Species-environment relationships have been extensively explored through species distribution models (SDM) and species abundance models (SAM), which have become key components to understand the spatial ecology and population dynamics directed at biodiversity conservation. Nonetheless, within the internal structure of species\' ranges, habitat suitability and species abundance do not always show similar patterns, and using information derived from either SDM or SAM could be incomplete and mislead conservation efforts. We gauged support for the abundance-suitability relationship and used the combined information to prioritize the conservation of South American dwarf caimans (Paleosuchus palpebrosus and P. trigonatus). We used 7 environmental predictor sets (surface water, human impact, topography, precipitation, temperature, dynamic habitat indices, soil temperature), 2 regressions methods (Generalized Linear Models-GLM, Generalized Additive Models-GAM), and 4 parametric distributions (Binomial, Poisson, Negative binomial, Gamma) to develop distribution and abundance models. We used the best predictive models to define four categories (low, medium, high, very high) to plan species conservation. The best distribution and abundance models for both Paleosuchus species included a combination of all predictor sets, except for the best abundance model for P. trigonatus which incorporated only temperature, precipitation, surface water, human impact, and topography. We found non-consistent and low explanatory power of environmental suitability to predict abundance which aligns with previous studies relating SDM-SAM. We extracted the most relevant information from each optimal SDM and SAM and created a consensus model (2,790,583 km2) that we categorized as low (39.6%), medium (42.7%), high (14.9%), and very high (2.8%) conservation priorities. We identified 279,338 km2 where conservation must be critically prioritized and only 29% of these areas are under protection. We concluded that optimal models from correlative methods can be used to provide a systematic prioritization scheme to promote conservation and as surrogates to generate insights for quantifying ecological patterns.

Algae are highly sensitive to environmental factors, especially nutrient fluctuations; excessive nutrients can lead to the proliferation of specific algae species, resulting in dominance. In this study, we aimed to reevaluate changes in algal dominance from the perspective of resource utilization efficiency (RUE). We established 80 monitoring sites across different water systems, collecting water and phytoplankton samples. Using canonical correspondence analysis (CCA) and a generalized additive model (GAM), we analyzed the correlation between phytoplankton RUE and nutrient concentrations, quantifying the corresponding relationship between algal dominance and RUE. Our results indicate a significant negative correlation between the RUE of total phosphorus (TP) and total nitrogen (TN) concentration, but a positive correlation with N:P. The RUE of TN was negatively correlated with TN concentration and N:P. We constructed GAMs with interaction terms and confirmed a nonlinear relationship between algal dominance and RUE. When the RUE of TN was low, a positive correlation was observed, while a negative correlation was observed otherwise. These findings reveal the ecological adaptability of algal communities and provide valuable insights for predicting the risk of algal bloom outbreaks.

Understanding and quantifying the influences and contributions of air pollution emissions on water quality variations is critically important for surface water quality protection and management. To address this, we created a five-year daily data matrix of six water quality indicators-permanganate index (CODMn), NH3-N, pH, turbidity, conductivity, and dissolved organic matter (DOM)-and six air pollution indicators-O3, CO, NO2, SO2, 2.5 μm particulate matter (PM2.5), and inhalable particles (PM10)-using data from seven national monitoring stations along the world\'s longest water-diversion project, the Middle Route of the South-to-North Water Diversion Project in China (MR-SNWD). Multivariate techniques (Mann-Kendall, Spearman\'s correlation, lag correlation, and Generalized Additive Models [GAMs]) were applied to examine the nonlinear relationships and lag effects of air pollution on water quality. Air pollution and water quality exhibited marked spatial heterogeneity along the MR-SNWD, with all water quality parameters meeting Class I or II national standards and the air pollution indicators exceeding those thresholds. Except for CODMn and DOM, the other water quality and air pollution indicators exhibited significant seasonal differences. Air pollution exhibited significant lag effects on water quality at the northern stations, with NO2, SO2, PM2.5, and PM10 being highly correlated with changes in pH, with an average lag of 17 d. Based on the GAMs, lag effects enhanced the significant nonlinear relationships between air pollution and water quality, increasing the average deviance explained for CODMn, NH3-N, and pH by 93%, 24%, and 41%, respectively. These findings provide a scientific basis for protecting water quality along the long-distance inter-basin water-diversion project under anthropogenic air pollution.

