Search Results (204)

Direct methods provide a model-free approach to solving the crystallographic phase problem and deliver unbiased atomic structures. However, conventional iterative projection algorithms such as Hybrid Input–Output (HIO) face two critical challenges: discontinuous density modification at the protein-solvent boundary and inaccurate molecular envelope reconstruction that fails to account for trapped solvent, particularly in crystals with solvent content approaching the lower limits of direct phasing applicability. We introduced four continuous iterative projection algorithms, including our improved continuous version, which implements smooth density modification at protein-solvent interfaces. To address envelope inaccuracy, we developed a two-step refined reconstruction scheme using sequential large-radius and small-radius Gaussian filters to identify trapped solvent molecules within surface cavities and internal channels. This scheme enhances the performance of both continuous and classical algorithms, including HIO, the difference map, and our improved versions. Benchmarking on 28 protein structures (solvent contents 55–78%, resolutions 1.46–3.2 Å, reported R-factor less than 0.22) showed that the refined envelope scheme increased average success rates of continuous algorithms by 45.7% and classical algorithms by 60.5%. The performance of continuous algorithms and improved classical algorithms proved comparable to the well-established HIO algorithm, forming a top-tier group that exceeded other classical algorithms. Integrating a genetic algorithm co-evolution strategy further enhanced average success rates by approximately 2.5-fold and accelerated convergence through population-wide information sharing. Although the success rate correlates with solvent content, our strategy improved success probability at any given solvent level, extending the practical boundaries of direct methods. The high success rate enabled averaging of multiple independent solutions, which reduced mean phase error by approximately 6.83° and yielded atomic models with backbone root-mean-square deviation (RMSD) typically below 0.5 Å relative to structures reported in the Protein Data Bank (PDB). This work introduces novel algorithms, a refined envelope reconstruction methodology, and an effective optimization strategy with genetic algorithm evolution. The complete framework enhances the capability and reliability of direct methods for phasing protein crystals with limited solvent content and provides a toolkit for addressing challenging cases in structural biology. Full article

Cryo-electron microscopy (cryo-EM) has revolutionized structural virology, enabling routine structure determination at 2–4 Å resolution, with exceptional cases reaching 1.56 Å. The structural diversity of viruses across vertebrate, plant, and insect hosts provides fundamental insights into infection mechanisms, host–pathogen coevolution, and therapeutic target identification. However, analysis of Electron Microscopy Data Bank entries reveals notable disparities in structural coverage: among 11,717 eukaryotic virus structures (excluding bacteriophages), vertebrate viruses constitute 97.6% (n = 11,432) of deposited entries, while plant viruses (1.0%; n = 117) and insect viruses (1.4%; n = 168) remain significantly underrepresented. This bias stems from distinct technical barriers including size limitations for giant viruses exceeding 200 nm, the loss of asymmetric information during symmetry-imposed processing, and the morphological complexity of filamentous and pleomorphic viruses. Each barrier has driven the development of specialized methodological solutions: block-based local refinement overcomes through-focus variations in giant viruses, cryo-electron tomography (cryo-ET) validates and reveals asymmetric features lost in symmetrized reconstructions, and subtomogram averaging enables structural analysis of pleomorphic assemblies. This review synthesizes recent methodological advances, critically evaluates their capacity to address specific technical barriers, and proposes strategies for expanding structural investigations across underrepresented host systems to achieve comprehensive understanding of viral structural biology. Full article

Geomorphological change is a fundamental consequence of high-magnitude flood events, as extreme hydraulic forcing can rapidly reshape river channels, redistribute sediment, and alter floodplain connectivity. This study applies multi-temporal UAS-based Structure-from-Motion (SfM) photogrammetry to quantify flood-induced geomorphological changes along two representative reaches of the Lilas River (Evia Island, Central Greece) affected by the extreme August 2020 flash flood. High-resolution aerial surveys were conducted prior to the event (June 2018) and shortly after the flood (September 2020), producing Digital Surface Models (DSMs) and orthomosaics with a ground sampling distance of ~2.5 cm. Differential DSM analysis reveals pronounced spatial heterogeneity in erosion and deposition, with net erosional lowering locally exceeding 7 m and depositional aggradation reaching up to ~5 m after accounting for vegetation effects. Channel widening was the dominant response, with cross-sectional widths increasing by a factor of three to nine at selected locations, driven primarily by lateral bank erosion. The results highlight the strong interaction between extreme hydrological forcing, loose alluvial sediments, vegetation removal, and human interventions such as roads and engineered terraces. The study demonstrates how repeatable UAS–SfM workflows can provide quantitative evidence to support post-flood assessment, guide infrastructure adaptation, and inform river restoration and flood risk management in Mediterranean catchments prone to extreme events. Full article

