Epidemiological characteristics, diagnosis and treatment effect of rifampicin-resistant pulmonary tuberculosis (RR-PTB) in Guizhou Province.

BACKGROUND: Rifampicin-resistant pulmonary tuberculosis (RR-PTB) presents a significant threat to global public health security. China bears a substantial burden of RR-PTB cases globally, with Guizhou Province experiencing particularly alarming trends, marked by a continual increase in patient numbers. Understanding the population characteristics and treatment modalities for RR-PTB is crucial for mitigating morbidity and mortality associated with this disease. METHODS: We gathered epidemiological, diagnostic, and treatment data of all RR-PTB cases recorded in Guizhou Province from January 1, 2017 to December 31, 2023. Utilizing composition ratios as the analytical metric, we employed Chi-square tests to examine the spatiotemporal distribution patterns of RR-PTB patients and the evolving trends among different patient classifications over the study period. RESULTS: In our study, 3396 cases of RR-PTB were analyzed, with an average age of 45 years. The number of RR-PTB patients rose significantly from 176 in 2017 to 960 in 2023, peaking notably among individuals aged 23-28 and 44-54, with a rising proportion in the 51-80 age group (P < 0.001). Since 2021, there has been a notable increase in the proportion of female patients. While individuals of Han ethnic group comprised the largest group, their proportion decreased over time (P < 0.001). Conversely, the Miao ethnicity showed an increasing trend (P < 0.05). The majority of patients were farmers, with their proportion showing an upward trajectory (P < 0.001), while students represented 4.33% of the cases. Geographically, most patients were registered in Guiyang and Zunyi, with a declining trend (P < 0.001), yet household addresses primarily clustered in Bijie, Tongren, and Zunyi. The proportion of floating population patients gradually decreased, alongside an increase in newly treated patients and those without prior anti-tuberculosis therapy. Additionally, there was a notable rise in molecular biological diagnostic drug sensitivity (real-time PCR and melting curve analysis) (P < 0.001). However, the cure rate declined, coupled with an increasing proportion of RR-PTB patients lost to follow-up and untreated (P < 0.05). CONCLUSIONS: Enhanced surveillance is crucial for detecting tuberculosis patients aged 23-28 and 44-54 years. The distribution of cases varies among nationalities and occupations, potentially influenced by cultural and environmental factors. Regional patterns in RR-PTB incidence suggest tailored prevention and control strategies are necessary. Despite molecular tests advances, challenges persist with low cure rates and high loss to follow-up. Strengthening long-term management, resource allocation, and social support systems for RR-PTB patients is essential.

Intelligent management of carbon emissions of urban domestic sewage based on the Internet of Things.

Domestic wastewater is one of the major carbon sources that cannot be ignored by human society. Against the background of carbon peaking & carbon neutrality (Double Carbon) goals, the continuous urbanization has put heavy pressure on urban drainage systems. Nevertheless, the complex subjective and objective conditions of drainage systems restrict the field monitoring, measurement, and analysis of drainage systems, which has become a great obstacle to the study of carbon emissions from drainage system. In this paper, 3389 sensor terminals of Internet of Things (IoT) are used to build a field monitoring IoT for urban domestic wastewater methane (CH4) carbon emission, with 21 main districts of Chongqing Municipality in China as the study area. Incorporating Fick's law of diffusion, this field monitoring IoT derives a measurement model for methane carbon emissions based on measured concentrations, and solves the problems of long-term and stable monitoring and measurement of methane gas in complex underground environment. With GIS spatio-temporal analysis used to analyze the spatial and temporal evolution patterns of carbon emissions from septic tanks in drainage systems, it successfully reveals the spatial and temporal distribution of methane carbon emissions from drainage systems in different seasons, as well as the relationship between methane carbon emissions from drainage systems and the latitude of direct sunlight. Applying the DTW method, it quantifies the stability of methane monitoring in drainage systems and evaluates the effects of Sampling Frequency (SF) and Number of Devices Terminal (NDT) on the stability of methane monitoring. Consequently, an intelligent management system for carbon emissions from urban domestic wastewater is constructed on the base of IoT, which integrates methane monitoring, measurement and analysis in septic tanks of drainage systems.

Characterization of spatial-temporal distribution and microenvironment source contribution of PM2.5 concentrations using a low-cost sensor network with artificial neural network/kriging techniques.

Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM2.5 in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM2.5 by the LCS network. The calibrated PM2.5 were shown to agree with reference PM2.5 measured by the BAM-1020 with R2 of 0.85, MNE of 30.91%, and RMSE of 3.73 µg/m3, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM2.5 concentrations in the urban area. Results showed that the highest average PM2.5 concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM2.5 concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM2.5. The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM2.5 concentrations and formulating regional air pollution control strategies.

The days we never forget: Flashbulb memories across the life span in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by autobiographical memory deficits, with the ability to retrieve episodic-rich memories being particularly affected. Here, we investigated the influence of AD on a specific subtype of episodic memories known as flashbulb memories (i.e., the ability to remember the personal circumstances for the reception of important news events). We examined the frequency, characteristics, and the temporal distribution of flashbulb memories across the life span. To this aim, 28 older adults diagnosed with AD and a matched sample of 29 healthy older controls were probed for flashbulb memories for two historical events from each decade of their lives. They also estimated the subjective degree of reexperiencing for the memories reported. AD participants showed impaired access to flashbulb memories, the frequency of reported memories being lower than for healthy older adults. However, qualitative aspects of AD participants' flashbulb memories were quite similar to those of the controls, as no group differences were obtained with respect to the canonical categories or degree of reexperience. AD participants' flashbulb memories clustered during the early years of their life, consistent with a reminiscence bump, whereas healthy controls also reported memories dated to later lifetime periods. Our results suggest that probing for personal memories of important public events may serve as a powerful cue for detailed episodic memories in AD.

Pedestrian flow-environmental pollutants interactions and health risks to residents in high-occupancy public areas of apartment buildings.

The current interaction of pedestrian flow and environmental pollutants in high-occupancy public areas of apartment and the risks of residents being exposed to environmental pollutants are issues that are often overlooked but urgently need to be addressed. In this study, we provide a comprehensive of pedestrian flow-environmental pollutants interactions and health risks to residents in first-floor public areas of apartment with high-occupancy. The main findings indicate that under closed management conditions, there is a significant increase in TVOC and noise levels during the peak periods of nighttime pedestrian flow. In the correlation analysis, the significant impact of time granularity selection in clarifying the correlation between pedestrian flow and environmental pollutants has been highlighted, with larger time granularities generally showing stronger correlations, while finer time granularities may help identify specific risks in areas directly connected to the external environment. There is a significant correlation exists between pedestrian flow and environmental pollutants (TVOC, ozone, and noise), with higher concentrations of these pollutants observed during peak pedestrian flow periods, thereby increasing the risk of residents being exposed to adverse environmental conditions. To mitigate the risks associated with TVOC pollution and noise exposure, it is crucial to maintain proper ventilation, avoid conducting cleaning or maintenance activities during peak hours, and implement noise-reducing measures, such as distancing noise sources from residential areas or installing soundproofing barriers. Additionally, the study identifies total volatile organic compounds originating from property maintenance activities and clarifies their dispersion patterns, emphasizing the importance of developing robust, standardized maintenance protocols for indoor environmental quality assurance. This research can improve the environmental sustainability of apartment buildings and provide a theoretical basis for the development of environmental health strategies for high-occupancy public areas of apartment buildings.

Assessing Derawan Island's Coral Reefs over Two Decades: A Machine Learning Classification Perspective.

This study aims to understand the dynamic changes in the coral reef habitats of Derawan Island over two decades (2003, 2011, and 2021) using advanced machine learning classification techniques. The motivation stems from the urgent need for accurate, detailed environmental monitoring to inform conservation strategies, particularly in ecologically sensitive areas like coral reefs. We employed non-parametric machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), to assess spatial and temporal changes in coral habitats. Our analysis utilized high-resolution data from Landsat 9, Landsat 7, Sentinel-2, and Multispectral Aerial Photos. The RF algorithm proved to be the most accurate, achieving an accuracy of 71.43% with Landsat 9, 73.68% with Sentinel-2, and 78.28% with Multispectral Aerial Photos. Our findings indicate that the classification accuracy is significantly influenced by the geographic resolution and the quality of the field and satellite/aerial image data. Over the two decades, there was a notable decrease in the coral reef area from 2003 to 2011, with a reduction to 16 hectares, followed by a slight increase in area but with more heterogeneous densities between 2011 and 2021. The study underscores the dynamic nature of coral reef habitats and the efficacy of machine learning in environmental monitoring. The insights gained highlight the importance of advanced analytical methods in guiding conservation efforts and understanding ecological changes over time.

