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The longitudinal course of atopic dermatitis (AD) is heterogeneous and complex. While previously thought to be a childhood disorder, recent studies demonstrated that childhood-onset AD may take several different courses that may involve persistence into adulthood becoming a lifelong condition. Other patients only demonstrated adult-onset AD. Different factors may play a role in the timing of AD onset. Assessing the longitudinal course also involves understanding the changing temporal pattern of AD. Understanding the dynamic course of AD is important in identifying individualized treatment recommendations for patients.

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BACKGROUND: Temporal trends of routinely obtained parameters may provide valuable information for predicting BSIs, but this association has not yet been established in LVAD patients. METHODS: This retrospective analysis included data from 347 consecutive recipients of three rotary LVAD types. Study endpoints included the incidence of BSI, the association of temporal trends of routinely obtained blood biomarkers with the development of BSIs, the incidence of BSIs, and survival on LVAD support. RESULTS: During follow-up, 47.8% (n = 166) of the patients developed BSI. In multivariate analyses, the development of BSI was a significant predictor of mortality (HR 5.78, 95% CI 4.08-8.19, p < 0.0001). In univariate analyses, after adjusting for potential confounders, albumin (SHR 0.94, 95% CI 0.91-0.97, p < 0.00010), creatinine (SHR 1.49, 95% CI 1.03-2.15, p = 0.033), and C-reactive protein (SHR 1.19, 95% CI 1.08-1.32, p = 0.0007) significantly predicted the development of BSIs during LVAD support. Notably, the strength of the association of parameter changes with the prediction of BSIs demonstrated a time-dependent correlation in the cases of albumin (p = 0.045) and creatinine (p = 0.003). CONCLUSION: Bloodstream infections are highly prevalent among LVAD recipients and are independent predictors of mortality. Temporal biomarker trends significantly predict the development of BSIs. These findings suggest opportunities for interventions aiming to reduce the incidence of BSIs.

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The grey parrot (Psittacus erithacus), once abundant, has become increasingly threatened due to the combined effects of capture for the global pet trade and habitat loss. Although grey parrots are well studied in captivity, effective conservation efforts require a better understanding of their ecological requirements in the wild. The aim of this paper is to quantify grey parrot behaviours across the annual cycle. To do that, we studied groups of grey parrots gathering in a natural forest clearing in the Nkuba Conservation Area, eastern Democratic Republic of the Congo. Using parrot counts and focal sampling, combined with descriptive statistics, we found that an average of 40 grey parrots visited the clearing each day, following a regular pattern in which they first perched in the trees surrounding the clearing, vocalizing loudly, until the group landed in the clearing to feed, drink and interact with each other. Generalized linear models (GLMs) and generalized additive models (GAMs) showed that the time at which parrots arrived, landed at, and left clearings was influenced by the weather, seasonality and the month of the year. We also found that parrots shortened their visits when disturbed by predators or the presence of humans. Although the underlying mechanisms for grey parrot visits to forest clearings remains unclear, the consistency of this behaviour observed elsewhere in Africa and the feeding observed in the clearing in this study suggest that these area support important foraging habitat for the wild grey parrots. Therefore, ensuring the availability of such clearings is paramount to the long-term survival of the species. We suggest that future efforts to protect grey parrots in their native habitats focus on identifying clearings visited by parrots, monitoring these clearings and allowing parrots to visit them without disturbance or risk of capture.

