Development of a coupled model to simulate and assess arsenic contamination and impact factors in the Jinsha River Basin, China.

With the increasing severity of arsenic (As) pollution, quantifying the environmental behavior of pollutant based on numerical model has become an important approach to determine the potential impacts and finalize the precise control strategies. Taking the industrial-intensive Jinsha River Basin as typical area, a two-dimensional hydrodynamic water quality model coupled with Soil and Water Assessment Tool (SWAT) model was developed to accurately simulate the watershed-scale distribution and transport of As in the terrestrial and aquatic environment at high spatial and temporal resolution. The effects of hydro-climate change, hydropower station construction and non-point source emissions on As were quantified based on the coupled model. The result indicated that higher As concentration areas mainly centralized in urban districts and concentration slowly decreased from upstream to downstream. Due to the enhanced rainfall, the As concentration was significantly higher during the rainy season than the dry season. Hydro-climate change and the construction of hydropower station not only affected the dissolved As concentration, but also affected the adsorption and desorption of As in sediment. Furthermore, As concentration increased with the input of non-point source pollution, with the maximum increase about 30%, resulting that non-point sources contributed important pollutant impacts to waterways. The coupled model used in pollutant behavior analysis is general with high potential application to predict and mitigate water pollution.

Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

Combustion aerosols and suspended dust with controlled processes in Lhasa: Elemental analysis and size distribution characteristics.

To explore air contamination resulting from special biomass combustion and suspended dust in Lhasa, the present study focused on the size distribution and chemical characteristics of particulate matter (PM) emission resulting from 7 types of non-fossil pollution sources. We investigated the concentration and size distribution of trace elements from 7 pollution sources collected in Lhasa. Combining Lhasa's atmospheric particulate matter data, enrichment factors (EFs) have been calculated to examine the potential impact of those pollution sources on the atmosphere quality of Lhasa. The highest mass concentration of total elements of biomass combustion appeared at PM0.4, and the second highest concentration existed in the size fraction 0.4-1 µm; the higher proportion (12 %) of toxic metals was produced by biomass combustion. The elemental composition of suspended dust and atmospheric particulate matter was close (except for As and Cd); the highest concentration of elements was all noted in PM2.5-10 (PM3-10). Potassium was found to be one of the main biomass markers. The proportion of Cu in suspended dust is significantly lower than that of atmospheric particulate matter (0.53 % and 3.75 %), which indicates that there are other anthropogenic sources. The EFs analysis showed that the Cr, Cu, Zn, and Pb produced by biomass combustion were highly enriched (EFs > 100) in all particle sizes. The EFs of most trace elements increased with decreasing particle size, indicating the greater influence of humanfactors on smaller particles.

Assessing the impact of disease incidence and immunization on the resilience of complex networks during epidemics.

Disease severity through an immunized population ensconced on a physical network topology is a key technique for preventing epidemic spreading. Its influence can be quantified by adjusting the common (basic) methodology for analyzing the percolation and connectivity of contact networks. Stochastic spreading properties are difficult to express, and physical networks significantly influence them. Visualizing physical networks is crucial for studying and intervening in disease transmission. The multi-agent simulation method is useful for measuring randomness, and this study explores stochastic characteristics of epidemic transmission in various homogeneous and heterogeneous networks. This work thoroughly explores stochastic characteristics of epidemic propagation in homogeneous and heterogeneous networks through extensive theoretical analysis (positivity and boundedness of solutions, disease-free equilibrium point, basic reproduction number, endemic equilibrium point, stability analysis) and multi-agent simulation approach using the Gilespie algorithm. Results show that Ring and Lattice networks have small stochastic variations in the ultimate epidemic size, while BA-SF networks have disease transmission starting before the threshold value. The theoretical and deterministic aftermaths strongly agree with multi-agent simulations (MAS) and could shed light on various multi-dynamic spreading process applications. The study also proposes a novel concept of void nodes, Empty nodes and disease severity, which reduces the incidence of contagious diseases through immunization and topologies.

NMR-Based Stable Isotope Tracing of Cancer Metabolism.