Monitoring Off-Shore Fishing in the Northern Indian Ocean Based on Satellite Automatic Identification System and Remote Sensing Data.

Satellite-derived Sea Surface Temperature (SST) and sea-surface Chlorophyll a concentration (Chl-a), along with Automatic Identification System (AIS) data of fishing vessels, were used in the examination of the correlation between fishing operations and oceanographic factors within the northern Indian Ocean from March 2020 to February 2023. Frequency analysis and the empirical cumulative distribution function (ECDF) were used to calculate the optimum ranges of two oceanographic factors for fishing operations. The results revealed a substantial influence of the northeast and southwest monsoons significantly impacting fishing operations in the northern Indian Ocean, with extensive and active operations during the period from October to March and a notable reduction from April to September. Spatially, fishing vessels were mainly concentrated between 20° N and 6° S, extending from west of 90° E to the eastern coast of Africa. Observable seasonal variations in the distribution of fishing vessels were observed in the central and southeastern Arabian Sea, along with its adjacent high sea of the Indian Ocean. Concerning the marine environment, it was observed that during the northeast monsoon, the suitable SST contributed to high CPUEs in fishing operation areas. Fishing vessels were widely distributed in the areas with both mid-range and low-range Chl-a concentrations, with a small part distributed in high-concentration areas. Moreover, the monthly numbers of fishing vessels showed seasonal fluctuations between March 2020 and February 2023, displaying a periodic pattern with an overall increasing trend. The total number of fishing vessels decreased due to the impact of the COVID-19 pandemic in 2020, but this was followed by a gradual recovery in the subsequent two years. For fishing operations in the northern Indian Ocean, the optimum ranges for SST and Chl-a concentration were 27.96 to 29.47 °C and 0.03 to 1.81 mg/m3, respectively. The preliminary findings of this study revealed the spatial-temporal distribution characteristics of fishing vessels in the northern Indian Ocean and the suitable ranges of SST and Chl-a concentration for fishing operations. These results can serve as theoretical references for the production and resource management of off-shore fishing operations in the northern Indian Ocean.

Spatial-temporal distribution and source analysis of atmospheric particulate-bound cadmium from 1998 to 2021 in China.

Cadmium (Cd) is one of the most serious atmospheric heavy metal pollutants in China. PM2.5, PM10, and total suspended particle (TSP) are all important media for population Cd exposure. However, no studies so far have systematically explored the spatial and temporal distribution of atmospheric Cd bound to all these media in China, and the specific industrial sectors that contribute to the airborne Cd level are still unclear at present. In this study, we constructed the spatial and temporal distribution of PM (PM2.5, PM10, and TSP) binding Cd concentrations in China. Quantitative source apportionment of atmospheric Cd was carried out by analyzing the association of 23 industrial or energy-consuming sectors with Cd concentrations. Our results showed PM2.5, PM10, and TSP binding Cd concentrations decreased by 5.8%, 5.9%, and 6.1% per year at the national level, respectively. High PM-Cd concentrations were concentrated and distributed mainly in central and northwestern China. In addition, the medians of atmospheric PM2.5, PM10, and TSP binding Cd concentrations at the national level were 0.0026 µg/m3, 0.0036 µg/m3, and 0.0042 µg/m3, respectively. The main sources of PM-Cd include nonferrous metal smelting (Zn, Pb, Al) (47%), glass production (13%), pesticide production (12%), cement production (10%), and coal consumption (9%). This study analyzes comprehensively the atmospheric PM-bound Cd pollution, identifies the major industrial sectors that affect atmospheric Cd concentrations at the macroscale for the first time, and provides a basis for further reduction in the atmospheric Cd pollution.

Unseen riverine risk: Spatio-temporal shifts of microplastic pollution and its bioavailability in freshwater fish within the Ikopa River urban system.

