Search

Lineages of rod-shaped bacteria such as Escherichia coli exhibit a temporal decline in elongation rate in a manner comparable to cellular or biological aging. The effect results from the production of asymmetrical daughters, one with a lower elongation rate, by the division of a mother cell. The slower daughter compared to the faster daughter, denoted respectively as the old and new daughters, has more aggregates of damaged proteins and fewer expressed gene products. We have examined further the degree of asymmetry by measuring the density of ribosomes between old and new daughters and between their poles. We found that ribosomes were denser in the new daughter and also in the new pole of the daughters. These ribosome patterns match the ones we previously found for expressed gene products. This outcome suggests that the asymmetry is not likely to result from properties unique to the gene expressed in our previous study, but rather from a more fundamental upstream process affecting the distribution of ribosomal abundance. Because damage aggregates and ribosomes are both more abundant at the poles of E. coli cells, we suggest that competition for space between the two could explain the reduced ribosomal density in old daughters. Using published values for aggregate sizes and the relationship between ribosomal number and elongation rates, we show that the aggregate volumes could in principle displace quantitatively the amount of ribosomes needed to reduce the elongation rate of the old daughters.

California's diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989-2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively. Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations. To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)-NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions. Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy.

Dialysis patients experience frequent hospitalizations and a higher mortality rate compared to other Medicare populations, in whom hospitalizations are a major contributor to morbidity, mortality, and healthcare costs. Patients also typically remain on dialysis for the duration of their lives or until kidney transplantation. Hence, there is growing interest in studying the spatiotemporal trends in the correlated outcomes of hospitalization and mortality among dialysis patients as a function of time starting from transition to dialysis across the United States Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multivariate spatiotemporal functional principal component analysis model to study the joint spatiotemporal patterns of hospitalization and mortality rates among dialysis patients. The proposal is based on a multivariate Karhunen-Loéve expansion that describes leading directions of variation across time and induces spatial correlations among region-specific scores. An efficient estimation procedure is proposed using only univariate principal components decompositions and a Markov Chain Monte Carlo framework for targeting the spatial correlations. The finite sample performance of the proposed method is studied through simulations. Novel applications to the USRDS data highlight hot spots across the United States with higher hospitalization and/or mortality rates and time periods of elevated risk.

Fungi are among the most diverse and ecologically important kingdoms in life. However, the distributional ranges of fungi remain largely unknown as do the ecological mechanisms that shape their distributions1,2. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardized aerial sampling of fungal spores3. The vast majority of operational taxonomic units were detected within only one climatic zone, and the spatiotemporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating for only a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with a microbial lifestyle follow the global biodiversity paradigms known for macroorganisms4,5.

Global biodiversity gradients are generally expected to reflect greater species replacement closer to the equator. However, empirical validation of global biodiversity gradients largely relies on vertebrates, plants, and other less diverse taxa. Here we assess the temporal and spatial dynamics of global arthropod biodiversity dynamics using a beta-diversity framework. Sampling includes 129 sampling sites whereby malaise traps are deployed to monitor temporal changes in arthropod communities. Overall, we encountered more than 150,000 unique barcode index numbers (BINs) (i.e. species proxies). We assess between site differences in community diversity using beta-diversity and the partitioned components of species replacement and richness difference. Global total beta-diversity (dissimilarity) increases with decreasing latitude, greater spatial distance and greater temporal distance. Species replacement and richness difference patterns vary across biogeographic regions. Our findings support long-standing, general expectations of global biodiversity patterns. However, we also show that the underlying processes driving patterns may be regionally linked.

In the wake of the COVID-19 pandemic, global efforts to mitigate the spread of the virus have led to widespread lockdowns and movement restrictions. Earlier studies have reported a notable positive correlation between nitrogen dioxide (NO2) levels and mobility during the initial 2020 lockdowns. However, our explorative investigation in Southeast Asia observed that, despite their similar spatial distribution, NO2 does not consistently align with mobility patterns. This observation indicates the existence of additional influential factors apart from mobility contributing to NO2 variation, necessitating a more comprehensive examination. Subsequently, we developed a trained Multi-Layer Perceptron (MLP) model and leveraged SHapley Additive exPlanations (SHAP) values. Our analysis extends beyond mobility to encompass diverse potential factors such as travel modes and meteorological factors. The model results suggest that, while as expected mobility has a strong impact on NO2 column density, a more accurate prediction requires considering different travel modes (i.e. driving and walking). Furthermore, the study reveals that spatio-temporal heterogeneity and meteorological factors also play roles in shaping NO2 level. These findings emphasize the importance of integrating such multifaceted considerations in future NO2 study for a more accurate and holistic understanding. [ABSTRACT FROM AUTHOR]

In Canada, Gonorrhea infection ranks as the second most prevalent sexually transmitted infection. In 2018, Manitoba reported an incidence rate three times greater than the national average. This study aims to investigate the spatial, temporal, and spatio-temporal patterns of Gonorrhea infection in Manitoba, using individual-level laboratory-confirmed administrative data provided by Manitoba Health from 2000 to 2016. Age and sex patterns indicate that females are affected by infections at younger ages compared to males. Moreover, there is an increase in repeated infections in 2016, accounting for 16% of the total infections. Spatial analysis at the 96 Manitoba regional health authority districts highlights significant positive spatial autocorrelation, demonstrating a clustered distribution of the infection. Northern districts of Manitoba and central Winnipeg were identified as significant clusters. Temporal analysis shows seasonal patterns, with higher infections in late summer and fall. Additionally, spatio-temporal analysis reveals clusters during high-risk periods, with the most likely cluster in the northern districts of Manitoba from January 2006 to June 2014, and a secondary cluster in central Winnipeg from June 2004 to November 2012. This study identifies that Gonorrhea infection transmission in Manitoba has temporal, spatial, and spatio-temporal variations. The findings provide vital insights for public health and Manitoba Health by revealing high-risk clusters and emphasizing the need for focused and localized prevention, control measures, and resource allocation. [ABSTRACT FROM AUTHOR]