NMR is widely used for metabolite profiling (metabolomics, metabonomics) particularly of various readily obtainable biofluids such as plasma and urine. It is especially valuable for stable isotope tracer studies to track metabolic pathways under control or perturbed conditions in a wide range of cell models as well as animal models and human subjects. NMR has unique properties for utilizing stable isotopes to edit or simplify otherwise complex spectra acquired in vitro and in vivo, while quantifying the level of enrichment at specific atomic positions in various metabolites (i.e., isotopomer distribution analysis).In this protocol, we give an overview with specific protocols for NMR-based stable isotope-resolved metabolomics, or SIRM, with a workflow from administration of isotope-enriched precursors, via sample preparation through to NMR data collection and reduction. We focus on indirect detection of common NMR-active stable isotopes including 13C, 15N, 31P, and 2H, using a variety of 1H-based two-dimensional experiments. We also include the application and analyses of multiplex tracer experiments.

Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015-2020 in Shaanxi, China.

Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables. In this study, we propose a machine learning algorithm for carbon emissions, a Bayesian optimized XGboost regression model, using multi-year energy carbon emission data and nighttime lights (NTL) remote sensing data from Shaanxi Province, China. Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models, with an R2 of 0.906 and RMSE of 5.687. We observe an annual increase in carbon emissions, with high-emission counties primarily concentrated in northern and central Shaanxi Province, displaying a shift from discrete, sporadic points to contiguous, extended spatial distribution. Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns, with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering. Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissions more accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment. This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.

Impact of coking plant to heavy metal characteristics in groundwater of surrounding areas: Spatial distribution, source apportionment and risk assessments.

Coking industry is a potential source of heavy metals (HMs) pollution. However, its impacts to the groundwater of surrounding residential areas have not been well understood. This study investigated the pollution characteristics and health risks of HMs in groundwater nearby a typical coking plant. Nine HMs including Fe, Zn, Mo, As, Cu, Ni, Cr, Pb and Cd were analyzed. The average concentration of total HMs was higher in the nearby area (244.27 µg/L) than that of remote area away the coking plant (89.15 µg/L). The spatial distribution of pollution indices including heavy metal pollution index (HPI), Nemerow index (NI) and contamination degree (CD), all demonstrated higher values at the nearby residential areas, suggesting coking activity could significantly impact the HMs distribution characteristics. Four sources of HMs were identified by Positive Matrix Factorization (PMF) model, which indicated coal washing and coking emission were the dominant sources, accounted for 40.4%, and 31.0%, respectively. Oral ingestion was found to be the dominant exposure pathway with higher exposure dose to children than adults. Hazard quotient (HQ) values were below 1.0, suggesting negligible non-carcinogenic health risks, while potential carcinogenic risks were from Pb and Ni with cancer risk (CR) values > 10-6. Monte Carlo simulation matched well with the calculated results with HMs concentrations to be the most sensitive parameters. This study provides insights into understanding how the industrial coking activities can impact the HMs pollution characteristics in groundwater, thus facilitating the implement of HMs regulation in coking industries.

In-silico study of the impact of system design parameters on microcalcification detection in wide-angle digital breast tomosynthesis.

Purpose: Accurate detection of microcalcifications ( µ Calcs ) is crucial for the early detection of breast cancer. Some clinical studies have indicated that digital breast tomosynthesis (DBT) systems with a wide angular range have inferior µ Calc detectability compared with those with a narrow angular range. This study aims to (1) provide guidance for optimizing wide-angle (WA) DBT for improving µ Calcs detectability and (2) prioritize key optimization factors. Approach: An in-silico DBT pipeline was constructed to evaluate µ Calc detectability of a WA DBT system under various imaging conditions: focal spot motion (FSM), angular dose distribution (ADS), detector pixel pitch, and detector electronic noise (EN). Images were simulated using a digital anthropomorphic breast phantom inserted with 120 µ m µ Calc clusters. Evaluation metrics included the signal-to-noise ratio (SNR) of the filtered channel observer and the area under the receiver operator curve (AUC) of multiple-reader multiple-case analysis. Results: Results showed that FSM degraded µ Calcs sharpness and decreased the SNR and AUC by 5.2% and 1.8%, respectively. Non-uniform ADS increased the SNR by 62.8% and the AUC by 10.2% for filtered backprojection reconstruction with a typical clinical filter setting. When EN decreased from 2000 to 200 electrons, the SNR and AUC increased by 21.6% and 5.0%, respectively. Decreasing the detector pixel pitch from 85 to 50 µ m improved the SNR and AUC by 55.6% and 7.5%, respectively. The combined improvement of a 50 µ m pixel pitch and EN200 was 89.2% in the SNR and 12.8% in the AUC. Conclusions: Based on the magnitude of impact, the priority for enhancing µ Calc detectability in WA DBT is as follows: (1) utilizing detectors with a small pixel pitch and low EN level, (2) allocating a higher dose to central projections, and (3) reducing FSM. The results from this study can potentially provide guidance for DBT system optimization in the future.

