Search Results (34)

During August 2025, a single adult yellow-bellied watersnake, Nerodia erythrogaster transversa was found dead on the road in Montgomery County, Arkansas, USA, salvaged, and its feces examined for coccidian parasites. Fecal material from the rectum was placed in a vial of 2.5% potassium dichromate and examined by light microscopy. Partially sporulated oocysts were initially found and allowed to completely sporulate. The snake was found to be passing a new species of Eimeria. Oocysts of Eimeria speairsi sp. n. were ovoidal to spheroidal with a slightly rough bi-layered wall, measured (L × W) 28.0 × 18.2 µm, and had a length/width (L/W) ratio of 1.5; a micropyle, oocyst residuum, and polar granule was absent. Sporocysts are ellipsoidal and measured 13.7 × 8.6 µm, L/W ratio of 1.6; a flattened Stieda body was present but sub-Stieda and para-Stieda bodies were absent. The sporocyst residuum was composed of various-sized granules in a compact rounded or irregular mass, sometimes dispersed between the sporozoites. A 412 bp sequence of the SSU rRNA gene produced for E. speairsi sp. n. showed a relatively low level of similarity. The species description is based primarily on oocyst morphology and partial SSU rRNA sequence data from the single host snake. In addition, an updated summary of the coccidians of North American watersnakes is provided. Full article

AI-enabled chatbots are increasingly positioned as a remedy for breastfeeding support gaps in rural maternal health, offering private, immediate assistance amid persistent shortages of lactation specialists and limited access to care. However, their clinical promise remains constrained by the probabilistic nature of large language models, which can generate hallucinations that undermine maternal–infant safety. This article argues that safely integrating AI into breastfeeding support requires treating hallucination not as a singular technical flaw but as a systems-level risk shaped by design, governance, and use context. We identified key risks of AI systems that could result in hallucination such as, false citations, transcription errors, prompt injection and jailbreaking, and incorrect generalization or personalization, and analyze how each error introduces distinct safety vulnerabilities. Drawing from systems thinking, we outline mitigation strategies including retrieval-augmented generation grounded in authoritative breastfeeding sources, layered guardrails, adversarial testing, uncertainty-aware messaging, and domain-specific fine-tuning. By linking AI system design choices to downstream health consequences in resource-constrained settings, this paper reframes AI-assisted breastfeeding support as a governance challenge central to equitable, safe maternal health innovation. Full article

Given the increasing demand for rapid emergency management decision-making, which must be both timely and reliable, even slight delays can result in substantial human and economic losses. However, current systems and recent state-of-the-art work often use inflexible rule-based logic that cannot adapt to rapidly changing emergency conditions or dynamically optimise response allocation. As a result, our study presents the Calculation Logic Optimisation Framework (CLOF), a novel data-driven approach that enhances decision-making intelligently and strategically through learning-based predictive and multi-objective optimisation, utilising the 911 Emergency Calls data set, comprising more than half a million records from Montgomery County, Pennsylvania, USA. The CLOF examines patterns over space and time and uses optimised calculation logic to reduce response latency and increase decision reliability. The suggested framework outperforms the standard Decision Tree, Random Forest, Gradient Boosting, and XGBoost baselines, achieving 94.68% accuracy, a log-loss of 0.081, and a reliability score (R²) of 0.955. The mean response time error is reported to have been reduced by 19%, illustrating robustness to real-world uncertainty. The CLOF aims to deliver results that confirm the scalability, interpretability, and efficiency of modern EM frameworks, thereby improving safety, risk awareness, and operational quality in large-scale emergency networks. Full article

The green sunfish, Lepomis cyanellus, is a common centrarchid that has been previously reported to harbor several myxosporeans. In May 2022, six L. cyanellus were collected from the Caddo River, Montgomery County, Arkansas, USA, and had their gills, gall bladders, urinary bladders, fins, integument, other major organs, and musculature examined for myxosporeans. A single individual was found to harbor a new species of Myxobolus infecting the soft dorsal fin. A qualitative and quantitative morphological description was based on fresh plasmodia and myxospores. Elliptoid to obovoid myxospores of Myxobolus picassoi sp. n. are asymmetrical, 12.2 µm long × 9.1 µm wide, with two broadly pyriform to broadly ovoid subequal polar capsules. Molecular data consisted of a 2042 base pair sequence of the partial small subunit rRNA gene (SSU). Phylogenetic analysis revealed that M. picassoi sp. n. is a member of a clade of myxosporean species that predominately infect centrarchid sunfishes from North America. This is the fifth report of a Myxobolus from L. cyanellus, but the first report of a species infecting the soft dorsal fin. This article was registered in the Official Register of Zoological Nomenclature (ZooBank) as urn:lsid:zoobank.org:pub:146D21D1-E416-41C7-A1F6-8AB2AC6D9260. Full article

