Search Results (445)

In recent years, phylogenomic approaches have significantly deepened our understanding of moss diversity. These techniques have uncovered numerous previously overlooked species and provided greater clarity in resolving complex taxonomic relationships. In this context, the genus Rehubryum is particularly outstanding, because of its close morphological similarity to both Ulota and Atlantichella. The challenges posed by its segregation are addressed in this study, which integrates morphological and molecular data to reassess the circumscription of Rehubryum and its phylogenetic placement within the subtribe Lewinskyinae. Our results support the recognition of a new species, R. kiwi, and show that its inclusion within the genus further complicates the morphological delimitation of Rehubryum from Ulota, as both genera are distinguishable by only two consistent gametophytic characteristics: a submarginal leaf band of elongated cells, and the presence of geminate denticulations in the margins of the basal half of the leaf. Moreover, R. kiwi challenges the current morphological circumscription of Rehubryum itself, as it overlaps in key characteristics with its sister genus Atlantichella, rendering their morphological separation untenable. The striking interhemispheric disjunction between Rehubryum and Atlantichella raises new questions about long-distance dispersal and historical biogeography in mosses, despite these complexities at the generic level. Nevertheless, species-level distinctions remain well defined, especially in sporophytic traits and geographic distribution. These findings highlight the pervasive cryptic diversity within Orthotrichaceae, underscoring the need for integrative taxonomic frameworks that synthesize morphology, molecular phylogenetics, and biogeography to resolve evolutionary histories. Full article

In 2022, the Russia–Ukraine conflict has severely impacted the EU’s energy supply chain, and the EU’s natural gas import pattern has begun to reconstruct, and exploring the spatiotemporal differentiation of EU natural gas trade and its driving factors is the basis for improving the resilience of its supply chain and ensuring the stable supply of energy resources. This paper summarizes the law of the change of its import volume by using the complex network method, constructs a multi-dimensional index system such as demand, economy, and security, and uses the geographic detector model to mine the driving factors affecting the spatiotemporal evolution of natural gas imports in EU countries and propose different sustainable development paths. The results show that from 2000 to 2023, Europe’s natural gas imports generally show an upward trend, and the import structure has undergone great changes, from pipeline gas dominance to LNG diversification. After the conflict between Russia and Ukraine, the number of import source countries has increased, the market network has become looser, France has become the core hub of the EU natural gas market, the importance of Russia has declined rapidly, and the status of countries in the United States, North Africa, and the Middle East has increased rapidly; natural gas consumption is the leading factor in the spatiotemporal differentiation of EU natural gas imports, and the influence of import distance and geopolitical risk is gradually expanding, and the proportion of energy consumption is significantly higher than that of other factors in the interaction with other factors. Combined with the driving factors, three different evolutionary directions of natural gas imports in EU countries are identified, and energy security paths such as improving supply chain control capabilities, ensuring export stability, and using location advantages to become hub nodes are proposed for different development trends. Full article

As distant hybridization has profound implications for evolutionary biology, aquaculture, and biodiversity conservation, this study aims to elucidate patterns of maternal inheritance, genetic divergence, and phylogenetic relationships by synthesizing mitochondrial genome (mitogenome) data from 74 distant hybrid fish species. These hybrids span diverse taxa, including 48 freshwater and 26 marine species, with a focus on Cyprinidae (n = 35) and Epinephelus (n = 14), representing the most frequently hybridized groups in freshwater and marine systems, respectively. Mitogenome lengths were highly conserved (15,973 to 17,114 bp); however, the genetic distances between hybrids and maternal species varied from 0.001 to 0.17, with 19 hybrids (25.7%) showing distances >0.02. Variable sites in these hybrids were randomly distributed but enriched in hypervariable regions, such as the D-loop and NADH dehydrogenase subunits 1, 3 and 6 (ND2, ND3, and ND6) genes, likely reflecting maternal inheritance (reported in Cyprinus carpio × Carassius auratus). Moreover, these genes were under purifying selection pressure, revealing their conserved nature. Phylogenetic reconstruction using complete mitogenomes revealed three distinct clades in hybrids: (1) Acipenseriformes, (2) a freshwater cluster dominated by Cypriniformes and Siluriformes, and (3) a marine cluster comprising Centrarchiformes, Pleuronectiformes, Scombriformes, Cichliformes, Anabantiformes, Tetraodontiformes, Perciformes, and Salmoniformes. The prevalence of Cyprinidae hybrids underscores their importance in aquaculture for hybridization, where traits such as rapid growth and disease resistance are enhanced. In contrast, marine hybrids are valued for their market value and adaptability. While mitogenome data robustly support maternal inheritance in most cases, exceptions suggest complex mechanisms, such as doubly uniparental inheritance (DUI), in distantly related crosses. Moreover, AT-skew of genes in hybrids revealed a paternal leakage of traits in mitogenomes. This study also highlights ecological risks, such as genetic swamping in native populations, emphasizing the need for responsible hybridization practices. These findings advance our understanding of the role of hybridization in fish evolution and aquaculture, providing a genomic framework and policy recommendations for optimizing breeding programs, hybrid introduction, and mitigating conservation challenges. Full article

