Explore the vast range of titles available.

Background Trachoma is the world’s major infectious cause of blindness, responsible for blinding 1.9 million people, including 1.2 million irreversibly. It is still endemic predominantly in sub-Saharan Africa, including Ethiopia. Five or more follicles in the upper tarsal conjunctiva measuring at least 0.5 mm indicate trachomatous inflammation-follicular (TF) disease. No previous study determined the prevalence of TF, and it had not been determined for the study area to satisfy adequate geospatial representation/spatial distribution of TF among children 1–9 years old. These study findings can help programmers understand the prevalence of TF and identify the villages in the study area where TF will be clustered to implement appropriate intervention strategies to support the current trachoma control and elimination program and to help achieve SDG Goal 3 target 3.3 and Goal 6. Therefore, this study addressed those gaps by identifying TF’s prevalence and spatial distribution using spatial analytical techniques and models in Yilmana Densa and Gonji Kolela Districts. Methods The study utilizes spatial autocorrelation methodologies, including Global Moran’s I and Local Getis-Ord statistics, to describe and map spatial clusters. The global Moran’s I statistic was used to evaluate the global spatial autocorrelation of TF prevalence. The Gi_Bin field was computed in hot spot spatial analysis, independent of the False Discovery Rate correction (FDR), to detect important hot spots and cold spots. Bins of +/-3, +/-2, and +/-1 indicated statistically significant clustering of the TF distribution with 99%, 95%, and 90% confidence levels, respectively. However, non-significant TF clusters were identified with a 0 bin. Results This study found that the prevalence of TF was 17.8% (95% CI: 15.3–20.2%). From spatial analytical techniques and models, the global spatial autocorrelation analysis based on feature locations and attribute values revealed a clustering of TF among children aged 1–9 years across the study area (Global Moran’s I = 0.849, p-value < 0.0001). In hot spot spatial analysis, fourteen hot spot clusters were detected. Eight clusters were detected as significantly clustered from those fourteen hot spot areas at the 99% confidence level. The study also found that the distribution of TF was not spatially random. It was clustered at the village levels and showed strong spatial patterns. It was affected by different locations based on sociodemographic, environmental, and behavioral factors. It was more clustered in Gonji Kolela District compared to Yilmana Densa District. This study showed that trachoma is a family-based disease. Conclusion TF was found to be higher than the WHO recommended threshold of 10% to say that trachoma is a severe public health problem to conduct MDA and eliminate trachoma as a Public Health problem in a community when the prevalence of TF is less than 5%. The results of the study may be used to support the current trachoma control and elimination program, and to help achieve SDG Goal 3 target 3.3 and Goal 6. Intervention against TF may also have an impact on poverty (SDG1) and hunger (SDG2), may improve education (SDG4), work, and economic growth (SDG8). These will be helpful to decide whether the Yilmana Densa and Gonji Kolela Districts meet VISION 2020, “The Right to Sight” (elimination of the major causes of avoidable blindness), an initiative launched in Ethiopia in September 2002. It is recommended that coordinated work on implementing the WHO endorsed SAFE strategy in particular, and enhancing the overall living conditions of the community be given a high priority.

The Mediterranean region has long been identified as a climate change hotspot. However, within the Mediterranean, there are smaller sub-areas that exhibit a higher risk of climate change and extremes. Previous research has often focused on indices based on mean climate values, yet extremes are typically more impactful on humans and ecosystems. This study aims to identify the most vulnerable sub-areas of the Mediterranean as climate change hotspots using two indices: the newly introduced Mediterranean Hotspot Index (MED-HOT) and the well-defined Regional Climate Change Index (RCCI). The MED-HOT focuses on extreme high maximum and minimum temperatures, rainfall, and drought, while RCCI assesses changes in mean climate conditions. By combining these indices, we provide an identification of Mediterranean hotspots, capturing both mean climate shifts and extremes. The spatiotemporal variation of both indices across the Mediterranean region is presented and the 20 subregions are categorized into distinct groups. The results reveal that the southeastern Mediterranean is at high risk according to both indices. Additionally, southern Italy is identified as high risk due to changes in mean climate (RCCI), while the northern part is at risk due to extreme events (MED-HOT). The Iberian Peninsula and Greece are also highlighted as vulnerable areas requiring extra attention.