Accurate segmentation of lung contours from computed tomography (CT) scans is essential for developing reliable computer-aided diagnostic systems. Although deep learning models, especially convolutional neural networks, have advanced the automation of pulmonary region extraction, their performance is often limited by low contrast and atypical anatomical appearances in CT images. This paper presents MSDC-AM U-Net, a hierarchically supervised segmentation framework built upon the U-Net architecture, integrated with a newly designed Multi-Scale Dilated Convolution (MSDC) module and an Attention Module (AM). The MSDC component employs dilated convolutions with varying receptive fields to improve edge detection and counteract contrast-related ambiguities. Furthermore, spatial attention mechanisms applied across different dimensions guide the model to focus more effectively on lung areas, thereby increasing localization precision. Extensive evaluations on multiple public lung imaging datasets (Luna16, Montgomery County, JSRT) confirm the superiority of the proposed approach. Our MSDC-AM U-Net achieved leading performance, notably attaining a Dice Coefficient of 0.974 on the Luna16 CT dataset and 0.981 on the JSRT X-ray dataset, thereby exceeding current leading methods in both qualitative and quantitative assessments. Full article

Accurate classification of chest X-ray (CXR) images is crucial for diagnosing lung diseases in medical imaging. Existing deep learning models for CXR image classification face challenges in distinguishing non-lung features. In this work, we propose a new segmentation-assisted fusion-based classification method. The method involves two stages: first, we use a lightweight segmentation model, Partial Convolutional Segmentation Network (PCSNet) designed based on an encoder–decoder architecture, to accurately obtain lung masks from CXR images. Then, a fusion of the masked CXR image with the original image enables classification using the improved lightweight ShuffleNetV2 model. The proposed method is trained and evaluated on segmentation datasets including the Montgomery County Dataset (MC) and Shenzhen Hospital Dataset (SH), and classification datasets such as Chest X-Ray Images for Pneumonia (CXIP) and COVIDx. Compared with seven segmentation models (U-Net, Attention-Net, SegNet, FPNNet, DANet, DMNet, and SETR), five classification models (ResNet34, ResNet50, DenseNet121, Swin-Transforms, and ShuffleNetV2), and state-of-the-art methods, our PCSNet model achieved high segmentation performance on CXR images. Compared to the state-of-the-art Attention-Net model, the accuracy of PCSNet increased by 0.19% (98.94% vs. 98.75%), and the boundary accuracy improved by 0.3% (97.86% vs. 97.56%), while requiring 62% fewer parameters. For pneumonia classification using the CXIP dataset, the proposed strategy outperforms the current best model by 0.14% in accuracy (98.55% vs. 98.41%). For COVID-19 classification with the COVIDx dataset, the model reached an accuracy of 97.50%, the absolute improvement in accuracy compared to CovXNet was 0.1%, and clinical metrics demonstrate more significant gains: specificity increased from 94.7% to 99.5%. These results highlight the model’s effectiveness in medical image analysis, demonstrating clinically meaningful improvements over state-of-the-art approaches. Full article

Tuberculosis (TB) is the most serious worldwide infectious disease and the leading cause of death among people with HIV. Early diagnosis and prompt treatment can cut off the rising number of TB deaths, and analysis of chest X-rays is a cost-effective method. We describe a deep learning-based cascade algorithm for detecting TB in chest X-rays. Firstly, the lung regions were segregated from other anatomical structures by an encoder–decoder with an atrous separable convolution network—DeepLabv3+ with an XceptionNet backbone, DLabv3+X, and then cropped by a bounding box. Using the cropped lung images, we trained several pre-trained Deep Convolutional Neural Networks (DCNNs) on the images with hyperparameters optimized by a Bayesian algorithm. Different combinations of trained DCNNs were compared, and the combination with the maximum accuracy was retained as the winning combination. The ensemble classifier was designed to predict the presence of TB by fusing DCNNs from the winning combination via weighted averaging. Our lung segmentation was evaluated on three publicly available datasets: it provided better Intercept over Union (IoU) values: 95.1% for Montgomery County (MC), 92.8% for Shenzhen (SZ), and 96.1% for JSRT datasets. For TB prediction, our ensemble classifier produced a better accuracy of 92.7% for the MC dataset and obtained a comparable accuracy of 95.5% for the SZ dataset. Finally, occlusion sensitivity and gradient-weighted class activation maps (Grad-CAM) were generated to indicate the most influential regions for the prediction of TB and to localize TB manifestations. Full article

