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.

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.

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.

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.

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.

Integration of cross-links, discrete fiber distributions and of a non-local theory in the Homogenized Constrained Mixture Model to Simulate Patient-Specific Thoracic Aortic Aneurysm Progression.

Thoracic aortic aneurysms (TAA) represent a critical health issue for which computational models can significantly contribute to better understand the physiopathology. Among different computational frameworks, the Homogenized Constrained Mixture Theory has shown to be a computationally efficient option, allowing the inclusion of several mechanically significant constituents into a layer-specific mixture. Different patient-specific Growth and Remodeling (G&R) models correctly predicted TAA progression, although simplifications such as the inclusion of a limited number of collagen fibers and imposed boundary conditions might limit extensive analyses. The current study aims to enhance existing models by incorporating several discrete collagen fibers and to remove restrictive boundary conditions of the previous models. The implementation of discretized fiber dispersion presents a more realistic description of the vessel, while the removal of boundary conditions was addressed by including cross-links in the model to provide a supplemental stiffness against through-thickness shearing, a feature that was previously absent, and by the development of a non-local framework that ensures the stable deposition and degradation of collagen fibers. With these improvements, the current model represents a step forward towards more robust and comprehensive simulations of TAA growth.

Modelling the age distribution of longevity leaders.

Human longevity leaders with remarkably long lifespan play a crucial role in the advancement of longevity research. In this paper, we propose a stochastic model to describe the evolution of the age of the oldest person in the world by a Markov process, in which we assume that the births of the individuals follow a Poisson process with increasing intensity, lifespans of individuals are independent and can be characterized by a gamma-Gompertz distribution with time-dependent parameters. We utilize a dataset of the world's oldest person title holders since 1955, and we compute the maximum likelihood estimate for the parameters iteratively by numerical integration. Based on our preliminary estimates, the model provides a good fit to the data and shows that the age of the oldest person alive increases over time in the future. The estimated parameters enable us to describe the distribution of the age of the record holder process at a future time point.

Two new species and a new record of the ant genus Meranoplus Smith, 1853 (Hymenoptera, Formicidae, Myrmicinae) from Thailand.

Meranoplus Smith, 1853 is distributed in the Old World tropics, from Africa, Asia, New Guinea to Australia. There are four species Meranoplusbicolor (Guérin-Méneville, 1844), M.castaneus Smith, 1857, M.laeviventris Emery, 1889, and M.mucronatus Smith, 1857 previously recorded from Thailand. In the present paper, two new species of the genus, M.siamensis Yodprasit & Jaitrong, sp. nov. and M.tanomtongi Yodprasit & Jaitrong, sp. nov., are described based on the worker caste. Additionally, M.malaysianus Schödl, 1998 is recorded for the first time for Thailand. A key to the Oriental and Indo-Australian species, based on the worker caste, is provided. The new species and the new record were found to nest in soil.

Sample size considerations for single-arm clinical trials with time-to-event endpoint using the gamma distribution.

Background: Time-to-event (TTE) endpoints are evaluated as the primary endpoint in single-arm clinical trials; however, limited options are available in statistical software for sample size calculation. In single-arm trials with TTE endpoints, the non-parametric log-rank test is commonly used. Parametric options for single-arm design assume survival times follow exponential distribution or Weibull distribution. Methods: The exponential- or Weibull-distributed survival time assumption does not always reflect hazard pattern of real-life diseases. We therefore propose gamma distribution as an alternative parametric option for designing single-arm studies with TTE endpoints. We outline a sample size calculation approach using gamma distribution with a known shape parameter and explain how to extract the gamma shape estimate from previously published resources. In addition, we conduct simulations to assess the accuracy of the extracted gamma shape parameter and to explore the impact on sample size calculation when survival time distribution is misspecified. Results: Our simulations show that if a previously published study (sample sizes ≥ 60 and censoring proportions ≤ 20 %) reported median and inter-quartile range of survival time, we can obtain a reasonably accurate gamma shape estimate, and use it to design new studies. When true survival time is Weibull-distributed, sample size calculation could be underestimated or overestimated depending on the hazard shape. Conclusions: We show how to use gamma distribution in designing a single-arm trial, thereby offering more options beyond the exponential and Weibull. We provide a simulation-based assessment to ensure an accurate estimation of the gamma shape and recommend caution to avoid misspecification of the underlying distribution.

Data analysis of patients with positive mould or dimorphic fungal cultures from sterile sites.