Background Gastric cancer remains a global health challenge due to the difficulty of detecting it early in asymptomatic, high-risk populations. Current invasive diagnostic methods are impractical for widespread screening. Liquid biopsy using circulating tumor DNA (ctDNA) shows promise, but early detection is hindered by the low abundance and heterogeneity of ctDNA. Methods We developed a multimodal cfDNA assay integrating methylation, fragmentomic, and hotspot mutation profiling from a single blood draw to detect gastric cancer-specific molecular signatures. Using these signatures, a machine-learning model was trained on a discovery cohort of 110 nonmetastatic GC patients and 119 healthy controls, then validated on an independent cohort of 58 patients and 65 controls. Results The ensemble model achieved an AUC of 0.87 (95% CI: 0.80–0.93), with 70.7% sensitivity and 92.3% specificity for detecting nonmetastatic GC. Incorporating hotspot mutation profiling increased overall sensitivity to 75.9% without affecting specificity. Compared to a previous multi-cancer model, our ensemble model showed improved sensitivity across all stages, particularly for early-stage GC (72.7% vs. 36.4%). Conclusions This multimodal cfDNA assay provides a minimally invasive and effective strategy for early GC detection, making it a potential screening tool for high-risk populations. Mini abstract This study presents a novel multimodal cfDNA assay that combines methylation, fragmentomic, and hotspot mutation profiling, achieving 75.9% sensitivity and 92.3% specificity for early gastric cancer detection.

The tropical Andes of South America, the world's richest biodiversity hotspot, are home to many rapid radiations. While geological, climatic, and ecological processes collectively explain such radiations, their relative contributions are seldom examined within a single clade. We explore the contribution of these factors by applying a series of diversification models that incorporate mountain building, climate change, and trait evolution to the first dated phylogeny of Andean bellflowers (Campanulaceae: Lobelioideae). Our framework is novel for its direct incorporation of geological data on Andean uplift into a macroevolutionary model. We show that speciation and extinction are differentially influenced by abiotic factors: speciation rates rose concurrently with Andean elevation, while extinction rates decreased during global cooling. Pollination syndrome and fruit type, both biotic traits known to facilitate mutualisms, played an additional role in driving diversification. These abiotic and biotic factors resulted in one of the fastest radiations reported to date: the centropogonids, whose 550 species arose in the last 5 million yr. Our study represents a significant advance in our understanding of plant evolution in Andean cloud forests. It further highlights the power of combining phylogenetic and Earth science models to explore the interplay of geology, climate, and ecology in generating the world's biodiversity.

Schistosomiasis is the second most important human parasitic disease in terms of socioeconomic impact, causing great morbidity and mortality, predominantly across the African continent. For intestinal schistosomiasis, severe morbidity manifests as periportal fibrosis (PPF) in which large tracts of macro-fibrosis of the liver, visible by ultrasound, can occlude the main portal vein leading to portal hypertension (PHT), sequelae such as ascites and collateral vasculature, and ultimately fatalities. For urogenital schistosomiasis, severe morbidity manifests as pathology throughout the urinary system and genitals, and is a definitive cause of squamous cell bladder carcinoma. Preventative chemotherapy (PC) programmes, delivered through mass drug administration (MDA) of praziquantel (PZQ), have been at the forefront of schistosomiasis control programmes in sub-Saharan Africa since their commencement in Uganda in 2003. However, despite many successes, ‘biological hotspots’ (as distinct from ‘operational hotspots’) of both persistent high transmission and morbidity remain. In some areas, this failure to gain control of schistosomiasis has devastating consequences, with not only persistently high infection intensities, but both “subtle” and severe morbidity remaining prevalent. These hotspots highlight the requirement to revisit research into severe morbidity and its mechanisms, a topic that has been out of favor during times of PC implementation. Indeed, the focality and spatially-structured epidemiology of schistosomiasis, its transmission persistence and the morbidity induced, has long suggested that gene-environmental-interactions playing out at the host-parasite interface are crucial. Here we review evidence of potential unique parasite factors, host factors, and their gene-environmental interactions in terms of explaining differential morbidity profiles in the human host. We then take the situation of schistosomiasis mansoni within the Albertine region of Uganda as a case study in terms of elucidating the factors behind the severe morbidity observed and the avenues and directions for future research currently underway within a new research and clinical trial programme (FibroScHot).

