Estimating Uncertainty of Geographic Atrophy Segmentations with Bayesian Deep Learning.

Purpose: To apply methods for quantifying uncertainty of deep learning segmentation of geographic atrophy (GA). Design: Retrospective analysis of OCT images and model comparison. Participants: One hundred twenty-six eyes from 87 participants with GA in the SWAGGER cohort of the Nonexudative Age-Related Macular Degeneration Imaged with Swept-Source OCT (SS-OCT) study. Methods: The manual segmentations of GA lesions were conducted on structural subretinal pigment epithelium en face images from the SS-OCT images. Models were developed for 2 approximate Bayesian deep learning techniques, Monte Carlo dropout and ensemble, to assess the uncertainty of GA semantic segmentation and compared to a traditional deep learning model. Main Outcome Measures: Model performance (Dice score) was compared. Uncertainty was calculated using the formula for Shannon Entropy. Results: The output of both Bayesian technique models showed a greater number of pixels with high entropy than the standard model. Dice scores for the Monte Carlo dropout method (0.90, 95% confidence interval 0.87-0.93) and the ensemble method (0.88, 95% confidence interval 0.85-0.91) were significantly higher (P < 0.001) than for the traditional model (0.82, 95% confidence interval 0.78-0.86). Conclusions: Quantifying the uncertainty in a prediction of GA may improve trustworthiness of the models and aid clinicians in decision-making. The Bayesian deep learning techniques generated pixel-wise estimates of model uncertainty for segmentation, while also improving model performance compared with traditionally trained deep learning models. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan.

Due to its diverse topography, Pakistan faces different types of floods each year, which cause substantial physical, environmental, and socioeconomic damage. However, the susceptibility of specific regions to different flood types remains unexplored. To the best of our knowledge for the first time, this study employed an integrated approach by leveraging a GIS-based Analytical Hierarchy Process (AHP), remote sensing, and machine learning (ML) algorithms, to assess susceptibility to three different types of flooding in Peshawar, Pakistan. The study first evaluated the degree of susceptibility to riverine, urban, and flash floods using the GIS-based AHP technique, and then employed ML models, (i.e., specifically Random Forest [RF] and Extreme Gradient Boosting [XG-Boost] to analyze multi-type flood susceptibility in the study region. The performance of the ML models was also evaluated, and the XG-Boost model outperforms RF, demonstrating a higher correlation coefficient (R2 = 0.561-0.922) and lower mean absolute error (MAE = 0.042-0.354), and root-mean-square error (RMSE = 0.119-0.415) for both training and testing datasets. The superior performance of the XG-Boost was further confirmed by the higher value of the area under the curve (AUC) values, which is relatively higher (0.87) than that of the AHP (0.70) and RF (0.86) models. Based on the relative best performance, the XG-Boost model was chosen for further susceptibility assessment of different types of floods, and the generated flood susceptibility maps revealed that 20.9% of the total area is susceptible to riverine flooding, while 30.27% and 48.68% of the total area is susceptible to urban and flash flooding, respectively. The study's findings are significant, offering valuable insights for relevant stakeholders in guiding future flood risk management and sustainable land use plans in the study area.

General Surgery Versus Integrated Surgical Sub-specialties: Predictors for Residency Match and Interview Invites Among Surgical Candidates.

OBJECTIVE: The goal of this study was to assess predictive factors for receiving interviews and matching in general surgery (GS), cardiothoracic surgery (TS), vascular surgery (VS), and plastic surgery (PS). DESIGN: The Texas Seeking Transparency in Applications to Residency (STAR) survey was analyzed for match years 2018-2023. Chi-Square Tests of Independence were used to assess differences among participants who received ≥16 vs <16 interviews and, separately, participants who matched vs went unmatched. Odds ratios (OR) for matching were adjusted for board scores, home region, publications, and honors in applicant specialty. SETTING: All US medical schools participating in the Texas STAR survey from 2018-2023. PARTICIPANTS: All fourth-year students who completed the survey during the study period. RESULTS: Of the 2,687 individuals included, 78.15% applied in GS, 13.58% in PS, 4.43% in VS, and 3.82% in TS. Participants had higher odds of receiving ≥16 interviews when having >240 step 1 score vs ≤239 (OR 1.76 (95% CI 1.46-2.12); p < 0.001), >250 step 2 score vs ≤249 (2.42 (2.00-2.91); p < 0.001), honors in their specialty (1.48 (1.21-1.80); p < 0.001), and >5 publications vs ≤4 (1.46 (1.16-1.83); p = 0.001). Odds of matching were lower among PS (0.50 (0.36-0.69); p < 0.001) and TS (0.2 (0.13-0.31); <0.001) compared to GS applicants. Participants had higher odds of matching when having >240 step 1 score vs ≤239 (1.33 (1.04-1.70); p = 0.026), >250 step 2 score vs ≤249 (1.52 (1.20-1.92); p < 0.001), and were more likely to match at a program where they indicated a geographic preference (5.49 (2.58-11.66); p < 0.0001) or program signal (3.87 (1.85-8.11); p < 0.001). CONCLUSIONS: The novel geographic preferencing and program signal functions were associated with increased match success. More studies are needed to assess the generalizability of these findings.