Background: Moulds and dimorphic fungi are increasingly recognised as pathogens carrying high morbidity and mortality in critically ill and immune-compromised patients. The lack of surveillance data limits our understanding of these infections. Objectives: To determine the distribution, patient characteristics, risk factors, therapy and treatment outcome in patients with positive mould or dimorphic fungal cultures from sterile sites at a tertiary hospital in central South Africa. Method: All moulds or dimorphic fungi cultured from sterile specimens from 1 July 2014 to 30 June 2017 were identified retrospectively. Laboratory and clinical records were reviewed. Information collected included gender and age, type of specimen collected for investigation, specific fungi isolated, underlying conditions, other contributing risk factors, treatment and outcome of the patients. Results: Forty-eight patient records were analysed. Male and female patients were equally distributed. The mean age was 40.5 years (range 7-78 years). Aspergillus spp. were most commonly isolated. The most common underlying condition was HIV infection, followed by haematological conditions. Twenty-six (54.2%) patients received treatment involving antifungal therapy alone (n = 19; 73.1%), surgery alone (n = 5; 19.2%) or a combined medical and surgical approach (n = 2; 7.7%). Twenty-two (45.8%) patients received no treatment. The overall mortality rate was 25.0% (n = 12). Conclusion: The diagnosis of fungal infections remains challenging. In the current study, moulds and dimorphic fungi were isolated from at-risk patients' specimens. Despite treatment with appropriate antifungal agents, the associated mortality rate was high. Contribution: This study contributes to the growing body of knowledge on these potentially life-threatening infections.

Purifying selection drives distinctive arsenic metabolism pathways in prokaryotic and eukaryotic microbes.

Microbes play a crucial role in the arsenic biogeochemical cycle through specific metabolic pathways to adapt to arsenic toxicity. However, the different arsenic-detoxification strategies between prokaryotic and eukaryotic microbes are poorly understood. This hampers our comprehension of how microbe-arsenic interactions drive the arsenic cycle and the development of microbial methods for remediation. In this study, we utilized conserved protein domains from 16 arsenic biotransformation genes (ABGs) to search for homologous proteins in 670 microbial genomes. Prokaryotes exhibited a wider species distribution of arsenic reduction- and arsenic efflux-related genes than fungi, whereas arsenic oxidation-related genes were more prevalent in fungi than in prokaryotes. This was supported by significantly higher acr3 (arsenite efflux permease) expression in bacteria (upregulated 3.72-fold) than in fungi (upregulated 1.54-fold) and higher aoxA (arsenite oxidase) expression in fungi (upregulated 5.11-fold) than in bacteria (upregulated 2.05-fold) under arsenite stress. The average values of nonsynonymous substitutions per nonsynonymous site to synonymous substitutions per synonymous site (dN/dS) of homologous ABGs were higher in archaea (0.098) and bacteria (0.124) than in fungi (0.051). Significant negative correlations between the dN/dS of ABGs and species distribution breadth and gene expression levels in archaea, bacteria, and fungi indicated that microbes establish the distinct strength of purifying selection for homologous ABGs. These differences contribute to the distinct arsenic metabolism pathways in prokaryotic and eukaryotic microbes. These observations facilitate a significant shift from studying individual or several ABGs to characterizing the comprehensive microbial strategies of arsenic detoxification.

Nine Lessons about Aquatic Invasive Species from the North Temperate Lakes Long-Term Ecological Research (NTL-LTER) Program.

Freshwater ecosystems can serve as model systems that reveal insights into biological invasions. In this article, we summarize nine lessons about aquatic invasive species from the North Temperate Lakes Long-Term Ecological Research program and affiliated projects. The lessons about aquatic invasive species are as follows: Invasive species are more widespread than has been documented; they are usually at low abundance; they can irrupt from low-density populations in response to environmental triggers; they can occasionally have enormous and far-reaching impacts; they can affect microbial communities; reservoirs act as invasive species hotspots; ecosystem vulnerability to invasion can be estimated; invasive species removal can produce long-term benefits; and the impacts of invasive species control may be greater than the impacts of the invasive species. This synthesis highlights how long-term research on a freshwater landscape can advance our understanding of invasions.

Deep learning models map rapid plant species changes from citizen science and remote sensing data.

Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model-Deepbiosphere-not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.

Bioaccumulation and biotransformation of selenium nanoparticles in soybean and natto, and the bioaccessibility of multi-elements and amino acids.

Soybean is a food crop with strong selenium (Se) enrichment ability. Selenium nanoparticles (SeNPs) are a low-toxic Se source. To develop strategies in SeNPs biofortification of soybean and natto, the effects of Se enrichment and natto fermentation on selenoamino acids, mineral elements, free amino acids, γ-polyglutamic acid, nattokinase, and bioaccessibility were investigated. Soybean grains were able to convert SeNPs into selenomethionine (SeMet). Selenium enrichment and natto fermentation influenced the enrichment and distribution of multi-elements in soybean, as well as the composition of free and bound amino acids. Selenium enrichment had no significant effect on the bioaccessibility of amino acids. After natto fermentation, the bioaccessibility of SeMet, Fe, Mn, Cu, and Zn in the gastrointestinal tract increased significantly by 10.1-18.9 %. These findings indicate that SeNPs can enhance the Se content of soybean grains, and natto fermentation can further improve the nutritional quality of Se-enriched soybean.