Reservoir bank collapse is a critical geological hazard during the operation of large-scale water conservancy projects, controlled by unique hydrodynamic mechanisms induced by reservoir impoundment, and differs significantly from ordinary landslides. Traditional risk assessment methods, however, often struggle to achieve effective integration between macro-regional zoning and micro-mechanical analysis. Against this limitation, this study proposes a GIS-integrated multi-scale risk screening framework to achieve the preliminary integration of qualitative regional evaluation and quantitative site-specific analysis. Compared with traditional multi-scale studies, the innovations of this research are as follows: (1) a customized GIS component was developed to realize semi-automatic profile extraction from high-resolution DEMs and batch Bishop stability calculations, overcoming the bottleneck of spatializing micro-models over large areas; (2) a “bottom-up” dynamic feedback mechanism was established, utilizing the quantitative safety factor from site-specific evaluations as an explicit indicator for the conservative screening correction of the macro-regional risk map. Applied to the Xiluodu Reservoir, this framework illustrates a potential multi-scale approach for cross-scale risk screening driven by physical–mechanical mechanisms. This provides both a global perspective and a localized physical basis, offering a strategic screening tool for reservoir management. By linking failure mechanisms directly to spatial impacts, the framework provides a plausible conservative feedback rule for risk-informed decision-making in complex reservoir settings. Full article

Mudstone badlands are critical hotspots of erosion and sediment yield, and their rapid morphological changes serve as an ideal site for studying erosion processes. This study used high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry to monitor erosion patterns on a mudstone badland platform in southwestern Taiwan over a 22-month period. Five UAV surveys conducted between 2017 and 2018 were processed using Structure-from-Motion photogrammetry to generate time-series digital surface models (DSMs). Topographic changes were quantified using DSMs of Difference (DoD). The results reveal intense surface lowering, with a mean erosion depth of 34.2 cm, equivalent to an average erosion rate of 18.7 cm yr⁻¹. Erosion is governed by a synergistic regime in which diffuse rain splash acts as the dominant background process, accounting for approximately 53% of total erosion, while concentrated flow drives localized gully incision. Morphometric analysis shows that erosion depth increases nonlinearly with slope, consistent with threshold hillslope behavior, but exhibits little dependence on the contributing area. Plan and profile curvature further influence the spatial distribution of erosion, with enhanced erosion on both strongly concave and convex surfaces relative to near-linear slopes. The gully network also exhibits rapid channel adjustment, including downstream meander migration and associated lateral bank erosion. These findings highlight the complex interactions among hillslope processes, gully dynamics, and base-level controls that govern badland landscape evolution and have important implications for erosion modeling and watershed management in high-intensity rainfall environments. Full article

The Asian tiger mosquito (Aedes albopictus) is an important vector of arboviruses, including dengue, chikungunya, and Zika. Its rapid global expansion has been facilitated by climate change and human activities. Phylogenetic studies of Ae. albopictus have largely relied on mitochondrial cytochrome c oxidase subunit 1 (COX1) and NADH dehydrogenase subunit 5 (ND5) markers, while the utility of cytochrome b (CYTB) remains underexplored. We collected Ae. albopictus from 13 sites in seven provinces of South Korea and analyzed COX1, ND5, and CYTB sequences. Genetic diversity indices were calculated, and phylogenetic relationships were reconstructed using maximum-likelihood trees and haplotype networks with a dataset obtained from GenBank. COX1 revealed 46 haplotypes, including six novel variants, with the highest diversity in southern coastal regions such as Busan and Suncheon. ND5 showed limited variation, with only two haplotypes. CYTB revealed three haplotypes, including region-specific variants in Busan and Wonju, supporting its role as a complementary marker. The Busan haplotype H41 bridged domestic and international lineages, suggesting Busan as a likely entry point. This study demonstrates that integrating COX1, ND5, and CYTB improves the resolution of Ae. albopictus phylogeography in Korea and highlights the need for continued molecular surveillance to guide vector control strategies. Full article

