Search Results (57)

Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, disaster early warning, and smart grids. However, sensor nodes face strict energy limitations. Unbalanced energy consumption and hotspots severely shorten the network lifetime. To address these problems, this paper proposes an optimized Spotted Hyena Optimization-Energy-Efficient Non-Uniform Clustering algorithm (SHOE) for cluster head selection and data transmission. The algorithm has three main innovations: combining a bio-inspired metaheuristic with an improved EEUC (Energy-Efficient Unequal Clustering) multi-hop relay and a Gaussian distribution model for non-uniform node deployment; designing a multi-dimensional fitness function considering energy, distance, and node location; and introducing empty cluster and isolated node repair mechanisms to balance exploration and exploitation. Specifically, the multi-dimensional fitness function guides the heuristic search process towards high-quality cluster head candidates, while the empty cluster and isolated node repair mechanisms dynamically rectify abnormal network structures, ensuring the robustness of the final architecture optimized by the bio-inspired framework. Simulations in MATLAB show that SHOE outperforms LEACH (Low-Energy Adaptive Clustering Hierarchy), PSOE (Particle Swarm Optimization with Evolutionary Strategy), PL-EBC (Probabilistic Localized Energy-Balanced Clustering), and CGWOA (Chaotic Grey Wolf Optimization Algorithm) in reducing node death, saving energy, and extending network lifetime. It improves adaptability to non-uniform distribution and optimizes energy balance, thus enhancing the efficiency and stability of WSNs. Full article

Background/Objectives: Multidrug-resistant (MDR) Escherichia coli resistant to third-generation cephalosporins are a growing One Health concern, but data on extraintestinal pathogenic E. coli (ExPEC) from wildlife in North Africa remain scarce. We aimed to characterize ESBL/AmpC-producing ExPEC from captive wild mammals in Tunisia and to situate these isolates in a global genomic context. Methods: In 2018, 30 fecal samples from 14 captive wild mammals in a private farm were screened on cefotaxime agar. Four cefotaxime-resistant E. coli isolates were recovered from a llama, lion, hyena, and tiger. Antimicrobial susceptibility testing and Illumina whole-genome sequencing were combined with in silico typing, resistome and virulome profiling, plasmid and mobile element analysis, human pathogenicity prediction and core-genome MLST-based minimum-spanning trees. Results: All isolates were MDR but remained susceptible to carbapenems, colistin and tigecycline. Two ST162/B1 isolates from the llama and tiger carried bla_CMY-2, whereas two ST69/D isolates from the lion and hyena harbored bla_CTX-M-15 and qnrS1. Genomes encoded 61–68 antimicrobial resistance genes and 114–131 virulence-associated genes, together with IncF-, IncI1- and IncY-type plasmids and IS26-rich insertion sequence profiles. Mating-out assays yielded cefotaxime-resistant transconjugants, supporting plasmid transferability of bla_CMY-2 or bla_CTX-M-15. PathogenFinder predicted a ≥0.93 probability of human pathogenicity for all isolates. cgMLST-based trees showed that Tunisian ST69 and ST162 clustered within internationally disseminated lineages containing human, animal and food isolates, rather than forming wildlife-restricted branches. Conclusions: Captive wild mammals in Tunisia can harbor high-risk ExPEC lineages combining ESBL/AmpC production, multidrug resistance and extensive virulence and mobility gene repertoires. These findings highlight captive wildlife as potential reservoirs and sentinels of clinically relevant E. coli and underscore the need for integrated WGS-based One Health surveillance at the human–animal–environment interface in North Africa. Full article

Diabetes mellitus is a chronic condition that frequently leads to severe complications that are difficult to detect in their early stages using conventional clinical monitoring. This paper presents a data-driven framework for predicting multiple diabetes-related complications using structured electronic health record data while ensuring clinically meaningful explainability. The proposed approach adapts a pretrained electronic health record foundation model to operate on static patient data and integrates it with classical machine learning baselines to address class imbalance, feature sparsity, and interpretability challenges. A multi-label prediction setting covering eight common diabetes complications is evaluated using a real-world dataset from a regional diabetes center in the United Arab Emirates. Synthetic data generation and clinical constraint enforcement are applied to improve robustness for underrepresented outcomes, while feature selection is guided by model importance and attribution-based explanations. The best-performing configuration, a weighted ensemble combining a low-rank adapted Hyena-based foundation model with a tree-based predictor, achieved an average F1-score of 0.77, an average recall of 0.85, and an example-based F1-score of 0.71, outperforming all individual models. In addition, this ensemble produced the most stable explanations under input perturbations, indicating improved consistency of dominant clinical risk drivers. These results demonstrate that explainable foundation model-based ensembles can deliver accurate, robust, and clinically transparent risk prediction for diabetes complications. Full article