Land managers across the United States (U.S.) are developing plans to mitigate climate change. Effective implementation and monitoring of these climate action plans require standardized methods and timely, accurate geospatial data at appropriate resolutions. Despite the abundance of geospatial and statistical data in the U.S., a significant gap remains in translating these data into actionable insights. To address this gap, we developed the Land Emissions and Removals Navigator (LEARN), an online tool that automates subnational greenhouse gas (GHG) inventories of forests and trees in nonforest lands using a standardized analytical framework consistent with national and international guidelines. LEARN integrates multiple datasets to calculate land cover and tree canopy changes, delineate areas of forest disturbance, and estimate carbon emissions and removals. To demonstrate the application of LEARN, this paper presents case studies in Jefferson County, Washington; Montgomery County, Maryland; and federally owned forests across the conterminous U.S. Our results highlight LEARN’s capacity to provide localized insights into carbon dynamics, enabling subnational entities to develop tailored climate strategies. By enhancing accessibility to standardized data, LEARN empowers community land managers to more effectively mitigate climate change. Future developments aim to expand LEARN’s scope to cover nonforest landscapes and incorporate additional decision-making functionalities. Full article

Background: In clinical practice, identifying the location and extent of tumors and lesions is crucial for disease diagnosis and treatment. Artificial intelligence, particularly deep neural networks, offers precise and automated segmentation, yet limited data and high computational demands often hinder its application. Transfer learning helps mitigate these challenges by significantly reducing computational costs, although applying these models can still be resource intensive. This study aims to present flexible and computationally efficient architecture that leverages transfer learning and delivers highly accurate results across various medical imaging problems. Methods: We evaluated three datasets with varying similarities to ImageNet: ISIC 2018 (skin lesions), CBIS-DDSM (breast masses), and the Shenzhen and Montgomery CXR Set (lung segmentation). An ablation study on ISIC 2018 tested various pre-trained backbones, architectures, and loss functions. Results: The optimal configuration—DeepLabV3+ with a pre-trained ResNet50 backbone and Log-Cosh Dice loss—was validated on the remaining datasets, achieving state-of-the-art results. Conclusion: Computationally simpler architectures can deliver robust performance without extensive resources, establishing DeepLabV3+ with the ResNet50 as a baseline for future studies. In the medical domain, enhancing data quality is more critical for improving segmentation accuracy than increasing model complexity. Full article

Traffic stops represent a crucial point of interaction between citizens and law enforcement, with potential implications for bias and discrimination. This study performs a rigorously validated comparative machine learning model analysis, creating artificial intelligence (AI) technologies to predict the results of traffic stops using a dataset sourced from the Montgomery County Maryland Data Centre, focusing on variables such as driver demographics, violation types, and stop outcomes. We repeated our rigorous validation of AI for the creation of models that predict outcomes with and without race and with and without gender informing the model. Feature selection employed regularly selects for gender and race as a predictor variable. We also observed correlations between model performance and both race and gender. While these findings imply the existence of discrimination based on race and gender, our large-scale analysis (>600,000 samples) demonstrates the ability to produce top performing models that are gender and race agnostic, implying the potential to create technology that can help mitigate bias in traffic stops. The findings encourage the need for unbiased data and robust algorithms to address biases in law enforcement practices and enhance public trust in AI technologies deployed in this domain. Full article

Green stormwater infrastructure (GSI) is increasingly implemented worldwide to address stormwater issues while providing co-benefits such as habitat provision. However, research on public perceptions of GSI’s ecosystem benefits is limited, and barriers such as perception and maintenance hinder biodiversity promotion in GSI. Through an online survey (n = 781), we explored how residents in four Northeast US urban areas—Prince George’s County and Montgomery County, MD, New York City, and Philadelphia, PA—perceived the benefits and concerns regarding two types of bioswales (biodiverse and turf). Biodiverse swales feature various plants to promote biodiversity, whereas turf swales are primarily grass-covered. Our analyses included paired-samples t-tests, independent t-tests, one-way repeated measures ANOVA tests, and one-way ANOVA tests to compare perceptions across bioswale types, aspects of benefit/concern, and locations. Both bioswale types were recognized for enhancing green spaces and neighborhood aesthetics. Residents perceived greater environmental and social benefits from biodiverse swales than turf swales, particularly for habitat provision. While overall concerns for both bioswale types were low, potential issues like pest cultivation and the unappealing appearance of biodiverse swales remain significant barriers. Notably, implementing biodiverse swales alleviated initial concerns, especially about pests, suggesting familiarity can enhance acceptance. Location-specific differences in perception were observed, with New York City showing higher perceived benefits and concerns and Montgomery County exhibiting the lowest concerns. This variance is likely due to distinct urban environments, levels of environmental awareness, and demographic profiles. Full article

