Search Results (4,488)

Cathepsins represent a crucial group of protein enzymes involved in insect metabolism. Within the Hemiptera order, comprising a diverse array of predatory, blood-feeding, and herbivorous species, the understanding of cathepsin types and their roles as venom components in predatory bugs remains limited. This investigation systematically identified cathepsin genes present in Hemiptera genomes, highlighting a prevalence of cathepsin B and L, with cathepsin D exhibiting a higher gene count in the Heteroptera suborder. Examining the predatory assassin bug Sycanus bifidus, eight cathepsin genes were notably expressed in its venom glands, with the SbCAB2 gene from the cathepsin B subfamily demonstrating the highest expression in the posterior main gland, indicating its significance as a venom component. Subsequent expression and purification of the recombinant SbCAB2 protein revealed heightened hydrolytic activity (0.91 U/mg protein) compared to extracts from the anterior main gland, accessory gland, and gut. Functional assays demonstrated that SbCAB2, at lower doses (0.625–2.5 μg), can impede phenoloxidase activity in Tenebrio molitor pupal hemolymph, with a 2.5 μg dose inhibiting 86.5% of this activity, thereby preventing hemolymph melanization. Conversely, a higher dose of 10 μg led to effects akin to human placental cathepsin B, promoting melanization in T. molitor pupal hemolymph. These findings lay the foundation for further exploration of the adaptive evolution of cathepsin genes in Hemiptera and offer crucial insights into the functional role of venomous cathepsins in predatory bugs. Full article

High-resolution meteorological satellite cloud imagery plays a crucial role in diagnosing and forecasting severe convective weather phenomena characterized by suddenness and locality, such as tropical cyclones. However, constrained by imaging principles and various internal/external interferences during satellite data acquisition, current satellite imagery often fails to meet the spatiotemporal resolution requirements for fine-scale monitoring of these weather systems. Particularly for real-time tracking of tropical cyclone genesis-evolution dynamics and capturing detailed cloud structure variations within cyclone cores, existing spatial resolutions remain insufficient. Therefore, developing super-resolution techniques for meteorological satellite cloud imagery through software-based approaches holds significant application potential. This paper proposes a Multi-scale Residual Deformable Attention Model (MRDAM) based on Generative Adversarial Networks (GANs), specifically designed for satellite cloud image super-resolution tasks considering their morphological diversity and non-rigid deformation characteristics. The generator architecture incorporates two key components: a Multi-scale Feature Progressive Fusion Module (MFPFM), which enhances texture detail preservation and spectral consistency in reconstructed images, and a Deformable Attention Additive Fusion Module (DAAFM), which captures irregular cloud pattern features through adaptive spatial-attention mechanisms. Comparative experiments against multiple GAN-based super-resolution baselines demonstrate that MRDAM achieves superior performance in both objective evaluation metrics (PSNR/SSIM) and subjective visual quality, proving its superior performance for satellite cloud image super-resolution tasks. Full article

The Yangtze River fishing ban policy is a central measure in China’s watershed governance, and the adaptability of its policy tools and collaborative mechanisms directly influences the sustainability and effectiveness of basin management. This study systematically examines the evolution of policy themes, the characteristics of policy tool combinations, and their alignment with intergovernmental collaborative governance needs, drawing on 120 central government policy texts issued between 2002 and 2024. Using frequency analysis and policy tool coding, the findings reveal that (1) policy themes have shifted from fishery resource control to comprehensive ecological protection and, more recently, to integrated watershed management, thereby driving progressively higher demands for intergovernmental collaboration. (2) The policy tool structure has long been dominated by environmental tools, supplemented by supply-side tools, while demand-side tools remain underdeveloped. Imbalances persist, such as excessive emphasis on resource inputs over capacity building in supply-side tools, rigid constraints with limited flexibility in environmental tools, and a reliance on publicity while underutilizing market incentives in demand-side tools. (3) Tool combinations have adapted to changing collaboration needs, evolving from rigid constraints and fiscal subsidies to institutional frameworks and cross-regional cooperation, ultimately forming a governance model characterized by systemic guarantees and diversified collaboration. Based on these findings, this study recommends strengthening long-term governance mechanisms, improving cross-regional collaborative structures, authorizing local governments to design context-specific implementation details, enhancing fishermen’s livelihood security and social development, expanding public participation and oversight, and exploring market mechanisms for realizing ecological product value. These measures aim to advance collaborative governance in the Yangtze River Basin and foster a balanced integration of ecological protection and social development. Full article