Nitrogen use efficiency remains the primary bottleneck for sustainable maize production. This study elucidates the functional mechanisms of the amino acid transporter ZmAAP1 in nitrogen absorption and stress resilience. Through systematic evolutionary analysis of 55 maize inbred lines, we discovered that the ZmAAP1 gene family exhibits distinct chromosomal localization (Chr7 and Chr9) and functional domain diversification (e.g., group 10-specific motifs 11/12), indicating species-specific adaptive evolution. Integrative analysis of promoter cis-elements and multi-omics data confirmed the root-preferential expression of ZmAAP1 under drought stress, mediated via the ABA-DRE signaling pathway. To validate its biological role, we generated transgenic maize lines expressing Arabidopsis thaliana AtAAP1 via Agrobacterium-mediated transformation. Three generations of genetic stability screening confirmed the stable genomic integration and root-specific accumulation of the AtAAP1 protein (Southern blot/Western blot). Field trials demonstrated that low-N conditions enhanced the following transgenic traits: the chlorophyll content increased by 13.5%, and the aboveground biomass improved by 7.2%. Under high-N regimes, the gene-pyramided hybrid ZD958 (AAP1 + AAP1) achieved a 12.3% yield advantage over conventional varieties. Our findings reveal ZmAAP1’s dual role in root development and long-distance nitrogen transport, establishing it as a pivotal target for molecular breeding. This study provides actionable genetic resources for enhancing NUE in maize production systems. Full article

In response to the challenge of multi-objective optimal scheduling and efficient solution of hydropower stations under large-scale renewable energy integration, this study develops a multi-objective optimization model with the dual goals of maximizing total power generation and minimizing the variance of residual load. Four complementarity evaluation indicators are used to analyze the wind–solar complementarity characteristics. Building upon this foundation, Hyper-dominance Evolutionary Algorithm (HEA)—capable of efficiently solving high-dimensional problems—is introduced for the first time in the context of wind–solar–hydropower integrated scheduling. The case study results show that the HEA performs better than the benchmark algorithms, with the best mean Hypervolume and Inverted Generational Distance Plus across nine Walking Fish Group (WFG) series test functions. For the hydro-wind-solar scheduling problem, HEA obtains Pareto frontier solutions with both maximum power generation and minimal residual load variance, thus effectively solving the multi-objective scheduling problem of the hydropower system. This work provides a valuable reference for modeling and efficiently solving the multi-objective scheduling problem of hydropower in the context of emerging power systems. This work provides a valuable reference for the modeling and efficient solution of hydropower multi-objective scheduling problems in the context of emerging power systems. Full article

Castor fiber birulai is a subspecies of the Eurasian beaver that has a relatively small population size compared to other Castor subspecies. There is limited genetic research on this subspecies. In this study, mitochondrial cytochrome b (Cytb) and D-loop sequences were analysed in genetic samples obtained from 19 individuals residing in the Buergen River Basin, Xinjiang, China. The Cytb region presented a single haplotype, whereas three haplotypes were identified in the D-loop region. The genetic diversity within the Chinese population was low (D-loop Hd = 0.444; Pi = 0.0043), markedly lower than that observed in other geographical populations of C. fiber. Phylogenetic reconstructions and haplotype network analyses revealed substantial genetic differentiation between C. f. birulai and other Eurasian lineages (Fst > 0.95), supporting the status of C. f. birulai as a distinct evolutionary lineage. Although the genetic distance between the Chinese and Mongolian populations was relatively small (distance = 0.00269), significant genetic differentiation was detected (Fst = 0.67055), indicating that anthropogenic disturbances—such as hydraulic infrastructure and fencing along the cross-border Bulgan River—may have impeded gene flow and dispersal. Demographic analyses provided no evidence of recent population expansion (Fu’s Fs = 0.19152), suggesting a demographically stable population. In subsequent studies, we recommend increasing nuclear gene data to verify whether the C. f. birulai population meets the criteria for Evolutionarily Significant Unit classification, and strengthening cross-border protection and cooperation between China and Mongolia. Full article

