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Lead poisoning is often considered a traditional disease; however, the specific mechanism of toxicity remains unclear. The study of Pb-induced alterations in cellular metabolic pathways is important to understand the biological response and disorders associated with environmental exposure to lead. Metabolomics studies have recently been paid considerable attention to understand in detail the biological response to lead exposure and the associated toxicity mechanisms. In the present study, wild rodents collected from an area contaminated with lead (N = 18) and a control area (N = 10) were investigated. This was the first ever experimental metabolomic study of wildlife exposed to lead in the field. While the levels of plasma phenylalanine and isoleucine were significantly higher in a lead-contaminated area versus the control area, hydroxybutyric acid was marginally significantly higher in the contaminated area, suggesting the possibility of enhancement of lipid metabolism. In the interregional least-absolute shrinkage and selection operator (lasso) regression model analysis, phenylalanine and isoleucine were identified as possible biomarkers, which is in agreement with the random forest model. In addition, in the random forest model, glutaric acid, glutamine, and hydroxybutyric acid were selected. In agreement with previous studies, enrichment analysis showed alterations in the urea cycle and ATP-binding cassette transporter pathways. Although regional rodent species bias was observed in this study, and the relatively small sample size should be taken into account, the present results are to some extent consistent with those of previous studies on humans and laboratory animals.

Fish growth can be highly variable among populations of the same species due to differences in abundance, system productivity and watershed characteristics. Because of this, understanding factors that influence fish growth and body condition is important to managers for fish conservation, to meet angler desires and to support local economies. As ecosystems respond to a changing climate, species compositions can change. In north temperate lakes, this is often exemplified by an increase in largemouth bass (LMB). These lakes are often managed for multiple fish species concurrently, making standardized fishery‐independent LMB data limited, creating challenges for managing this species. As such, a better understanding of factors influencing LMB body condition and growth may become critically important in the future. We assessed LMB age, length, and weight data to test for abiotic and biotic lake characteristics explaining variation in LMB body condition, asymptotic length, and mean length at age metrics in Wisconsin from 1994 to 2022. Macrophyte species composition and lake classification relationships were the two primary predictors of variation in LMB growth. Lakes with degraded macrophyte communities were associated with larger individual LMB sizes as were lake class types that contained cool water and riverine characteristics. Our results provide fisheries managers with options when dealing with diverse angler desires and a heterogenous landscape of lakes. Where available, macrophyte species composition data can be consulted by managers to identify opportunities to provide a trophy fishing experience in a system that otherwise would be undervalued. As populations of LMB increase in Wisconsin lakes, a better understanding of how to effectively reach goals set by managers, and what realistic goals might be, is required, and understanding what lake characteristics can explain variation of body condition gives insight to that end.

Satellite-based models for estimating concentrations of particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) have seldom been developed in islands with complex topography over the monsoon area, where the transport of PM2.5 is influenced by both the synoptic-scale winds and local-scale circulations compared with the continental regions. We validated Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) with ground observations in Japan and developed a 1-km-resolution national-scale model between 2011 and 2016 to estimate daily PM2.5 concentrations. A two-stage random forest model integrating MAIAC AOD with meteorological variables and land use data was applied to develop the model. The first-stage random forest model was used to impute the missing AOD values. The second-stage random forest model was then utilised to estimate ground PM2.5 concentrations. Ten-fold cross-validation was performed to evaluate the model performance. There was good consistency between MAIAC AOD and ground truth in Japan (correlation coefficient = 0.82 and 74.62% of data falling within the expected error). For model training, the model showed a training coefficient of determination (R2) of 0.98 and a root mean square error (RMSE) of 1.22 μg/m3. For the 10-fold cross-validation, the cross-validation R2 and RMSE of the model were 0.86 and 3.02 μg/m3, respectively. A subsite validation was used to validate the model at the grids overlapping with the AERONET sites, and the model performance was excellent at these sites with a validation R2 (RMSE) of 0.94 (1.78 μg/m3). Additionally, the model performance increased as increased AOD coverage. The top-ten important predictors for estimating ground PM2.5 concentrations were day of the year, temperature, AOD, relative humidity, 10-m-height zonal wind, 10-m-height meridional wind, boundary layer height, precipitation, surface pressure, and population density. MAIAC AOD showed high retrieval accuracy in Japan. The performance of the satellite-based model was excellent, which showed that PM2.5 estimates derived from the model were reliable and accurate. These estimates can be used to assess both the short-term and long-term effects of PM2.5 on health outcomes in epidemiological studies.