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Urban rivers are recognized as significant sources of methane (CH4) and carbon dioxide (CO2) emissions. Despite this, the influence of land use and urbanization on carbon emissions across rural-urban rivers at the watershed scale has been insufficiently explored. This study utilized in-situ surveys of the Liao River in northern China to investigate the spatial and temporal variations of CH4 and CO2 emissions and their relationship with urbanization and its potential controlling factors. The findings revealed that CH4 emissions peaked in fall, whereas CO2 emissions were highest in summer. The average fluxes of CH4 and CO2 at the water-gas interface were 1387.22 ± 2474.98 µmol·m-2·d-1 and 52.78 ± 54.44 mmol·m-2·d-1, respectively. Water quality parameters accounted for 80.49 % of the total variation in CH4 and CO2 concentrations and fluxes. Structural equation modeling indicated that TN, TP, DTC, and conductivity had direct effects on riverine CH4 and CO2 emissions, with standardized direct effects of 0.50 and 0.49, respectively. Nutrient input emerged as the primary driver, increasing CH4 and CO2 concentrations and fluxes, particularly in urban-adjacent river sections likely receiving higher nutrient loads. This study underscores that land use and urbanization indirectly influence riverine CH4 and CO2 emissions by modifying nutrient inputs. Effective land use management and nutrient input control are recommended strategies to mitigate riverine CH4 and CO2 emissions.

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Dietary and movement behaviors [physical activity (PA), sedentary behavior (SED), and sleep] occur throughout a 24-h day and involve multiple contexts. Understanding the temporal patterning of these 24-h behaviors and their contextual determinants is key to determining their combined effect on health. A scoping review was conducted to identify novel analytic methods for determining temporal behavior patterns and their contextual correlates. We searched Embase, ProQuest, and EBSCOhost databases in July 2022 to identify studies published between 1997 and 2022 on temporal patterns and their contextual correlates (e.g., locational, social, environmental, personal). We included 14 studies after title and abstract (n = 33,292) and full-text (n = 135) screening, of which 11 were published after 2018. Most studies (n = 4 in adults; n = 5 in children and adolescents), examined waking behavior patterns (i.e., both PA and SED) of which 3 also included sleep and 6 included contextual correlates. PA and diet were examined together in only 1 study of adults. Contextual correlates of dietary, PA, and sleep temporal behavior patterns were also examined. Machine learning with various clustering algorithms and model-based clustering techniques were most used to determine 24-h temporal behavior patterns. Although the included studies used a diverse range of methods, behavioral variables, and assessment periods, results showed that temporal patterns characterized by high SED and low PA were linked to poorer health outcomes, than those with low SED and high PA. This review identified temporal behavior patterns, and their contextual correlates, which were associated with adiposity and cardiometabolic disease risk, suggesting these methods hold promise for the discovery of holistic lifestyle exposures important to health. Standardized reporting of methods and patterns and multidisciplinary collaboration among nutrition, PA, and sleep researchers; statisticians; and computer scientists were identified as key pathways to advance future research on temporal behavior patterns in relation to health.

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Assessment and monitoring of the quality of the ecological environment in the area is a very important fundamental task in the development of ecological civilization in the Xiaojiang River Basin in Yunnan Province, which serves as a demonstration area for ecological restoration in the upper reaches of the Yangtze River. The Landsat remote sensing images from 1990, 1995, 2000, 2005, 2010, 2014, 2018, and 2022 were chosen, and the four indexes of greenness （NDMVI）, humidity （WET）, dryness （NDBSI）, and heat （LST） were extracted. The remote sensing ecological index （RSEI） was created using the principal component analysis method, then the spatial and temporal patterns and trends of ecological quality in the Xiaojiang River Basin between 1990 and 2022 were examined using the GEE platform, ArcGIS 10.7 platform, and Python platform, combining the analysis methods of geographic information mapping, coefficient of variation, Mann-Kendall trend test, Sen's slope estimation, and Hurst's index. The findings demonstrated that： ① the ecological quality of the study area had more obvious geographic differentiation spatially, and by 2022, the areas with excellent and good ecological quality grades were primarily distributed in the areas with better alpine vegetation cover, and those with poor ecological quality were primarily distributed in the areas of the mudslide ravines with relatively low terrain. On a time scale, the study area's RSEI index increased from 0.41 in 1990 to 0.55 in 2022, with a fluctuating overall trend of ecological quality improvement and an average increase of 0.048（10 a） -1； this progress was directly related to a number of ecological construction initiatives that have been energetically carried out, such as converting farms to forests, preventing mudslides, saving soil and water, managing heavy metal contamination, etc. ② The RSEI was more appropriate for the evaluation of ecological quality in alpine ravine areas because, in comparison to the NDVI index, the NDVMI adopted in this study was more sensitive to vegetation information in topographic undulation areas, especially in shaded areas, and could more accurately and quantitatively describe the vegetation information. ③ The RSEI in the Xiaojiang River Basin had a mean coefficient of variation of 0.202. Overall, its volatility was low, and its high volatility was mostly concentrated in the mudslide gully area along both sides of the Xiaojiang River fracture zone, where the surface was made up of bare rocks and sediment that was easily impacted by the changing of the seasons, the climate, and human activity. ④ The quality of the ecological environment in the region was significantly improving, with the rising area reaching 85.72% of the total area and the declining area accounting for approximately 10.15% of the total area. The future trend of change will be dominated by ongoing improvement and future degradation, accounting for 44.75% and 39.97%, respectively. It is important to pay close attention to areas that could potentially degrade. The findings of this study can serve as a theoretical foundation for additional ecological environmental conservation, management, and sustainable development in the Xiaojiang River Basin.