Background: Cutaneous leishmaniasis is one of the few infectious diseases whose global prevalence is on the rise. Iran ranks among the eight most affected countries in the world. Iranian military personnel are often sent to endemic areas for cutaneous leishmaniasis without prior immunity, and they have fewer health facilities in military centers than the general population. This study aims to comprehensively investigate the situation of cutaneous leishmaniasis in Iranian military personnel across all units from 2018 to 2022 and predict the disease trend using time series analysis up to the end of 2025. Methods: We analyzed data from the Iranian Ministry of Health to perform spatiotemporal and descriptive analyses based on patient frequency. Variables examined included age distribution, cutaneous leishmaniasis types (zoonotic or anthroponotic), month of healthcare facility visits, and lesion locations. This study employed the ARIMA model (p = 2, d = 0, q = 1)(P = 3, D = 0, Q = 0), for time series analysis and forecasting the disease trend up to 36 months after 2022. Results: Over five years, 2,894 patients were reported. The Esfahan, Khuzestan, and Ilam provinces had the highest average patient counts, with hot spots primarily found in central, south, southwestern, and western Iran. Although the total number of patients with zoonotic cutaneous leishmaniasis was almost equal to anthroponotic cutaneous leishmaniasis, in high-risk provinces such as Esfahan, Khuzestan, and Ilam, the confirmed cases of zoonotic cutaneous leishmaniasis were much more than anthroponotic cutaneous leishmaniasis. patient numbers peak in October and November. Demographic analysis revealed that younger patients outnumbered older patients. Lesion locations were frequent on the forelimbs and lower limbs. The time series analysis for 36 months after 2022 indicated the seasonal pattern of the disease and predicted an upward trend after 2022. Conclusion: While overall cases have declined, provinces such as Esfahan exhibit an upward trend. The expansion of hotspots from the west and southwestern to the center and south of Iran, coupled with an increasing trend in time series analysis, suggests the potential emergence of new foci and a rise in patient numbers in the future. In provinces with high disease prevalence, preventive measures should be prioritized, particularly in Ilam, Khuzestan, and Esfahan. [ABSTRACT FROM AUTHOR]

Multi-site PM2.5 prediction has emerged as a crucial approach, given that the accuracy of prediction models based solely on data from a single monitoring station may be constrained. However, existing multi-site PM2.5 prediction methods predominantly rely on recurrent networks for extracting temporal dependencies and overlook the domain knowledge related to air quality pollutant dispersion. This study aims to explore whether a superior prediction architecture exists that not only approximates the prediction performance of recurrent networks through feedforward networks but also integrates domain knowledge of PM2.5. Consequently, we propose a novel spatio-temporal attention causal convolutional neural network (Causal-STAN) architecture for predicting PM2.5 concentrations at multiple sites in the Yangtze River Delta region of China. Causal-STAN comprises two components: a multi-site spatiotemporal feature integration module, which identifies temporal local correlation trends and spatial correlations in the spatio-temporal data, and extracts inter-site PM2.5 concentrations from the directional residual block to delineate directional features of PM2.5 concentration dispersion between sites; and a temporal causal attention convolutional network that captures the internal correlation information and long-term dependencies in the time series. Causal-STAN was evaluated using one-year data from 247 sites in mainland China. Compared to six state-of-the-art baseline models, Causal-STAN achieves optimal performance in 6-hour future predictions, surpassing the recurrent network model and reducing the prediction error by 8%-10%. [ABSTRACT FROM AUTHOR]

Cervical cancer, despite being preventable through primary and secondary prevention strategies, remains one of the leading causes of morbidity and mortality among women in Brazil. This study aimed to analyze the temporal, spatial, and space-time patterns of cervical cancer mortality in Brazil. An ecological study was conducted using temporal, spatial, and space-time analysis techniques, using death certificates with cervical cancer as the underlying cause or associated condition among females in Brazil from 2000 to 2021. Death certificate and population data were provided by the Department of Health Informatics of the Unified Health System (DATASUS) and the Brazilian Institute of Geography and Statistics (IBGE), respectively. A total of 123,306 deaths associated with cervical cancer among women were registered during the study period. A rising trend in mortality was detected since 2014 onwards, after 14 years of decline. Particularly, an increase in mortality was observed among the younger age groups, and in the North and Northeast regions regardless of age. Heterogeneity in the spatial distribution of cervical cancer mortality was observed, with high mortality clusters around the country, but mostly concentrated in the North and Northeast regions. These findings suggest a need and an opportunity to develop efficient and effective health policies targeting those regions and groups of women at higher risk which in turn will allow for fast and significant reductions in cervical cancer mortality in Brazil. [ABSTRACT FROM AUTHOR]

The heaps of soil left behind after mining activities are termed as mine spoil or overburden. The environmental impacts due to mining, including vegetation destruction and soil degradation, necessitates restoration measures like mine spoil afforestation. This review examines approaches undertaken globally for mine spoil afforestation, highlighting the need for comprehensive assessments to guide strategic development. The effectiveness of spatio-temporal analysis (STA), a technique which combines spatial and temporal dimensions, in evaluating the evolution of vegetative covering in mine spoil regions over time is examined in this regard. Leveraging STA techniques like LULC and various indices including NDVI, EVI, RVI, NDMI, NDISI offer promising avenues for enhanced monitoring and assessment by integrating advancements in remote sensing and machine learning. In general, NDVI is more preferred for assessing vegetative cover. For fractional vegetation cover or the leaf area index, advanced indices like SAVI and GEMI are recommended. Further exploring the recent advances in STA holds potential for addressing various challenges involving data quality and spectral resolution and offers valuable insights for sustainable land management practices. Through a comprehensive analysis of contemporary literature, this study enhances our understanding of the complex dynamics associated with mine spoil afforestation. [ABSTRACT FROM AUTHOR]

This work aimed to analyze the temporal behavior of the concentration profiles of PM2.5 (period 2014-2023) and PM10 (period 2010-2023) collected in five districts of the metropolitan area of Lima (MAL). The year 2016 and 2021 showed the highest annual averages for PM2.5 while PM10 reported higher concentrations in 2013, 2016, and 2021 for most stations. For PM2.5 higher peaks were recorded between May and September and lower peaks between January and March. For PM10, higher peaks were found in March-May and August-October, and lower peaks were observed between January and June. Daily minimum and maximum values of PM2.5 ranged between 3.74 and 148 μg/m³, while PM10 ranged from 7.16 to 579 μg/m³, respectively. For hourly variations were observed peaks that occur between 6:00 and 10:00 a.m. and between 6:00 p.m. and 11:00 p.m. for both PMs. The information obtained will help to identify episodes of air pollution, pollution sources, monitor climate change, and can be used to alert communities and take immediate action. [ABSTRACT FROM AUTHOR]