Growing concern over microplastic pollution, driven by their widespread accumulation in the environment, stresses the need for comprehensive assessments. This study investigates the spatial and temporal distribution of microplastics in the Ikopa River (Antananarivo - Madagascar), which flows through a densely populated area, and examines their correlation with contamination levels in local fish species. By analyzing upstream and downstream stations across wet and dry seasons, only a notable increase in microplastic concentration downstream during the wet season was observed, ranging from 138.6 ± 9.0 to 222.0 ± 24.5 particles m-3, with polyethylene-co-vinyl acetate being the predominant polymer at 62.3 ± 5.13% of the total sampled polymers. This distribution underlines the impact of urban activities on pollution levels. Fish species, gambusia and Nile tilapia, were assessed for microplastic occurrence in gills and gastrointestinal tracts. Higher contamination rates were found in gambusia, enlightening the influence of feeding behaviour and fish habitat on microplastics contamination. Ingestion of microplastics directly from the water column was evident in both species, with the detection of high-density plastics such as polytetrafluoroethylene and polyvinyl chloride suggesting likely sediment contamination. This research highlights the widespread contamination of aquatic environments and its direct impact on local wildlife, pointing to a clear requirement for effective pollution management strategies.

High-resolution estimation of near-surface ozone concentration and population exposure risk in China.

Considering the spatial and temporal effects of atmospheric pollutants, using the geographically and temporally weighted regression and geo-intelligent random forest (GTWR-GeoiRF) model and Sentinel-5P satellite remote sensing data, combined with meteorological, emission inventory, site observation, population, elevation, and other data, the high-precision ozone concentration and its spatiotemporal distribution near the ground in China from March 2020 to February 2021 were estimated. On this basis, the pollution status, near-surface ozone concentration, and population exposure risk were analyzed. The findings demonstrate that the estimation outcomes of the GTWR-GeoiRF model have high precision, and the precision of the estimation results is higher compared with that of the non-hybrid model. The downscaling method enhances estimation results to some extent while addressing the issue of limited spatial resolution in some data. China's near-surface ozone concentration distribution in space shows obvious regional and seasonal characteristics. The eastern region has the highest ozone concentrations and the lowest in the northeastern region, and the wintertime low is higher than the summertime high. There are significant differences in ozone population exposure risks, with the highest exposure risks being found in China's eastern region, with population exposure risks mostly ranging from 0.8 to 5.

Spatial and Temporal Distribution Characteristics and Cytotoxicity of Atmospheric PM2.5 in the Main Urban Area of Lanzhou City.

PM2.5, as one of the most harmful pollutant in the atmospheric environment and population health, has received much attention. We monitored PM2.5 levels at five sampling sites in the Lanzhou City and collected PM2.5 particles from two representative sites for cytotoxicity experiment. The cytotoxicity of PM2.5 samples on A549 cells and migration ability of the cells were respectively detected by Cell Counting kit-8 (CCK-8) assay and scratch assay. We detected the levels of cellular inflammatory factors and oxidative damage-related biochemical indexes. RT-qPCR was used to detect the mRNA levels of NF-κB and epithelial-mesenchymal transition (EMT)-related genes. We found that the Lanlian Hotel station had the highest PM2.5 annual average concentration. The annual average concentration change curve of PM2.5 showed a roughly "U"-shaped distribution during the whole sampling period. The cytotoxicity experiment showed the viability of A549 cells decreased and the scratch healing rate increased in the 200 and 400 µg/mL PM2.5-treated groups. We also found 400 µg/mL PM2.5 induced changes in the mRNA levels of NF-κB and EMT-related genes, the mRNA levels of IKK-α, NIK, and NF-κB in the 400 µg/mL PM2.5 group were higher than those in the control group. The mRNA levels of E-cadherin decreased and α-SMA increased in the 400 µg/mL PM2.5 groups, and the mRNA levels of Fibronectin increased in the 400 µg/mL PM2.5 groups. Moreover, we found hydroxyl radical scavenging ability and T-AOC levels were lower, and LPO levels were higher in the 200 and 400 µg/mL PM2.5 groups, and the SOD activity of cells in the 400 µg/mL PM2.5 group decreased. And compared with the control group, the levels of TNF-α were higher in the 200 and 400 µg/mL PM2.5 groups and the levels of IL-1 were higher in the 400 µg/mL PM2.5 group. The results indicated that the cytotoxicity of atmospheric PM2.5 was related to oxidative damage, inflammatory response, NF-κB activity and EMT.

Long-term changes in Noctiluca scintillans blooms along the Chinese coast from 1933 to 2020.