In response to the issues of premature convergence and insufficient parameter control in Particle Swarm Optimization (PSO) for high-dimensional complex optimization problems, this paper proposes a Multi-Strategy Topological Particle Swarm Optimization algorithm (MSTPSO). The method builds upon a reinforcement learning-driven topological switching framework, where Q-learning dynamically selects among fully informed topology, small-world topology, and exemplar-set topology to achieve an adaptive balance between global exploration and local exploitation. Furthermore, the algorithm integrates differential evolution perturbations and a global optimal restart strategy based on stagnation detection, together with a dual-layer experience replay mechanism to enhance population diversity at multiple levels and strengthen the ability to escape local optima. Experimental results on 29 CEC2017 benchmark functions, compared against various PSO variants and other advanced evolutionary algorithms, show that MSTPSO achieves superior fitness performance and exhibits stronger stability on high-dimensional and complex functions. Ablation studies further validate the critical contribution of the Q-learning-based multi-topology control and stagnation detection mechanisms to performance improvement. Overall, MSTPSO demonstrates significant advantages in convergence accuracy and global search capability. Full article

Background: The interleukin-10 (IL-10) signaling system, comprising ligands (IL-10s) and receptors (IL-10Rs), plays critical roles in immune regulation, inflammation resolution, and disease pathogenesis. “IL-10 signaling system” here refers to the immunomodulatory signaling system composed of ligands (IL-10s) and receptors (IL-10Rs), which belong to different Protein families in evolution, but achieve functional synergy through the conserved JAK-STAT pathway. Understanding their evolutionary and functional dynamics is essential for elucidating immune mechanisms and therapeutic targeting. Methods: Through phylogenetic reconstruction, homology analysis, and sequence alignment across >400 animal species, we traced the evolutionary trajectory and structural–functional diversification of IL-10s and IL-10Rs. Results and Conclusions: IL-10 signaling components emerged in early vertebrates, with IL-10Rs originating in cartilaginous fishes (~450 Mya) and IL-10s diversifying in bony fishes (~400 Mya). Functional divergence yielded immunosuppressive (IL-10), barrier-protective (IL-20 subfamily), and antiviral (type III IFN) subgroups. Structurally, conserved motifs (e.g., IL-10R1 GYXXQ, IL-22 N54-glycosylation) underpin receptor–ligand binding and JAK/STAT signaling. Evolutionarily invariant residues suggest candidate therapeutic epitopes. This study provides an evolutionary framework highlighting functional conservation and species-specific adaptation within IL-10 signaling, with implications for immunotherapy and animal breeding. Full article

This study systematically examined shock wave velocity induced by millisecond–nanosecond combined-pulse laser (ms–ns CPL) at a fixed ns laser energy density of 6 J/cm², exploring the effects of varying pulse delays of 0 to 3 ms and ms laser energy densities of 226.13 J/cm², 301 J/cm² and 376.89 J/cm². The temporal evolution of shock wave velocity induced by varying laser parameters was predicted by an attention mechanism-based long short-term memory algorithm (Attention-LSTM). The dependence between laser parameters and the evolution of shock wave velocity was captured by the LSTM layer. An attention mechanism was utilized to adaptively increase the weights of important time points during the propagation of the shock wave, thereby improving prediction accuracy. The experimental data corresponding to ms laser energy densities of 226.13 J/cm² and 301 J/cm² were set as the training set. The ms laser energy density of 376.89 J/cm² experimental data was set as test set to evaluate the generalization ability of the model under unknown ms laser energy. The results indicate that when ms laser energy density is 376.8 J/cm², the pulse delay is 2.2 ms. The shock wave velocity induced by the CPL increased by 50.77% compared with that induced by a single ns laser. The proposed Attention-LSTM model effectively predicts the evolutionary characteristics of shock wave velocity. The mean absolute error (MAE), root mean square error (RMSE), mean bias error (MBE) and the correlation coefficient (R²) of the test set are 7.65, 9.01, 1.47 and 0.98, respectively. This study provides a new data-driven approach for predicting the shock wave behavior induced by combined laser parameters and provides valuable guidance for optimizing laser process parameter combinations. Full article