Genomic Language Models (GLMs), leveraging their vast parameter scales and the similarities between DNA sequences and natural languages, demonstrate immense potential in processing large-scale genomic data and elucidating gene regulation and evolutionary relationships. However, the cross-species generalization capability of large genomic models has not yet been systematically evaluated. This study addresses this critical gap by benchmarking five GLMs (DNABERT-2, GROVER, HyenaDNA, NT-V2, and AgroNT) and a CNN baseline model using human (Homo sapiens) and rice (Oryza sativa) genomes across four downstream tasks: promoter detection, transcription start site (TSS) scanning, species classification, and gene region identification, through both zero-shot testing and fine-tuning. During testing, factors such as hyperparameters, early stopping protocols, and computational resources were fixed to ensure fairness, enabling us to systematically evaluate their performance and cross-species generalization capabilities. The results were further analyzed from multiple mathematical and representational perspectives to provide a more rigorous and objective assessment of each model’s performance. The results show that AgroNT consistently leads on rice tasks, while NT-V2 and DNABERT-2 achieved the best overall performance in fine-tuning and zero-shot experiments, respectively. Although their pretraining data did not include plants, they demonstrate excellent performance on rice-related tasks thanks to cross-species pretraining that enhances their generalization ability across human–rice domains. This benchmark study offers guidance on selecting appropriate genomic language models based on task characteristics and provides insights for future development in this field. Full article

Background: The striped hyena (Hyaena hyaena) occurs in a wide range from north and east Africa, through southwest Asia to India, but its distribution is increasingly patchy and many of its populations are in decline due to intense human pressure. Its genetic diversity and structure, phylogeography, and evolutionary history, remain poorly understood. Methods: In this study, we investigated mitochondrial DNA variation in Algerian striped hyenas. Moreover, with the aim of contributing to our understanding of the evolutionary history of the species, we also examined samples from other geographic regions and compared our results with those of the only previous study in which individuals from across the range of the species were analyzed. In particular, we performed a wide range of analyses of demographic history and estimation of the age of the extant mitochondrial DNA variation. Results and Conclusions: The Algerian population sample was monomorphic. Overall, the global patterns of genetic diversity and the results of some demographic history analyses support a scenario of population growth in the species, estimated to have occurred in the Late Pleistocene, but many of the analyses did not detect a significant signal of growth, most likely a result of the limited power provided by a small number of segregating sites. The estimates, from three different methods, for the time to the most recent common ancestor (TMRCA) of the mitochondrial DNA variation hovered around 400 ka, coinciding with one of the longest and warmest interglacials of the last 800,000 years, with environmental conditions similar to the Holocene. Full article

There have been few reports of neoplasia in hyenas to date. In this report, we describe a captive adult female spotted hyena (Crocuta crocuta) that developed inappetence, lethargy and marked abdominal distension over a 3-day period. The hyena was chemically immobilised to allow clinical investigation of the severe symptoms; however, she died before any internal examination occurred. At necropsy, severe serosanguinous hydropericardium was evident, as well as pulmonary congestion and oedema, ascites and chronic passive congestion of the liver with mild fibrosis. Histopathological examination of the pericardial surface revealed fibrous proliferations lined by mostly a single layer of large proliferating neoplastic mesothelial cells forming papillary projections into the lumen of the pericardial sac as well as infiltration into the pericardial connective tissue, with innumerable haemosiderin-laden macrophages in places, suggestive of chronic haemorrhage. The liver revealed severe congestion and interstitial fibrosis, and the lung revealed congestion and oedema, with moderate numbers of alveolar macrophages and marked anthracosis. The diagnosis was pericardial well-differentiated papillary mesothelioma, with death under anaesthesia caused by cardiogenic shock due to pericardial mesothelioma-associated cardiac tamponade. As primary pericardial mesothelioma (PPM) is a rare tumour type for both animals and humans, and this is the first report of a PPM in a hyena, we compare the clinical findings with those seen in other species. Full article

Background: Due to the wide host range of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), vaccination has been recommended for susceptible species in zoological collections, particularly to protect endangered species. The Zoetis^® Experimental Mink Coronavirus Vaccine (Subunit) was temporarily authorized in 2021–2024 for emergency use in North America for this purpose. However, there are limited data regarding its safety or efficacy in non-domestic mammals. The present study was conducted to assess the ability of this vaccine to elicit serum neutralizing titers against SARS-CoV-2 in selected animals from the Toronto Zoo (TZ) vaccinated during 2022. Methods: Serum samples were collected from 24 individuals across four families (Cervidae, Felidae, Ursidae, and Hyaenidae) and tested using a surrogate virus neutralization test (sVNT) and a plaque-reduction neutralization test (PRNT). Results: The results showed that all species developed some neutralizing titers after at least one vaccine dose, except for polar bears, which showed no seroconversion. Felids and hyenas had the highest neutralizing titers, which peaked at 3 and declined between 4 and 6 months after boost. These differences may stem from species-specific immune responses or lack of vaccination protocols tailored to individual species. Conclusions: While natural infection with SARS-CoV-2 could not be ruled out in the cohort of this study, insights from our results have the potential to inform future vaccine recommendations for non-domestic species. Furthermore, our study highlighted the value of competitive assays in assessing serological responses across a broad range of exotic species, for which reagents, such as anti-isotype antibodies, are often unavailable. Full article