This study investigates the prevalence, distribution, and ecological consequences of 21 heavy metals (Ag, Al, As, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Li, Mg, Mn, Na, Ni, Pb, Sr, and Zn) in the soils between the Mahi and Dhadhar rivers in Gujarat, India. It aims to assess the seasonal variations in soil contamination and the potential human health risk associated with the heavy metal exposure through ingestion, inhalation, and dermal contact pathways. Soil samples were collected from 57 sites across three distinct seasons and analyzed using indices such as the geo-accumulation index, contamination factor, pollution load index, ecological risk factor, and global potential ecological risk. Human health risks were also evaluated for carcinogenic and non-carcinogenic effects. The findings reveal significant soil contamination, especially during the Monsoon and Post-Monsoon seasons, with heavy metals like Ag, Cd, Cr, Cu, Co, Ni, and Pb posing considerable ecological threats. Cr and Ni were identified as presenting the highest carcinogenic risks, while Fe and Cr posed major non-carcinogenic challenges. Seasonal variations significantly influenced heavy metal concentrations and distribution patterns. The study highlights the urgent need for comprehensive policies and sustainable practices to mitigate soil contamination and protect environmental health. It emphasizes the critical role of human activities, such as industrial, agricultural, and mining operations, in soil degradation and calls for increased community awareness and action to address these challenges. [ABSTRACT FROM AUTHOR]

In many administrative entities, there is little information on the accessibility of critical resources such as food, water, healthcare and fuel. Yet the lack of accessibility to critical resources can be a risk-mitigating factor. Indeed, people who are not physically threatened by a hazard can be put at risk by the fact that they lack critical resources. In the specific context of an area threatened by lava flows and characterized by a scarcity of accessible and reliable data, we assessed the accessibility of critical resources in Goma, in the Democratic Republic of Congo (DRC), a densely populated city located at the foot of the Nyiragongo volcano. Our study shows that by mapping the location of critical resources and characterizing their accessibility, we can systematically identify underserviced urban areas that are consequently more vulnerable to risks, and in our study to volcanic risks.

In Canada, Gonorrhea infection ranks as the second most prevalent sexually transmitted infection. In 2018, Manitoba reported an incidence rate three times greater than the national average. This study aims to investigate the spatial, temporal, and spatio-temporal patterns of Gonorrhea infection in Manitoba, using individual-level laboratory-confirmed administrative data provided by Manitoba Health from 2000 to 2016. Age and sex patterns indicate that females are affected by infections at younger ages compared to males. Moreover, there is an increase in repeated infections in 2016, accounting for 16% of the total infections. Spatial analysis at the 96 Manitoba regional health authority districts highlights significant positive spatial autocorrelation, demonstrating a clustered distribution of the infection. Northern districts of Manitoba and central Winnipeg were identified as significant clusters. Temporal analysis shows seasonal patterns, with higher infections in late summer and fall. Additionally, spatio-temporal analysis reveals clusters during high-risk periods, with the most likely cluster in the northern districts of Manitoba from January 2006 to June 2014, and a secondary cluster in central Winnipeg from June 2004 to November 2012. This study identifies that Gonorrhea infection transmission in Manitoba has temporal, spatial, and spatio-temporal variations. The findings provide vital insights for public health and Manitoba Health by revealing high-risk clusters and emphasizing the need for focused and localized prevention, control measures, and resource allocation.

Abstract Background Cutaneous leishmaniasis is one of the few infectious diseases whose global prevalence is on the rise. Iran ranks among the eight most affected countries in the world. Iranian military personnel are often sent to endemic areas for cutaneous leishmaniasis without prior immunity, and they have fewer health facilities in military centers than the general population. This study aims to comprehensively investigate the situation of cutaneous leishmaniasis in Iranian military personnel across all units from 2018 to 2022 and predict the disease trend using time series analysis up to the end of 2025. Methods We analyzed data from the Iranian Ministry of Health to perform spatiotemporal and descriptive analyses based on patient frequency. Variables examined included age distribution, cutaneous leishmaniasis types (zoonotic or anthroponotic), month of healthcare facility visits, and lesion locations. This study employed the ARIMA model (p = 2, d = 0, q = 1)(P = 3, D = 0, Q = 0), for time series analysis and forecasting the disease trend up to 36 months after 2022. Results Over five years, 2,894 patients were reported. The Esfahan, Khuzestan, and Ilam provinces had the highest average patient counts, with hot spots primarily found in central, south, southwestern, and western Iran. Although the total number of patients with zoonotic cutaneous leishmaniasis was almost equal to anthroponotic cutaneous leishmaniasis, in high-risk provinces such as Esfahan, Khuzestan, and Ilam, the confirmed cases of zoonotic cutaneous leishmaniasis were much more than anthroponotic cutaneous leishmaniasis. patient numbers peak in October and November. Demographic analysis revealed that younger patients outnumbered older patients. Lesion locations were frequent on the forelimbs and lower limbs. The time series analysis for 36 months after 2022 indicated the seasonal pattern of the disease and predicted an upward trend after 2022. Conclusion While overall cases have declined, provinces such as Esfahan exhibit an upward trend. The expansion of hotspots from the west and southwestern to the center and south of Iran, coupled with an increasing trend in time series analysis, suggests the potential emergence of new foci and a rise in patient numbers in the future. In provinces with high disease prevalence, preventive measures should be prioritized, particularly in Ilam, Khuzestan, and Esfahan.

In the wake of the COVID-19 pandemic, global efforts to mitigate the spread of the virus have led to widespread lockdowns and movement restrictions. Earlier studies have reported a notable positive correlation between nitrogen dioxide (NO2) levels and mobility during the initial 2020 lockdowns. However, our explorative investigation in Southeast Asia observed that, despite their similar spatial distribution, NO2 does not consistently align with mobility patterns. This observation indicates the existence of additional influential factors apart from mobility contributing to NO2 variation, necessitating a more comprehensive examination. Subsequently, we developed a trained Multi-Layer Perceptron (MLP) model and leveraged SHapley Additive exPlanations (SHAP) values. Our analysis extends beyond mobility to encompass diverse potential factors such as travel modes and meteorological factors. The model results suggest that, while as expected mobility has a strong impact on NO2 column density, a more accurate prediction requires considering different travel modes (i.e. driving and walking). Furthermore, the study reveals that spatio-temporal heterogeneity and meteorological factors also play roles in shaping NO2 level. These findings emphasize the importance of integrating such multifaceted considerations in future NO2 study for a more accurate and holistic understanding.