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Spatial transcriptomics, a groundbreaking field in cellular biology, faces the challenge of effectively deciphering complex spatial-temporal gene expression patterns. Traditional data analysis methods often fail to capture the intricate nuances of this data, limiting the depth of understanding in spatial distribution and gene interactions. In response, we present Spatial-Temporal Patterns for Downstream Analysis (STPDA), a sophisticated computational framework tailored for spatial transcriptomic data analysis. STPDA leverages high-resolution mapping to bridge the gap between genomics and histopathology, offering a comprehensive perspective on the spatial dynamics of gene expression within tissues. This approach enables a view of cellular function and organization, marking a paradigm shift in our comprehension of biological systems. By employing Autoregressive Moving Average (ARMA) and Long Short-Term Memory (LSTM) models, STPDA effectively deciphers both global and local spatio-temporal dynamics in cellular environments. This integration of spatial-temporal patterns for downstream analysis offers a transformative approach to spatial transcriptomics data analysis. STPDA excels in various single-cell analytical tasks, including the identification of ligand-receptor interactions and cell type classification. Its ability to harness spatial-temporal patterns not only matches but frequently surpasses the performance of existing state-of-the-art methods. To ensure widespread usability and impact, we have encapsulated STPDA in a scalable and accessible Python package, addressing single-cell tasks through advanced spatial-temporal pattern analysis. This development promises to enhance our understanding of cellular biology, offering novel insights and therapeutic strategies, and represents a substantial advancement in the field of spatial transcriptomics.

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Background: Studying how the bull sharks aggregate and how they can be driven by life history traits such as reproduction, prey availability, predator avoidance and social interaction in a National Park such as Cabo Pulmo, is key to understand and protect the species. Methods: The occurrence variability of 32 bull sharks tracked with passive acoustic telemetry were investigated via a hierarchical logistic regression model, with inference conducted in a Bayesian framework, comparing sex, and their response to temperature and chlorophyll. Results: Based on the fitted model, occurrence probability varied by sex and length. Juvenile females had the highest values, whereas adult males the lowest. A strong seasonality or day of the year was recorded, where sharks were generally absent during September-November. However, some sharks did not show the common pattern, being detected just for a short period. This is one of the first studies where the Bayesian framework is used to study passive acoustic telemetry proving the potential to be used in further studies.