Meadows or asphalt road - Does momentary affective well-being vary with immediate physical environment? Results from a geographic ecological momentary assessment study in three metropolitan areas in Germany.

We investigated the association between the immediate physical environment of individuals and individual fluctuations of momentary affective well-being in everyday life with a focus on medium sized metropolitan areas in Europe representing a typical living environment of a large proportion of the world's population. The sample comprised 365 individuals (54.8 % female) with participants ranging from 14.08 to 88.27 years of age (M = 43.9, SD = 20.9). In an geographic ecological momentary assessment (GEMA), participants were prompted six times a day on at least 9 days over the course of 3 weeks, covering a total of six weekdays and three weekend days on mobile phones to report their momentary affective well-being. Urban land use categories (forest, water, urban green) were assessed with GPS-localization as environmental variables. Additionally, sunshine, rainfall, whether participants were inside or outside as well as whether they were alone or had company were included into the analyses. We used dynamic structural equation modelling to model the inter- and intraindividual differences as well as fluctuations and assess potential covariates while acknowledging the autoregressive nature of affect. The results showed that on individual level, fluctuations of momentary affective well-being were associated with sunshine, having company and travelling. No significant association emerged for urban green, forest, and water neither within individuals nor between. Methodological as well as conceptual implications are discussed and an interpretation of the present findings are provided.

A continental-wide decline of occupancy and diversity in five Neotropical carnivores.

The Neotropics are a global biodiversity hotspot that has undergone dramatic land use changes over the last decades. However, a temporal perspective on the continental-wide distributions of species in this region is still missing. To unveil it, we model the entire area of occupancy of five Neotropical carnivore species at two time periods (2000-2013 and 2014-2021) using integrated species distribution models (ISDMs) in a Bayesian framework. The carnivores are the jaguarundi (Herpailurus yagouaroundi), margay (Leopardus wiedii), maned wolf (Chrysocyon brachyurus), tayra (Eira barbara), and giant otter (Pteronura brasiliensis). We mapped the temporal change, the areas where gains and losses accumulated for all species (hotspots of change) and calculated the temporal species turnover and change in spatial turnover. We show that (1) most carnivore species have declined their area of occupancy (i.e., range size) in the last two decades, (2) their diversity has decreased over time, mostly in the Chaco region, and (3) that hotspots of fast species composition turnover are in Chaco, the Caatinga region, and northwest of Mexico. We discuss how these newly identified hotspots of change overlap with regions of well-known and pronounced land use transformation. These estimated patterns of overall decline are alarming, more so given that four out of the five species had been classified as not threatened by IUCN. The official global threat status of these species may need to be re-evaluated. All this would be invisible if standard forecasts, local expert knowledge, or static threat criteria, such as range size, were used. We thus provide a new approach to evaluate past species range dynamics based on multiple lines of evidence, which can be employed over more species in the future, particularly in under-sampled regions.

Spatiotemporal dynamics and driving factors of ecosystem services value in Lanzhou City, China.