Abstract Cervical cancer, despite being preventable through primary and secondary prevention strategies, remains one of the leading causes of morbidity and mortality among women in Brazil. This study aimed to analyze the temporal, spatial, and space-time patterns of cervical cancer mortality in Brazil. An ecological study was conducted using temporal, spatial, and space-time analysis techniques, using death certificates with cervical cancer as the underlying cause or associated condition among females in Brazil from 2000 to 2021. Death certificate and population data were provided by the Department of Health Informatics of the Unified Health System (DATASUS) and the Brazilian Institute of Geography and Statistics (IBGE), respectively. A total of 123,306 deaths associated with cervical cancer among women were registered during the study period. A rising trend in mortality was detected since 2014 onwards, after 14 years of decline. Particularly, an increase in mortality was observed among the younger age groups, and in the North and Northeast regions regardless of age. Heterogeneity in the spatial distribution of cervical cancer mortality was observed, with high mortality clusters around the country, but mostly concentrated in the North and Northeast regions. These findings suggest a need and an opportunity to develop efficient and effective health policies targeting those regions and groups of women at higher risk which in turn will allow for fast and significant reductions in cervical cancer mortality in Brazil.

Illegal parking is a widespread issue in urban environments, leading to significant adverse effects on traffic flow, public safety, and the aesthetics of streetscapes. This paper introduces an innovative methodology for the identification and analysis of illegal parking hotspots. To achieve this, a custom Web-GIS platform is developed that employs crowdsourcing, enabling citizens to report the precise locations and types of parking violations they encounter. The platform collects this data, which is then analyzed using a Spatio-Temporal Density Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm. The methodology was applied in the city of Athens, Greece, with the findings revealing that parking violations are not uniformly distributed but instead show distinct spatial and temporal patterns. Specifically, certain areas and times during the day exhibit considerably higher levels of illegal parking. For instance, commercial areas tend to experience more violations during business hours, while residential areas see more issues during evenings and weekends, mainly due to an increasing number of already parked cars and a lack of organized parking spaces. By providing a data-driven approach to identifying critical areas of illegal parking, city officials can better allocate enforcement resources and develop targeted strategies to mitigate this issue, highlighting the significant implications of the study for urban management and policy-making. Therefore, the main goal of this research is to contribute to the enhancement of urban mobility and the overall quality of life by addressing one of the most persistent challenges in city management. [ABSTRACT FROM AUTHOR]

Abstract Background Despite global efforts to reduce and eventually interrupt malaria transmission, the disease remains a pressing public health problem, especially in sub-Saharan Africa. This study presents a detailed spatio-temporal analysis of malaria transmission in Rwanda from 2012 to 2022. The main objective was to gain insights into the evolving patterns of malaria and to inform and tailor effective public health strategies. Methods The study used yearly aggregated data of malaria cases from the Rwanda health management information system. We employed a multifaceted analytical approach, including descriptive statistics and spatio-temporal analysis across three demographic groups: children under the age of 5 years, and males and females above 5 years. Bayesian spatially explicit models and spatio scan statistics were utilised to examine geographic and temporal patterns of relative risks and to identify clusters of malaria transmission. Results We observed a significant increase in malaria cases from 2014 to 2018, peaking in 2016 for males and females aged above 5 years with counts of 98,645 and 116,627, respectively and in 2018 for under 5-year-old children with 84,440 cases with notable geographic disparities. Districts like Kamonyi (Southern Province), Ngoma, Kayonza and Bugesera (Eastern Province) exhibited high burdens, possibly influenced by factors such as climate, vector control practices, and cross-border dynamics. Bayesian spatially explicit modeling revealed elevated relative risks in numerous districts, underscoring the heterogeneity of malaria transmission in these districts, and thus contributing to an overall rising trend in malaria cases until 2018, followed by a subsequent decline. Our findings emphasize that the heterogeneity of malaria transmission is potentially driven by ecologic, socioeconomic, and behavioural factors. Conclusions The study underscores the complexity of malaria transmission in Rwanda and calls for climate adaptive, gender-, age- and district-specific strategies in the national malaria control program. The emergence of both artemisinin and pyrethoids resistance and persistent high transmission in some districts necessitates continuous monitoring and innovative, data-driven approaches for effective and sustainable malaria control. Graphical Abstract

Rapid urbanization causes significant changes in land use and land cover (LULC), contributing to Urban Heat Islands (UHIs) and environmental challenges. This study analyzes two decades of Landsat satellite imagery to examine LULC changes and their impact on Land Surface Temperature (LST) in Aligarh City, India. Supervised classification using maximum likelihood techniques produced LULC maps with 85% accuracy. The results show a 21.10% increase in built-up areas, from 2338.83 hectares in 2001 to 2832.66 hectares in 2021, alongside a 24.55% decline in vegetation, from 620.28 hectares to 467.91 hectares. LST increased by 11.35°C, with a total maximum rise of 10.70°C between 2001 and 2021. Regression analysis revealed strong correlations between LST and spectral indices, with NDVI showing a negative correlation (R² = 0.89, 0.83, 0.78) and NDBI a positive one (R² = 0.87, 0.89, 0.79). The Aligarh main city area consistently recorded higher LST values than the AMU campus, with 88.60% of the city area in the 40–45°C range by 2021, compared to 52.87% for the campus. This study underscores the need for sustainable urban strategies to mitigate UHI effects and promote climate resilience.