Noctiluca scintillans is one of the most common harmful algal species and widely known due to its bioluminescence. In this study, the spatial distribution, seasonal variations, and long-term trends of N. scintillans blooms in China and the related drivers were analyzed and discussed. From 1933 to 2020, a total of 265 events of N. scintillans blooms were recorded in Chinese coastal waters, with a total duration of 1052 days. The first N. scintillans bloom occurred in Zhejiang in 1933, and only three events were recorded before 1980. From 1981 to 2020, N. scintillans caused harmful algal blooms (HABs) almost every year, both the average duration and the proportion of multiphase HABs showed an increasing trend. 1986-1992, 2002-2004, and 2009-2016 were the three peak periods with a frequency of no less than five events of N. scintillans blooms per year. In terms of spatial distribution, N. scintillans blooms spread from the Southeast China Sea to the Bohai Sea after 2000, Guangdong, Fujian, and Hebei were the three provinces with the highest numbers of recorded events of N. scintillans blooms. Moreover, 86.8% of the events of N. scintillans blooms occurred in spring (March, April, and May) and summer (June, July, and August). Among environmental factors, the dissolved inorganic phosphate, dissolved silicate and chemical oxygen demand were significantly correlated with the cell density of N. scintillans during N. scintillans blooms, and most of N. scintillans blooms were recorded in the temperature range of 18.0-25.0°C. Precipitation, hydrodynamics, water temperature, and food availability might be the main factors affecting the spatial-temporal distribution of N. scintillans blooms along the Chinese coast.

Spatio-temporal distribution of organochlorine pesticides in agricultural soils of southeast China during 2014-2019.

Organochlorine pesticides (OCPs) are organic pollutants that are persistent and undegradable in the environment. To investigate their residual concentrations, spatial and temporal distributions, and the relationship with the crops planted, 12 individual OCPs in 687 soil samples from Jiangsu, Zhejiang and Jiangxi provinces of southeast China were examined. The detection frequencies of OCPs in the studied areas were 1.89%-64.9%. The concentrations of dichloro-diphenyl-trichloroethanes (DDTs), hexachlorocyclohexanes (HCHs), and endosulfans ranged from 0.01 to 5659 µg/kg, 0.03-3.58 µg/kg, and 0.05-3235 µg/kg, respectively. Jiangsu was mainly contaminated by p,p'-DDT, p,p'-DDD and endosulfan sulfate, Zhejiang was more polluted by OCPs except δ-HCH, and Jiangxi was more vulnerable to the contamination of OCPs except o,p'-DDE. The partial least-squares discrimination analysis (PLS-DA) model with RX2 36.3-36.8% revealed that compounds with similar chemical properties tended to appear in the same year and month. All crop lands were polluted by DDTs and Endosulfans. The highest concentrations of DDTs and Endosulfans were found in citrus and vegetable fields, respectively. This study offers new insight into the layout and partitioning of OCPs in agricultural land and into insecticide management on public health and ecological safety.

Spatio-temporal pattern, matching level and prediction of ageing and medical resources in China.

OBJECTIVE: Population ageing, as a hot issue in global development, increases the burden of medical resources in society. This study aims to assess the current spatiotemporal evolution and interaction between population ageing and medical resources in mainland China; evaluate the matching level of medical resources to population ageing; and forecast future trends of ageing, medical resources, and the indicator of ageing-resources (IAR). METHODS: Data on ageing (EPR) and medical resources (NHI, NBHI, and NHTP) were obtained from China Health Statistics Yearbook and China Statistical Yearbook (2011-2020). We employed spatial autocorrelation to examine the spatial-temporal distribution trends and analyzed the spatio-temporal interaction using a Bayesian spatio-temporal effect model. The IAR, an improved evaluation indicator, was used to measure the matching level of medical resources to population ageing with kernel density analysis for visualization. Finally, an ETS-DNN model was used to forecast the trends in population ageing, medical resources, and their matching level over the next decade. RESULTS: The study found that China's ageing population and medical resources are growing annually, yet distribution is uneven across districts. There is a spatio-temporal interaction effect between ageing and medical resources, with higher levels of both in Eastern China and lower levels in Western China. The IAR is relatively high in Northwest, North China, and the Yangtze River Delta, but showed a declining trend in North China and the Yangtze River Delta. The hybrid model (ETS-DNN) gained an R2 of 0.9719, and the predicted median IAR for 2030 (0.99) across 31 regions was higher than the median IAR for 2020 (0.93). CONCLUSION: This study analyzes the relationship between population ageing and medical resources, revealing a spatio-temporal interaction between them. The IAR evaluation indicator highlights the need to address ageing population challenges and cultivate a competent health workforce. The ETS-DNN forecasts indicate higher concentrations of both medical resources and ageing populations in eastern China, emphasizing the need for region-specific ageing security systems and health service industries. The findings provide valuable policy insights for addressing a hyper-aged society in the future.