Echinoderms are marine invertebrates that play important roles in structuring marine benthic ecosystems. DNA barcoding has become a valuable tool for species identification; however, reference DNA barcode libraries for echinoderms remain incomplete. This study aims to: (i) develop a COI-5′ reference dataset for echinoderms from Vietnam by integrating DNA barcodes with morphological data; (ii) evaluate species resolution and barcode gaps using multiple analytical approaches; (iii) assess the consistency of species assignments from BOLD and GenBank for echinoderms collected in Vietnam; (iv) make barcode data publicly available to support global reference database development. Thirty-two echinoderm specimens representing 16 species were analyzed for COI-5′ sequences, and BLAST assignments were highly concordant with those from GenBank and BOLD. Integrative validation confirmed that all taxa were monophyletic in the Neighbor Joining Tree, formed single OTUs in Cluster Sequences, and exhibited clear barcode gaps greater than 3% to the nearest-neighbor species. These results provided species-level resolution for 75% and genus-level resolution for 90% of the records. The dataset, spanning four classes, eight orders, and eleven families, enhances barcode coverage and contributes records (ProcessIDs. BINs; GenBank accessions) to public repositories. This study delivers the first curated COI-5′ reference library, supporting regional baselines for taxonomy, conservation, and biodiversity assessment. Full article

Persistence of seagrass meadows varies depending on community composition, substrate stability, environmental forcing, and water quality/clarity. Spatial trends in decadal scale persistence are difficult to assess at the meadow scale using in situ approaches and assessments using Earth Observation often lack temporal consistency. This study utilises a multi-decadal field monitoring dataset and high-resolution multispectral satellite imagery in a cloud-processing environment to assess species distribution, seagrass cover, and meadow persistence. In this work, we investigate long-term trends in overall meadow and species-specific persistence in the Eastern Banks, Moreton Bay, Australia, a shallow, semi-enclosed, subtropical embayment (∼200 km²). Here, we have identified an overall decline in seagrass cover (−15% of the total study area), between 2011 and 2025, through contraction of meadow extent, with most losses in colonising species (Halophila spinulosa and Halophila ovalis) across the deeper sections of the study area. We have also quantified the spatial extent of a previously identified broad-scale ecosystem shift from meadows dominated by Zostera muelleri to a prevalence of Oceana serrulata, and reduction in the sparse cover species H. spinulosa and H. ovalis. We have presented a semi-automated cloud-processing based pipeline to combine in situ seagrass observations, derived from an expertly trained machine learning model, with high resolution multispectral data to assess seagrass cover and persistence. The variability in decadal-scale persistence between the six key species found in this region has been assessed, with dense cover species (e.g., O. serrulata and Z. muelleri) exhibiting moderate persistence (>0.32) and sparse cover species (H. ovalis and H. spinulosa) with low persistence (∼0.15). Colonising/opportunistic growth patterns characterise the species examined in this study, indicating quick response to disturbance but a lack temporal consistency in meadow form, which has critical implications for resilience. Full article