Abstract Objective To analyse hospital case fatality and mortality related to Chagas disease (CD) in Brazil, 2000–2019. Method This is a mixed ecological study with spatial and temporal trends, based on national population data from the Brazilian Ministry of Health - hospital admissions (HA) and death certificates (DC). Records with CD as a primary or secondary cause of death in HA and/or as an underlying or associated cause of death in DC were evaluated. Temporal trends were analysed by Joinpoint regression and the spatial distribution of age- and gender-adjusted rates, spatial moving averages, and standardized morbidity ratios. Results There were a total of 4,376 HA due to CD resulting in death in Brazil, with a hospital case fatality rate of 0.11/100,000 inhabitants. The Southeast region had the highest rate (63.9%, n = 2,796; 0.17/100,000 inhabitants). The general trend for this indicator in Brazil is upwards (average annual percentage change [AAPC] 7.5; 95% confidence interval [CI] 5.3 to 9.9), with increases in the North, Northeast and Southeast regions. During the same period 122,275 deaths from CD were registered in DC, with a mortality rate of 3.14/100,000 inhabitants. The highest risk of CD-related death was found among men (relative risk [RR] 1.27) and Afro-Brazilians (RR 1.63). There was a downward trend in CD mortality in the country (AAPC − 0.7%, 95%CI -0.9 to -0.5), with an increase in the Northeast region (AAPC 1.1%, 95%CI 0.6 to 1.6). Municipalities with a very high Brazilian Deprivation Index tended to show an increase in mortality (AAPC 2.1%, 95%CI 1.6 to 2.7), while the others showed a decrease. Conclusion Hospital case fatality and mortality due to CD are a relevant public health problem in Brazil. Differences related to gender, ethnicity, and social vulnerability reinforce the need for comprehensive care, and to ensure equity in access to health in the country. Municipalities, states, and regions with indicators that reveal higher morbidity and mortality need to be prioritized.

Abstract Background Breastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019. Methods Data from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables. Results The trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section. Conclusions Despite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health.

Introduction. The rapid spread of African swine fever in the Kaliningrad region makes it necessary to use the methods of molecular epidemiology to determine the dynamics and direction of ASF spread in this region of Russia. The aim of the study was to determine single nucleotide polymorphisms within molecular markers K145R, O174L and MGF 505-5R of ASFVs isolated in Kaliningrad region and to study the circulating of the pathogen in European countries by subgenotyping and spatio-temporal clustering analysis. Materials and methods. Blood samples from living domestic pigs and organs from dead domestic pigs and wild boars, collected in the Kaliningrad region between 2017 and 2022 were used. Virus isolation was carried out in porcine bone-marrow primary cell culture. Amplicons of genome markers were amplified by PCR with electrophoretic detection and subsequent extraction of fragments from agarose gel. Sequencing was performed using the Sanger method. Results. The circulation of two genetic clusters of ASFV isolates on the territory of the Kaliningrad has been established: epidemic (K145R-III, MGF 505-5R-II, O174L-I – 94.3% of the studied isolates) and sporadic (K145R-II, MGF 505-5R-II, O174L-I – 5.7%). Conclusion. The broaden molecular genetic surveillance of ASFV isolates based on sequencing of genome markers is necessary in the countries of the Eurasian continent to perform a more detailed analysis of ASF spread between countries and within regions.

After myocardial infarction, there exists a spatiotemporal variation of cardiac tissue stiffness across the infarcted border region outward to remote regions, influencing adverse remodeling and cardiac fibrosis, and this stiffness gradient changes over time. Here, a platform with dynamic, tunable, and reversible gradient stiffness can recapitulate in vitro the time‐dependent stiffness range across the infarction border that occurs as part of the remodeling process is presented. This platform enables the observation of time‐dependent interaction between cardiac fibroblasts and their mechanical microenvironment in a spatiotemporal manner. Specifically, the competition and cooperation of a chemical cue (antifibrotic drug) and mechanical cue (gradient softening) in tandem to attenuate the fibrotic responses of cardiac fibroblasts is examined. Applying a combined intervention showed either additive or antagonistic effects on fibrosis‐related gene regulation compared to separate interventions of drug or softening. This work reveals the spatiotemporal variation of fibrotic response in cardiac fibroblasts as well as the complexity of antifibrotic drug dosing with stiffness changes and their combinatory effect on cardiac fibroblasts. This platform provides a unique in vitro tool to study disease progression mechanisms in a more clinically relevant microenvironment and also serves as a cost‐effective model for potential therapeutic screening. [ABSTRACT FROM AUTHOR]

Background: Despite global efforts to reduce and eventually interrupt malaria transmission, the disease remains a pressing public health problem, especially in sub-Saharan Africa. This study presents a detailed spatio-temporal analysis of malaria transmission in Rwanda from 2012 to 2022. The main objective was to gain insights into the evolving patterns of malaria and to inform and tailor effective public health strategies. Methods: The study used yearly aggregated data of malaria cases from the Rwanda health management information system. We employed a multifaceted analytical approach, including descriptive statistics and spatio-temporal analysis across three demographic groups: children under the age of 5 years, and males and females above 5 years. Bayesian spatially explicit models and spatio scan statistics were utilised to examine geographic and temporal patterns of relative risks and to identify clusters of malaria transmission. Results: We observed a significant increase in malaria cases from 2014 to 2018, peaking in 2016 for males and females aged above 5 years with counts of 98,645 and 116,627, respectively and in 2018 for under 5-year-old children with 84,440 cases with notable geographic disparities. Districts like Kamonyi (Southern Province), Ngoma, Kayonza and Bugesera (Eastern Province) exhibited high burdens, possibly influenced by factors such as climate, vector control practices, and cross-border dynamics. Bayesian spatially explicit modeling revealed elevated relative risks in numerous districts, underscoring the heterogeneity of malaria transmission in these districts, and thus contributing to an overall rising trend in malaria cases until 2018, followed by a subsequent decline. Our findings emphasize that the heterogeneity of malaria transmission is potentially driven by ecologic, socioeconomic, and behavioural factors. Conclusions: The study underscores the complexity of malaria transmission in Rwanda and calls for climate adaptive, gender-, age- and district-specific strategies in the national malaria control program. The emergence of both artemisinin and pyrethoids resistance and persistent high transmission in some districts necessitates continuous monitoring and innovative, data-driven approaches for effective and sustainable malaria control. [ABSTRACT FROM AUTHOR]