High-speed railways (HSRs) are established all over the world owing to their advantages of high speed, ride comfort, and low vibration and noise. A ballastless track slab is a crucial part of the HSR, and its working condition directly affects the safe operation of the train. With increasing train operation time, track slabs suffer from various defects such as track slab warping and arching as well as interlayer disengagement defect. These defects will eventually lead to the deformation of track slabs and thus jeopardize safe train operation. Therefore, it is important to monitor the condition of ballastless track slabs and identify their defects. This paper proposes a method for monitoring track slab deformation using fiber optic sensing technology and an intelligent method for identifying track slab deformation using the random-forest model. The results show that track-side monitoring can effectively capture the vibration signals caused by train vibration, track slab deformation, noise, and environmental vibration. The proposed intelligent algorithm can identify track slab deformation effectively, and the recognition rate can reach 96.09%. This paper provides new methods for track slab deformation monitoring and intelligent identification.

Chinese New Year has traditionally been welcomed with fireworks, but this has meant this holiday can experience intense peaks of pollutants, particularly as particulate matter. Such environmental issues add to other risks (e.g., accident, fire, and ecological and health threats) posed by firework displays, but cultural reasons encourage such celebrations. This study examines air pollution from fireworks across a time of increasingly stringent bans as a time series from 2014–2021 using a random forest (decision-tree) model to explore the effect of year-to-year weather changes on pollutant concentrations at Chinese New Year. Peak concentrations of firework pollutants have decreased in cities and hint at the importance of well-enforced regulation of these traditional celebrations, e.g., Beijing, Tianjin, and Chongqing. The model suggested relative humidity was an important controlling variable, perhaps as the presence of water vapor might also accelerate particle growth but also as a surrogate parameter related to atmospheric mixing. Bans on fireworks, resisted at first, have shown evidence of growing public acceptance. The regulations are increasingly effective, even in the outer parts of cities. Celebrations might safely return as public firework displays, including light shows and the use of lanterns.

Debris flow events often pose significant damage and are a threat to infrastructure and even livelihoods. Recent studies have mainly focused on determining the susceptibility of debris flow using deterministic or heuristic/probabilistic models. However, each type of model has its own significant advantages with some irreparable disadvantages. The random forest model, which is sensitive to the region where the terrain conditions are suitable for the occurrence of debris flow, was applied along with the steady-state infinite slope method, which is capable of describing the initiation mechanism of debris flow. In this manner, a random-forest-based steady-state infinite slope method was used to conduct susceptibility assessment of debris-flow at Changbai mountain area. Results showed that the assessment accuracy of the proposed random-forest-based steady-state infinite slope method reached 90.88%; however, the accuracy of just the random forest model or steady-state infinite slope method was only 88.48% or 60.45%, respectively. Compared with the single-model assessment results, the assessment accuracy of the proposed method improved by 2.4% and 30.43%, respectively. Meanwhile, the debris-flow-prone area of the proposed method was reduced. The random-forest-based steady-state infinite slope method inherited the excellent diagnostic performance of the random-forest models in the region where the debris flow disaster already occurred; meanwhile, this method further refined the debris-flow-prone area from the suitable terrain area based on physico-mechanical properties; thus, the performance of this method was better than those of the other two models.

As climate change progresses rapidly, biodiversity declines, and ecosystems shift, it is becoming increasingly difficult to document dynamic populations, track fluctuations, and predict responses to climate change. Concurrently, publicly available databases and tools are improving scientific accessibility, increasing collaboration, and generating more data than ever before. One of the most successful projects is iNaturalist, an AI-driven social network doubling as a public database designed to allow citizen scientists to report personal biodiversity reports with accuracy. iNaturalist is especially useful for the research of rare, dangerous, and charismatic organisms, but requires better integration into the marine system. Despite their abundance and ecological relevance, there are few long-term, high-sample datasets for jellyfish, which makes management difficult. To provide some high-sample datasets and demonstrate the utility of publicly collected data, we synthesized two global datasets for ten genera of jellyfishes in the order Rhizostomeae containing 8412 curated datapoints from both iNaturalist (n = 7807) and the published literature (n = 605). We then used these reports in conjunction with publicly available environmental data to predict global niche partitioning and distributions. Initial niche models inferred that only two of ten genera have distinct niche spaces; however, the application of machine learning-based random forest models suggests genus-specific variation in the relevance of abiotic environmental variables used to predict jellyfish occurrence. Our approach to incorporating reports from the literature with iNaturalist data helped evaluate the quality of the models and, more importantly, the quality of the underlying data. We find that free, accessible online data is valuable, yet subject to biases through limited taxonomic, geographic, and environmental resolution. To improve data resolution, and in turn its informative power, we recommend increasing global participation through collaboration with experts, public figures, and hobbyists in underrepresented regions capable of implementing regionally coordinated projects.