Cultural identity and participation play a critical role in understanding culture and its influence on different cultural groups. The Carpatho-Rusyns originate in the Carpathian Rus, which is in the Carpathian Mountains. The Carpatho-Rusyns are a stateless group, and many historically immigrated to other countries. This mixed-method study examines cultural participation and identity among Carpatho-Rusyn descendants (n = 51). Data collection comprised both open-ended and closed-ended survey questions. A link to the survey was shared in Facebook groups that relate to Carpatho-Rusyn culture, genealogy, and history. Closed-ended survey items were analyzed using descriptive statistics, while open-ended items were thematically coded. The findings indicate that most participants do not align with particular Carpatho-Rusyn groups, yet many still uphold cultural traditions, especially related to food and holidays. Qualitative insights emphasize the significance of cultural pride and distinction. Ultimately, this study highlights unique facets of Carpatho-Rusyn heritage and its lasting importance for descendants living in various countries, especially the United States. Finally, this paper concludes with practical implications that center on the importance of developing educational programs, community engagement strategies, and cultural awareness initiatives to preserve and promote the culture. Full article

Children with developmental central hypotonia have reduced muscle tone secondary to non-progressive damage to the brain or brainstem. Children may have transient delays, mild or global functional impairments, and the lack of a clear understanding of this diagnosis makes evaluating appropriate interventions challenging. This overview aimed to systematically describe the best available evidence for tools to identify and evaluate children with developmental central hypotonia aged 2 months to 6 years. A systematic review of systematic reviews or syntheses was conducted with electronic searches in PubMed, Medline, CINAHL, Scopus, Cochrane Database of Systematic Reviews, Google Scholar, and PEDro and supplemented with hand-searching. Methodological quality and risk-of-bias were evaluated, and included reviews and tools were compared and contrasted. Three systematic reviews, an evidence-based clinical assessment algorithm, three measurement protocols, and two additional measurement tools were identified. For children aged 2 months to 2 years, the Hammersmith Infant Neurological Examination has the strongest measurement properties and contains a subset of items that may be useful for quantifying the severity of hypotonia. For children aged 2–6 years, a clinical algorithm and individual tools provide guidance. Further research is required to develop and validate all evaluative tools for children with developmental central hypotonia. Full article

Globally, floods are becoming more severe, lasting longer, and occurring more frequently because of changes in climate, rapid urbanization, and changing human demographics. Although traditional structural flood mitigation infrastructures (e.g., drainage systems, levees) are effective in urban areas, their functionalities in the face of extreme rainfall events and increased development largely depend on the capacity and location of such systems, making complementary solutions such as nature-based solutions (NBS) important. The concept of NBS within the context of flood mitigation has gained traction in the last decade; however, the success of NBS depends on their effectiveness and distribution over urban regions. This article seeks to examine the potential of NBS as a flood loss mitigation tool in one of the fastest-growing and flood-prone counties of Pennsylvania, Montgomery County, using Generalized Linear Model (GLR) and Geographically Weighted Regression (GWR) techniques. The analysis integrates the National Risk Index dataset for river flooding, a 100-year flood zone layer from National Flood Hazard Layer (NFHL) provided by FEMA, with land use and impervious surface percent data derived from National Land Cover Database (NLCD) for 2019 and socioeconomic data at the U.S. census tract level from the 2019 U.S. Census. This study’s findings partially contradict previous research by revealing an unexpected relationship between NBS quantity in floodplains and expected annual loss. Findings also suggest that small size and disconnected patches of NBS in floodplains in some dense urban areas effectively reduce total losses from flood events. Full article

Mobility experience has a positive impact on activity, participation, socialisation, language and cognition, but children with cerebral palsy (CP), Gross Motor Function Classification System (GMFCS) level V require assistive devices or assistance in all environments. Supported standing devices afford upright, weight-bearing positions to promote muscle, bone, joint and overall health. Supported stepping devices afford stepping and upright independent mobility, positively impacting self-esteem and participation, while power mobility is the only possibility for effective, independent community mobility. These devices and opportunities should be introduced at the age when children who are typically developing are pulling to stand, moving and exploring their environment. A detailed case description including lived experience and device use data is presented for female twins with dystonic tetraplegic CP born at 25 weeks gestational age and functioning at GMFCS level V. The feasibility of using power mobility, standing and stepping devices in home and community settings within the first two years is illustrated. The twins transitioned from spending 24 h in lying positions or being held in arms to spending more than 2 h daily in upright positions and having opportunities to move independently. Positioning and mobility devices can help to address all the F-words for child development: functioning, family, fitness, fun, friends and future. Full article