Aligned with the imperatives of national ecological civilization construction, the systematic investigation into the intricate interplay between shifts in land utilization and the assessment of ecosystem services plays a pivotal and indispensable role in advancing ecological civilization. This endeavor holds significant implications. It aids in optimizing the ecological landscape at the regional level and fosters harmonious coexistence between humanity and the natural world. The study utilizes land-use remote sensing interpretation data from three time periods (2000, 2010, and 2020) and employs various methodologies, including equivalent factor coefficient correction, sensitivity analysis, and spatial autocorrelation. The objective is to uncover the spatiotemporal dynamics of land-use changes and Ecosystem Service Value (ESV) in Lanzhou City. Furthermore, geographic detectors are applied to explore the driving factors influencing ESV spatial heterogeneity and their interactions. The research findings indicate the following: (1) From 2000 to 2020, grassland and cropland were the predominant land-use types in Lanzhou City, with cropland and urban land experiencing the most active changes. (2) ESV in Lanzhou City increased from 179.37 billion RMB in 2000 to 193.86 billion RMB in 2020, reflecting an ESV total growth rate of 8.07% and a gradual improvement in the ecological environment. Spatially, ESV exhibits a "west high, east low" distribution pattern, with the center shifting towards the northwest and southeast, gradually reducing spatial imbalance. (3) Analysis of ESV spatial autocorrelation reveals that high-high clusters are predominantly found within the Tulu Gou National Forest Park and the Xinglong Mountain National Natural Reserve, while low-low clusters are primarily concentrated in the central urban area of Lanzhou City. Over the period from 2000 to 2020, the spatial clustering effect of ESV within the study area has progressively intensified. 4)NDVI, precipitation, and GDP emerge as pivotal factors influencing spatial differentiation within Lanzhou City, with natural and societal elements exerting interactive effects on ESV spatial disparities. The research results integrate environmental considerations into the decision-making process, offering valuable insights for formulating targeted ecological protection policies in Lanzhou City. This study embodies concrete measures taken by Lanzhou City in practicing China's concept of "green water and green mountains are golden silver mountains," providing a theoretical basis for the harmonious and sustainable development of the ecological economy.

Detection of spatial and temporal variation characteristics of vegetation cover in the Lower Mekong region and the influencing factors.

As the main component of terrestrial ecosystem, vegetation plays a very important role in regional ecosystem environmental change, global carbon cycle and climate regulation. The Lower Mekong region (LMR) is at the core of Southeast Asia, its vegetation changes will affect the regional ecosystem and climate. Five countries of LMR were selected as the study area, based on MODIS (Moderate-Resolution Imaging Spectroradiometer) NDVI(Normal Difference Vegetation Index) data from 2000 to 2022, using the Sen's slope estimator, Mann-Kendall trend test and geographic detector to study the spatial and temporal variation trends and driving forces of vegetation coverage. The results showed that:(1) From 2000 to 2022,the vegetation coverage in the LMR showed an overall fluctuating upward trend, the annual average Fractional Vegetation Cover(FVC) value was 0.70, mainly with high vegetation coverage and relatively high vegetation coverage. Vegetation distribution had obviously spatial heterogeneity, and the vegetation of Myanmar, Laos and Vietnam was significantly larger than Thailand and Cambodia.(2) The variation trend analysis of Sen_MK showed that the proportion of improved and degraded vegetation coverage areas in the LMR were 56.33% and 37.55% respectively. The vegetation improvement area was much larger than the vegetation degradation area during 2000-2022. According to the variation trend analysis of different countries, the vegetation coverage improvement area in Vietnam, Myanmar and Thailand were larger than the degraded , the overall vegetation coverage variation trend were good. However, in Laos and Cambodia, the degraded areas were larger than the improved, the overall variation trends of coverage were not good.(3) The results of geographic detector showed that the Land Use and Land Cover(LULC) had the greatest influence on vegetation coverage in the study area.The influencing factors of vegetation coverage were different in the LMR. For Vietnam, Thailand and Laos,elevation and slope factors were second only to LULC, for Myanmar and Cambodia, the influence of precipitation factor was second only to LULC. The results provide scientific data support for understanding the ecological environment status and future changes in the research area.

Spatial prediction of armed conflicts from the perspective of political geography using bivariate frequency ratio method (FR) in East African States.

Armed conflicts, as significant human phenomena, profoundly impact populations and reflect a state's capacity to fulfill its responsibilities. These conflicts arise from various causes, necessitating robust predictive models to understand their spatial distribution. This study employs the Bivariate Frequency Ratio (FR) method to spatially predict the occurrence of armed conflicts across the East African States, drawing on 42 political geography-related criteria. The development of the predictive model involved classifying the region into five conflict-prone categories influenced by critical political geography factors. Geospatial datasets, curated in a GIS environment, were sourced from approved online portals. The findings indicate that Burundi exhibits the highest vulnerability to armed conflict, followed closely by Rwanda, Uganda, and Somalia. Ethiopia and South Sudan show a moderate risk, while predictions for Zimbabwe, Zambia, and Mozambique suggest lower likelihoods of conflict. The model's accuracy was validated using the Receiver Operating Characteristic (ROC) curve, demonstrating its effectiveness. Furthermore, the model's applicability extends to other regions, offering a valuable tool for global conflict prediction.