Remembering a life: an examination of open-ended life stories and the reminiscence bump in patients with Alzheimer's disease.

Autobiographical memory impairments are prominent in Alzheimer's disease (AD). Yet, life narratives of AD patients are scarcely examined. Here, twenty-one older adults diagnosed with probably AD and 22 age-matched healthy controls told their life story, and dated the events mentioned in these narratives. AD patients provided significantly fewer life story memories overall, but the proportion of memories with reference to specific events did not differ between groups. Patients included fewer negative events in their life story, while the emotional tone of narratives was similar across groups. Impairments were found on most structural aspects, with patients' narratives being less coherent and ending somewhere in the past, while no differences were seen for life story beginnings. Both groups showed a peak in remembered events dated to young adulthood, consistent with a reminiscence bump. However, in contrast to controls, patients displayed a steep drop in life story memories after the age of 30. For both groups, a high proportion of memories within the bump period were of life script events, consistent with the idea that the life script helps to structure the recall of personal life story memories, thus suggesting that AD patients still benefit from the retrieval support of this semantic structure.

Evaluating the spatial and temporal distribution of emerging contaminants in the Pearl River Basin for regulating purposes.

Little information is available on how the types, concentrations, and distribution of chemicals have evolved over the years. The objective of the present study is therefore to review the spatial and temporal distribution profile of emerging contaminants with limited toxicology data in the pearl river basin over the years to build up the emerging contaminants database in this region for risk assessment and regulatory purposes. The result revealed that seven groups of emerging contaminants were abundant in this region, and many emerging contaminants had been detected at much higher concentrations before 2011. Specifically, antibiotics, phenolic compounds, and acidic pharmaceuticals were the most abundant emerging contaminants detected in the aquatic compartment, while phenolic compounds were of the most profound concern in soil. Flame retardants and plastics were the most frequently studied chemicals in organisms. The abundance of the field concentrations and frequencies varied considerably over the years, and currently available data can hardly be used for regulation purposes. It is suggested that watershed management should establish a regular monitoring scheme and comprehensive database to monitor the distribution of emerging contaminants considering the highly condensed population in this region. The priority monitoring list should be formed in consideration of historical abundance, potential toxic effects of emerging contaminants as well as the distribution of heavily polluting industries in the region.

Online micro solid phase extraction coupled with ultra-performance liquid chromatography-tandem mass spectrometry for trace analysis of endogenous plant hormones in Ulva linza.

OBJECTIVE: Ulva linza (L.) is a species of green algae widely distributed in China. We aimed to establish a sensitive online analytical method for quantification of endogenous phytohormones in fresh minute seaweed samples. METHOD: The method for quantification of endogenous plant hormones in fresh minute samples was developed based on a homemade online micro solid phase extraction (m-SPE) system coupled with an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) platform. The online m-SPE instrument injected the eluent of m-SPE directly onto the LC separation column, improving the utilization of samples and saving time. The m-SPE column, of which the effective size was 9.6 × 2 mm i.d., was filled with 19 mg of C18 (10 µm). RESULTS: Under optimized conditions, the limits of detection were 0.002-0.060 ng ml-1 for five plant hormones. The actual sample recoveries of phytohormones were 76.4-103.4% and the coefficients of variance were below 14.1%. The temporal distribution of these endogenous plant hormones of U. linza during different growth periods is described. CONCLUSION: The proposed online m-SPE method was successfully applied to quantification of endogenous acidic and alkaline plant hormones in U. linza. It provides important information for the further study of the physiological and ecological effects of plant hormones in lower algal species.

Tracking spatio-temporal distribution and transmission of antibiotic resistance in aquatic environments by using ESBL-producing Escherichia coli as an indicator.