Tracking small, fast-moving unmanned aerial vehicles (UAVs) in thermal infrared (TIR) imagery is a significant challenge due to low-resolution targets, Dynamic Background Clutter, and frequent occlusions. To address this, we introduce MemLoTrack, a novel onestream Vision Transformer tracker that integrates a memory mechanism into a parameterefficient LoRA framework. MemLoTrack enhances a baseline tracker (LoRAT) with two key components: (i) a gated First-In, First-Out (FIFO) memory bank (MB) for temporal context aggregation and (ii) a lightweight Memory Attention Layer (MAL) for effective information retrieval. A key component of our method is a selective memory update policy, which commits a frame to the memory bank only when it satisfies both a classification confidence threshold ($\tau$) and a Kalman filter-based motion consistency check. This gating mechanism robustly prevents memory contamination due to distractors, occlusions, and reappearance events. Our training is highly efficient, updating only the LoRA adapters, MAL, and prediction head while the pretrained DINOv2 backbone remains frozen. Evaluated on the challenging Anti-UAV410 benchmark, MemLoTrack (L_mem = 7, $\tau$ = 0.8) achieves an AUC of 63.6 and a State Accuracy (SA) of 64.0, representing a significant improvement over the LoRAT baseline by +1.4 AUC and +1.5 SA. Compared to the state-of-the-art method FocusTrack, MemLoTrack demonstrates superior robustness with higher AUC (63.6 vs. 62.8) and SA (64.0 vs. 63.9), while trading lower precision (P/P-Norm) scores. Furthermore, MemLoTrack operates at 153 FPS on a single RTX 4070 Ti SUPER, demonstrating that parameter-efficient fine-tuning with a selective memory mechanism is a powerful and deployable strategy for real-time Anti-UAV tracking in demanding TIR environments. Full article

After a series of successful reintroductions, the Eurasian beaver (Castor fiber) is expanding its range throughout Europe. Timely monitoring of beaver activity contributes to early detection of environmental impacts and aids in mitigating human–wildlife conflicts and other threats. However, the signs of beaver presence are difficult to detect in some environments, e.g., densely vegetated river banks or in areas with considerable water level variability. In these cases, new technologies can offer opportunities for easier and faster monitoring. In the current study, we provide a characterisation of the wood-gnawing activity of a newly established beaver population in Northern Bulgaria, using a traditional transect method. In addition, we test the application of unmanned aerial vehicles (UAVs) to detect and map the signs of beaver activity. The overall gnawing-activity characteristics of newly established Castor fiber populations in Bulgaria follow the pattern documented in earlier studies: the affected trees were mainly willow and poplar, located at less than 10 m from the riverbank, with a diameter mostly under 30 cm. However, there were considerable differences in the tree size and distance from the water between the two studied habitats—the Danube River and its tributaries. No dams were recorded, probably due to the rivers’ sizes. We found no significant difference in the detection rates of the UAV with and without canopy cover. Overall, the UAV-based transects were reliable for the detection of the species’ presence, but not for quantification of its activity patterns, due to the low detection rates, in comparison with ground-level transects. We believe that the method is promising because it is cost- and time-saving but could be improved using cameras with better resolution and by involving machine learning algorithms. The drone detection method could help identify the areas with the densest populations of the species, where Natura 2000 protected zones could then be established. Full article

Understanding spatial characteristics of urban building systems is critical for unraveling urban building stock growth patterns, addressing housing vacancy challenges, and advancing urban carbon neutrality. However, existing research on built environment stocks and housing vacancy spatial distribution remains limited, particularly in underdeveloped cross-river cities—where rapid urbanization often prioritizes scale expansion over demand matching, leading to unresolved issues of resource waste and environmental pressure. This study integrated material stocks analysis (MSA) and geographical information system (GIS) to uncover the spatial patterns of urban building material stocks and housing vacancy at a high spatial resolution for Nanchang, China—a typical underdeveloped cross-river city facing the “scale expansion trap” in its urbanization across the Ganjiang River. Results show that (1) Nanchang’s building stock exhibits a “butterfly-shaped” spatial pattern centered on the Ganjiang River, with simultaneous horizontal expansion (40-fold urban area growth since 1949) and vertical growth (super high-rises in new west-bank districts), reflecting aggressive cross-river scale expansion; (2) the total building material stock reached 1034 Mt (204 t/cap) in 2021, with over 85% accumulated post-2000—coinciding with large-scale cross-river development. Vacant buildings locked in 405 Mt of materials (39.17%), which is a direct consequence of the “scale expansion trap” where construction outpaced actual demand; (3) total embodied carbon emissions from building materials amounted to 264 Mt, with 104 Mt (39.39%) attributed to vacant stocks. This “vacant carbon lock-in” stems from mismatched urban construction and actual demand in the process of cross-river scale expansion; (4) spatially, high-value clusters of material stocks and carbon emissions overlapped at two cores (old town and Honggutan CBD), while housing vacancy rates were significantly higher in the urban periphery and Ganjiang’s west bank—the primary areas of cross-river scale expansion—than in the old town and east bank. These findings empirically demonstrate how the “scale expansion trap” in cross-river urbanization drives building stock vacancy and carbon lock-in. These findings also offer data-driven strategies for optimizing urban resource allocation, reducing housing vacancy, and promoting low-carbon transitions, especially for other underdeveloped cross-river cities globally. Full article