Plasmodium parasites are the causative agents of malaria and can infect humans and other vertebrates, impacting socioeconomic development and causing significant health issues globally. Plasmodium falciparum causes the most severe type of infection, which can lead to chronic morbidity and other severe complications like anemia and cerebral malaria. The onset of infection is marked by the injection of sporozoites into the skin through the bite of a female Anopheles mosquito. This triggers a cascade of reactions elicited both by the host immune system in response to infection and by the parasite in a bid to evade the host immune system, survive, and replicate. The dynamics of this host–parasite relationship have prompted extensive research in an attempt to understand and exploit it in the fight against malaria. Thus, understanding the temporal and spatial dimensions of adaptation in host–parasite relationships is critical for forecasting parasite evolution and spread within and between host populations. One such relationship is the complex interplay between malaria and cellular aging processes. Understanding this dynamic will provide novel insights into the pathophysiology of the disease. This comprehensive review takes us on that journey by providing an overview of the interaction between the Plasmodium parasite and its host and the interplay between infection mechanisms, host immune response, and parasite evasion strategies, narrowing it down to how it affects cellular aging biomarkers and how this can be explored as a platform in the fight against the disease. Full article

With an emphasis on the adaptation and mediation of Buddhist meditation within Western societies, this study explores the transformative interaction of traditional contemplative practices and modern technologies. By means of an extensive ethnographic investigation carried out in multiple European locations, this study sheds light on the significant influence that digital devices—specifically, smartphone applications—have on the accessibility, practice, and conception of meditation. These digital tools become guides that not only democratize access to meditation but also fundamentally change its nature, making it more individualized, commodified, and integrated into the field of self-care and therapeutic modalities from a deeply philosophical and communal practice. This inquiry critically looks at the two outcomes of this shift: the good that meditation is now more widely available and the bad that it is losing its conventional discipline and philosophical profundity. This paper raises concerns about the integrity of spiritual practices in the digital age and their evolution under the influence of Western epistemologies by arguing that the digital facilitation of meditation practices parallels broader societal tendencies towards personalization and digitization. Full article

We propose State-DynAttn, a hybrid architecture for robust air traffic flow prediction under weather disruptions, which integrates state-space models (SSMs) with dynamic graph attention to address the challenges of long-range dependency modeling and adaptive spatial–temporal relationship learning. The increasing complexity of air traffic systems, exacerbated by unpredictable weather events, demands methods that can simultaneously capture global temporal patterns and localized disruptions; existing approaches often struggle to balance these requirements efficiently. The proposed method employs two parallel branches: an SSM branch for continuous-time recurrent modeling of long-range dependencies with linear complexity, and a dynamic graph attention branch that adaptively computes node-pair weights while incorporating weather severity features through sparsification strategies for scalability. These branches are fused via a data-dependent gating mechanism, enabling the model to dynamically prioritize either global temporal dynamics or localized spatial interactions based on input conditions. Moreover, the architecture leverages memory-efficient attention computation and HiPPO initialization to ensure stable training and inference. Experiments on real-world air traffic datasets demonstrate that State-DynAttn outperforms existing baselines in prediction accuracy and robustness, particularly under severe weather scenarios. The framework’s ability to handle both gradual traffic evolution and abrupt disruption-induced changes makes it suitable for real-world deployment in air traffic management systems. Furthermore, the design principles of State-DynAttn can be extended to other spatiotemporal prediction tasks where long-range dependencies and dynamic relational structures coexist. This work contributes a principled approach to hybridizing state-space models with graph-based attention, offering insights into the trade-offs between computational efficiency and modeling flexibility in complex dynamical systems. Full article