To compare the random forest (RF) model and the frequency ratio (FR) model for landslide susceptibility mapping (LSM), this research selected Yunyang Country as the study area for its frequent natural disasters; especially landslides. A landslide inventory was built by historical records; satellite images; and extensive field surveys. Subsequently; a geospatial database was established based on 987 historical landslides in the study area. Then; all the landslides were randomly divided into two datasets: 70% of them were used as the training dataset and 30% as the test dataset. Furthermore; under five primary conditioning factors (i.e., topography factors; geological factors; environmental factors; human engineering activities; and triggering factors), 22 secondary conditioning factors were selected to form an evaluation factor library for analyzing the landslide susceptibility. On this basis; the RF model training and the FR model mathematical analysis were performed; and the established models were used for the landslide susceptibility simulation in the entire area of Yunyang County. Next; based on the analysis results; the susceptibility maps were divided into five classes: very low; low; medium; high; and very high. In addition; the importance of conditioning factors was ranked and the influence of landslides was explored by using the RF model. The area under the curve (AUC) value of receiver operating characteristic (ROC) curve; precision; accuracy; and recall ratio were used to analyze the predictive ability of the above two LSM models. The results indicated a difference in the performances between the two models. The RF model (AUC = 0.988) performed better than the FR model (AUC = 0.716). Moreover; compared with the FR model; the RF model showed a higher coincidence degree between the areas in the high and the very low susceptibility classes; on the one hand; and the geographical spatial distribution of historical landslides; on the other hand. Therefore; it was concluded that the RF model was more suitable for landslide susceptibility evaluation in Yunyang County; because of its significant model performance; reliability; and stability. The outcome also provided a theoretical basis for application of machine learning techniques (e.g., RF) in landslide prevention; mitigation; and urban planning; so as to deliver an adequate response to the increasing demand for effective and low-cost tools in landslide susceptibility assessments.

IntroductionMyopia is a common refractive disorder in adolescents, and its association with the gut microbiota remains incompletely defined.MethodsWe performed 16S rRNA sequencing on fecal samples from 102 adolescents (49 myopic, 53 non-myopic; aged 6–18 years) and analyzed microbial diversity, taxonomy, function, and random forest-based classification, stratified by sexResultsMyopic adolescents showed lower gut microbial richness (observed and Chao1, both P < 0.05) than non-myopic controls, but overall community structure did not differ. Sex-stratified analyses revealed that these diversity reductions occurred only in myopic males (all α-diversity indices, P < 0.01), not in females. Several genera differed between myopic and non-myopic males (e.g., lower Akkermansia, Alistipes, Oscillibacter; higher Veillonella, Sutterella), whereas fewer differences were found in females. Functional predictions indicated altered metabolism and immune pathways. A random forest model achieved moderate overall accuracy (AUC = 76.84%), with higher performance in males (89.13%) than females (69.21%).DiscussionAdolescent myopia is associated with reduced gut microbial richness and sex-specific compositional changes, particularly in males, underscoring the importance of sex in gut–eye axis research.

The objectives of this research were developing a model for forecasting vegetable prices in Nakhon Si Thammarat Province using random forest and comparing the forecast results of different crops. The information used in this paper were monthly climate data and average monthly vegetable prices collected between 2011 – 2020 from Nakhon Si Thammarat meteorological station and Nakhon Si Thammarat Provincial Commercial Office, respectively. We evaluated model performance based on mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE). The experimental results showed that the random forest model was able to predict the prices of vegetables, including pumpkin, eggplant, and lentils with high accuracy with MAPE values of 0.09, 0.07, and 0.15, with RMSE values of 1.82, 1.46, and 2.33, and with MAE values of 3.32, 2.15, and 5.42, respectively. The forecast model derived from this research can be beneficial for vegetable planting planning in the Pak Phanang River Basin of Nakhon Si Thammarat Province, Thailand.