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There is always a contradiction between the limited health resources and the unlimited demand of the population for health services, and only by improving the productivity of health resources can the health level of the population be improved as much as possible. Using prefecture-level administrative regions as spatial units, the paper analyzes the spatial pattern and changes of health productivity of health resources in China from 2000 to 2010, and uses a spatial panel Tobit model to examine the effects of factors such as technical level of health institutions, health service accessibility, public health policies and ecological environment quality on health productivity of health resources. The results show that with the Hu Huanyong line as the dividing line, the spatial heterogeneity of "high in the southeast and low in the northwest" in the health productivity of China's health resources is clear; as the regional differences narrow, the spatial correlation increases, and the spatial pattern of "overall dispersion and partial agglomeration" becomes more obvious. The fitting results of the spatial Durbin model reveal the direction and degree of influence of local and adjacent factors on the production efficiency of health resources. The positive influence of technical level of local health institutions and the accessibility of health services, the literacy level and the ability to pay for health services of residents in adjacent areas, the degree of urbanization of regional health resource allocation, climate suitability and the quality of the atmospheric environment are significant. And the negative influence of local residents' literacy and ability to pay for health services, the technical level of health institutions in adjacent areas and the degree of medicalization of health resource allocation are also significant. The influence of the degree of medicalization of local health resource allocation and the accessibility of health services in adjacent areas are significantly spatial-heterogeneous.

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The irregular eating patterns of both shift workers and evening chronotypes adversely affect cardiometabolic health. A tool that conveniently captures temporal patterns of eating alongside an indicator of circadian rhythm such as chronotype will enable researchers to explore relationships with diverse health outcome measures. We aimed to investigate the test-retest reliability and convergent validity of a Chrononutrition Questionnaire (CNQ) that captures temporal patterns of eating and chronotype in the general population (non-shift workers, university students, retirees, unemployed individuals) and shift work population. Participants attended two face-to-face/virtual sessions and completed the CNQ and food/sleep/work diaries. Outcomes included subjective chronotype, wake/sleep/mid-sleep time, sleep duration, meal/snack regularity, meal/snack/total frequency, times of first/last/largest eating occasions (EO), main meal (MM) 1/2/3, and duration of eating window (DEW). 116 participants enrolled (44.5 ± 16.5 years, BMI: 27.3 ± 5.8 kg/m2, 73% female, 52% general population); 105 completed the study. Reliability was acceptable for chronotype, sleep, and all temporal eating patterns except on night shifts. Convergent validity was good for chronotype and sleep except for certain shift/shift-free days. Generally, meal/snack regularity and frequency, and times of first/last EO showed good validity for the general population but not shift workers. Validity was good for DEW (except work-free days and afternoon shifts) and times of MM 1/2/3 (except afternoon and night shifts), while time of largest EO had poor validity. The CNQ has good test-retest reliability and acceptable convergent validity for the general and shift work population, although it will benefit from further validation, especially regarding regularity, frequency, and times of first and last eating occasions across more days amongst a larger sample size of shift workers. Use of the CNQ by researchers will expand our current understanding of chrononutrition as relationships between timing of food intake and the multitude of health outcomes are examined.

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Background: China is confronted with the significant menace posed by hemorrhagic fever with renal syndrome (HFRS). Nevertheless, the long-term spatial-temporal variations, regional prevalence patterns, and fundamental determinants' mechanisms for HFRS remain inadequately elucidated. Methods: Newly diagnosed cases of HFRS from January 2004 to December 2019 were acquired from the China Public Health Science Data repository. We used Age-period-cohort and Bayesian Spacetime Hierarchy models to identify high-risk populations and regions in mainland China. Additionally, the Geographical Detector model was employed to quantify the determinant powers of significant driver factors to the disease. Results: A total of 199,799 cases of HFRS were reported in mainland China during 2004-2019. The incidence of HFRS declined from 1.93 per 100,000 in 2004 to 0.69 per 100,000 in 2019. The incidence demonstrated an inverted U-shaped trend with advancing age, peaking in the 50-54 age group, with higher incidences observed among individuals aged 20-74 years. Hyperendemic areas were mainly concentrated in the northeastern regions of China, while some western provinces exhibited a potential upward trend. Geographical detector model identified that the spatial variations of HFRS were significantly associated with the relative humidity (Q = 0.36), forest cover (Q = 0.26), rainfall (Q = 0.18), temperature (Q = 0.16), and the surface water resources (Q = 0.14). Conclusions: This study offered comprehensive examinations of epidemic patterns, identified high-risk areas quantitatively, and analyzed factors influencing HFRS transmission in China. The findings may contribute to the necessary implementations for the effective prevention and control of HFRS.