Background/Objectives: While morphological similarity and incomplete specimens pose a challenge to the precise identification of Coelogyne orchids, accurate species and genus assignment is essential for conservation and CITES enforcement. This study evaluated the efficacy of five DNA barcode regions—rbcL, matK, trnH-psbA, atpF-atpH, and ITS2—and their combinations for species- and genus-level discrimination within the genus Coelogyne, aiming to develop a rapid and simple diagnostic tool for use by customs officers and trade inspectors. This is the first comprehensive comparative analysis of these five barcode regions specifically within Coelogyne, a genus underrepresented in molecular identification studies, and the first to propose multi-locus combinations for potential practical use. This study identified DNA barcode regions with high resolution and reliability, providing a solid basis for practical identification kits. Such tools will enhance CITES enforcement by enabling rapid detection of Coelogyne species in trade, directly supporting their conservation and contributing to the reduction in illegal orchid trade. Methods: Using a CTAB protocol, genomic DNA was extracted from leaf samples belonging to 19 Coelogyne species. Sanger sequencing was performed after PCR amplification using published primer sets for every barcode region. Sequences were modified in BioEdit, and BLASTn (accessed 15 June 2025) was used to compare them to GenBank (NCBI Nucleotide). Amplification efficiency was calculated per locus. Species and genus identification success rates were determined by the congruence of top BLAST hits with morphologically pre-identified taxa. Multi-barcode combinations (matK + rbcL, ITS2 + matK, matK + trnH-psbA, rbcL + trnH-psbA, and matK + rbcL + trnH-psbA) were also assessed. Results: With rbcL, atpF-atpH, and ITS2 yielding ≤11%, the highest single-locus species identification rates were for trnH-psbA (21%) and matK (16%). Among single-locus barcodes, matK showed the highest performance, with 84% genus assignment. ITS2 reached 27%, but genus-level resolution remained limited for the rbcL, trnH-psbA and atpF-atpH barcodes. Multi-barcode approaches maintained species resolution: matK + rbcL + trnH-psbA, matK + rbcL, and matK + trnH-psbA correctly identified 16% of species and achieved 74–79% genus assignment. Conclusions: No single locus achieves robust species discrimination in Coelogyne, but trnH-psbA, matK and atpF-atpH provide the best single-marker performance. Using the matK locus alone, in combination with either trnH-psbA or rbcL, or all three together ensures consistent genus-level identification and significantly improves taxonomic resolution. This study introduces a novel multi-locus barcode strategy tailored to Coelogyne, offering a practical solution for identification and enforcement. While promising, this approach represents a potential application that requires further validation before routine implementation. Full article

Leptospirosis is a globally distributed zoonosis of major public health and veterinary relevance, caused by pathogenic species of the genus Leptospira. Brazil is a hotspot for transmission due to its ecological diversity and complex host–environment interfaces. This study explored the genetic diversity and structure of circulating pathogenic Leptospira spp. in Brazil through a single-locus sequence typing (SLST) analysis based on the secY gene. A total of 531 sequences were retrieved from GenBank and subjected to phylogenetic and haplotype diversity analyses. Maximum likelihood reconstruction revealed strongly supported clades for seven species, with L. interrogans being the most prevalent and broadly distributed across hosts and regions. This species showed evidence of clonal expansion, with a dominant haplotype (n = 242) shared by humans, domestic animals, and wildlife. In contrast, L. santarosai and L. noguchii exhibited high haplotypic diversity and reticulated network structures, reflecting greater evolutionary variability. The species L. kirschneri and L. borgpetersenii displayed reduced haplotypic variation, the latter mainly associated with cattle, consistent with its host-adapted profile. Host- and biome-based haplotype networks revealed both the broad ecological adaptability of certain lineages and the exclusive presence of haplotypes restricted to specific environments, such as those found in marine mammals from the Atlantic Ocean. Genetic distance analyses confirmed the strong taxonomic resolution of the gene secY, which effectively distinguished closely related species while capturing intraspecific diversity. These findings provide a comprehensive molecular overview of pathogenic Leptospira in Brazil, highlighting ecological connectivity across hosts and biomes, as well as the contrasting evolutionary dynamics among species. Beyond describing genetic patterns, our analyses emphasize evolutionary processes, host–environment connectivity, and the implications for One Health. This integrative framework strengthens the basis for surveillance and control strategies in other endemic regions in the world. Full article