Peruvian origin and global invasions of five continents by the highly damaging agricultural pest Liriomyza huidobrensis (Diptera: Agromyzidae).

Identification of the geographic origin of invasive species can be critical to effective management and amelioration of negative impacts in the introduced range. Liriomyza huidobrensis is a polyphagous leafmining fly that is a devastating pest of many vegetable and floriculture crops around the world. Considered native to South and possibly Central America, L. huidobrensis became invasive in the 1980s and has since spread to at least 30 countries on five continents. We used phylogeographic analysis of over 2 kb of mitochondrial cytochrome oxidase I and II sequence data from 403 field-collected specimens from both native and introduced populations to investigate the geographic origins of invasive L. huidobrensis worldwide. Within South America, there was substantial genetic variation, as well as the strong phylogeographic structure typical of a native range. In contrast, leafminers from the introduced range and Central America all contained little genetic variation and shared the same small set of haplotypes. These haplotypes trace to Peru as the ultimate geographic origin of invasive populations. Central America is rejected as part of the original geographic range of L. huidobrensis. Within Peru, the primary export region of Lima shared an extremely similar pattern of reduced haplotype variation to the invasive populations. An additional 18 specimens collected at US ports of entry did not share the same haplotype profile as contemporary invasive populations, raising perplexing questions on global pathways and establishment success in this species.

Exploring the effects of multi-dimensional geographic environment on daily sleep and physical activity based on the Actigraph data.

Understanding the interplay between people's daily sleep and physical activity and how geographic environment influences them are important for developing healthy cities. However, such research has been limited. This study aims to explore the bidirectional and nonlinear relationship between daily sleep and physical activity, and further investigate the comprehensive influences of multi-dimensional geographic environment on these health behaviors. Based on the objective data on sleep and physical activity over seven consecutive days using wrist-based accelerometers in Beijing, China, we developed a series of models to analyze the mutual influences between people's daily sleep and physical activity, and employed the generalized additive model (GAM) to examine their potential nonlinear relationships and how geographic environment - including meteorological conditions, built environment, and social environment - influences them. The results show that sleep and physical activity exhibit notable bidirectional relationship. Moderate-to-vigorous physical activity (MVPA) is observed to improve sleep quality, but it decreases sleep duration. In contrast, total sleep time (TST) exhibits an inverted U-shaped pattern with both MVPA and total step counts, with the optimal sleep duration at 5 h. Furthermore, meteorological factors, built environment characteristics, and social environment have significant linear or nonlinear effects on people's daily sleep and physical activity. The outcomes of this study offer valuable insights for enhancing residents' health and developing healthy cities.

Geographic atrophy progression secondary to age-related macular degeneration: Five years of follow-up.

PURPOSE: To study the progression of geographic atrophy (GA) secondary to age-related macular degeneration over a five-year follow-up. METHODS: Eyes with GA included to assess demographic data, yearly optical coherence tomography (OCT) findings and the GA growth rate on infra-red (IR) images. RESULTS: A total of 41 eyes of 29 patients were included with a mean age of 81.76 ± 6.37 at baseline, and 65.51% were females. Over five years, there was a significant increase in the mean GA area from 8.44 ± 8.98 mm² to 13.32 ± 10.07 mm² (P < 0.001), with an annual growth rate of 1.14 ± 0.78 mm². The annual growth rates in females were slightly higher compared to males (1.29 ± 0.89 mm2 vs 0.96 ± 0.49 mm2, p = 0.569), and in smokers was slightly higher than non-smokers (1.35 ± 0.85 mm2 vs 0.94 ± 0.66 mm2, p = 0.100). Larger GA areas at the baseline showed higher GA progression in mm2 per year (P = 0.04). Smaller GA areas and fovea-spared GA at the baseline exhibited a larger percentage increase (P < 0.001 and P = 0.015, respectively). There was a lower GA progression rate in eyes with outer retinal tubulations (ORT) (P = 0.027), yet no significant correlation was found between GA progression and other OCT features. CONCLUSIONS: Smaller, fovea-sparing GA eyes experienced a more substantial proportional increase over five years. Also, The presence of ORT was associated with a slower rate of GA progression. Additionally, we observed a trend of faster GA growth in smokers and female genders.