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High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33µg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.

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The flagellar movement of the mammalian sperm plays a crucial role in fertilization. In the female reproductive tract, human spermatozoa undergo a process called capacitation which promotes changes in their motility. Only capacitated spermatozoa may be hyperactivated and only those that transition to hyperactivated motility are capable of fertilizing the egg. Hyperactivated motility is characterized by asymmetric flagellar bends of greater amplitude and lower frequency. Historically, clinical fertilization studies have used two-dimensional analysis to classify sperm motility, despite the inherently three-dimensional (3D) nature of sperm motion. Recent research has described several 3D beating features of sperm flagella. However, the 3D motility pattern of hyperactivated spermatozoa has not yet been characterized. One of the main challenges in classifying these patterns in 3D is the lack of a ground-truth reference, as it can be difficult to visually assess differences in flagellar beat patterns. Additionally, it is worth noting that only a relatively small proportion, approximately 10-20% of sperm incubated under capacitating conditions exhibit hyperactivated motility. In this work, we used a multifocal image acquisition system that can acquire, segment, and track sperm flagella in 3D+t. We developed a feature-based vector that describes the spatio-temporal flagellar sperm motility patterns by an envelope of ellipses. The classification results obtained using our 3D feature-based descriptors can serve as potential label for future work involving deep neural networks. By using the classification results as labels, it will be possible to train a deep neural network to automatically classify spermatozoa based on their 3D flagellar beating patterns. We demonstrated the effectiveness of the descriptors by applying them to a dataset of human sperm cells and showing that they can accurately differentiate between non-hyperactivated and hyperactivated 3D motility patterns of the sperm cells. This work contributes to the understanding of 3D flagellar hyperactive motility patterns and provides a framework for research in the fields of human and animal fertility.

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Haemorrhagic septicaemia (HS) is an economically important disease affecting cattle and buffaloes and the livelihoods of small-holder farmers that depend upon them. The disease is caused by Gram-negative bacterium, Pasteurella multocida, and is considered to be endemic in many states of India with more than 25,000 outbreaks in the past three decades. Currently, there is no national policy for control of HS in India. In this study, we analysed thirty year (1987-2016) monthly data on HS outbreaks using different statistical and mathematical methods to identify spatial variability and temporal patterns (seasonality, periodicity). There was zonal variation in the trend and seasonality of HS outbreaks. Overall, South zone reported maximum proportion of the outbreaks (70.2%), followed by East zone (7.2%), Central zone (6.4%), North zone (5.6%), West zone (5.5%) and North-East zone (4.9%). Annual state level analysis indicated that the reporting of HS outbreaks started at different years independently and there was no apparent transmission between the states. The results of the current study are useful for the policy makers to design national control programme on HS in India and implement state specific strategies. Further, our study and strategies could aid in implementation of similar approaches in HS endemic tropical countries around the world.

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PURPOSE: Suicide is a leading cause of death in children and adolescents. Prevention efforts may benefit from examining temporal patterns and precipitating circumstances of youth suicide deaths. The purpose of this study was to investigate if there are distinct temporal patterns of suicide death for youth. METHODS: We examined the distributions of suicide deaths across months of the year and days of the week in youth ages 10-18 using data from the National Violent Death Reporting System. RESULTS: We found that youth suicides peaked during March, April, and October on Monday, Tuesday, and Wednesday. When disaggregated by separate years of age, we discovered unique patterns in 18-year-olds and 10-year-olds. Suicides peaked during July for 18-year-olds and on Saturday for 10-year-olds. DISCUSSION: These findings suggest a need to examine temporal patterns by separate years of age, as grouping ages together may mask unique trends.