Effects of slurry ice treatment on the physicochemical changes and proteome of large yellow croaker (Larimichthys crocea).

Large yellow croaker (Larimichthys crocea) is susceptible to oxidative denaturation during storage. This work is to investigate the quality alterations by analyzing its physicochemical changes and proteomics throughout preservation under refrigeration, frozen, and slurry ice (SI) conditions. Results revealed that the freshness of large yellow croaker, as evaluated by indicators such as total volatile basic nitrogen, total viable count, and thiobarbituric acid reactive substances, was well maintained while stored in the SI group. Meanwhile, the water distribution in the muscle tissue of group SI exhibited slower fluctuations, thereby preserving the integrity of fish muscle cells. Based on label-free proteomic analysis, a considerable downregulation was observed in the mitogen-activated protein kinase (MAPK) signaling pathway, indicating that SI decelerated this metabolic pathway and effectively delayed the deterioration of muscle. Therefore, the application of SI provides potential for maintaining the quality stability of large yellow croaker.

Trace element contamination in water and sediment in an estuarine ecosystem connected to the Bay of Bengal: A preliminary assessment of ecological and human health risks.

The research, focusing on the analysis of nine trace elements, namely As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn, completely analyzed their quantities in both water and sediment inside the Rabnabad Channel. Samples were collected during the post-monsoon and analyzed by ICP-OES following acid digestion. The mean concentrations of elements in water and sediments are as follows: Fe > Mn > Pb > Cu > Ni > Zn > Cr > As>Cd, and Zn > Fe > Pb > Mn > As>Cu > Cr > Ni > Cd. To understand the state of ecological and human health risk, several indices were incorporated. Health risk assessment revealed that children posed higher risk than adults. PERI, TRI, and Igeo indices for water sediment indicate a significant ecological risk. Moreover, Mn and Pb exhibit elevated HPI values and contribute substantially to contamination factors. Correlation and PCA implicate both anthropogenic and geogenic sources, such as agricultural practices, coal-based power plants, and the Payra seaport, in the elevated concentrations of Cd, Cr, Mn, and Fe in both water and sediment samples.

A deep-level decomposed model to accelerate hydraulic simulations in large water distribution networks.

As the size of water distribution network (WDN) models continues to grow, developing and applying real-time models or digital twins to simulate hydraulic behaviors in large-scale WDNs is becoming increasingly challenging. The long response time incurred when performing multiple hydraulic simulations in large-scale WDNs can no longer meet the current requirements for the efficient and real-time application of WDN models. To address this issue, there is a rising interest in accelerating hydraulic calculations in WDN models by integrating new model structures with abundant computational resources and mature parallel computing frameworks. This paper presents a novel and efficient framework for steady-state hydraulic calculations, comprising a joint topology-calculation decomposition method that decomposes the hydraulic calculation process and a high-performance decomposed gradient algorithm that integrates with parallel computation. Tests in four WDNs of different sizes with 8 to 85,118 nodes demonstrate that the framework maintains high calculation accuracy consistent with EPANET and can reduce calculation time by up to 51.93 % compared to EPANET in the largest WDN model. Further investigation found that factors affecting the acceleration include the decomposition level, consistency of sub-model sizes and sub-model structures. The framework aims to help develop rapid-responding models for large-scale WDNs and improve their efficiency in integrating multiple application algorithms, thereby supporting the water supply industry in achieving more adaptive and intelligent management of large-scale WDNs.

Disturbances of thalamus and prefrontal cortex contribute to cognitive aging: A structure-function coupling analysis based on KL divergence.

Normal aging is accompanied by changes in brain structure and function associated with cognitive decline. Structural and functional abnormalities, particularly the prefrontal cortex (PFC) and subcortical regions, contributed to cognitive aging. However, it remains unclear how the synchronized changes in structure and function of individual brain regions affect the cognition in aging. Using 3D T1-weighted structural data and movie watching functional magnetic resonance imaging data in a sample of 422 healthy individuals (ages from 18 to 87 years), we constructed regional structure-function coupling (SFC) of cortical and subcortical regions by quantifying the distribution similarity of gray matter volume (GMV) and amplitude of low-frequency fluctuation (ALFF). Further, we investigated age-related changes in SFC and its relationship with cognition. With aging, increased SFC localized in PFC, thalamus and caudate nucleus, decreased SFC in temporal cortex, lateral occipital cortex and putamen. Moreover, the SFC in the PFC was associated with executive function and thalamus was associated with the fluid intelligence, and partially mediated age-related cognitive decline. Collectively, our results highlight that tighter structure-function synchron of the PFC and thalamus might contribute to age-related cognitive decline, and provide insight into the substrate of the thalamo-prefrontal pathway with cognitive aging.