Global warming is driving significant changes in aquatic ecosystems, where temperature fluctuations influence biological processes across multiple levels of organisation. As ectothermic organisms, fish are particularly susceptible, with even minor thermal shifts affecting their metabolism, behaviour, and overall fitness. Understanding these responses is essential for evaluating the ecological and evolutionary consequences of climate change. This study investigates the effects of acute (4-day) and chronic (21-day) exposure to three temperature regimes—18 °C (low), 26 °C (control), and 34 °C (high)—on the spatio-temporal shoaling behaviour of adult zebrafish (Danio rerio). Groups of four fish were tested for six minutes in water maintained at the same temperature as their prior acclimation. Shoaling behaviour was assessed by analysing shoal structure—encompassing shoal dimensions and cohesion—as well as spatial positioning. Parameters measured included inter-fish distance, shoal volume, shoal area, homogeneity index, distance to the centroid, and the shoal’s vertical and horizontal distribution. Results revealed complex behavioural changes influenced by both temperature and duration of exposure. At 18 °C, zebrafish showed a marked preference for the bottom zone and exhibited no significant temporal modulation in exploratory behaviour—patterns indicative of heightened anxiety-like responses. In contrast, exposure to 34 °C resulted in increased shoal cohesion, particularly under chronic conditions, and a progressive increase in environmental exploration over the six-minute test period. This enhancement in exploratory activity was especially evident when compared to the first minute of the test and was characterised by greater vertical movement—reflected in the increased use of the upper zone—and broader horizontal exploration, including more frequent occupation of peripheral areas. These findings align with previous research linking thermal variation to neurobiological and proteomic alterations in zebrafish. By elucidating how temperature modulates social behaviour in ectotherms, this study offers valuable insights into the potential behavioural impacts of climate change on aquatic ecosystems. Full article

This study aims to enhance the accuracy and interpretability of flood susceptibility mapping (FSM) in Seoul, South Korea, by integrating automated machine learning (AutoML) with explainable artificial intelligence (XAI) techniques. Ten topographic and environmental conditioning factors were selected as model inputs. We first employed the Tree-based Pipeline Optimization Tool (TPOT), an evolutionary AutoML algorithm, to construct baseline ensemble models using Gradient Boosting (GB), Random Forest (RF), and XGBoost (XGB). These models were further fine-tuned using Bayesian optimization via Optuna. To interpret the model outcomes, SHAP (SHapley Additive exPlanations) was applied to analyze both the global and local contributions of each factor. The SHAP analysis revealed that lower elevation, slope, and stream distance, as well as higher stream density and built-up areas, were the most influential factors contributing to flood susceptibility. Moreover, interactions between these factors, such as built-up areas located on gentle slopes near streams, further intensified flood risk. The susceptibility maps were reclassified into five categories (very low to very high), and the GB model identified that approximately 15.047% of the study area falls under very-high-flood-risk zones. Among the models, the GB classifier achieved the highest performance, followed by XGB and RF. The proposed framework, which integrates TPOT, Optuna, and SHAP within an XAI pipeline, not only improves predictive capability but also offers transparent insights into feature behavior and model logic. These findings support more robust and interpretable flood risk assessments for effective disaster management in urban areas. Full article