Background: Emergency Medical Services (EMS) is the first point of service for people who are in critical conditions and need emergency services, and high blood pressure is one of the most important causes of death in Iran and the world. Materials and Methods: This applied and descriptive-analytical research, using spatial statistics and geographic information system (GIS), analyzed the epidemiologic space-time of blood pressure in the city of Mashhad. The statistical population, the total number of blood pressure patients (3555 people), are within the legal limits of the city of Mashhad, who contacted the EMS center between 2018 and 2019. To identify the spatial pattern of blood pressure disease, in the analysis of the spatial distribution pattern of central complication techniques, standard deviation curve, high/low clustering analysis, hot spot analysis, global and local Moran index and kernel density analysis to analyze and how the pattern Spatial distribution of blood pressure disease has been used by geographic information system. Results: The findings of the research show that the index of clustering analysis in the spatial pattern is in the form of severe clustering. Cornell's density estimation model in the period of 2018-2019 showed that the most areas involved in blood pressure diseases include the northeastern areas towards the center of Mashhad city, where patients with high or low blood pressure have gathered in hot or cold clusters. Conclusion: By using the analytical results of this research, a comprehensive understanding of the centers of blood pressure disease, and the spatial patterns and geographical distribution of health, in Mashhad city, has been achieved, which can be taken into account in preventive measures. [ABSTRACT FROM AUTHOR]

In the American regions, Brazil accounts for 97% of visceral leishmaniasis (VL) cases, with a case fatality rate of approximately 10%. This study aimed to investigate the VL mortality distribution in Brazil and identify high-priority and high-risk areas for intervention strategies. This was an ecological study that analysed the spatial–temporal patterns of VL mortality in Brazilian municipalities. Age-standardised VL mortality rates from the Global Burden of Disease study from 2001 to 2018 were used. The distribution of mortality in the municipalities was assessed, and subsequently the Local Index of Spatial Autocorrelation (LISA) analysis was conducted to identify contiguous areas with high mortality rates. Scan analysis identified clusters of high spatial–temporal risks. The highest mortality rates and clusters were in municipalities located in the Northeast region and in the states of Tocantins and Roraima (North region), Mato Grosso do Sul (Central-West region), and Minas Gerais (Southeast region). According to LISA, there was an increase in the number of municipalities classified as high priority from the first 3-year period (n = 434) to the last 3-year period (n = 644). The spatio-temporal analysis identified 21 high-risk clusters for VL mortality. Areas with a high risk of VL mortality should prioritise preventing transmission, invest in early diagnosis and treatment, and promote the training of healthcare professionals. [ABSTRACT FROM AUTHOR]

Machine intelligence is at great height these days and has been evident with its effective provenance in almost all domains of science and technology. This work will focus on one handy and profound application of machine intelligence-time series forecast, and that too on visual data points, i.e., our objective is to predict future visual data points, given a subtle lag to work on. For the same, we would propose a deep learner, Newtonian physics informed neural network (NwPiNN) with the critical modelling capabilities of the physics informed neural networks, modelled on the laws of Newtonian physics. For computational efficacy, we would work on the gray-scale values of pixels. Since the variation in data pixel values is not only provoked by the pixel gray values but also by the velocity component of each pixel, the final prediction of the model would be a weighted average of the gray value forecast and the kinematics of each pixel, as modelled by the PINN. Besides its' proposal, NwPiNN is subjected to benchmark visual dataset, and compared with existing models for visual time series forecast, like ConvLSTM, and CNN-LSTM, and in most of the occasions, NwPiNN is found to outperform its preliminaries. [ABSTRACT FROM AUTHOR]

Objective: To analyse hospital case fatality and mortality related to Chagas disease (CD) in Brazil, 2000–2019. Method: This is a mixed ecological study with spatial and temporal trends, based on national population data from the Brazilian Ministry of Health - hospital admissions (HA) and death certificates (DC). Records with CD as a primary or secondary cause of death in HA and/or as an underlying or associated cause of death in DC were evaluated. Temporal trends were analysed by Joinpoint regression and the spatial distribution of age- and gender-adjusted rates, spatial moving averages, and standardized morbidity ratios. Results: There were a total of 4,376 HA due to CD resulting in death in Brazil, with a hospital case fatality rate of 0.11/100,000 inhabitants. The Southeast region had the highest rate (63.9%, n = 2,796; 0.17/100,000 inhabitants). The general trend for this indicator in Brazil is upwards (average annual percentage change [AAPC] 7.5; 95% confidence interval [CI] 5.3 to 9.9), with increases in the North, Northeast and Southeast regions. During the same period 122,275 deaths from CD were registered in DC, with a mortality rate of 3.14/100,000 inhabitants. The highest risk of CD-related death was found among men (relative risk [RR] 1.27) and Afro-Brazilians (RR 1.63). There was a downward trend in CD mortality in the country (AAPC − 0.7%, 95%CI -0.9 to -0.5), with an increase in the Northeast region (AAPC 1.1%, 95%CI 0.6 to 1.6). Municipalities with a very high Brazilian Deprivation Index tended to show an increase in mortality (AAPC 2.1%, 95%CI 1.6 to 2.7), while the others showed a decrease. Conclusion: Hospital case fatality and mortality due to CD are a relevant public health problem in Brazil. Differences related to gender, ethnicity, and social vulnerability reinforce the need for comprehensive care, and to ensure equity in access to health in the country. Municipalities, states, and regions with indicators that reveal higher morbidity and mortality need to be prioritized. [ABSTRACT FROM AUTHOR]

Monitoring the LST(Land surface temperature) is crucial for understanding environmental conditions and their impact on energy exchange between the Earth and atmosphere, as well as the water cycle and overall environmental evolution. To this end, Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LST, 8-Day (MOD11A2,061) products were utilized to study the day and night time LST in Iran and its watersheds from 2001–2022. The findings revealed that during the day, 1.5% of Iran area had a temperature below 0 °C, 21% had an LST between 0–20 °C, 37% above 40 °C, with the largest region (40%) falling within the 20–40 °C range. At night, 16.7% had a temperature below 0 °C, 1.7% of the area had an LST above 30 °C, 39% between 15–30 °C, with the largest region (43%) falling between 0–15 °C. Furthermore, the study revealed a significant increase in LST during the summer season, with LST day showing a positive trend in ten watersheds with a confidence level of 95% (p = 0.05) and a very strong positive trend in Helle watershed with a confidence level of 99% (p = 0.01). while LST night showed a significant positive trend in 6 watersheds (p = 0.05) and a very strong significant positive trend in 18 watersheds (p = 0.01). Additionally, LST night showed a significantly greater increasing trend compared to LST day. [ABSTRACT FROM AUTHOR]