Exploring Contact Distance Distributions with Google Colaboratory.

Hi-C and Micro-C are the three-dimensional (3D) genome assays that use high-throughput sequencing. In the analysis, the sequenced paired-end reads are mapped to a reference genome to generate a two-dimensional contact matrix for identifying topologically associating domains (TADs), chromatin loops, and chromosomal compartments. On the other hand, the distance distribution of the paired-end mapped reads also provides insight into the 3D genome structure by highlighting global contact frequency patterns at distances indicative of loops, TADs, and compartments. This chapter presents a basic workflow for visualizing and analyzing contact distance distributions from Hi-C data. The workflow can be run on Google Colaboratory, which provides a ready-to-use Python environment accessible through a web browser. The notebook that demonstrates the workflow is available in the GitHub repository at https://github.com/rnakato/Springer_contact_distance_plot.

Binding and distribution regularity of water molecules in high-moisture textured Antarctic krill (Euphausia superba) meat.

High-moisture extrusion technique with the advantage of high efficiency and low energy consumption is a promising strategy for processing Antarctic krill meat. Consequently, this study aimed to prepare high-moisture textured Antarctic krill meat (HMTAKM) with a rich fiber structure at different water contents (53 %, 57 %, and 61 %) and to reveal the binding and distribution regularity of water molecules, which is closely related to the fiber structure of HMTAKM and has been less studied. The hydrogen-bond network results indicated the presence of at least two or more types of water molecules with different hydrogen bonds. Increasing the water content of HMTAKM promoted the formation of hydrogen bonds between the water molecules and protein molecules, leading to the transition of the ß-sheet to the α-helix. These findings offer a novel viable processing technique for Antarctic krill and a new understanding of the fiber formation of high-moisture textured proteins.

Long-term spatiotemporal variations of ammonia in the Yangtze River Delta region of China and its driving factors.

This study focuses on the spatiotemporal distribution, urban-rural variations, and driving factors of ammonia Vertical Column Densities (VCDs) in China's Yangtze River Delta region (YRD) from 2008 to 2020. Utilizing data from the Infrared Atmospheric Sounding Interferometer (IASI), Generalized Additive Models (GAM), and the GEOS-Chem chemical transport model, we observed a significant increase of NH3 VCDs in the YRD between 2014 and 2020. The spatial distribution analysis revealed higher NH3 concentrations in the northern part of the YRD region, primarily due to lower precipitation, alkaline soil, and intensive agricultural activities. NH3 VCDs in the YRD region increased significantly (65.18%) from 2008 to 2020. The highest growth rate occurs in the summer, with an annual average growth rate of 7.2% during the period from 2014 to 2020. Agricultural emissions dominated NH3 VCDs during spring and summer, with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones. Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH3 VCDs. The rise in NH3 VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO2, as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model. The concentration changes of acidic gases contribute to over 80% of the interannual variations in NH3 VCDs. This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases. Effective emission control is urgent to mitigate environmental hazards and secondary particulate matter, especially in the northern YRD.

Occurrence and potential risks of organophosphate esters in agricultural soils: A case study of Fuzhou City, Southeast China.