Flavonoid synthases (FNSs) are key enzymes catalyzing the conversion of flavanones to flavonoids, yet their functions in citrus remain functionally uncharacterized. In this study, we identified three FNSII genes in the citrus genome. Phylogenetic analysis revealed that citrus FNSII genes share the closest evolutionary distance with apple FNSII genes. Chromosomal localization demonstrated that the three FNSII genes are distributed across two out of nine chromosomes. Gene structure analysis indicated that the majority of motifs within these three FNSII genes are highly conserved. We cloned a gene called CitFNSII-1 from citrus. Transient overexpression of CitFNSII-1 in citrus leaves significantly increased flavonoid content, while simultaneous virus-induced silencing of CitFNSII-1 led to synchronously and significantly reduced gene expression levels and flavonoid content in citrus seedlings. Through the Agrobacterium rhizogenes-mediated genetic transformation system, overexpression of CitFNSII-1 was found to markedly enhance flavonoid accumulation in hairy roots, whereas knockout of CitFNSII-1 resulted in a significant decrease in flavonoid content in hairy roots. Further experiments verified an interaction between CitFNSII-1 and the Chalcone isomerase-1 (CHI-1) protein. The results demonstrated that the flavonoid accumulation patterns of CHI-1 and CitFNSII-1 are highly similar. In conclusion, this study advances the understanding of the flavonoid biosynthesis pathway in citrus and provides a theoretical foundation for molecular breeding strategies in citrus. Full article

Cloud resource brokerage is a fundamental challenge in cloud computing, requiring the efficient selection and allocation of services from multiple providers to optimize performance, sustainability, and cost-effectiveness. Traditional approaches often struggle with balancing conflicting objectives, such as minimizing the response time, reducing energy consumption, and maximizing broker profits. This paper presents NSGA-III-GKM++, an advanced multi-objective optimization model that integrates the NSGA-III evolutionary algorithm with an enhanced K-means++ clustering technique to improve the convergence speed, solution diversity, and computational efficiency. The proposed framework is extensively evaluated using Deb–Thiele–Laumanns–Zitzler (DTLZ) and Unconstrained Function (UF) benchmark problems and real-world cloud brokerage scenarios. Comparative analysis against NSGA-II, MOPSO, and NSGA-III-GKM demonstrates the superiority of NSGA-III-GKM++ in achieving high-quality tradeoffs between performance and cost. The results indicate a 20% reduction in the response time, 15% lower energy consumption, and a 25% increase in the broker’s profit, validating its effectiveness in real-world deployments. Statistical significance tests further confirm the robustness of the proposed model, particularly in terms of hypervolume and Inverted Generational Distance (IGD) metrics. By leveraging intelligent clustering and evolutionary computation, NSGA-III-GKM++ serves as a powerful decision support tool for cloud brokerage, facilitating optimal service selection while ensuring sustainability and economic feasibility. Full article

This study examined genetic diversity in the endangered medicinal plant Celastrus paniculatus using 62 individual samples from seven natural populations in northern and northeastern Thailand to inform conservation strategies. The analysis of the nuclear internal transcribed spacer (ITS) and ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL) markers revealed 17 haplotypes (CpI1–CpI17) across these populations, with 15 being population-specific. The genetic diversity varied significantly among populations: CMI showed the highest diversity (Hd = 0.944 ± 0.070), while LEI and LPN displayed complete homogeneity. The haplotype network identified a central shared haplotype (CpI4), suggesting a common ancestry, with the PLK population showing a distinct genetic divergence through unique haplotypes separated by multiple mutation steps. Genetic distance calculations revealed close relationships between LEI and NPM populations (distance = 0.0004), with greater differentiation between PLK and other populations (distances > 0.005). Phylogenetic analyses confirmed the species integrity while highlighting population clusters, especially PLK in ITS analyses and LPN in rbcL analyses. This genetic structure information provides a foundation for targeted conservation planning. Results suggest that conservation efforts should prioritize both genetically diverse populations (like CMI and MKM) and genetically distinct ones (like PLK) to preserve the maximum evolutionary potential. This study delivers crucial molecular data for developing evidence-based conservation strategies to protect this valuable medicinal species from further decline. Full article

The multi-station agricultural machinery scheduling process mainly involves two key stages: order allocation and path planning. Order allocation methods based solely on spatial distance cannot ensure the continuity of agricultural operations. Multi-objective evolutionary algorithms are sensitive to the initial population quality and local search strategies for path planning, where unreasonable initial solutions or improper local search strategies can affect the diversity of solutions. Therefore, we propose a spatiotemporal allocation algorithm that constructs a spatiotemporal distance function to describe the feasibility of continuous operations and evaluates the spatiotemporal proximity of operation points and stations for clustering allocation. In terms of path planning, we design a learnable multi-objective evolutionary algorithm (LMOEA). First, a hybrid initialization strategy is used to enhance the initial population quality; second, a Q-learning-based local search method is constructed to adaptively adjust the search strategy to reduce ineffective iterations; finally, a dynamically adjusted crowding distance mechanism is introduced to improve the distribution of the solution set. Experimental results show that the spatiotemporal allocation algorithm improves the average cost and satisfaction by 4.09% and 3.28% compared to the spatial method. Compared with INSGA-II, HTSMOGA, and NNITSA algorithms, the LMOEA can obtain solutions of higher quality and greater diversity. Full article