Wildfires are an intrinsic and vital driving factor in the Miombo ecosystem. Understanding fire regimes in Miombo is crucial for its ecological sustainability. Miombo is dominant in Central Mozambique, having one of the highest fire incidences in the country. This study evaluated the spatio-temporal patterns of fire regimes (intensity, seasonality, frequency and fire return interval) in the LevasFlor Forest Concession (LFC), Central Mozambique using remotely sensed data from 2001 to 2022. We conducted hotspot spatial statistics using the Getis-Ord Gi* method to assess fire distribution and patterns. The results revealed that 88% of the study area was burnt at least once from 2001 to 2022, with an average burned area of 9733 ha/year (21% of LFC's total area). Fires were more likely to occur (74.4%) in open and deciduous Miombo types. A total of 84% of the studied area, burned in a range of 4 to 22 years of fire return interval (FRI) over the 21 assessed. Only 16% of the area was affected by high to very high FRI (1 to 4 years), with an average FRI of 4.43 years. Generally, fires are more frequent and intense in September and October. These results highlight the usefulness of remote sensing in evaluating long-term spatiotemporal fire trends for effective fire management strategies and control measures in African savanna ecosystems. [ABSTRACT FROM AUTHOR]

Background: Breastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019. Methods: Data from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables. Results: The trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section. Conclusions: Despite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health. [ABSTRACT FROM AUTHOR]

Accurate identification of the spatio-temporal planting structure and analysis of its driving factors in an irrigation district are the important bases for scientific and reasonable utilization of irrigation water resources. In pursuit of this goal, the training sample migration method combined with the random forest algorithm were used to classify land use and planting structure over 2001–2022 in the lower Yellow River Basin. Moreover, an econometric regression model was applied to quantify the driving factors of the change in the crop-planted area. The results illustrated that the identification method has relatively high accuracy in identifying historical periods of land use and planting structures, with the average kappa coefficient equating to 0.953. From 2001 to 2022, the area of cultivated land was the largest, with the proportion of the total area increasing from 45.72% to 58.12%. The planted area of winter wheat–summer maize rotation increased from 74.84% to 88.11% of the cultivated land. While the planted area of cotton declined by 96.36%, about 50% of cotton planting was converted to the winter wheat–summer maize rotation planting. The government policies about grain purchase and storage were the dominant factors for the change in the crop-planted area. This resulted in an increase of 63.32 × 103 ha and 63.98 × 103 ha in the planted area of winter wheat and summer maize, respectively. The findings are of great significance to the allocation of water resources in irrigation districts of the lower Yellow River Basin. [ABSTRACT FROM AUTHOR]

Knowledge regarding soil wetness dynamics is relevant for its application in hydrological and climatological studies. It is also a parameter that allows the analysis of processes related to land cover and land use. Due to this importance, the spatio-temporal dynamics of the surface soil humidity (SSH) in the southern of the Argentinean Pampean Plains have been analyzed, based on data corresponding to six Argentine localities and in relation to a climatological index that incorporates rainfall and evapotranspiration variables. The methodology includes statistical correlation and multivariate analysis based on modeled and reanalysis data. The results obtained reveal a different response of the SSH at a spatial level, with maximum values in the highest rainfall region, diminishing towards the most arid zone. This allows us to infer the influence of the topographic homogeneity of the area and the predominance of vertical processes in the hydrological balance. At both a spatial and temporal scale, the SSH dynamics concur with the climatological index pattern, confirmed by a Pearson coefficient. It has also been shown that the SSH responds to the climatological parameters and that the highest correlation occurs at a three-month temporal scale. This response may be observed at an interannual and annual level, with maximum and minimum values during the wet and dry years, respectively and a decrease in the SSH during the warmest seasons (summer). The SSH displays the same tendency as the climatological index, from NE to SW, reaching a maximum in the rainiest area and minimum in the arid region. The manuscript, based on diverse sources analysis, pluviometric periods classification and statistical methods application, is a contribution related to the edaphic variable dynamics in a region in which farming activity constitutes the main land use and where the droughts and floods alternance has effects on the flooding risks and the economic losses as a consequence of intensive droughts, respectively. [ABSTRACT FROM AUTHOR]

This study conducts a spatio-temporal analysis to identify trends and clusters of child sexual abuse in Mexico City before, during, and after the COVID-19 pandemic. Sexual abuses of children were analyzed considering various crime theories. Trends and patterns were identified using time series decomposition and spatial autocorrelation techniques. Time series considered three relevant periods. Anselin's Local Moran's I identified the spatial distribution of significant clusters. The child sexual abuse rate presented similar values following school closures. The resumption of classes entailed a decrease of −1.5% (children under 15) and an increase of 29% (children over 15). Particular locations in Mexico City experienced significant clusters among those over 15. There were eight noteworthy clusters displaying recidivism patterns with lower poverty rates and a high level of education. Efforts to combat child sexual abuse should prioritize specific areas in Mexico City where female children over 15 are at high risk of becoming victims of sexual abuse. [ABSTRACT FROM AUTHOR]

Shillong, the capital of Meghalaya located in North East India is situated in the highest part of Meghalaya plateau, Shillong as an urban centre had its inception in the colonial period, built with a vision to house a population of less than a 0.1 million. Shillong was then a cluster of a few scattered hamlets, the Cantonment and the Municipality which at present has grown tremendously with 12 contiguous units forming the Shillong Urban Agglomeration (SUA). The population induced haphazard urban growth in this hill station leaves its impact on the environment, as overcrowded residential areas, traffic congestion, water shortage, poor sanitation and waste disposal leading to pollution of the numerous streams are common. The paper attempts to assess the pattern of growth of SUA in the recent decades, using Remote Sensing (RS) and Geographical Information System (GIS) and discusses its impact on the city's environment. [ABSTRACT FROM AUTHOR]

Droughts can affect forest ecosystems and lead to soil degradation, biodiversity loss, and desertification. Not all regions of Mexico are affected in the same way, as some areas are naturally more prone to drought due to their geographical location. Therefore, the objective of this work was to carry out a spatio-temporal analysis of the occurrence of droughts (severe and extreme) in Mexican forest systems, covering the period 2000–2021, and to study the area covered by these events in Mexican forest systems. This analysis was divided into three stages: the classification of land use and vegetation, spatial mapping and the classification of drought intensity, and an analysis of drought frequency and probability in forest systems. The results show that more than 46% of Mexico's forest area experienced severe and extreme droughts during the 21-year period studied. Broadleaved forests were most affected by severe and extreme droughts, with a frequency of 6 years. The increasing frequency of droughts poses a major challenge to the resilience of forest ecosystems in Mexico, highlighting the need to implement climate change adaptation and forest management measures to protect the country's biodiversity and natural resources. [ABSTRACT FROM AUTHOR]

