Search

Introduction: Sporotrichosis is a chronic infectious mycosis caused by traumatic implantation of Sporothrix spp., which poses a significant challenge to public health because of its zoonotic nature. It affects humans and other animals, particularly cats. This study investigated epidemiological trends and spatial distribution of feline sporotrichosis in Espírito Santo between 2017 and 2022. Methods: This study collected diagnostic data on sporotrichosis from the Veterinary Pathology Laboratory and Center for Infectious Diseases in Espírito Santo from 2017 to 2022. Statistical analyses were performed using STATA and spatial analyses were performed using ArcGIS software to create maps and identify hotspots. Kernel density estimation and directional mean statistics were applied to visualise the disease concentration and transmission trends. Results: The results showed a significant increase in feline sporotrichosis cases in Espírito Santo from 2017 to 2022, with hotspots identified in urban and densely populated areas. Anchieta, Aracruz and Vila Velha reported the highest numbers of cases. Spatial analysis indicated a spread towards the central and northern coastal regions. Conclusion: This study highlights the growing threat of feline sporotrichosis in Espírito Santo, Brazil, particularly in densely populated urban areas. This underscores the urgent need for comprehensive public health strategies that address urbanisation, human–animal interactions and managing stray cat populations. Effective control measures and enhanced surveillance are crucial to mitigate the spread of this zoonotic disease. Importantly, this study provides valuable insights into the epidemiology of sporotrichosis in Brazil and offers a framework for other regions identified as emerging hotspots that face similar challenges. [ABSTRACT FROM AUTHOR]

The Ningxia region in Northwest China, a significant grain-producing area, heavily relies on the Yellow River for agricultural irrigation. Maintaining the ecological health of the Yellow River is crucial due to its role as the primary water source. This research comprehensively assessed heavy metal (HM) levels in surface water and sediments within the Ningxia section of the Yellow River basin. It specifically examined the concentrations of Sr, Zn, Mn, Cu, As, Cd, Cr, Co, Sb, Pb, Tl, Ni, and Hg, detailing their spatial distribution and associated risks. Sources of pollution were identified, and their relationships were explored using statistical analysis and positive matrix factorization (PMF). The risk assessment results indicated elevated pollution levels of Tl and slight pollution of Hg in surface water. Integrated Nemerow Pollution Index (PN) calculations revealed that 18% and 20% of surface water samples exhibited pollution during the wet and dry seasons, respectively. In sediments, mean concentrations of Mn, As, Ni, Cr, Zn, Cu, Cd, Sr, Co, Sb, and Tl exceeded background levels, with Mn being the highest. Sediments exhibited low to moderate HM pollution, with higher concentrations found in northern Ningxia's irrigated areas. Major sources of HM pollution included agriculture, traffic emissions, and natural sources. Overall, this study provides essential data to improve water resource management and mitigate HM pollution in the Ningxia section of the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Ecologically sustainable urban design plays a pivotal role in mitigating climate change. This study develops an indicator group consisting of urban ecological emergy, land use change, population density, ecological services, habitat quality, enhanced vegetation index, carbon emissions, and carbon storage to assess urban sustainability. By leveraging a dataset from 2000 to 2020, we employ a neural network to predict emergy sustainability indicators over a time series, projecting the sustainable status of Xuzhou City from 2020 to 2050. The findings indicate that urbanization has led to significant changes in land use, population distribution, ecological service patterns, habitat quality degradation, vegetation fragmentation, and fluctuating carbon dynamics. Cropland constitutes the predominant land type (90.6%), followed by built-up land (8.49%). The neural network predictions suggest that Xuzhou City's sustainable status is subject to volatility (15–20%), with stability expected only as the city matures into a developed urban area. This research introduces a novel approach to urban sustainability analysis and provides insights for policy development aimed at fostering sustainable urban growth. [ABSTRACT FROM AUTHOR]

This article addresses a significant aspect of evaluating and comparing spatial forest data from various databases developed and maintained in Poland and globally. The study focused on the application of these data to create terrain passability maps, which are useful in planning military and crisis operations. The research was conducted in a test area near Warsaw, encompassing the Kampinos Forest. In the study, the "forest" layers from the tested databases were compared. Their spatial extents were analyzed, and terrain passability maps were generated in different configurations, which were comprehensively compared with one another. The results indicated that the quality and detail of forest data are not critical for generating passability maps. Only in the case of creating highly detailed maps does the use of precise data prove justified. As the level of detail in the maps decreases, they become increasingly similar, reducing the influence of the forest data on their accuracy and operational applicability. The study enabled the selection of the most accurate data sources on forested areas—those that most faithfully represent the structure of forested regions in Poland. [ABSTRACT FROM AUTHOR]

The impacts on living conditions and natural habitats deriving from planning decisions require complex analysis of cross-acting factors, which in turn require interdisciplinary data. At the municipal level, both data collection and the knowledge needed to interpret it are often lacking. Additionally, climate change and species extinction demand rapid and effective policies in order to preserve soil resources for future generations. Ex-ante evaluation of planning measures is insufficient owing to a lack of data and linear models capable of simulating the impacts of complex systemic relationships. Integrating machine learning (ML) into systemic planning increases awareness of impacts by providing decision-makers with predictive analysis and risk mitigation tools. ML can predict future scenarios beyond rigid linear models, identifying patterns, trends, and correlations within complex systems and depicting hidden relationships. This article focuses on a case study of single-family houses in Upper Austria, chosen for its transferability to other regions. It critically reflects on an ML approach, linking data on past and current planning regulations and decisions to the physical environment. We create an inventory of categories of areas with different features to inform nature-based solutions and backcasting planning decisions and build a training dataset for ML models. Our model predicts the effects of planning decisions on soil sealing. We discuss how ML can support local planning by providing area assessments in soil sealing within the case study. The article presents a working approach to planning and demonstrates that more data is needed to achieve well-founded planning statements.