A gypsum karst sinkhole at Westeregeln (north-central Germany) was filled during the Late Pleistocene, first by fluvial flooding, then by solifluctation, and finally with wind-transported loess. Pleistocene mollusks and bones of snakes, birds, micro- and macromammals, and hyena coprolites were accumulated, often mixed in gravel or sand layers with Middle Paleolithic artifacts, whereas ice wedges reach deep into the sinkhole. The high amount of small flint debris prove on-site tool production by using 99% local Saalian transported brownish-to-dark Upper Cretaceous flint, which could have been collected from the Bode River gravels near-site. Only a single quartzite and one jasper flake prove other local gravel sources or importation. A large bifacial flaked knife of layer 4 dates to the early/middle Weichselian/Wuermian (MIS 5-4), similar to two triangular handaxes in the MTA tradition and an absolutely dated woolly rhinoceros bone (50,310 + 1580/−1320 BP). A cold period of Late Pleistocene glacial mammoth steppe megafauna is represented, but the material is mostly strongly fragmented and smashed by humans. Neanderthal camp use on the gypsum hill is indicated also by small charcoal pieces, burned bone fragments, and fire-dehydrated flint fragments. Crocuta crocuta spelaea (Goldfuss) hyenas are well known from Westeregeln, with an open-air commuting den site, which was marked with feces. Full article

Animal rabies remains underreported in low-income countries, hindering effective control. This scoping review aimed to map reported animal rabies cases, identify key reservoir species, and assess gaps in surveillance coverage in mainland Tanzania from 1993 to 2023. Specifically, it addressed the distribution of cases, species involved, and the extent of surveillance coverage during this period. Literature searches in PubMed, Google Scholar, and Science Direct were screened using Rayyan. Twenty articles published between 1993 and 2023 reported 7319 animal rabies cases across the Northern Zone (NZ), Southeastern Zone (SEZ), and Coastal Zone (CZ). In the NZ, domestic dogs accounted for most cases (5387), followed by jackals (225), cats (77), livestock (311), and various wildlife species including African wild dogs, bat-eared foxes, lions, cheetahs, and striped hyenas. Additionally, 102 cases involved unidentified animals. In SEZ, domestic dogs (588) were the primary source, followed by jackals (262), hyenas (8), cats (10), honey badgers (5), and leopards (2). In CZ, domestic dogs accounted for 94 cases. The findings confirm domestic dogs as the main rabies reservoir, highlighting the need for strengthened surveillance and control. The role of wildlife in rabies maintenance and spillover remains poorly understood and warrants further investigation, especially in enzootic hotspots. Full article

Effective analysis of central bank communications is critical for anticipating monetary policy changes and guiding market expectations. However, traditional natural language processing models face significant challenges in processing lengthy and nuanced policy documents, which often exceed tens of thousands of tokens. This study addresses these challenges by proposing a novel integrated deep learning framework based on Hyena Hierarchy architectures, which utilize sub-quadratic convolution mechanisms to efficiently process ultra-long sequences. The framework employs Delta-LoRA (low-rank adaptation) for parameter-efficient fine-tuning, updating less than 1% of the total parameters without additional inference overhead. To ensure robust performance across institutions and policy cycles, domain-adversarial neural networks are incorporated to learn domain-invariant representations, and a multi-task learning approach integrates auxiliary hawkish/dovish sentiment signals. Evaluations conducted on a comprehensive dataset comprising Federal Open Market Committee statements and European Central Bank speeches from 1977 to 2024 demonstrate state-of-the-art performance, achieving over 6% improvement in macro-F1 score compared to baseline models while significantly reducing inference latency by 65%. This work offers a powerful and efficient new paradigm for handling ultra-long financial policy texts and demonstrates the effectiveness of integrating advanced sequence modeling, efficient fine-tuning, and domain adaptation techniques for extracting timely economic signals, with the aim to open new avenues for quantitative policy analysis and financial market forecasting. Full article