Fifty agricultural soil samples collected from Fuzhou, southeast China, were first investigated for the occurrence, distribution, and potential risks of twelve organophosphate esters (OPEs). The total concentration of OPEs (ΣOPEs) in soil ranged from 1.33 to 96.5 ng/g dry weight (dw), with an average value of 17.1 ng/g dw. Especially, halogenated-OPEs were the predominant group with a mean level of 9.75 ng/g dw, and tris(1-chloro-2-propyl) phosphate (TCIPP) was the most abundant OPEs, accounting for 51.1% of ΣOPEs. The concentrations of TCIPP and ∑OPEs were found to be significantly higher (P < 0.05) in soils of urban areas than those in suburban areas. In addition, the use of agricultural plastic films and total organic carbon had a positive effect on the occurrence of OPE in this study. The positive matrix factorization model suggested complex sources of OPEs in agricultural soils from Fuzhou. The ecological risk assessment demonstrated that tricresyl phosphate presented a medium risk to land-based organisms (0.1 ≤ risk quotient < 1.0). Nevertheless, the carcinogenic and non-carcinogenic risks for human exposure to OPEs through soil ingestion and dermal absorption were negligible. These findings would facilitate further investigations into the pollution management and risk control of OPEs.

Characterization and function of particulate organic matter: Evidence from lakes undergoing ecological restoration.

Particulate organic matter (POM) plays a crucial role in the organic composition of lakes; however, its characteristics remain poorly understood. This study aimed to characterize the structure and composition of POM in Lake Baiyangdian using many kinds of techniques and investigate the effects of different extracted forms of POM on water quality. The suspended particulate matter in the lake had complex compositions, with its components primarily derived from aquatic plants and their detritus. The organic matter content of the suspended particulate matter was relatively high (organic carbon content 27.29-145.94 g/kg) for the sum of three extractable states (water-extracted organic matter [WEOM], humic acid, and fulvic acid) and one stable bound state (humin). Spatial distribution analysis revealed that the POM content in the water increased from west to east, which was consistent with the water flow pattern influenced by the Baiyangdian water diversion project. Fluorescence spectroscopy analysis of the WEOM showed three prominent peaks with excitation/emission wavelengths similar to those of dissolved organic matter peaks. These peaks were potentially initial products of POM conversion into dissolved organic matter. Furthermore, the intensity of the WEOM fluorescence peak (total fluorescence peak intensity) was negatively correlated with the inorganic nitrogen concentration in water (p < 0.01), while the intensity of the HA fluorescence peak showed a positive correlation with the inorganic nitrogen concentration (p < 0.01). This suggested that exogenous organic matter inputs led to the diffusion of alkaline dissolved nitrogen from sediment into water, while degradation processes of aquatic plant debris contributed to the decrease in inorganic nitrogen concentrations in the water column. These findings enhance our understanding of POM characteristics in shallow lakes and the role of POM in shallow lake ecosystems.

Environmental sympatry through time: spatio-temporal distribution and conservation status of two sympatric anuran species (Leptodactylidae) in South America / Simpatría ambiental a través del tiempo: distribución espacio-temporal y estado de conservación de dos especies de anuros simpátricos (Leptodactylidae) en América del Sur

Abstract Introduction: Leptodactylus latinasus and Physalaemus cuqui are sympatric anuran species with similar environmental requirements and contrasting reproductive modes. Climatic configuration determines distribution patterns and promotes sympatry of environmental niches, but specificity/selectivity determines the success of reproductive modes. Species distribution models (SDM) are a valuable tool to predict spatio-temporal distributions based on the extrapolation of environmental predictors. Objectives: To determine the spatio-temporal distribution of environmental niches and assess whether the protected areas of the World Database of Protected Areas (WDPA) allow the conservation of these species in the current scenario and future. Methods: We applied different algorithms to predict the distribution and spatio-temporal overlap of environmental niches of L. latinasus and P. cuqui within South America in the last glacial maximum (LGM), middle-Holocene, current and future scenarios. We assess the conservation status of both species with the WDPA conservation units. Results: All applied algorithms showed high performance for both species (TSS = 0.87, AUC = 0.95). The L. latinasus predictions showed wide environmental niches from LGM to the current scenario (49 % stable niches, 37 % gained niches, and 13 % lost niches), suggesting historical fidelity to stable climatic-environmental regions. In the current-future transition, L. latinasus would increase the number of stable (70 %) and lost (20 %) niches, suggesting fidelity to lowland regions and a possible trend toward microendemism. P. cuqui loses environmental niches from the LGM to the current scenario (25 %) and in the current-future transition (63 %), increasing the environmental sympathy between both species; 31 % spatial overlap in the current scenario and 70 % in the future. Conclusion: Extreme drought events and rainfall variations, derived from climate change, suggest the loss of environmental niches for these species that are not currently threatened but are not adequately protected by conservation units. The loss of environmental niches increases spatial sympatry which represents a new challenge for anurans and the conservation of their populations.