Spatial Disparities in Mifepristone Use for Early Miscarriage and Induced Abortion Among Obstetrician-Gynecologists Practicing in Massachusetts.

Background: About 25% of pregnancies end in early miscarriage or abortion annually in the United States. While mifepristone is part of the most effective medication regimen for miscarriage and abortion, regulatory burdens and legal restrictions limit its provision in obstetric-gynecological practice. The extent of geographic disparities in mifepristone use is unknown. Objectives: We sought to ascertain whether regional "deserts" for mifepristone-based miscarriage and abortion care exist in Massachusetts using geographic regions specified by the Commonwealth's Executive Office of Health and Human Services. Methods: We fielded a cross-sectional survey of obstetrician-gynecologists practicing in Massachusetts. We weighted survey data to account for differential nonresponse by provider sex, region, and years in independent practice. Results: Among obstetrician-gynecologists in independent practice with region data (n = 148), 51.0% reported using mifepristone for miscarriage and 43.5% for abortion. Significant differences in reported use were observed across regions (p < 0.001 for both indications). Barriers to using mifepristone for miscarriage management also varied across regions. Respondents outside of Boston and Western Massachusetts were more likely to report gaps in knowledge about regulations and prescribing and had less prior experience using mifepristone. In a multivariable model adjusting for provider sex and practice type, obstetrician-gynecologists outside of Boston had significantly lower odds of using mifepristone for miscarriage (adjusted odds ratio [aOR] = 0.14, 95% confidence interval [95% CI] = 0.08-0.25) and abortion (aOR = 0.46, 95% CI = 0.26-0.82), compared to Boston-based obstetrician-gynecologists. Conclusion: Mifepristone provision varies significantly by Massachusetts region. This may lead to spatial disparities in reproductive health outcomes.

Defining blood deserts and access to blood products for 660 million people: a geospatial analysis of eight states in Northern India.

INTRODUCTION: Blood transfusion is crucial, but low-income and middle-income countries like India face a severe shortage of banked blood. This study focuses on the Empowered Action Group (EAG) states in India, where healthcare is limited, and health outcomes are poor. Our objective was to assess the blood banking infrastructure and access to blood products in these states. METHODS: We used e-Rakht Khosh, an online platform for blood availability data. We collected data on blood bank locations and stocks from 18 January to 9 February 2022 and used ArcGIS to determine the population residing within 30-60-90 min of a blood bank. Availability ratios were calculated by dividing available blood products by population in these catchment areas. Descriptive analysis characterised availability, and statistical tests evaluated differences across states and over the 4-week period. RESULTS: 806 of 824 blood banks reported data on blood stocks. Our analysis showed that 25.72% of the EAG states' population live within 30 min of a blood bank, while 61.45% and 92.46% live within 60 and 90 min, respectively. CONCLUSION: Blood availability rates were low in the EAG states, with only 0.6 units per 1000 people. Additionally, only 61% of the population had access to blood-equipped facilities within an hour. These rates fell below the standards of the Lancet Commission on Global Surgery (15 units per 1000 population) and the WHO (10 donations per 1000 population). The study highlights the challenges in meeting demand for blood in emergencies due to inadequate blood banking infrastructure.

Summary Analysis of National Surveillance on Kashin-Beck Disease from 1990 to 2023.

Objective: To analyze the epidemiological characteristics and epidemic situation of children with Kashin-Beck disease (KBD) in China, and provide the basis for formulating prevention and control measures. Methods: Fixed-point monitoring, moving-point monitoring, and full coverage of monitoring were promoted successively from 1990 to 2023. Some children (7-12 years old) underwent clinical and right-hand X-ray examinations every year. According to the KBD diagnosis criteria, clinical and X-ray assessments were used to confirm the diagnosis. Results: In 1990, the national KBD detectable rate was 21.01%. X-ray detection decreased to below 10% in 2003 and below 5% in 2007. Between 2010 and 2018, the prevalence of KBD in children was less than 0.4%, which fluctuated at a low level, and has decreased to 0% since 2019. Spatial epidemiological analysis indicated a spatial clustering of adult patients prevalence rate in the KBD areas. Conclusion: The evaluation results of the elimination of KBD in China over the last 5 years showed that all villages in the monitored areas have reached the elimination standard. While the adult KBD patients still need for policy consideration and care.

