type 1 compared to SV, along with higher activation and senescence in CD4+ and CD8+ EM and terminally differentiated effector memory RA+ (TEMRA) subpopulations. No significant differences in systemic inflammation were observed between PWH groups.ConclusionLLV between 50 and 200 copies/mL leads to reduced cytotoxic activity and T-cell dysfunction that could affect cytokine production, being unable to control and eliminate infected cells. The increase in senescence markers suggests a progressive loss of immunological memory and a reduction in the proliferative capacity of immune cells. This accelerated immune aging could lead to an increased risk of developing future comorbidities. These findings strongly advocate for heightened surveillance of these PWH to promptly identify potential future complications.

Distributional modelling of positively skewed data via the flexible Weibull extension distribution

SummaryThe time until an event occurs is often known to have a skewed distribution. To model this, a statistical distribution called the two‐parameter flexible Weibull extension (FWE) has been proposed. In this paper, the FWE distribution is used to model datasets through the use of generalised additive models for location, scale and shape (GAMLSS) distributional regression. GAMLSS is the only regression technique that can examine the effects of both categorical and numeric predictors on all the parameters of the distribution used to fit the dependent variable. To make it easier to use the FWE distribution through GAMLSS, the RelDists R package is proposed. A simulation study shows that FWE modelling through GAMLSS provides reliable parameter estimates even in the presence of factors that affect the distribution.

Assessment of dynamic hydrological drought risk from a non‐stationary perspective

AbstractThe stationarity hypothesis of hydrometeorological elements has been questioned in the context of global warming and intense human disturbance. The conventional drought index and methods of frequency analysis may no longer be applicable for hydrological drought risk evaluation under a changing environment. In this study, a new dynamic hydrological drought risk evaluation framework is proposed for application to the Hanjiang River basin (HRB), which simultaneously considers the non‐stationarity in the construction of drought index as well as in the frequency analysis. First, a non‐stationary standardized runoff index (NSRI) is developed using a generalized additive model for location, scale and shape (GAMLSS) framework. Then, hydrological drought characteristics including duration and severity are identified, and their marginal distributions are established. Finally, based on the dynamic copula, considering the non‐stationarity of the dependence structure, the dynamic joint probability distribution, conditional probability distribution and return period of the bivariate hydrological drought properties are analysed. Results showed that NSRI, which integrates the impacts of climate change and anthropogenic activities on the non‐stationarity of runoff series, had a better ability to capture runoff extremes than had SRI. In addition, it is indispensable to consider the non‐stationarity of the dependence structure between variables when discussing the multivariate joint risk of hydrological drought. The risk of hydrological drought in the study area has shown an increasing trend in the past 65 years, and the drought conditions from upstream to downstream have been alleviated first and then intensified. This study provides valuable information for regional drought risk estimation and water resources management from a non‐stationary perspective.

Viability of high-frequency environmental DNA (eDNA) sampling as a fish enumeration tool

Despite growing interest in environmental DNA (eDNA) monitoring as a low-cost, non-invasive tool for monitoring fish presence and relative abundance, quantitative comparisons between fish abundance and eDNA are still rare, especially for anadromous species of conservation concern. To examine the relationship between eDNA data and management-relevant abundance data, we assessed eDNA abundance of river herring, an ecologically relevant anadromous sister-species pair, alongside sonar-generated fish counts at a well-characterized site in the Choptank River, Chesapeake Bay, MD. Over the 2021 spawning season, there was a strong correlation between daily eDNA concentration and daily counts of fish swimming upstream (Spearman’s rho = 0.86). A generalized additive model for location scale and shape (GAMLSS) was used to estimate fish count as a function of flow-corrected eDNA concentration and relevant covariates. Model- and sonar-generated run counts were 729,385 and 733,208 fish respectively, differing by just 0.5 %. A sub-sampling analysis showed that a model built on at least five weeks of data could utilize eDNA abundance to produce accurate estimates for the remainder of the season, while a reduction in eDNA sampling frequency substantially reduced run count accuracy. This work emphasizes the utility of quantitative eDNA monitoring for river herring populations and shows the necessity of high-frequency eDNA sampling to estimate run count.