Background: Reconstructing gene regulatory networks (GRNs) from gene expression data remains a major challenge in systems biology due to the inherent complexity of biological systems and the limitations of existing reconstruction methods, which often yield high false-positive rates. This study aims to develop a robust and adaptive approach to enhance the accuracy of inferred GRNs by integrating multiple modelling paradigms. Methods: We introduce EvoFuzzy, a novel algorithm that integrates evolutionary computation and fuzzy logic to aggregate GRNs reconstructed using Boolean, regression, and fuzzy modelling techniques. The algorithm initializes an equal number of individuals from each modelling method to form a diverse population, which evolves through fuzzy trigonometric differential evolution. Gene expression values are predicted using a fuzzy logic-based predictor with confidence levels, and a fitness function is applied to identify the optimal consensus network. Results: The proposed method was evaluated using simulated benchmark datasets and a real-world SOS gene repair dataset. Experimental results demonstrated that EvoFuzzy consistently outperformed existing state-of-the-art GRN reconstruction methods in terms of accuracy and robustness. Conclusions: The fuzzy trigonometric differential evolution approach plays a pivotal role in refining and aggregating multiple network outputs into a single, optimal consensus network, making EvoFuzzy a powerful and reliable framework for reconstructing biologically meaningful gene regulatory networks. Full article

In the Chondrichthyes Raja asterias and Myliobatis aquila and in the Teleost Sparus aurata, the appendicular skeleton of the pectoral fins (including the calcified structures of the mouth in M. aquila) was investigated to find out how the specific skeletal segments were formed and stiffened over the course of evolution, not only with regard to the adaptation of the ontogenesis of the cartilage “anlagen” to the mechanical requirements of locomotion in the water column, but also to the specific feeding habits (durophagy) of M. aquila. The morphology of the pectoral fins of the three species showed a different layout, characterized by the geometry of the basic units (aligned tesserae and calcified radial columns), which provide varied flexibility of the pectoral fins, suggesting an adaptation to the “pelagic” and “benthic” locomotion patterns in the environment where the species live. The morphology of the calcified structures in the mouth of M. aquila showed the presence of two different masticatory systems: the first (external) with the rows of teeth resting on the maxillary and mandibular arches, and the second (internal, in the oral cavity) with the symphyseal plates specialized for durophagy. Chemical–physical analyses revealed that the calcified cartilage matrix of the Chondrichthyes fin rays, teeth and durophagy plates is stiffened by the same Ca₃(PO₄)₂ mineral phase deposed in the organic matrix of the Teleost S. aurata fins (with the characteristic SEM morphological texture of calcified bone matrix). The hitherto unknown presence of two different chewing systems in M. aquila documents an evolutionary adaptation to nutritional requirements that can be explained by two hypotheses: the coexistence of two functioning systems in current specimens, allowing for the ingestion of harder and softer prey (or plant food), or the persistence of a rudimentary dentition that is no longer used (vestigial dentition). Furthermore, the texture of the calcified matrix in teleost fishes, as observed by scanning electron microscopy, may indicate a bone-like organic matrix substrate, similar to that found in endochondral ossification. Full article

Snakes are model animals to study energy balance, but studies on the gut microbiota of the animals are rather scarce. To fill the gap, we used metagenome sequencing to investigate the microbial community composition and adaptability in the stomach, small intestine, and large intestine of Big-eyed Bamboo Snake. The results showed that there was no significant differences in α-diversity among different gastrointestinal segments. Pseudomonadota, Bacteroidota, and Bacillota were the most abundant phyla. The dominant genera in the stomach and small intestine were similar, while those in the large intestine were distinct. The abundance of Bacteroides, Citrobacter and Clostridium was significantly higher in the large intestine than in the small intestine. The LEfSe analysis revealed that the small intestine had the most characteristic bacteria, with a total of 20 species, while the stomach and large intestine each had two species. Additionally, in the current study, we also focused on the impact of the microbial community structure on functions through functional annotations in the KEGG and CAZy. There were significant differences in the KEGG level 2 between the stomach and the small intestine. The LEfSe analysis revealed the differences in the CAZy level 2 between the large intestine and the small intestine. Overall, our study provided a comparative and contrastive analysis of the gut microbiota in different gastrointestinal segments of Big-eyed Bamboo Snake, offering valuable insights for the co-evolution mechanism of the host and the gut microbiota. Full article