Resumen Introducción: Leptodactylus latinasus y Physalaemus cuqui son especies de anuros simpátricos con requerimientos ambientales similares y modos reproductivos contrastantes. La configuración climática determina los patrones de distribución y promueve la simpatría de los nichos ambientales, pero la especificidad/selectividad determina el éxito de los modos reproductivos. Los modelos de distribución de especies (MDE) son una herramienta valiosa para predecir distribuciones espacio-temporales basadas en la extrapolación de predictores ambientales. Objetivos: Determinar la distribución espacio-temporal de los nichos ambientales y evaluar si las áreas protegidas de la base de Datos Mundial de Áreas Protegidas (DMAP) permiten la conservación de estas especies en el escenario actual y futuro. Métodos: Aplicamos diferentes algoritmos para predecir la distribución y superposición espacio-temporal de nichos ambientales de L. latinasus y P. cuqui dentro de América del Sur en el último máximo glacial (UGM), Holoceno medio, actual y futuro. Evaluamos el estado de conservación de ambas especies con las unidades de conservación de la DMAP. Resultados: Todos los algoritmos aplicados mostraron un alto rendimiento para ambas especies (TSS = 0.87, AUC = 0.95). Las predicciones de L. latinasus mostraron amplios nichos ambientales desde LGM hasta el escenario actual (49 % de nichos estables, 37 % de nichos ganados y 13 % de nichos perdidos), sugiriendo fidelidad histórica por regiones climático-ambientales estables. En la transición actual-futura L. latinasus incrementaría la cantidad de nichos estables (70 %) y perdidos (20 %), sugiriendo fidelidad por regiones de tierras bajas y la posible tendencia hacia el microendemismo. P. cuqui pierde nichos ambientales desde el LGM al escenario actual (25 %) y en la transición actual-futura (63 %), incrementando la simpatría ambiental entre ambas especies; 31 % de superposición espacial en el escenario actual y 70 % en el futuro. Conclusión: Los eventos de sequía extrema y las variaciones de precipitaciones, derivados del cambio climático, sugieren la pérdida de nichos ambientales para estas especies, actualmente no se encuentran amenazadas, pero no están adecuadamente protegidas por las unidades de conservación. La pérdida de nichos ambientales aumenta la simpatría espacial que representa un nuevo desafío para estos anuros y la conservación de sus poblaciones.

Prevalence Estimation of Dementia/Alzheimer's Disease Using Health and Retirement Study Database in the United States.

BACKGROUND: Updated prevalence estimates along the continuum of Alzheimer's disease (AD) can foster a more nuanced and effective approach to managing AD within the current healthcare landscape. OBJECTIVES: This study aims to estimate the prevalence and severity distribution of dementia/AD (including mild, moderate, and severe stages) and all-cause mild cognitive impairment (MCI) in the United States using data from the Health and Retirement Study (HRS). DESIGN: Retrospective study. SETTING: Data from the bi-annual HRS surveys involving in-depth interviews of a representative sample of Americans aged >50 years. PARTICIPANTS: Dementia/AD and all-cause MCI patients from the 4 most recent HRS surveys (2014, 2016, 2018 and 2020). MEASUREMENTS: AD was identified based on diagnosis (self-report). Cognitive performance (modified Telephone Interview of Cognitive Status [TICS-m]) scores in the dementia/AD range were also captured; all-cause MCI was similarly identified using the TICS-m. Dementia/AD and MCI prevalence, as well as the distribution by dementia/AD stage (mild, moderate, or severe), were estimated. Sampling weights developed by HRS were applied to ensure the sample's representativeness of the target population and unbiased estimates for population parameters. RESULTS: Across the four HRS surveys, the total number of HRS respondents ranged from 15,000 to 21,000 (unweighted); 7,000 to 14,000 had TICS-m scores. The estimated prevalence of AD (all severity categories combined) in the 2014, 2016, 2018, and 2020 HRS surveys was 1.2%, 1.2%, 1.3% and 1.0%, respectively using the diagnosis-based approach; using the cognitive performance-based approach, 23-27% patients had scores in the dementia/AD ranges across the 4 surveys. The estimated prevalence of all-cause MCI was consistently 23% in each survey. In the 2020 survey, the distribution of mild, moderate, and severe disease stages was 34%, 45%, and 21%, respectively, in patients self-reporting an AD diagnosis, and 55%, 40%, and 5%, respectively in all patients meeting TICS-m threshold for dementia/AD. CONCLUSION: The prevalence of AD diagnosis based on self-report was approximately 1% across the 4 most recent HRS surveys and may reflect the proportion of patients who have actively sought healthcare for AD. Among HRS survey respondents with cognitive scores available, over 20% were in the dementia/AD range. The distribution of disease by stage differed for self-reported AD diagnosis vs dementia/AD based on cognitive scores. Discordance in estimates of dementia/AD and stage distributions underscores a need for better understanding of clinical practice patterns in AD diagnosis, use of clinical assessment tools, and severity classification in the United States. Accurate patient identification is needed, especially early in the AD disease continuum, to allow for timely and appropriate initiation of new anti-amyloid treatments.

