Search Results (2,361)

Hosts generating unsolicited network traffic increasingly operate in a coordinated manner rather than in isolation. Scanning and exploitation activities are often distributed across multiple hosts that share common infrastructure, toolchains, and behavioral patterns, forming loosely coupled yet persistently aligned sender groups. Accurately attributing such groups is critical for understanding organized activities and strengthening network defense capabilities. However, existing attribution approaches face notable limitations. Methods that rely on threat intelligence suffer from delayed updates and limited coverage. Static feature-based approaches ignore temporal ordering and therefore fail to capture multi-stage behavioral evolution. Although dynamic sequence models incorporate temporal patterns, they typically overlook the collaborative structural relationships among coordinated senders. In this paper, we propose SentinelGraph, a temporal graph reasoning framework for sender group attribution from honeypot traffic. SentinelGraph constructs a temporal knowledge graph and integrates a recurrent graph evolution module to jointly model coordination structures and their temporal dynamics. A structure enhancement module further exploits contextual information available at the target time, while an auxiliary relation loss encourages the learning of enriched entity representations. This design enables accurate attribution even for previously unseen senders by leveraging information from their observed neighbors. Experiments on real-world honeypot data demonstrate that SentinelGraph substantially outperforms state-of-the-art methods in modeling coordinated network behaviors. Full article

Unmanned aerial vehicle (UAV) imagery poses persistent challenges for object detection, including dense small objects, large-scale variation, cluttered backgrounds, and stringent localization requirements, where conventional two-stage detectors often fall short in fine-grained small-object representation, efficient global context modeling, and classification–localization consistency. We specifically target low-altitude UAV-captured imagery with highly flexible viewpoints (near-nadir to oblique) and frequent platform-induced motion blur, which makes dense small-object localization substantially more challenging than in conventional remote-sensing imagery. To address these issues, we propose CASA-RCNN, a context-adaptive and scale-aware two-stage detection framework tailored to UAV scenarios. CASA-RCNN introduces a shallow-level enhancement module, ConvSwinMerge, which strengthens position-sensitive cues and suppresses background interference by combining coordinate attention with channel excitation, thereby improving discriminative high-resolution features for small objects. For deeper semantic features, we incorporate an adaptive sequence modeling module based on MambaBlock to capture long-range dependencies and support context reasoning in crowded or occluded scenes with practical computational overheadon a desktop GPU. In addition, we adopt Varifocal Loss for quality-aware classification to better align confidence scores with localization quality, and we design a ScaleAdaptiveLoss to dynamically reweight regression objectives across object scales, compensating for the reduced gradient contribution of small targets during training. Experiments on the VisDrone2021 validation benchmark show that CASA-RCNN achieves 22.9% mAP, improving Faster R-CNN by 9.0 points; it also reaches 36.6% mAP${}_{50}$ and 25.7% mAP${}_{75}$. Notably, performance on small objects improves to 12.5% mAP${}_s$ (from 6.9%), and ablation studies confirm the effectiveness and complementarity of the proposed components. Full article

We propose Reinforcement Learning Contrastive Optimization (RLCO), a novel quadruped robot locomotion control framework that synergistically integrates contrastive learning with reinforcement learning. This framework addresses two critical limitations of existing reinforcement learning methods in quadruped motion control: low sample efficiency and insufficient stability in action sequences. To meet the temporal coherence requirements of motion policies in complex environments, we develop a history–prediction action alignment mechanism through contrastive learning. This approach ensures that an action sequence is consistent over time. It does this by reducing the difference between past actions and predicted actions. This approach greatly enhances the stability and reliability of motion control. The proposed co-optimization mechanism preserves reinforcement learning’s exploration capability for complex tasks while improving the physical plausibility and predictability of action sequences. Experimental results demonstrate that our method achieves notable improvements in motion control precision and environmental adaptability in unstructured terrains. Through comparative analysis of different training strategies, we systematically validate the effectiveness of the RLCO framework. Field tests in outdoor environments with stairs, slopes, and grassy terrain confirm the robot’s capabilities. The quadruped robot rapidly adapts to diverse ground conditions. Full article