The structural diversity of microbial communities plays a pivotal role in microbiological research and applications. However, the study of microbial transitions has remained challenging due to a lack of effective methods, limiting our understanding of microbial dynamics and their underlying mechanisms. To address this gap, we introduce MT-tracker (microbiome transition tracker), a novel algorithm designed to capture the transitional trajectories of microbial communities. Grounded in diversity and phylogenetic principles, MT-tracker reconstructs the virtual common ancestors of microbiomes at the community level. By calculating distances between microbiomes and their ancestors, MT-tracker deduces their transitional directions and probabilities, achieving a substantial speed advantage over conventional approaches. The accuracy and robustness of MT-tracker were first validated by a phylosymbiosis analysis using samples from 28 mammals and 24 nonmammal animals, describing the co-evolutionary pattern between hosts and their associated microbiomes. We then expanded the usage of MT-tracker to 456,702 microbiomes sampled world-wide, uncovering the global transitional directions among 21 ecosystems for the first time. This effort provides new insights into the macro-scale dynamic patterns of microbial communities. Additionally, MT-tracker revealed intricate longitudinal transition trends in human microbiomes over a sampling period exceeding 400 days, capturing temporal dynamics often overlooked by normal diversity analyses. In summary, MT-tracker offers robust support for the qualitative and quantitative analysis of microbial community diversity, offering significant potential for studying and utilizing the macrobiome variation. Full article

Forests harbor most of the world’s terrestrial biodiversity; however, traditional conservation frameworks prioritize species richness over evolutionary diversity. Phylogenetic diversity (PD) reflects the complete evolutionary history contained within a community, offering a more comprehensive understanding of biodiversity. This review examines the theoretical foundations of PD, highlights methodological advancements in its assessment, and discusses its conservation applications in forest ecosystems. We discuss key metrics, including Faith’s PD, mean pairwise distance (MPD), mean nearest taxon distance (MNTD), and indices, including the net relatedness index (NRI) and nearest taxon index (NTI), as well as analytical tools (Picante, Phylocom, Biodiverse) and frameworks like the categorical analysis of neo- and paleo-endemism (CANAPE) and the evolutionarily distinct and globally endangered (EDGE) index, evaluating their effectiveness in identifying evolutionarily significant conservation areas. We examine global and regional forest PD patterns, including elevational and latitudinal gradients, using case studies from the Pan-Himalayan region, Tibetan Plateau, and northern Pakistan, along with the environmental and anthropogenic drivers, e.g., soil pH, precipitation, land-use change, and invasive species, and historical biogeographic forces that shape lineage diversification. We emphasize the need for data standardization, regional research expansion, and the inclusion of PD in national biodiversity strategies and global policy frameworks. This review highlights the transformative potential of shifting from species-centric to evolutionarily informed conservation, and provides a critical framework for enhancing the long-term resilience and adaptive capacity of forest ecosystems. Full article

The circadian rhythm plays a crucial role in regulating biological processes, and its disruption is linked to various health issues. Identifying small molecules that influence the circadian period is essential for developing targeted therapies. This study explores the use of evolutionary optimization techniques to enhance the classification of these molecules. We applied a genetic algorithm to optimize feature selection and classification performance. Several tree-based learning classification algorithms (Decision Trees, Extra Trees, Random Forest, XGBoost) and a distance-based classifier (kNN) were employed. Their performance was evaluated using accuracy and F1-score, while considering their generalization ability with a validation set. The findings demonstrate that the proposed genetic algorithm improves classification accuracy and reduces overfitting compared to baseline models. Additionally, the use of variance in accuracy as a penalty factor may enhance the model’s reliability for real-world applications. Our study confirms that evolutionary optimization is an effective strategy for classifying small molecules regulating the circadian rhythm. The proposed approach not only improves predictive performance but also ensures a more robust model. Full article