Establishment of discrete reference interval and next-generation reference interval for copper and zinc during pregnancy using real-world data

BackgroundCopper and zinc are two trace elements that are essential to maintain normal pregnancy and fetal development. But only few research established specific reference intervals (RIs) for pregnant women. In this study, we aim to establish discrete RIs and next-generation RIs for copper and zinc during pregnancy by real-world data. MethodWe retrospectively collected 710 healthy pregnant women and 300 age-matched non-pregnant women attending the hospital and compared copper and zinc levels among them. Further, we analyzed multiple factors (gestational age, maternal age, and parity) that may affect copper and zinc during pregnancy by multivariate regression. Two types of reference intervals (RIs) of copper and zinc for pregnant women were established: discrete reference intervals (RIs) by non-parametric method and next-generation RIs by Generalized Additive Models for Location, Scale, and Shape (GAMLSS) model. ResultCopper levels were higher (median: 1st trimester: 1203.00 μg/L, 2nd trimester: 1818.00 μg/L, 3rd trimester: 1795.00 μg/L) than in non-pregnant women (median: 900.00 μg/L, p＜0.001), whereas zinc levels were lower in pregnant women (median: 1st trimester: 836.00 μg/L, 2nd trimester: 639.00 μg/L, 3rd trimester: 618.00 μg/L) than in non-pregnant women (median: 767.00 μg/L, p＜0.001). Additionally, copper and zinc levels varied among trimesters. Moreover, copper and zinc were affected significantly by gestational age but maternal age only had a weak effect on them. Based on the effect of gestational age, we established discrete RIs partitioned by trimesters and next-generation RIs for copper and zinc respectively during pregnancy. ConclusionTaken together, this research elucidated the remarkable effect of gestational age on copper and zinc during pregnancy. The next-generation RIs visualized trends and subtle changes in copper and zinc levels during pregnancy. The discrepancy between discrete RIs and next-generation RIs suggested a more detailed continuous RIs could be considered for pregnant women.

Non-stationary low flow frequency analysis under climate change

Analysis of low river flows provides important information for effective management of water resources in a region. Despite the critical importance of understanding low flow dynamics, there is a gap in the literature regarding the use of non-stationary models to analyze low flow data under climate change in Turkey. In this research, low flow series from 80 measuring stations in Turkey are investigated by employing both stationary and non-stationary models based on the Generalized Additive Models for Location, Scale and Shape (GAMLSS). For constructing non-stationary models, 31 explanatory variables consisting of time, precipitation, temperature and atmospheric oscillation indices were used to model the parameters of the chosen distributions. The results show that stationary models are more successful at 7 stations, while non-stationary models are more successful at 73 stations. Comparisons between non-stationary models showed that for most stations, the best performing models were non-stationary models with annual precipitation as covariates. In addition, successful results were obtained when Western Mediterranean Oscillation and North Atlantic Oscillation indices were used as explanatory variables. Additionally, this study investigated 20 and 50-year return levels by fitting the non-stationary frequency distribution models for low flows over historical and projection periods under SSP2-4.5 and SSP5-8.5 climate scenarios. GAMLSS incorporated annual total precipitation, which is the most effective explanatory variable for low flows, as a covariate, and thus changes in low flows were analyzed. The results show that decreases are expected in low flows, except for the stations in the upper Euphrates basin compared to the historical period.

Field Data Analysis of Pavement Marking Retroreflectivity and Its Relationship with Paint and Glass Bead Characteristics

Pavement marking retroreflectivity, a critical factor for safe driving, depends on the characteristics of both the paint and the embedded glass beads. However, traditional methods for predicting pavement marking service life often overlook these materials properties. This study investigates the influence of paint and glass bead characteristics on pavement marking retroreflectivity performance and addresses the characterization of glass bead size distribution by the coefficient of uniformity and curvature. Three field test sites on a Brazilian highway with various paint and glass bead combinations were evaluated. A statistical model, GAMLSS (Generalized Additive Model for Location, Scale, and Shape), was adjusted to evaluate the performance of the markings’ retroreflectivity as a function of paint and glass bead characteristics. The model revealed that well-graded glass beads increased retroreflectivity by around 10%, while paints with a higher volume of solids improved service life around 65%. Therefore, the results show that acrylic water-based paints with higher volumes of solids and well-graded glass beads with better shape characteristics should be preferred to improve pavement markings’ retroreflectivity and service life. The statistical model identified the key characteristics with the greatest impact on pavement marking retroreflectivity, offering valuable insights for real-world applications, which will assist pavement marking practitioners and road authorities in selecting appropriate materials to achieve enhanced durability.