This paper introduces advanced computational methods for stock price prediction, integrating Fast Recurrent Neural Networks (FastRNN) with meta-heuristic algorithms such as the Horse Herd Optimization Algorithm (HOA) and the Spotted Hyena Optimizer (SHO). By challenging the Efficient Market Hypothesis (EMH) and Random Walk Hypothesis, our research demonstrates the effectiveness of these hybrid models in semi-strong or weak-form efficient markets. The study leverages data from five listed Iranian companies (2011–2021) and 25 factors encompassing technical, fundamental, and economic considerations. Our findings highlight the superior accuracy of the FastRNN optimised by HOA, SHO, and a Generative Adversarial Network (GAN) in forecasting stock prices compared to conventional FastRNN models. This research contributes to the multidisciplinary field of computational economics, emphasising advanced computing capabilities to address complex economic problems through innovative econometrics, optimisation, and machine learning approaches. Full article

Spotted hyenas live in fission–fusion social societies, requiring them to adopt a flexible multimodal communication system across variable spatial scales. However, researchers have extensively studied acoustic and olfactory signals for conspecific communication compared to visual signals, especially in wild populations. Here, we reviewed 46 articles on the Web of Science on social communication in wild and captive spotted hyena populations to synthesize our collective knowledge of the extent to which spotted hyenas utilize sensory cues to communicate and how flexible they are between captive and wild populations. Across all articles, 54% focused on acoustic communication (n = 25), 33% on olfaction (n = 15), leaving only 13% on vision (n = 6). Most of this research studied wild populations (82%; n = 38), leaving an intriguing gap in our knowledge of captive populations and their potential for developing behavioral innovations due to their robust social cognition (i.e., modifying behavioral form and/or function observed in wild populations to better accommodate the captive performer’s environment and social needs). Improving our understanding of innovation development in this species has possible benefits for studying behavioral evolution and improving captive welfare (e.g., identifying normal vs. stereotypic behavior) in this social carnivore. Full article

Paleontologists and archeologists reconstruct ancient ecosystems using data from carnivores’ food remains. Carnivores have evolved to employ two primary feeding strategies: consuming mostly meat and focusing on both meat and bones, and these strategies result in the production of different feces. Hyenas are exemplary meat-eaters and bone-crushers. While fecal characteristics like shape, color, size, and inclusions are often used for species identification, the detailed composition of hyena feces remains largely unexplored. To address this, we conducted a multimodal analysis of feces-like coprolites from four modern Hyaenid species, using scanning electron microscopy (SEM) and Fourier transform infrared spectrometry (FTIR). This approach allowed for the detection and quantification of the proportions of calcium phosphate/carbonate, silts, organic matter, and crystallinity in the coprolites. Our preliminary findings suggest that multivariate statistical analysis of these components could provide a reliable method for species identification based solely on fecal content, results which can be applied in research on fossil materials. Full article

In ecosystems where multiple carnivores coexist, interspecific interactions are crucial in shaping behavioral adaptations and resource utilization strategies. This study examines the competitive dynamics between Indian leopards (Panthera pardus fusca) and striped hyenas (Hyaena hyaena) in the Jhalana Reserve Forest, an urban-enclosed wildlife habitat in Jaipur, India. Using direct observations, citizen science contributions, and camera-trap data, we document kleptoparasitism and competitive exclusion instances where hyenas successfully outnumber leopards to gain access to food. Our findings indicate that hyenas exhibit an acute ability to locate leopard kills, often arriving within minutes of a leopard beginning to feed. Additionally, spatial constraints imposed by the reserve’s fencing create an ecological imbalance, as leopards can access external food sources while hyenas remain confined. We also discuss the potential consequences of supplementary feeding practices, which may influence predator behavior and interspecific interactions. This study highlights the importance of understanding competitive dynamics in fragmented landscapes to inform conservation strategies that promote coexistence. Full article

Feature engineering for whole-genome DNA sequences plays a critical role in predicting plant phenotypic traits. However, due to limitations in the models’ analytical capabilities and computational resources, the existing methods are predominantly confined to SNP-based approaches, which typically extract genetic variation sites for dimensionality reduction before feature extraction. These methods not only suffer from incomplete locus coverage and insufficient genetic information but also overlook the relationships between nucleotides, thereby restricting the accuracy of phenotypic trait prediction. Inspired by the parallels between gene sequences and natural language, the emergence of large language models (LLMs) offers novel approaches for addressing the challenge of constructing genome-wide feature representations with nucleotide granularity. This study proposes FE-WDNA, a whole-genome DNA sequence feature engineering method, using HyenaDNA to fine-tune it on whole-genome data from 1000 soybean samples. We thus provide deep insights into the contextual and long-range dependencies among nucleotide sites to derive comprehensive genome-wide feature vectors. We further evaluated the application of FE-WDNA in agronomic trait prediction, examining factors such as the context window length of the DNA input, feature vector dimensions, and trait prediction methods, achieving significant improvements compared to the existing SNP-based approaches. FE-WDNA provides a mode of high-quality DNA sequence feature engineering at nucleotide resolution, which can be transformed to other plants and directly applied to various computational breeding tasks. Full article