Distribution of tetracyclines and sulfonamides resistance genes around a smallholder pig farm: Modeling and attribution.

Livestock farm is a major source of antibiotics and antibiotic resistance genes (ARGs) pollution. ARGs can directly enter the environment through runoff and air deposition. The impact extent and the driving factors require further investigation to inform effective policies and actions to mitigate their spread. This study investigated a smallholder pig farm and its surrounding areas to understand the spread of ARGs. Topsoil samples were collected from 56 different sites within one kilometer of the farm, and a comprehensive analysis was conducted to reveal effects of soil properties, antibiotic residues, microbiome, mobilome on the variation of typical ARGs. The results confirmed that the ARGs reduced exponentially with increasing distance from the farm, with a goodness of fit (R2) of 0.7 for total ARGs. For tetracyclines (TC) and sulfonamides (SA) resistance genes, the fitting R2 exceeded 0.9. Model estimates allowed for quantitative comparisons of in-farm increments, out-farm background levels, and spread abilities of ARGs with distinct resistance mechanisms. SA-specific resistance genes (SRGs, 0.097 copies/16S rRNA gene) and TC-specific resistance genes (TRGs, 0.036 copies/16S rRNA gene) showed higher within-farm increases compared to multidrug resistance genes (MDRGs, 0.020 copies/16S rRNA gene). MDRGs, however, had a higher background level and a greater impact distance (0.18 km, 4.4 times the farm radius). Additionally spread abilities of TRGs varied by resistance mechanism, with ribosome protection proteins showing greater spread than TC inactivating enzymes and TC efflux pumps, likely due to different fitness costs. Correlation analysis and structural equation modeling indicated that changes in bacterial community composition and mobilome are primary factors influencing ARGs variation during their spread. Abiotic factors like soil nutrients and antibiotics also selectively enriched ARGs within the farm. These findings provide insights into the ARGs dissemination and could inform strategies to prevent their spread from smallholder livestock farms.

Unveiling the non-linear effects of water and oil on hyperspectral imaging-based characterization of solid waste by hyperspectral unmixing.

Rapid characterization of solid waste using near-infrared hyperspectral imaging (HSI) coupled with machine learning models has been increasingly investigated to replace the traditional time- and labor-intensive methods. However, contamination by waste-derived leachates or other fractions etc., can cause the spectra evolutions and significantly influences the identification performance, which has not been investigated before. The first attempt was made by using hyperspectral unmixing (HU) to extract the endmember components and demonstrate their contributions (abundance) to solid waste, taking the non-linear reflectance changes due to the O-H vibration of water and unclear variation associated with oil and leachates as an example. The HSI spectra of various solid waste components influenced by pure water, oil and three kinds of leachates were acquired. A novel method based on HU models, including multivariate curve resolution with alternating least squares and state-of-the-art autoencoder architectures (deep learning models), was developed to estimate the spectra of endmembers as well as their abundances in individual pixel. Their spatial distribution overview in solid waste was then yielded. The selected models were validated via an independent test data set, with lower spectral angle distance, 12.3° ± 6.5°, indicating the similarity of the predicted endmembers with real components. And the lowest root of mean square error on endmember distribution maps was 0.17. The non-linear liquid's effects by water and oil on spectra variations of solid waste were clearly illuminated. Additionally, the proposed method can extract information from mixed spectroscopic images and generate reconstructed spectra.

