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Description of two new species of freshwater leeches of the genus Helobdella (Hirudinea, Glossiphoniidae) from Mexico, with a redescription of Helobdella socimulcensis (Caballero, 1931)
No full textFreshwater leeches of the genus Helobdella Blanchard, 1896 (Annelida: Clitellata) are widely distributed in the New World, with most of the species occurring in the neotropics and a single species native to Europe. Species of the genus are characterized by the presence of an eversible proboscis, dorsoventrally flattened body, presence of a single pair of eyespots, and a preference for feeding on the hemolymph of aquatic invertebrates. In this study, two new species of Helobdella are described based on specimens collected in Mexico, Helobdella papilloprocta sp. nov. and Helobdella gulloae sp. nov. These specimens were preliminarily identified as H. socimulcensis (Caballero, 1931), but detailed morphological and molecular analyses confirmed their status as separate species. In addition, a neotype for Helobdella socimulcensis is designated, and a redescription of the species is provided based on specimens from the type locality: Xochimilco, Mexico City, Mexico. Finally, H. austinensis in Nuevo León and H. europaea in Morelos, Mexico are reported for the first time, representing new records for the country. In total, ten species of Helobdella are known from Mexico
The dual economic impact of COVID-19 vaccine hesitancy - Bulgaria’s conundrum case
No full textBulgaria is assumed to be the country with the lowest COVID-19 vaccination coverage in the EU. The present study aims to highlight the economic impact of vaccine hesitancy. The period analyzed is from 1 January 2021 to 31 December 2021, examining hospitalization costs for COVID-19 patients, vaccine application costs, and vaccine type and dose administered. Results demonstrate a net impact of BGN 97 686 089 (€49 946 104) if 30% of the eligible population had been vaccinated and BGN 146 529 133 (€74 919 156) if a 45% rate had been achieved. In addition, 5.1 million unused doses were scrapped—more than those administered. The public health impact of vaccine hesitancy in Bulgaria led to a preventable medical crisis, substantial economic losses due to avoidable hospitalizations, and significant additional costs associated with the destruction of unused vaccines
SNAP Framework: Linked Prediction Based Anomaly Prevention With Suspicious Nodes on Social Network Graph
No full textIn previous studies, the focus has predominantly been on anomaly detection, with minimal attention given to anomaly prevention. However, anomaly prevention holds greater significance than anomaly detection. Preventing anomalous behavior before it occurs and identifying potential anomalies in advance to enable timely intervention is both challenging and crucial. In this study, a Suspicious Nodes Anomaly Prevention framework for anomaly prevention has been developed. First, a novel K-medoid based Salp Swarm Anomaly Detection method is proposed within the framework. This method reveals unclustered data by applying clustering and determines the boundaries of clusters using a nature-inspired algorithm that optimizes the threshold. Since threshold determination is an optimization problem, it aligns well with nature-inspired algorithms. Additionally, the Enron email dataset was selected as it is a real-world dataset with accessible content information. Initially, content and node features were extracted from the Enron email dataset. The proposed anomaly detection method was then applied separately to each of these features. Nodes identified as anomalous by one feature but normal by others were of particular interest. These nodes were labeled as “suspicious nodes,” and their connections were analyzed to detect potentially harmful email content. This framework fills a significant gap in the anomaly detection literature by contributing an unprecedented approach to anomaly prevention, offering early intervention capabilities in various sectors by identifying risks in advance. In this study, the proposed framework demonstrates high efficacy in detecting anomalies, achieving a True Positive Rate of 94% in node-based anomaly detection and 78% in content-based anomaly detection, indicating a robust capability for early intervention and risk identification
Genetic-based square jigsaw puzzle solver using the combined color+texture compatibility criterion
No full textWhen reconstructing jigsaw puzzles, the state-of-the-art algorithms struggle to distinguish between identically colored pieces that belong to different objects. This limitation significantly impacts the accuracy of puzzle solvers, especially in complex images with repetitive colors or textures. To address this issue, we propose a new GA-based square jigsaw puzzle solver. A combined color and texture discriminator is incorporated into the proposed solver to prevent pieces that have the same color but come from distinct objects from being joined together incorrectly. Color and texture features are extracted separately using the sum of square distances and Gabor filter. To evaluate the performance of the proposed solver, we used a dataset consisting 66 images: 20 puzzles with 432 pieces from the MIT collection, 20 puzzles with 540 pieces, and 20 puzzles with 805 pieces from the McGill collection, and 3 puzzles with 2360 pieces, and 3 puzzles with 3300 pieces from the Pomeranz collection. For the direct, neighbor, and largest component comparisons, the proposed method’s accuracy is 92.91%, 96.66%, and 90.83%, respectively. The proposed method demonstrates an improvement of 11.9%, and 3.65% in accuracy based on direct and neighbor comparison criteria, on the database images when compared to current state-of-the-art GA-based square jigsaw puzzle solver
AI and telemedicine in management of diabetes
No full textThis review explores how two cutting-edge technologies—telemedicine and artificial intelligence (AI)—are reshaping diabetes care. Diabetes remains one of healthcare’s toughest challenges, demanding round-the-clock monitoring and treatments that adapt to each patient’s needs. During COVID-19, telemedicine proved its worth as a vital tool for maintaining patient care and improving health outcomes. Meanwhile, AI—through machine learning (ML) and deep learning (DL)—brings fresh capabilities for catching diabetes early, assessing patient risk, and spotting complications like eye and nerve damage before they become serious. We examined recent research on these technologies, particularly their roles in predicting who might develop diabetes, using Natural Language Processing (NLP) to decode messy patient records, and supporting doctors through clinical decision support systems (CDSS). Our findings reveal that telemedicine works—it helps patients control their blood sugar better and keeps them satisfied with their care. However, not everyone has equal access to technology, and some healthcare providers remain skeptical. AI diagnostic tools, especially for eye screening, now match human doctors in accuracy. Though merging these technologies could revolutionize personalized diabetes care, we first need to tackle real-world obstacles: ensuring fair access for all patients, protecting sensitive health data, and making different systems work together seamlessly