Peptide-based vaccines offer a modular and readily manufacturable platform for both prophylactic and therapeutic immunization. However, their broader translation has been constrained by the limited capacity to predict protective immunity directly from sequence-level features. Recent advances in systems vaccinology and high-throughput immune profiling have substantially expanded the experimental evidence, while generative artificial intelligence now enables de novo design of peptide immunogens and multi-epitope antigens under precisely controlled constraints. This review approaches how these complementary developments are transforming peptide vaccine research, moving beyond classical reverse vaccinology and conventional epitope prediction toward integrated, data-driven design frameworks. We discuss key generative model architectures and conditioning strategies aligned with vaccine objectives, including approaches that account for structural presentation, antigen processing and population-level human leukocyte antigen (HLA) diversity. Central to this perspective is the requirement for rigorous experimental validation and for strengthening the computational–experimental feedback loop through iterative in vitro and in vivo testing informed by systems-level immune readouts. We highlight representative applications spanning infectious diseases, cancer immunotherapy and vector-borne vaccinology, and we outline major technical and translational challenges that must be addressed to enable robust real-world deployment. Finally, we propose future directions for precision peptide vaccinology, emphasizing standardized functional benchmarks, the development of richer curated datasets linking sequence space to immune outcomes, and the early incorporation of formulation and delivery constraints into generative design pipelines. Full article

Background: Poultry-associated Enterococcus spp. are widespread commensals but may serve as One Health indicators when virulence-associated determinants and antimicrobial resistance co-occur. We characterized paired phenotypic and genomic profiles to delineate species-stratified virulome and resistome patterns. Methods: Isolates originated from a previously established poultry collection with MIC testing. Genotype–phenotype analyses were restricted to the whole-genome sequenced subset (n = 31). The acquired antimicrobial resistance genes were identified using the Comprehensive Antibiotic Resistance Database (CARD), and virulence-associated determinants were screened using the Virulence Factors Database (VFDB). Results were summarized as isolate-level presence/absence matrices and integrated with MIC-derived susceptible/intermediate/resistant categories. Results: The WGS subset comprised E. faecalis (n = 23) and E. faecium (n = 8) with diverse sequence types. Virulome architecture was strongly species-dependent: E. faecalis carried a broad repertoire of adhesion/biofilm-associated determinants, whereas E. faecium showed a limited set of high-confidence virulence-associated hits. Acquired resistance determinants were common across isolates, and resistome profiles displayed structured co-occurrence. Integrated analyses suggested only a modest overall association between virulence-gene burden and acquired resistome size, largely driven by species-level differences. Genotype–phenotype concordance was class-dependent, with incomplete alignment in several antimicrobial classes, consistent with mechanisms beyond the screened acquired gene set. The acquired resistance determinants detected in the WGS subset predominantly mapped to antimicrobial classes commonly used in food-producing animals (e.g., tetracyclines, macrolides, lincosamides, aminoglycosides, and phenicols), supporting interpretation in the context of production-associated antimicrobial selection rather than implying last-line clinical resistance by default. Conclusions: Poultry-derived enterococci may combine genetic features compatible with persistence/colonization and acquired antimicrobial resistance, with co-occurrence patterns shaped primarily by species/lineage background. These findings support risk-stratified One Health surveillance and targeted functional and mechanism-focused follow-up. This integrated virulome–resistome view highlights species-specific risk signatures in poultry-associated Enterococcus and identifies discordant high-level phenotypes that merit targeted mechanistic follow-up. Full article

Viral diseases pose a serious threat to pumpkin cultivation, which is an important cucurbitaceous vegetable crop. Recently, multi-virus mixed infections in plants have been continuously detected and reported. However, studies on mixed virus infections in pumpkins are limited. Through metavirome and polymerase chain reaction (PCR) analysis, we found that pumpkins exhibiting severe viral symptoms were co-infected with squash leaf curl China virus and tomato leaf curl New Delhi virus. Transcriptome analysis revealed that 2927 genes were upregulated, and 2273 were downregulated in virus-infected pumpkin plants, compared to the gene expression in healthy pumpkin plants. Cluster analysis showed that the expression of genes related to RNA silencing and the salicylic acid resistance pathway was higher in virus-infected pumpkin plants than in healthy pumpkin plants. Furthermore, quantitative real-time PCR confirmed that the expression pattern of genes related to RNA silencing and the salicylic acid resistance pathway aligned with the transcriptome sequencing results. Our findings provide a reference for investigating the mechanism of mixed infections by these two viruses to aid in the prevention and control of viral diseases in pumpkins. Full article