Occurrence and characteristics of microplastics across the watershed of the world's third-largest river.

While rivers as primary conduits for land-based plastic particles transferring to their "ultimate" destination, the ocean, have garnered increasing attention, research on microplastic pollution at the scale of whole large river basins remains limited. Here we conducted a large-scale investigation of microplastic contamination in water and sediment of the world's third-largest river, the Yangtze River. We found concentrations of microplastics in water and sediment to be 5.13 items/L and 113.9 items/kg (dry weight), respectively. Moreover, microplastic pollution levels exhibited a clear decreasing trend from upstream to downstream. The detected microplastics were predominantly transparent in color, with fibrous shapes predominating, sizes mainly concentrated below 1 mm and composed primarily of PP and PE polymers. Our analysis results indicated that compared to geographical and water quality parameters, anthropogenic factors primarily determined the spatial distribution pattern of microplastics. Moreover, the microplastic abundance in sediment upstream of the dam was significantly higher than that in the downstream sediment, while the trend of microplastic concentrations in water was opposite. Therefore, more effort is needed to monitor microplastic contamination and their ecological environmental effects of sediment before dams in future research.

Anthropogenic activities significantly interfered distribution and co-occurrence patterns of antibiotic resistance genes in a small rural watershed in Southwest China.

The prevalence and spread of antibiotic resistance genes (ARGs) have been a significant concern for global public health in recent years. Small rural watersheds are the smallest units of factor mobility for agricultural production in China, and their ARG profiles are the best scale of the contamination status, but the mapping and the distribution and diffusion of ARGs in the water and soil of small rural watersheds are inadequate. In this study, based on microbial metagenomics, we invested prevalence maps of 209 ARGs corresponding to typical commonly used antibiotics (including multidrug, aminoglycoside, macrolide-lincosamide-streptogramin B (MLSB), and ß-Lactamase) in water and soil in different agricultural types, as well as within water-soil interfaces in small rural watersheds in Southwest China. The results revealed that the most abundant ARGs in water and soil were consistent, but different in subtypes, and anthropogenic activities affect the transport of ARGs between water and soils. Livestock wastewater discharges influenced the diversity and abundance of ARGs in water, while in soil it is planting type and fertilizer management, and thus interfered with the co-occurrence patterns between bacteria and ARGs. Co-occurrence analysis revealed that Proteobacteria, Actinobacteria, and Bacteroidetes were the predominant ARG hosts in water and soil, but soil exhibited a more intricate ARG-bacterial association. Overall, this study provides integrated profiles of ARGs in water and soil influenced by anthropogenic activities at the small watershed scale in a typical rural area and provides a baseline for comparisons of ARGs.

Nitrate removal by a novel aerobic denitrifying Pelomonas puraquae WJ1 in oligotrophic condition: Performance and carbon source metabolism.

Reducing nitrate contamination in drinking water has become a critical issue in urban water resource management. Here a novel oligotrophic aerobic denitrifying bacterium, Pelomonas puraquae WJ1, was isolated and purified from artificial lake sediments. For the first time, excellent aerobic denitrification capabilities were demonstrated. At a carbon-to­nitrogen ratio of 5.0, strain WJ1 achieved 100.0 % nitrate removal and 84.92 % total nitrogen removal within 24 h, with no nitrite accumulation. PCR amplification and sequencing confirmed the presence of the denitrification genes napA, nirS, and nosZ in the strain. The nitrogen balance demonstrated that approximately 74.95 % of the initial nitrogen was eliminated as gaseous products under aerobic conditions. Furthermore, carbon balance analysis showed that most electron donors from strain WJ1 were directed towards oxygen, with limited availability for nitrate reduction. A combination of bio-ECO analysis and network modeling indicated that strain WJ1 has robust metabolic capabilities for diverse carbon sources and exhibits high adaptability to complex carbon environments. Overall, Pelomonas puraquae WJ1 removed approximately 45.89 % of the nitrates in raw water, demonstrating significant potential for practical applications in oligotrophic denitrification.