In cancer, recurrent somatic single-nucleotide variants—which are rare in most paediatric cancers—are confined largely to protein-coding genes 1 – 3 . Here we report highly recurrent hotspot mutations (r.3A>G) of U1 spliceosomal small nuclear RNAs (snRNAs) in about 50% of Sonic hedgehog (SHH) medulloblastomas. These mutations were not present across other subgroups of medulloblastoma, and we identified these hotspot mutations in U1 snRNA in only <0.1% of 2,442 cancers, across 36 other tumour types. The mutations occur in 97% of adults (subtype SHHδ) and 25% of adolescents (subtype SHHα) with SHH medulloblastoma, but are largely absent from SHH medulloblastoma in infants. The U1 snRNA mutations occur in the 5′ splice-site binding region, and snRNA-mutant tumours have significantly disrupted RNA splicing and an excess of 5′ cryptic splicing events. Alternative splicing mediated by mutant U1 snRNA inactivates tumour-suppressor genes ( PTCH1 ) and activates oncogenes ( GLI2 and CCND2 ), and represents a target for therapy. These U1 snRNA mutations provide an example of highly recurrent and tissue-specific mutations of a non-protein-coding gene in cancer. Highly recurrent hotspot r.3A>G mutations are identified in U1 splicesomal small nuclear RNAs in about 50% of Sonic hedgehog medulloblastomas, which result in disrupted RNA splicing and the activation of oncogenes.

The development of robust and efficient analytical tools for informed decision making, mainly in epidemiological contexts, remains a persistent challenge. This study presents an enhanced algorithm designed to accurately detect vulnerable spatiotemporal hotspots associated with unexpected disease outbreaks. We introduce an improved novel Multi-EigenSpot algorithm by systematically integrating the functionalities of both EigenSpot and its Multi-HotSpot extension. The EigenSpot algorithm effectively identifies single spatiotemporal clusters, it is unable to detect multiple hotspots. The Multi-EigenSpot algorithm overcomes this limitation through an iterative process of cluster detection and removal. However, challenges persist regarding computational efficiency and sensitivity in identifying rare clusters. To address these limitations, we propose an efficient Novel Multi-EigenSpot algorithm. This method is designed to detect multiple irregularly shaped, rare spatiotemporal clusters with significantly improved computational performance. Furthermore, the proposed algorithm integrates heatmap visualizations to enhance the interpretability of detected clusters. We evaluated our method using monthly waterborne disease surveillance data from Khyber Pakhtunkhwa, Pakistan (January - December 2024), comparing its performance against both the original EigenSpot and Multi-EigenSpot algorithms. Empirical results demonstrate the proposed algorithm’s superior performance in accurately identifying multiple spatiotemporal clusters. Beyond public health surveillance, this algorithm is readily adaptable to diverse domains, including crime analysis, environmental hazard detection, and other applications requiring spatiotemporal clustering.