Monitoring urban subsidence over ultra-long periods using time-series Interferometric synthetic aperture radar (InSAR) technology is critically important. Conventional approaches, however, face two main limitations: significant atmospheric phase residuals in complex urban settings, and discontinuous temporal time-series with short temporal coverage due to single-platform data constraints. To address these limitations, this study presents a new method for estimating ultra-long-term subsidence time series in urban areas, which combines Interferometric Subset Stacking (ISS) with multi-platform data fusion (DF). The methodology firstly processes TerraSAR-X and Sentinel-1A datasets through differential interferometry and applies ISS for atmospheric phase suppression. Next, bilinear interpolation unifies the spatial resolution and aligns the spatial reference frames of the two datasets. Subsequently, joint modeling derives subsidence velocities. Finally, temporal integration via linear interpolation and moving averaging produces a unified spatio-temporal deformation sequence. Applied to the Beijing region, China, this approach generated a 12-year ultra-long-term subsidence time series result (2012–2024), revealing maximum cumulative subsidence of 1100 mm spatially correlated with groundwater extraction patterns. Validation against Global Navigation Satellite System (GNSS) data showed strong agreement (correlation coefficient: 0.94, Root Mean Square Error (RMSE): 6.3 mm). The method achieved substantial atmospheric reduction—67.7% for Sentinel-1A and 24.1% for TerraSAR-X—representing approximately 15–20% accuracy improvement over conventional Generic Atmospheric Correction Online Service (GACOS) for InSAR. By effectively utilizing multi-platform data, this approach makes fuller use of the available phase information and compensates for the temporal gaps inherent in single-satellite datasets. It thus offers a valuable framework for long-term urban deformation monitoring. Full article

Background: Ankylosis of the temporomandibular joint (TMJ) is a rare developmental disorder that involves fibrous or bony fusion within the joint. It is a severe structural and functional disorder. Typically, the phenotype manifests as joint immobilization and results in facial deformity and trismus. To date, ankylosis is rarely diagnosed as congenital and its occurrence mechanism has not been thoroughly understood. We observed a female patient who as a newborn showed slight facial asymmetry and impaired mandibular retraction. In addition, non-uniform occlusal fissures were noted; the lower part of the left earlobe was slightly smaller than the right earlobe. The aim of the work was the identification of pathogenic variants in the genome related to ankylosis. Ankylosis has no known causative gene yet; thus, Whole Exome Sequencing (WES) was performed. Materials and Methods: We observed a female patient with facial asymmetry and impaired mandibular retraction from birth. No phenotypic abnormalities were noted on the head or elsewhere on the body. A diagnostic computed tomography (CT) scan of the head performed at five months of age led to the diagnosis of congenital zygomatic-coronoid ankylosis. Genomic DNA samples were subjected to WES. Library preparation was carried out using the Twist Library Preparation EF Kit 2.0, followed by target enrichment with the Twist Exome 2.0 Plus Comprehensive Exome. Sequencing reads were aligned to the human reference genome (GRCh38), and variant calling was performed using standard bioinformatics workflows. Variants were subsequently filtered, annotated, and interpreted using VariantStudio. Assessment of variant pathogenicity was primarily based on comparisons with public databases, including ClinVar and VarSome, and was supported by in silico prediction tools such as SIFT and PolyPhen-2. Results: In genes responsible for disorders of the I and II pharyngeal arches, three pathogenic variants were identified: in the genes TCOF1 and POLR1B, responsible for the development of Treacher Collins syndrome (TCS), and one in the DHODH gene, responsible for Miller syndrome. Additionally, in genes that have not been linked so far with rare facial disorders, 42 variants were identified, of which 8 are listed as pathogenic. We present the first described patient with congenital ankylosis, who, although showing no phenotypic features of these syndromes, has identified pathogenic variants in genes responsible for craniofacial dysostosis. Conclusions: Variants in TCOF1, POLR1B and DHODH may represent candidate genetic factors associated with susceptibility to ankylosis. WES analysis is an appropriate method in the case of patients with congenital diseases with unknown genetic origin. In this study we provide a comprehensive list of all identified pathogenic variants. This might be useful for scientists searching for the genetic background of skeletal system issues, one of which could be bone and fibrous tissue remodeling. Full article