BACKGROUND: Wastewater monitoring data can be used to estimate disease trends to inform public health responses. One commonly estimated metric is the rate of change in pathogen quantity, which typically correlates with clinical surveillance in retrospective analyses. However, the accuracy of rate of change estimation approaches has not previously been evaluated.
OBJECTIVE: We assessed the performance of approaches for estimating rates of change in wastewater pathogen loads by generating synthetic wastewater time series data for which rates of change were known. Each approach was also evaluated on real-world data.
METHODS: Smooth trends and their first derivatives were jointly sampled from Gaussian processes (GP) and independent errors were added to generate synthetic viral load measurements; the range hyperparameter and error variance were varied to produce nine simulation scenarios representing different potential disease patterns. The directions and magnitudes of the rate of change estimates from four estimation approaches (two established and two developed in this work) were compared to the GP first derivative to evaluate classification and quantitative accuracy. Each approach was also implemented for public SARS-CoV-2 wastewater monitoring data collected January 2021-May 2023 at 25 sites in North Carolina, USA.
RESULTS: All four approaches inconsistently identified the correct direction of the trend given by the sign of the GP first derivative. Across all nine simulated disease patterns, between a quarter and a half of all estimates indicated the wrong trend direction, regardless of estimation approach. The proportion of trends classified as plateaus (statistically indistinguishable from zero) for the North Carolina SARS-CoV-2 data varied considerably by estimation method but not by site.
CONCLUSIONS: Our results suggest that wastewater measurements alone might not provide sufficient data to reliably track disease trends in real-time. Instead, wastewater viral loads could be combined with additional public health surveillance data to improve predictions of other outcomes.

We examined PM2.5 and Hazard Mapping System smoke plume satellite data at ∼600 United States (US) air monitoring stations to identify surface smoke on 14.0% of all May-September days for 2018-2023, with large influences in 2020 and 2021, due to California fires, and 2023, due to Canadian fires. Days with smoke have an average of 11 μg m-3 more PM2.5 and 8 ppb higher maximum daily 8 h average (MDA8) O3 concentrations than nonsmoke days, and they also account for 94% of all days that exceed the daily PM2.5 health standard (35 μg m-3) and 36% of all days that exceed the O3 health standard (70 ppb). To estimate the smoke contributions to the O3 MDA8, Generalized Additive Models (GAMs) were built for each site using the nonsmoke day data and up to 8 predictors. The mean and standard deviation of the residuals from the GAMs were 0 ± 6.1 ppb for the nonsmoke day data and 4.3 ± 7.9 ppb for the smoke day data, indicating a significant enhancement in the MDA8 O3 on smoke days. We found positive residuals on 72% of the smoke days and for these days, we calculate an average smoke contribution to the O3 MDA8 of 7.8 ± 6.0 ppb. Over the 6 year period, the percentage of exceedance days due to smoke in the continental US was 25% of all exceedance days, and the highest was in 2023 (38%). In 2023, the Central US experienced an unusually high number of exceedance days, 1522, with 52% of these impacted by smoke, while the Eastern US had fewer exceedance days, 288, with 78% of these impacted by smoke. Our results demonstrate the importance of wildland fires as contributors to exceedances of the health-based national air quality standards for PM2.5 and O3.

Since steroids are crucial for diagnosing endocrine disorders, the lack of research on factors that affect hormone levels makes interpreting the results difficult. Our study aims to assess the stability of the pre-analytical procedure and the impact of hormonal physiological fluctuations using real-world data. The datasets were created using 12,418 records from individuals whose steroid hormone measurements were taken in our laboratory between September 2019 and March 2024. 22 steroid hormones in plasma by a well-validated liquid chromatography tandem mass spectrometry method were measured. After normalization transformation, outlier removal, and z-score normalization, generalized additive models were constructed to evaluate preanalytic stability and age, sex, and sample time-dependent hormonal fluctuations. Most hormones exhibit significant variability with age, particularly steroid hormone precursors, sex hormones, and certain corticosteroids such as aldosterone. 18-hydroxycortisol, 18-oxocortisol. Sex hormones varied between males and females. Levels of certain hormones, including cortisol, cortisone, 11-deoxycortisol, 18-hydroxycortisol, 18-oxocortisol, corticosterone, aldosterone, estrone, testosterone, dihydrotestosterone, dehydroepiandrosterone sulfate, 11-ketotestosterone, and 11-hydroxytestosterone, fluctuated with sampling time. Moreover, levels of pregnenolone and progesterone decreased within 1 hour of sampling, with pregnenolone becoming unstable with storage time at 4 degrees after centrifugation, while other hormone levels remained relatively stable for a short period of time without or after centrifugation of the sample. This is the first instance real-world data has been used to assess the pre-analytic stability of plasma hormones and to evaluate the impact of physiological factors on steroid hormones.

BACKGROUND: The inconsistent relationship between chemical exposure and estimated glomerular filtration rate (eGFR) has been examined in only a few studies. We investigated the association between paraben exposure and indicators of renal function in a total of 361 individuals recruiting from a representative study.
METHODS: The levels of urinary parabens, including methylparaben (MeP), ethylparaben (EtP), propylparaben (PrP), and butylparaben (BuP), were measured using UPLC-MS/MS. The association between paraben exposure and indices of renal function was assessed using multiple logistic regression and Bayesian Kernel Machine Regression (BKMR).
RESULTS: The median levels of urinary parabens in the adult group were significantly higher than those in the minor group, that is, 397 vs. 148 ng/mL for MeP, 38.8 vs. 13.6 ng/mL for EtP, 117 vs. 57.7 ng/mL for PrP, and 6.61 vs. 2.79 ng/mL for BuP (all P < 0.001). In the adult group, multivariate regression models confirmed a positive association between the albumin-to-creatinine ratio and urinary MeP (β = 0.580) and a positive association of BUN (β = 0.061) and a negative association of eGFR (β = -0.051) with urinary EtP (all P < 0.001). In the adult group, compared with the lowest tertile group, the adjusted odds ratio in the third tertile (T3) of urinary EtP levels indicated a 3.08 times increased risk of eGFR abnormalities, followed by the second tertile (T2) with a 2.63 times increased risk. The generalized additive model (GAM) and BKMR models showed a non-linear correlation between urinary EtP levels and early CKD, as well as reduced eGFR. We observed a significant positive cumulative effect of urinary paraben on eGFR, and a significant positive single exposure effect of urinary EtP with eGFR abnormality.
CONCLUSIONS: We found a significant association between exposure to EtP and an increased risk of high BUN levels and decreased eGFR.