Geographical disparities in temporal trends of low birth weight in Saskatchewan from 2002/2003 to 2021/2022: insights from a joinpoint regression analysis.

Low birth weight (LBW) is an important public health indicator that is associated with various negative health outcomes in infants. To effectively implement interventions that would improve health outcomes in children, it is important to understand both the historical trends and current levels of LBW rates. In this study, trends and regional differences in LBW rates in Saskatchewan from 2002/2003 to 2021/2022 were assessed. A joinpoint regression analysis was conducted using historical LBW rates, obtained from the Canadian Institute for Health Information database. Data were analysed using average percent change and average annual percent change. Spatial patterns and trends were identified using a choropleth map. From a provincial and national rate of 5.2% in 2002/2003, the LBW rate in Saskatchewan increased to 6.5% in 2021/2022, approaching the national rate of 6.8%. Over the 20-year period, average annual changes for Canada were 1.4% and 1.0% for Saskatchewan. There was a turning point in the study: 2004/2005 for Canada and 2011/2012 for Saskatchewan. Initially, Saskatchewan had stable LBW rates, increasing yearly by 0.1%, while the national rate was 5.7%. However, in recent years, Saskatchewan's rate increased to 1.8% annually, surpassing the national rate of 0.9%. Geographical differences were also observed within Saskatchewan, with the Far North region having the highest LBW rate (9.2%), and the Central West region having the lowest rate (4.3%) in 2021/2022. The Central East, Regina Qu'Appelle, and southern Saskatchewan saw significant upwards trends in LBW rates between 2015/2016 and 2021/2022. There is an increasing trend in LBW rates in Canada and Saskatchewan, as well as geographical disparities within the province. The geographical disparities in LBW rates underscore the need for tailored interventions in high-risk regions in the province.

Development and validation of a geographic search filter for MEDLINE (PubMed) to identify studies about Germany.

While geographic search filters exist, few of them are validated and there are currently none that focus on Germany. We aimed to develop and validate a highly sensitive geographic search filter for MEDLINE (PubMed) that identifies studies about Germany. First, using the relative recall method, we created a gold standard set of studies about Germany, dividing it into 'development' and 'testing' sets. Next, candidate search terms were identified using (i) term frequency analyses in the 'development set' and a random set of MEDLINE records; and (ii) a list of German geographic locations, compiled by our team. Then, we iteratively created the filter, evaluating it against the 'development' and 'testing' sets. To validate the filter, we conducted a number of case studies (CSs) and a simulation study. For this validation we used systematic reviews (SRs) that had included studies about Germany but did not restrict their search strategy geographically. When applying the filter to the original search strategies of the 17 SRs eligible for CSs, the median precision was 2.64% (interquartile range [IQR]: 1.34%-6.88%) versus 0.16% (IQR: 0.10%-0.49%) without the filter. The median number-needed-to-read (NNR) decreased from 625 (IQR: 211-1042) to 38 (IQR: 15-76). The filter achieved 100% sensitivity in 13 CSs, 85.71% in 2 CSs and 87.50% and 80% in the remaining 2 CSs. In a simulation study, the filter demonstrated an overall sensitivity of 97.19% and NNR of 42. The filter reliably identifies studies about Germany, enhancing screening efficiency and can be applied in evidence syntheses focusing on Germany.

Silicosis mortality in Spain (1999-2020): A temporal and geographical approach.