Oudemansiella raphanipes is a prized edible mushroom renowned for its “three-high, one-low” nutritional profile (high protein, fiber, vitamins; low fat). However, the stage-specific molecular dynamics governing its development and their potential link to its superior nutrition remain unknown, hindering targeted genetic improvement. This study aimed to decipher the first comprehensive transcriptomic atlas across its five key developmental stages and to explore potential molecular signatures linked to its distinctive nutrition. We first confirmed the superior nutritional profile of O. raphanipes via comparative analysis with nine commercial mushrooms. RNA sequencing (RNA-seq) was performed on samples from five defined developmental stages (spores, mycelia, primordia, closed-cap and open-cap fruiting bodies), followed by de novo transcriptome assembly, functional annotation, and differential expression analysis. Results revealed extensive transcriptional reprogramming, with the most dramatic changes occurring at the spore-to-mycelium transition (19,827 differentially expressed genes). Stage-specific pathway enrichment highlighted regulators of germination (e.g., ribosome, transmembrane transport), primordium formation (e.g., glycerophospholipid metabolism, GTPase signaling), fruiting body development (e.g., starch/sucrose metabolism, terpenoid synthesis), and maturation (e.g., glycolysis, fatty acid biosynthesis, transcription factors MADS-box/bZIP). We identified 588 stage-exclusive genes in spores and 515 constitutively upregulated genes linked to energy metabolism and proteostasis. Crucially, integrating nutritional phenotypes with stage-resolved transcriptomics revealed that sustained transcriptional programs in mature fruiting bodies are associated with its nutritional excellence; e.g., upregulation of ribosomal/amino acid metabolic pathways aligns with high protein content, while active fatty acid degradation correlates with low fat levels. Our study provides the first multi-stage transcriptomic blueprint for O. raphanipes development, revealing stage-specific regulators and proposing molecular associations for its nutritional traits. This resource offers a foundational basis and candidate genetic targets for future breeding strategies aimed at enhancing agronomic and nutritional traits in this prized fungus. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

Background: The Camellia genus is widely recognized for its remarkable diversity in floral morphology and coloration, with Camellia petelotii (Merr.) Sealy being particularly notable for its rare golden-yellow flowers, which possess exceptional ornamental value. Despite its horticultural significance, the molecular mechanisms governing its flowering process remain poorly elucidated, presenting a substantial barrier to effective conservation and breeding initiatives. Methods: To address this knowledge gap, we conducted a comprehensive transcriptomic analysis, focusing on three distinct developmental stages of C. petelotii floral organs: the alabastrum stage (S1), the half-opened flower stage (S2), and the full bloom stage (S3). These samples were subjected to high-throughput sequencing using the Illumina platform. Following rigorous quality control and alignment with the reference genome, we performed transcript assembly and integrated comprehensive gene annotation data with quantitative gene expression profiles. Results: Our analysis identified 18,732 differentially expressed genes (DEGs) showing significant expression changes across developmental stages. Notably, we identified 134 DEGs as potential flowering-related genes, which were functionally associated with key pathways involved in floral regulation, including plant hormone signal transduction (e.g., AUX/IAA, ARF, SAUR, GH3, JAR4, GID1 and SOC1), starch (SS, SUS, BAM) and sucrose metabolism (HK, FrK, and GH32), circadian rhythm regulation (e.g., PIF3, ELF3, LHY, and PRR), and the Autonomous pathway. Building upon these findings, we have proposed a comprehensive model illustrating the regulatory network underlying flowering transition in C. petelotii. The reliability of the transcriptomic data was demonstrated through the validation of 11 genes using quantitative real-time PCR (qRT-PCR). Conclusions: These insights not only enhance our understanding of the molecular basis of flowering in this species but also provide a valuable theoretical framework for future genetic improvement and breeding programs of C. petelotii. Full article

A case of peritonitis in a female cynomolgus monkey (Macaca fascicularis) caused by suspected bacterial infection was found. In order to identify the pathogenic bacteria, pathological tissues from the peritoneum of the animal were collected, and a bacterial strain designated MF080196 was isolated. Morphological observations were conducted, followed by biochemical testing and sequencing of its 16S rRNA gene. Additionally, pathogenicity test and drug sensitivity analyses were performed. The findings indicated that colonies grown on blood agar medium appeared white, circular, and raised; the bacteria were identified as Gram-negative; biochemical tests aligned with the characteristics of Acinetobacter lwoffii (A. lwoffii). PCR amplification was performed using a 16S rRNA sequence measuring 1443 bp; there was a remarkable similarity of 99.8% with previously reported sequences of A. lwoffii from various sources. The isolated strain MF080196 is capable of mortality in mice; furthermore, drug susceptibility testing revealed that the isolated strain exhibited high sensitivity to 23 antibiotics, including ceftriaxone, gentamicin, and levofloxacin. The pathogen isolated from the peritoneal tissue of the dead cynomolgus monkey was identified as A. lwoffii, which demonstrated sensitivity to most antibiotics tested in this study. This research provides significant insights for preventing and treating bacterial diseases in non-human primates. Full article