Vegetation maps are crucial for ecologists and decision-makers, providing essential information on the spatial distribution of various vegetation types to support ecosystem exploration and management. Despite advancements in Earth observation and machine learning enabling large-scale vegetation mapping, creating detailed and accurate maps in biodiversity hotspots remains challenging due to significant environmental heterogeneity and frequent human disturbances. The lack of sufficient ground-based data and complex climate-vegetation interactions further limits mapping accuracy. In this study, we developed an integrated framework for multi-source data fusion to enhance vegetation mapping and validation in Yunnan Province, a global biodiversity hotspot region in Southwest China. The mapping process involved four key steps: (1) vegetation classification using random forest and Landsat imagery, (2) boundary calibration based on a locally calibrated static climate-vegetation model, (3) patch correction with independent forest inventory data, and (4) validation using adequate field observations. This approach enabled the mapping of 17 vegetation types and 44 subtypes in Yunnan Province (1:50000), categorized based on the growth-form composition of dominant species of the community. The overall accuracies were 0.747 and 0.710 for natural vegetation types and subtypes, and 0.905 and 0.891 for artificial types and subtypes. This high-resolution map enhances our understanding of vegetation distribution and ecological complexity in this region, offering valuable insights for policymakers to support conservation efforts and sustainable management strategies.

Biodiversity hotspots are conservation priorities. We identify the North American Coastal Plain (NACP) as a global hotspot based on the classic definition, a region with > 1500 endemic plant species and > 70% habitat loss. This region has been bypassed in prior designations due to misconceptions and myths about its ecology and history. These fallacies include: (1) young age of the NACP, climatic instability over time and submergence during high sea-level stands; (2) climatic and environmental homogeneity; (3) closed forest as the climax vegetation; and (4) fire regimes that are mostly anthropogenic. We show that the NACP is older and more climatically stable than usually assumed, spatially heterogeneous and extremely rich in species and endemics for its range of latitude, especially within pine savannas and other mostly herbaceous and fire-dependent communities. We suspect systematic biases and misconceptions, in addition to missing information, obscure the existence of similarly biologically significant regions world-wide. Potential solutions to this problem include (1) increased field biological surveys and taxonomic determinations, especially within grassy biomes and regions with low soil fertility, which tend to have much overlooked biodiversity; (2) more research on the climatic refugium role of hotspots, given that regions of high endemism often coincide with regions with low velocity of climate change; (3) in low-lying coastal regions, consideration of the heterogeneity in land area generated by historically fluctuating sea levels, which likely enhanced opportunities for evolution of endemic species; and (4) immediate actions to establish new protected areas and implement science-based management to restore evolutionary environmental conditions in newly recognized hotspots.

We determined the relationship between acoustic diversity and metrics of vertical forest structure derived from light detection and ranging (LIDAR) data in a neotropical rainforest in Costa Rica. We then used the LIDAR-derived metrics to predict acoustic diversity across the forest landscape. Sound recordings were obtained from 14 sites for six consecutive days during dusk chorus (6 pm). Acoustic diversity was calculated for each day as the total intensity across acoustic frequency bands using the Shannon index and then averaged over the 6 days at each site. A 10 m radius around each site was used to obtain several LIDAR-derived metrics describing the vertical structural attributes of the forest canopy. Multiple linear regression (MLR) with Akaike information criterion was used to determine a top-ranked model with acoustic diversity as the dependent variable and the LIDAR metrics as independent variables. Acoustic diversity was modeled for forested areas (where canopy height was >20 m) at 20 m resolution using coefficients obtained from the MLR, and a hotspot analysis was conducted on the resulting layer. Acoustic diversity was strongly correlated ( R 2  = 0.75) with the LIDAR metrics suggesting that LIDAR-derived metrics can be used to determine canopy structural attributes important to vocal fauna species. The hotspot analysis revealed that the spatial distribution of these canopy structural attributes across the La Selva forest is not random. Our approach can be used to identify forest patches of potentially high acoustic diversity for conservation or management purposes.