Wastewater treatment plants (WWTPs) play an important role in the production, and transmission of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) as a convergence for human, animal, and environmental wastewater. The aim of this study was to investigate the spatio-temporal variation and influencing factors of ARB in different functional areas of the urban WWTP and the connecting rivers for 1-year monitoring using extended-spectrum ß-lactamase-producing Escherichia coli (ESBL-Ec) as an indicator bacteria, and to study the transmission patterns of ARB in the aquatic environment. The results showed that ESBL-Ec isolates were identified from the WWTP (n = 219), including influent (n = 53), anaerobiotic tank (n = 40), aerobiotic tank (n = 36), activated sludge tank (n = 31), sludge thickner tank (n = 30), effluent (n = 16), and mudcake storage area (n = 13). The dehydration process can significantly remove the ESBL-Ec isolates; however, ESBL-Ec was still detected in samples collected from the effluent of the WWTP (37.0%). The detection rate of ESBL-Ec was significantly different across seasons (P < 0.05), and ambient temperature was negatively correlated with the detection rate of ESBL-Ec (P < 0.05). Furthermore, a high prevalence of ESBL-Ec isolates (29/187, 15.5%) was detected in samples collected from the river system. These findings emphasize that the high majority of ESBL-Ec in aquatic environments is alarming because it poses a significant threat to public health. Clonal transmission of ESBL-Ec isolates between the WWTP and rivers based on the spatio-temporal scale was observed by pulsed-field gel electrophoresis analysis, ST38 and ST69 ESBL-Ec clone were selected as prioritized isolates for antibiotic resistance monitoring in the aquatic environment. Further phylogenetic analysis showed human-associated (feces, blood) E. coli was the main source contributing to the presence of antibiotic resistance in aquatic environments. Longitudinal and targeted monitoring of ESBL-Ec in WWTPs and the development of effective wastewater disinfection strategies before effluent discharge from WWTPs are urgently required, to prevent and control the spread of antibiotic resistance in the environment.

Spatial-temporal distribution, occurrence, water quality, and risk assessment of trace elements in ten rivers surrounding Chaohu Lake in China.

As one of the five great lakes in China, the Chaohu Lake Basin is the main water source for regional economic and ecological development in Hefei city and is considered a source of drinking water. The spatial-temporal distributions and occurrence of soluble trace elements were studied in the surrounding ten rivers in the Chaohu Lake Basin as well as water quality and risk assessment during the normal and wet seasons. Principal component analysis (PCA) showed that the main source of river pollution during the two seasons was the mining industry. High values of most elements were found in the northwestern rivers in the two seasons. The temporal changes in the elements showed that the distributions of As, Mn, Cd, and Cu in the two seasons were very different, but the trends of Ni, Co, and V were basically the same. The sodium adsorption ratio (SAR) showed that almost all river samples needed to be treated before irrigation, and the water quality index (WQI) showed that most samples were of excellent water quality for drinking. The ecological risk assessment results showed that the risks in the two seasons were all slight. The results of the health risk evaluation suggested that no noncarcinogenic risks were found in the normal season and that the carcinogenic risks from Cr and As reached their highest levels in the normal and wet seasons, respectively. This research can provide vital data for rational water control and water quality conservation, offer a scientific basis for ecological environment safety, and offer a reference for carcinogenic and noncarcinogenic health risks to regional residents.

Exploring temperature dynamics in Madhya Pradesh: a spatial-temporal analysis.

Understanding the dynamics of temperature trends is vital for assessing the impacts of climate change on a regional scale. In this context, the present study focuses on Madhya Pradesh state in Central Indian region to explore the spatial-temporal distribution patterns of temperature changes from 1951 to 2021. Gridded temperature data obtained from the Indian Meteorological Department (IMD) in 1° × 1° across the state are utilised to analyse long-term trends and variations in temperature. The Mann-Kendall (MK) test and Sen's slope (SS) estimator were used to detect the trends, and Pettitt's test was utilised for change point detection. The analysis reveals significant warming trends in Madhya Pradesh during the study period during specific time frames. The temperature variables, such as the annual mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin), consistently increase, with the most pronounced warming observed during winter. The trend analysis reveals that the rate of warming has increased in the past few years, particularly since the 1990s. However, Pettitt's test points out significant changes in the temperature, with Tmean rising from 25.46 °C in 1951-2004 to 25.78 °C in 2005-2021 (+0.33 °C), Tmax shifting from 45.77 °C in 1951-2010 to 46.24 °C in 2011-2021 (+0.47°C), and Tmin increasing from 2.65 °C in 1951-1999 to 3.19 °C in 2000-2021 (+0.46 °C). These results, along with spatial-temporal distribution maps, shed important light on the alterations and variations in monthly Tmean, Tmax, and Tmin across the area, underlining the dynamic character of climate change and highlighting the demand for methods for adaptation and mitigation.