Alternative splicing (AS) is a crucial post-transcriptional regulatory mechanism that enhances transcriptomic and proteomic diversity by generating multiple mRNA isoforms from a single gene. In plants, AS plays a central role in modulating growth, development, and stress responses. We summarize the prevalence and functional roles of AS in plant development and stress adaptation, highlighting mechanisms that link AS to hormone signaling, RNA surveillance, and epigenetic regulation. Polyploid crops, with their duplicated genomes, exhibit expanded AS complexity, contributing to phenotypic plasticity, stress tolerance, and adaptive evolution. Thus, this review synthesizes current knowledge on AS in plants, with a focus on three economically important polyploid crops—Brassica napus, Gossypium hirsutum, and Triticum aestivum. We also discuss how subgenome interactions shape diversity in polyploids and influence trait variation. Despite significant advances enabled by high-throughput sequencing, mechanistic studies that directly link specific AS events to phenotypic outcomes remain limited. Understanding how polyploidy reprograms AS and how isoform variation contributes to stress adaptation will be critical for harnessing AS in crop improvement. Full article

Xylophagous insects represent a diverse group of species whose life cycles are trophically associated with wood at various stages of decomposition. In forest ecosystems, they play a pivotal role in wood degradation and biogeochemical nutrient cycling. Their remarkable adaptation to feeding on structurally complex and nutrient-poor woody substrates has been largely mediated by long-term symbiotic interactions with gut microbiota. This review synthesizes current knowledge on the molecular and ecological mechanisms underlying insect–microbiota interactions, with particular attention paid to the impact of environmental stressors—including elevated temperature, shifts in moisture regimes, and pollution—on microbial community structure and host adaptive responses. We critically evaluate the strength of evidence linking climate-driven microbiome shifts to functional consequences for the host and the ecosystem. The ecological implications of microbiota restructuring, such as impaired wood decomposition, decreased disease resistance, facilitation of xylophagous species spread, and alterations in key biotic interactions within forest biocenoses, are discussed. Particular emphasis is placed on the integration of multi-omics technologies and functional assays for a deeper, mechanistic understanding of microbiota roles. We also assess the potential and limitations of microbiome-based approaches for insect population management, with the overall goal of maintaining and enhancing the resilience of forest ecosystems under ongoing climate change. Full article

Microwave devices based on microstrip resonators are widely used today in communication, radar, and navigation systems. The requirements to these devices may include specific frequency-selective properties, as well as size and production costs. The design of resonators and filters are mostly performed manually, as the process requires expert knowledge and computationally expensive modeling, so practitioners are usually limited to tuning a chosen example from a set of known, typical topologies. However, the set of possible topologies remains unexplored and may contain specific constructions, which have not been discovered yet. In this study we propose an approach to automatically search the space multimode resonator topologies using a zero-order optimization algorithm and numerous computational experiments. In particular, a family of symmetrical resonators constructed out of four rectangles is considered, and the parameters are tuned by the recently proposed L-SRTDE algorithm. We state the problem of building the topology of a microwave device conductor with specified frequency-selective characteristics as an optimization problem, and the minimized function (target function) in this problem is based on the evaluation of the deviation between the specified frequency-selective characteristics and their values obtained via electrodynamic modeling. The experiments with two target function formulations have shown that the proposed approach allows finding novel topologies and automatically tune them according to the required frequency-selective properties. It is shown that some of the topologies are different from the known ones but still demonstrate high-quality properties. Full article