The Flaviviruses Dengue virus (DENV), West Nile virus (WNV), Zika virus (ZIKV), and Yellow Fever virus (YFV), are mosquito-borne viruses that represent a persistent challenge to global health due to the emergence and re-emergence of outbreaks of significant magnitudes. Their positive-sense RNA genome, about 11,000 nucleotides long, encodes structural and nonstructural proteins. These viruses evolve rapidly through mutations and genetic recombination, which can lead to more virulent and transmissible strains. Although whole-genome sequencing is ideal for studying their evolution and geographic spread, its cost is a limitation. We investigated the genetic variability of DENV, ZIKV, WNV, and YFV to identify genomic regions that accurately reflect the phylogeny of the complete coding sequence and evaluated the utility of these regions in reconstructing the geographic dispersal patterns of viral genotypes and lineages. Publicly available sequences from GenBank were examined to assess variability, reconstruct phylogenies, and identify the most informative genomic regions. Once representative regions were identified, they were used to infer the global phylogeographic structure of each virus. The virus depicted distinct variation patterns, but conserved regions of high and low variability were common to all. Highly variable regions of ~2700 nt offered greater resolution in phylogenetic trees, improving the definition of internal branches and statistical support for nodes. In some cases, combined multiple highly variable regions enhanced phylogenetic accuracy. Phylogeographic reconstruction consistently grouped sequences by genotype and geographic origin, with temporal structuring revealing evolutionarily distinct clusters that diverged over decades. These findings highlight the value of targeting genomic regions for phylogenetic and phylogeographic analysis, providing an efficient alternative for genomic surveillance. Full article

Coastal vegetated ecosystems, including tidal marshes, vegetated dunes, and shrub- and forest-dominated wetlands, can mitigate hurricane impacts such as coastal flooding and erosion by increasing surface roughness and reducing wave energy. Land cover maps can be used as input to improve simulations of surface roughness in advanced hydro-morphological models. Consequently, there is a need for efficient tools to develop up-to-date land cover maps that include the accurate distribution of vegetation types prior to an extreme storm. In response, we developed the RUSH tool (Rapid remote sensing Updates of land cover for Storm and Hurricane forecast models). RUSH delivers high-resolution maps of coastal vegetation for near-real-time or historical conditions via a Jupyter Notebook application and a graphical user interface (GUI). The application generates 3 m spatial resolution land cover maps with classes relevant to coastal settings, especially along mainland beaches, headlands, and barrier islands, as follows: (1) open water; (2) emergent wetlands; (3) dune grass; (4) woody wetlands; and (5) bare ground. These maps are developed by applying one of two seasonal random-forest machine learning models to Planet Labs SuperDove multispectral imagery. Cool Season and Warm Season Models were trained on 665 and 594 reference points, respectively, located across study regions in the North Carolina Outer Banks, the Mississippi Delta in Louisiana, and a portion of the Florida Gulf Coast near Apalachicola. Cool Season and Warm Season Models were tested with 666 and 595 independent points, with an overall accuracy of 93% and 94%, respectively. The Jupyter Notebook application provides users with a flexible platform for customization for advanced users, whereas the GUI, designed with user-experience feedback, provides non-experts access to remote sensing capabilities. This application can also be used for long-term coastal geomorphic and ecosystem change assessments. Full article