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Temporal variation in community composition is central to our understanding of the assembly and functioning of microbial communities, yet the controls over temporal dynamics for microbiomes of long-lived plants, such as trees, remain unclear. Temporal variation in tree microbiomes could arise primarily from seasonal (i.e., intra-annual) fluctuations in community composition or from longer-term changes across years as host plants age. To test these alternatives, we experimentally isolated temporal variation in plant microbiome composition using a common garden and clonally propagated plants, and we used amplicon sequencing to characterize bacterial/archaeal and fungal communities in the leaf endosphere, root endosphere, and rhizosphere of two Populus spp. over four seasons across two consecutive years. Microbial community composition differed among seasons and years (which accounted for up to 21% of the variation in microbial community composition) and was correlated with seasonal dissimilarity in climatic conditions. However, microbial community dissimilarity was also positively correlated with time, reflecting longer-term compositional shifts as host trees aged. Together, our findings demonstrate that temporal patterns in tree microbiomes arise from both seasonal fluctuations and longer-term changes, which interact to generate unique seasonal patterns each year. In addition to shedding light on two important controls over the assembly of plant microbiomes, our results also suggest future studies of tree microbiomes should account for background temporal dynamics when testing the drivers of spatial patterns in microbial community composition and temporal responses of plant microbiomes to environmental change.IMPORTANCEMicrobiomes are integral to the health of host plants, but we have a limited understanding of the factors that control how the composition of plant microbiomes changes over time. Especially little is known about the microbiome of long-lived trees, relative to annual and non-woody plants. We tested how tree microbiomes changed between seasons and years in poplar (genus Populus), which are widespread and ecologically important tree species that also serve as important biofuel feedstocks. We found the composition of bacterial, archaeal, and fungal communities differed among seasons, but these seasonal differences depended on year. This dependence was driven by longer-term changes in microbial composition as host trees developed across consecutive years. Our findings suggest that temporal variation in tree microbiomes is driven by both seasonal fluctuations and longer-term (i.e., multiyear) development.

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The aim of this research is to verify whether there is a difference in the average duration of the active and passive phases of the game between volleyball and beach volleyball. A total of 2392 active and passive phases were measured during high-level volleyball and beach volleyball matches for males. Matches were played by four teams (53 players) in indoor volleyball (age 29 ± 4.94 years) and five teams (10 players) in beach volleyball (age 28.27 ± 6.64 years). Possible differences were evaluated using the Mann-Whitney U test. The average duration of the active phase in volleyball is 5.55 s ± 4.38 s. In beach volleyball, the active phase lasts marginally longer, at 6.00 s ± 3.44 s. The average duration of the passive phase in volleyball is 35.27 s ± 25.96 s and it lasts longer than the average passive phase in beach volleyball at 33.82 s ± 22.98 s. The Mann-Whitney U test showed a statistically significant difference (p = 0.00) between the active phases in volleyball (Md = 3.53, n = 727) and beach volleyball (Md = 3.43, n = 484), U = 140770.00, z = -5.90 with small effect according to Cohen's criterion (r = 0.14). The Mann-Whitney U test (U = 160773.00, z = -1.10) showed no statistically significant difference in the average duration of the passive phases at volleyball and beach volleyball. This research determined that there is a statistically significant difference in the average duration of the active phase between volleyball and beach volleyball. The new insights gained in this research can support a more precise programming of training intensity in high-level volleyball and beach volleyball.