Background: Silicosis is an occupational respiratory disease linked to silica dust inhalation. The main driver was traditional coal mining, but in recent decades, new sources of exposure have emerged. Our aim in this study was to assess the temporal and spatial distribution of mortality due to this disease over a 22-year period in Spain. Methods: Silicosis records, as an Underlying Cause of Death, were extracted from the National Institute of Statistics from 1999 to 2020 using the International Classification of Diseases 10th revision (code J62.8). Age- and sex-adjusted mortality rates per 1,000,000 inhabitants were calculated for the territory and by province. A geographic analysis was performed, and clusters of deaths were identified at the municipal level, and then the outcomes were compared in two periods of 11 years. Results: There were 2618 deaths due to silicosis in Spain. The mean age of death increased significantly by 0.66% annually from 1999 to 2013. The age-adjusted mortality rate decreased by 7.30% per year, falling from 3.00 to 0.65 per 1,000,000 inhabitants. The temporal pattern showed a significant decrease of mortality rate in 31% of the provinces (16 out of 52), while it increased in Pontevedra. Regarding the spatial analysis, 11 clusters were found in both periods, but some variations were observed in terms of their distribution in the Spanish territory, as well as in the affected municipalities. Conclusions: The decrease in mortality due to Silicosis could be related to less exposure to silica dust over the years and an improvement in the survival of those affected. It is thus essential to analyze the role of preventive measures for this occupational disease.

Modeling climate-related global risk maps of rice bacterial blight caused by Xanthomonas oryzae (Ishiyama 1922) using geographical information system (GIS).

Rice is a critical staple crop that feeds more than half of the world's population. Still, its production confronts various biotic risks, notably the severe bacterial blight disease produced by Xanthomonas oryzae. Understanding the possible effects of climate change on the geographic distribution of this virus is critical to ensuring food security. This work used ecological niche modeling and the Maxent algorithm to create future risk maps for the range of X. oryzae under several climate change scenarios between 2050 and 2070. The model was trained using 93 occurrence records of X. oryzae and five critical bioclimatic variables. It has an excellent predictive performance, with an AUC of 0.889. The results show that X. oryzae's potential geographic range and habitat suitability are expected to increase significantly under low (RCP2.6) and high (RCP8.5) emission scenarios. Key climatic drivers allowing this development include increased yearly precipitation, precipitation during the wettest quarter, and the wettest quarter's mean temperature. These findings are consistent with broader research revealing that climate change is allowing many plant diseases and other dangerous microbes to spread across the globe. Integrating these spatial predictions with data on host susceptibility, agricultural practices, and socioeconomic vulnerabilities can help to improve targeted surveillance, preventative, and management methods for reducing the growing threat of bacterial blight to rice production. Proactive, multidisciplinary efforts to manage the changing disease dynamics caused by climate change will be critical to assuring global food security in the future decades.

Mapping the influence: Institutional blockholder coordination and climate change risk disclosure.

This study investigates the relationship between institutional blockholder coordination, proxied by geographic proximity, and climate change risk disclosure. Using a sample of 2,887 firm-year observations for S&P 500 companies from 2010 to 2022, we reveal that a firm's climate change risk disclosure decreases when its institutional blockholders are more coordinated. In addition, we find that the negative relationship between institutional blockholder coordination and climate change risk disclosure manifests more in firms with less diversified institutional blockholders, a smaller number of institutional blockholders, a prominent position to their blockholders, and more dedicated institutional blockholder ownership. Moreover, we find that the negative association between institutional blockholder coordination and climate change risk disclosure is more pronounced in firms with corporate general counsels, a non-concentrated customer base, higher asset tangibility, and those that are environmentally sensitive. Our main conclusion still holds after using an alternative measure for climate change risk disclosure as well as a battery of endogeneity tests. Finally, we propose that institutional blockholder coordination lessens climate change risk disclosure through the channel of increased performance-induced CEO dismissal. Collectively, this study provides insightful implications for academics, financial statement users, regulators, and policymakers.

An integrated mapping approach highlights extended distribution and high environmental status of Irish seagrass meadows.

To address the remaining knowledge gap regarding the distribution of seagrasses in Ireland, this study aimed a) to create an updated seagrass (Zostera spp.) distribution map, and b) to evaluate the environmental quality to which seagrass meadows are exposed. To achieve the first objective, we (i) combined the available data on seagrass distribution published to date, and (ii) mapped additional meadows by implementing an integrated method based on species distribution models, satellite-derived images, and snorkelling-based surveys. We mapped 209 new seagrass meadows (14.98 km2), representing a 37.03 % increase over previously reported extents. Consequently, the total extent of Irish seagrass meadows is estimated to be at least 54.85 km2. To address the second objective, we assessed the level of anthropogenic pressure of seagrass meadows based on the index provided by the Water Framework Directive of the European Environment Agency. This study demonstrates that Irish meadows are primarily located in areas with 'HIGH' and 'GOOD' water status.