Nightlife in urban areas is gaining interest due to its role as a stimulus of the local economy, tourism, safety, and public services. However, most studies analyse nighttime activity from a qualitative perspective and at a general urban scale, without going into a detailed spatial analysis. In this paper, we have used the spatio-temporal details of Google Places data, and the activity trends at a variety of facilities that provide Google Popular Times. We studied and mapped the recovery of nighttime activity in the city of Madrid after the pandemic restrictions. The results reveal that there was a significant decrease of over 80% in the activity levels of open premises, which also led to a decrease in their average occupancy rate. These reductions were particularly noticeable in the city center and during the late-night hours.

ABSTRACT Background: Chagas disease is a silent illness with high mortality burden in many Latin American countries, such as Brazil. Bahia has the fourth highest mortality rate in Brazil. This study analyzed the temporal trends and regional differences in the mortality rate of Chagas disease in Bahia State from 2008 to 2018. Methods: A time-series analysis of Chagas disease-related deaths was conducted using data from the Mortality Information System of Brazil. We compared the mortality rate due to Chagas disease as the primary cause and mention of the disease in the death certificate, standardized by age and health macroregion/residence municipality, and mapped hot and coldspots. Results: The Chagas Disease Mortality Rate in Bahia during the study period revealed a stationary trend, ranging from 5.34 (2008) to 5.33 (2018) deaths per 100,000 inhabitants. However, the four health macroregions showed an upward trend in mortality rates. The mortality rate (age-adjusted) ranged from 4.3 to 5.1 deaths per 100,000 inhabitants between 2008 and 2018. We observed a upward trend in the mortality rate among individuals aged ≥70 years and a higher incidence of death among men than among women. Of the total number of deaths (8,834), 79.3% had Chagas disease as the primary cause and the death certificates of 20.7% mentioned the disease. Cardiac complications were reported in 85.1% of the deaths due to Chagas disease. Conclusions: The regional and individual differences in the mortality rate of Chagas disease highlighted in this study may support health planning that considers the peculiarities of the territory.

Terrestrial ecosystems have taken up about 32% of the total anthropogenic CO2 emissions in the past six decades1. Large uncertainties in terrestrial carbon-climate feedbacks, however, make it difficult to predict how the land carbon sink will respond to future climate change2. Interannual variations in the atmospheric CO2 growth rate (CGR) are dominated by land-atmosphere carbon fluxes in the tropics, providing an opportunity to explore land carbon-climate interactions3-6. It is thought that variations in CGR are largely controlled by temperature7-10 but there is also evidence for a tight coupling between water availability and CGR11. Here, we use a record of global atmospheric CO2, terrestrial water storage and precipitation data to investigate changes in the interannual relationship between tropical land climate conditions and CGR under a changing climate. We find that the interannual relationship between tropical water availability and CGR became increasingly negative during 1989-2018 compared to 1960-1989. This could be related to spatiotemporal changes in tropical water availability anomalies driven by shifts in El Niño/Southern Oscillation teleconnections, including declining spatial compensatory water effects9. We also demonstrate that most state-of-the-art coupled Earth System and Land Surface models do not reproduce the intensifying water-carbon coupling. Our results indicate that tropical water availability is increasingly controlling the interannual variability of the terrestrial carbon cycle and modulating tropical terrestrial carbon-climate feedbacks.

Summary Background In Haiti, reported incidence and mortality rates for COVID-19 were lower than expected. We aimed to analyze factors at communal and individual level that might lead to an underestimation of the true burden of the COVID-19 epidemic in Haiti during its first two years. Methods We analyzed national COVID-19 surveillance data from March 2020 to December 2021, to describe the epidemic using cluster detection, time series, and cartographic approach. We performed multivariate Quasi-Poisson regression models to determine socioeconomic factors associated with incidence and mortality. We performed a mixed-effect logistic regression model to determine individual factors associated with the infection. Results Among the 140 communes of Haiti, 57 (40.7%) had a COVID-19 screening center, and the incidence was six times higher in these than in those without. Only 22 (15.7%) communes had a COVID-19 care center, and the mortality was five times higher in these than in those without. All the richest communes had a COVID-19 screening center while only 30.8% of the poorest had one. And 75% of the richest communes had a COVID-19 care center while only 15.4% of the poorest had one. Having more than three healthcare workers per 1000 population in the commune was positively associated with the incidence (SIR: 3.31; IC95%: 2.50, 3.93) and the mortality (SMR: 2.73; IC95%: 2.03, 3.66). At the individual level, male gender (adjusted OR: 1.11; IC95%: 1.01, 1.22), age with a progressive increase of the risk compared to youngers, and having Haitian nationality only (adjusted OR:2.07; IC95%: 1.53, 2.82) were associated with the infection. Conclusions This study highlights the weakness of SARS-CoV-2 screening and care system in Haiti, particularly in the poorest communes, suggesting that the number of COVID-19 cases and deaths were probably greatly underestimated.

Abstract Machine intelligence is at great height these days and has been evident with its effective provenance in almost all domains of science and technology. This work will focus on one handy and profound application of machine intelligence-time series forecast, and that too on visual data points, i.e., our objective is to predict future visual data points, given a subtle lag to work on. For the same, we would propose a deep learner, Newtonian physics informed neural network (NwPiNN) with the critical modelling capabilities of the physics informed neural networks, modelled on the laws of Newtonian physics. For computational efficacy, we would work on the gray-scale values of pixels. Since the variation in data pixel values is not only provoked by the pixel gray values but also by the velocity component of each pixel, the final prediction of the model would be a weighted average of the gray value forecast and the kinematics of each pixel, as modelled by the PINN. Besides its’ proposal, NwPiNN is subjected to benchmark visual dataset, and compared with existing models for visual time series forecast, like ConvLSTM, and CNN-LSTM, and in most of the occasions, NwPiNN is found to outperform its preliminaries.