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Stoichiometry determines the key characteristics of organisms and ecosystems on a global scale and provides strong instructions on the fate of sediment carbon, nitrogen, and phosphorus (C-N-P) during the sedimentation process, contributing to the Earth's C-N-P balance. However, the mechanisms underlying C-N-P stoichiometry in response to intensive human activity and organic matter sources remain underexplored, especially in freshwater ecosystems. This study identifies the temporal patterns of C-N-P stoichiometry, reveals the inner driving factors, and clarifies its impact path, especially in eutrophication (the late 1970s). The results revealed that sediment RCP and RNP increased significantly and were controlled by TCAR and TNAR, respectively, indicating the direct impact of burial rate on C-N-P stoichiometry. Based on redundancy analysis and the STM model, autochthonous origin, GDP, and population had positive effects on sediment TCAR, TNAR, and TPAR, which, in turn, affected RCN, RCP, and RNP. Organic matter sources and human activities have a significant influence on RCN, RCP, and RNP, possibly regulated by the variation of TCAR and TNAR. Autochthonous origin had an indirect positive impact on RCN and RCP through the mediating effect of TCAR. Similarly, through the mediating effect of TNAR, it had an indirect negative impact on RCN and an indirect positive impact on RNP. This study showed that TCAR, TNAR, TPAR, GDP, autochthonous, allochthonous and population better explained the changes in RCN, RCP, and RNP over a-hundred-year deposition, highlighting an in-depth understanding of the dynamic change mechanism of sediment C-N-P stoichiometry during the lake deposition process.

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Wastewater-based epidemiology (WBE) is used for mining information about public health such as antibiotics resistance. This study investigated the distribution profiles of six types of antibiotic resistance genes (ARGs) in wastewater and rivers in Wuhu City, China. The levels of ARGs found in the Qingyijiang River were significantly higher than other rivers, and were comparable to effluent levels. Among the ARGs, sulfonamides ARGs and intI1 were the predominant in both wastewaters and rivers. Additionally, the concentrations of ARGs were higher on weekends as opposed to weekdays. Their distribution patterns remained consistent inter-week and inter-season using linear regression analysis (p < 0.001). Interestingly, the occurrence levels of ARGs in wastewaters during spring were significantly higher than in autumn, although insignificant in rivers. The apparent removal rate of ARGs in domestic wastewater sources ranged from 61.52-99.29%, except for qepA (-1.91% to 81.09%), whereas the removal rates in mixed domestic and industrial wastewaters showed a marked decrease (-92.94% to 76.67%). A correlation network analysis revealed that azithromycin and erythromycin were key antibiotics, while blaNDM-1, tetM, tetB, and ermB were identified as key ARGs. Sulfonamide and fluoroquinolone antibiotics, and tetracycline and macrolide ARGs were the primary contributors. Linear mixed models demonstrated that socio-economic variables positively impacted the occurrence levels of ARGs, whereas wastewater flow and river runoff were the negative drivers for their concentrations in wastewaters and surface waters, respectively. Overall, this WBE study contributes to the understanding of spatiotemporal profiles and main drivers of the occurrence of ARGs in wastewater and receiving water.

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Air pollution levels tend to be higher in urban areas than in surrounding rural areas, and this air pollution has a negative effect on human health. However, the spatiotemporal patterns of urban-rural air pollution differences and the determinants of these differences remain unclear. Here, we calculate the Urban Air Pollution Island (UAPI) intensity for PM2.5 and PM10 on a monthly, seasonal, and annual scale for 2273 cities in China from 2000 to 2020. Subsequently, we analyze the influence of urban characteristics using a combined approach of a two-way fixed effects model and a spatial Durbin model. Results show a strong downward trend in the UAPI intensity since 2013, with reductions ranging from 42 % to 61 % until 2020, for both pollutants and in summer as well as winter. Consistently, the proportion of the cities experiencing the UAPI phenomenon decreased from 94.5 % to 77.3 % for both PM2.5 and PM10. We find a significant influence of urban morphology on UAPI. Specifically, urban sprawl, polycentric development, and an increase in urban green spaces are associated with a reduction in UAPI, while dense urban areas intensify it. Our study also reveals a robust inverted U-shaped relationship between stages of economic development and UAPI. Moreover, economic development and air pollution itself show spillover effects that oppose their direct impacts. These results suggest that urban and regional planning and more ambitious climate change mitigation policies could be more effective strategies for mitigating air pollution in cities than end-of-pipe control.

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