OBJECTIVE: Previous research shows conflicting views on the relationship between obesity and osteoporosis, partly due to variations in obesity classification and the nonlinear nature of these relationships. This study investigated the association between adiposity indices and osteoporosis, diagnosed using dual-energy X-ray absorptiometry (DXA), employing nonlinear models and offering optimal thresholds to prevent further bone mineral density decline.
METHODS: In 2019, a prospective study enrolled males over 50 years and postmenopausal women. Anthropometric measurements, blood biochemistry, and osteoporosis measured by DXA were collected. Associations between adiposity indices and osteoporosis were analyzed using a generalized additive model and segmented regression model.
RESULTS: The study included 872 women and 1321 men. Indices such as abdominal volume index (AVI), visceral adiposity index (VAI), waist circumference (WC), hip circumference, body mass index (BMI), waist-to-hip ratio, and waist-to-height ratio (WHtR) were inversely associated with osteoporosis. In women, the relationship between the risk of osteoporosis and the adiposity indices was U-shaped, with thresholds of WC = 94 cm, AVI = 17.67 cm2, BMI = 25.74 kg/m2, VAI = 4.29, and WHtR = 0.61, considering changes in bone mineral density. Conversely, men exhibited a linear patterns for the inverse association.
CONCLUSIONS: The impact of obesity and adiposity on osteoporosis varies significantly between women and men. In postmenopausal women, the relationship is nonlinear (U-shaped), with both very low and very high adiposity linked to higher osteoporosis risk. In men over 50, the relationship is linear, with higher adiposity associated with lower osteoporosis risk. The study suggests that maintaining specific levels of adiposity could help prevent osteoporosis in postmenopausal women.

UNASSIGNED: Research exploring the link between dietary riboflavin intake and cognitive decline in this demographic is limited. Our aim was to examine the association between riboflavin intake levels and cognitive decline.
UNASSIGNED: The National Health and Nutrition Examination Survey (NHANES) data from 2011 to 2014 were utilized in this cross-sectional analysis. The Consortium to Establish a Registry for Alzheimer\'s Disease test Word Learning delayed recall trial (DR), Digit Symbol Substitution Test (DSST), Animal Fluency Test(AFT) and Z test were used to evaluate cognitive performance. Multivariate logistic regression, restricted cubic spline and subgroup analysis were performed to evaluate the associations between riboflavin intake and cognitive decline.
UNASSIGNED: The study included a total of 2255 patients, with 47.9% being male. The incidence of cognitive decline was 23.8%. After adjusting for all selected covariates, we found that high riboflavin intake was associated with a lower risk of cognitive impairment in adults in the United States. When riboflavin intake was used as a Categorical variable, compared to those with the lowest intake, the odds ratio (OR) of individuals with the highest riboflavin intake for DR test, AFT test, DSST test and Z test were 0.73 (95% CI: 0.53~1), 0.68(95% CI: 0.49-0.96),0.53(95% CI: 0.37-0.77) and 0.56(95% CI: 0.39-0.8). The study also found an L-shaped association between riboflavin intake and cognitive decline, with an inflection point at approximately 2.984 mg/d.
UNASSIGNED: Our cross-sectional study in a nationwide sample of American old adults suggests that dietary riboflavin intake was negative associated with cognitive decline.

Plant phenology is an important indicator of the impact of climate change on ecosystems. We have continuously monitored vegetation phenology using near-surface remote sensing, i.e., the PhenoCam in a gully region of the Loess Plateau of China from March 2020 to November 2022. In each image, three regions of interest (ROIs) were selected to represent different types of vegetation (scrub, arbor, and grassland), and five vegetation indexes were calculated within each ROI. The results showed that the green chromatic coordinate (GCC), excess green index (ExG), and vegetation contrast index (VCI) all well-captured seasonal changes in vegetation greenness. The PhenoCam captured seasonal trajectories of different vegetation that reflect differences in vegetation growth. Such differences may be influenced by external abiotic environmental factors. We analyzed the nonlinear response of the GCC series to environmental variables with the generalized additive model (GAM). Our results suggested that soil temperature was an important driver affecting plant phenology in the Loess gully region, especially the scrub showed a significant nonlinear response to soil temperature change. Since in situ phenology monitoring experiments of the small-scale on the Loess Plateau are still relatively rare, our work provides a reference for further understanding of vegetation phenological variations and ecosystem functions on the Loess Plateau.