Treating severe junctional epidermolysis bullosa with artesunate
No full textTreating severe junctional epidermolysis bullosa with artesunat
Taxonomy of the genus Elasmopus (Crustacea, Amphipoda) in Japan and South Korea, with description of a new species
No full textTo advance the limited understanding of the East Asian Elasmopus fauna, field surveys were conducted in Japan and South Korea. In the present study, a new species, E. lumbiniger sp. nov., is described. The new species is distinguished from its congeners by a strongly projected posteroventral corner of the epimeral plate 3, long slender setae on the anterodistal corner of the male gnathopod 2 carpus, a mid-palmar ridge on the male gnathopod 2 propodus, and a smooth posterior margin on the basis of pereopods 5–7. In addition, E. koreanus is recorded in Japan for the first time, and the known distribution range of E. mukuinu has been significantly extended. The nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI) of these species were determined, and the genetic distances among Elasmopus species were provided. A key to the species of Elasmopus found in East Asia is also provided
Environmental impacts of agricultural pest insects: five case studies reveal overlooked impact mechanisms and specify knowledge gaps
No full textInvasive species can cause environmental impacts through various mechanisms. Assessing their impact can inform management decisions and illuminate risks to non-invaded areas. Research on the environmental impacts of invasive insects is heavily focused on a few well-known examples, with agricultural pests in particular receiving little attention. We aimed to investigate whether evidence for environmental impacts of insect pests of agriculture may be overlooked. We conducted in-depth literature reviews of three globally relevant insect agricultural pests–Halyomorpha halys, Helicoverpa armigera, and Spodoptera frugiperda. For comparison, we reviewed two forest pathogens known for their environmental impacts–Bursaphelenchus xylophilus and Phytophthora ramorum. We identified many published articles containing evidence of environmental impacts among the three insect agricultural pests that were not captured by existing reviews on invasive insects, with some demonstrating high levels of impact severity. Crucially, a preponderance of the identified articles did not directly address the findings in relation to environmental impacts. As expected, we recorded more conspicuous examples of environmental impacts among the case-study forest pathogens, though we also identified underappreciated impact mechanisms. We further provide evidence that supports the importance of considering management interventions as a key mechanism of non-target environmental impacts. This review raises awareness about the underreported environmental impacts of agricultural insect pests and specifies knowledge gaps that should guide future research
Dietary supplementation of conjugated linoleic acid in adult Creole goats: Effects on weight gain, carcass yield and meat quality
No full textThe study evaluated the effects of dietary supplementation with rumen-protected conjugated linoleic acid (CLA) on productive performance, carcass characteristics and meat quality in lactating adult Creole goats. The trial included fifteen lactating Creole goats, divided into three dietary treatments: a control group (no CLA), and groups receiving 50 or 90 g/day of CLA supplementation. The experiment lasted seven weeks after a two-week adaptation period. Results showed no significant differences between dietary treatments in terms of dry matter intake, daily weight gain, feed efficiency or back fat thickness. However, goats supplemented with 90 g/day CLA showed a slight but significant reduction in cold carcass yield. Meat quality parameters, such as final pH, shear strength, water holding capacity and chemical composition, were not affected by dietary supplementation with CLA, although all goats produced relatively tough meat, commensurate with their age. In particular, dietary supplementation with CLA significantly increased the content of the cis-9, trans-11 CLA isomer in muscle tissue, especially at the 90 g/day dose, without significantly affecting other major fatty acids. The trans-10, cis-12 CLA isomer did not noticeably accumulate in muscle tissue. Thus, although CLA supplementation effectively enriched goat meat with beneficial CLA isomers, it did not improve growth performance or standard measures of meat quality. In conclusion, this study demonstrates that supplementation with protected CLA can beneficially modify the fatty acid profile of goat meat without negatively affecting meat quality traits, potentially adding nutritional value to meat from lactating adult goats
Assessing the effect of sampling proxies on plesiosaur taxic diversity with comments on implications for Mesozoic sampling design
No full textThe fossil record of plesiosaurs, while extensive, is strongly affected by geological and anthropogenic biases that may obscure true diversity patterns. This study evaluates the influence of rock availability and sampling effort on taxic diversity estimates (TDE) of plesiosaurians throughout the Mesozoic period and proposes a method for determining priority stages for future fossil sampling. Two primary proxies—the number of fossiliferous marine formations (FMF) and fossil marine collections (FMC)—are considered. Our findings indicate that neither FMF nor FMC alone accounts for observed diversity peaks and troughs in plesiosaurian TDE. To address this, we integrate phylogenetic diversity estimates (PDE) into our framework, using the difference between PDE and TDE (i.e., phylogenetic residuals) to identify stratigraphic stages where recorded diversity likely underrepresents true lineage richness. This combined approach reveals that some previously recognized declines in TDE—such as during the Berriasian—are consistent with low sampling proxy values and may reflect genuine sampling bias. In contrast, other intervals, particularly the Aalenian–Bathonian, Valanginian and Coniacian ages, exhibit low TDE despite limited rock and collection data but high phylogenetic residuals, highlighting them as high-priority targets for future paleontological fieldwork. These stages represent critical intervals for testing the consistency between diversity models based on sampling proxies and phylogenetically inferred diversity trends