Leg kicks are fundamental techniques in combat sports based on a proximal-distal sequence involving several factors that can affect mechanical efficiency and injury risk. However, there is a lack of comprehensive reviews that integrate biomechanical and epidemiological evidence on injuries in an interdisciplinary context. Background/Objectives: This narrative review synthesizes current evidence to explore the relationship between mechanical efficiency and injury risk in kick-based combat sports. Methods: The search was conducted across Web of Science and Scopus (January 2000–March 2025) or studies investigating the biomechanics and injury risk factors associated with leg kicks in Taekwondo, Karate, Muay Thai, Kickboxing, and MMA. Results: Analysis of 23 studies identified three primary technical determinants of efficiency: stance mechanics, hip rotation, and striking-surface selection. High-impact force is consistently associated with a pivoted support leg stance and proximal-to-distal coordination. However, these same mechanics create specific “load concentrations” that align with documented injury profiles: pivoted stances increase rotational stress on the support leg knee (ACL/meniscal strain), while striking-surface choice (shin vs. instep) dictates the trade-off between tibial stress and metatarsal/ankle trauma. Conclusions: This review proposes an Integrated mechanical efficiency–injury model that suggests that performance optimization and injury awareness are two sides of the same biomechanical process. Future research should fill the gaps relating to the subject’s age and gender parity, as well as direct comparisons between different sports. Full article

Trade competitiveness can coexist with structurally fragile value chains. When chain feasibility fractures from trade competitiveness, competitiveness without coherence becomes sustainability’s opposite. This paper proposes revisiting the concept of sustainability in agri-food systems, through the lens of structural coherence, understood as the alignment between trade competitiveness, export-destination diversification, and value chain capacity. The research goal is to design and operationalize a diagnostic instrument for structural coherence testing through the triangulation of constant market share analysis (CMSA), the Herfindahl–Hirschman Index (HHI), and physical structural input–output analysis (I-OA). CMSA measures two elements: demand- and competitiveness-driven export dynamics. Export patterns are further explored to verify if there are any destination-market concentration risks (HHI). I-OA closes the loop by linking trade outcomes to internal value chain capacity and efficiency. With clear upstream–downstream segmentation, the sunflower oilseed value chain of the European Union (EU) represents an empirically fertile ground, relevant in the context of the geopolitical disruptions of Black Sea trade corridors and double-cropping dynamics with food-fuel and land-use trade-offs. Focusing on Bulgaria, France, Hungary, Romania, and Spain, which collectively account for more than 85% of EU sunflower seed production, this paper benchmarks post-2013 Common Agricultural Policy (CAP) programming effects, utilized as a proxy for a period of stability, against the post-2020 window, marked by a sequence of crises. Diagnosis is facilitated through findings triangulation, enabling deriving CAP-relevant policy recommendations, aligned with country-specific binding constraints. Results show heterogeneous structurally incoherent profiles: Bulgaria suffers from growth-induced stress, France’s chain efficiency is eroded, the Hungarian chain lacks competitiveness, Romania is raw-export dependent with value-added leakage, and Spain is structurally constrained by physical limits. Policy recommendations target reorienting market-driven low value-added trade behaviors toward structurally sustainable value chain trajectories. Full article

Tick-borne diseases (TBDs) pose a significant global threat to both canine and public health, largely attributable to the extensive geographic distribution of ticks and their ability to harbor diverse pathogens. To evaluate regional risk, this study examined the developmental biology of a prevalent tick species in Xinjiang, China, and performed molecular surveillance for key pathogens in both tick vectors and canine hosts. Observations of reproductive biology revealed that Riphicephalus turanicus ticks could complete their development from egg to adult in approximately 50 days on laboratory rabbits, indicating a rapid lifecycle and high reproductive potential. Polymerase chain reaction (PCR)-based screening of 379 canine blood samples detected infection rates of 14.25% for Anaplasma spp., 2.64% for Hepatozoon spp., 21.64% for Rickettsia spp., and 21.90% for Babesia spp. Concurrently, screening of 184 tick samples revealed detection rates of 15.22% for Anaplasma ovis, 8.15% for Hepatozoon spp., and 21.74% for Rickettsia spp. Statistical analysis identified significant regional variation in pathogen prevalence across the surveyed locations. The BLASTn (BLAST: Basic Local Alignment Search Tool) alignment revealed high sequence identity (99.30–100%) with known strains of Babesia, Rickettsia, and Anaplasma circulating in Asia and Europe. confirming the presence of these pathogens in the local ecosystem and evolutionary linkage to global lineages. Collectively, these findings provide valuable epidemiological insight into the endemic nature of TBDs in Xinjiang and emphasize the importance of integrated tick management and sustained disease surveillance programs. Full article