Search Results (195)

Understanding ecosystem recovery following flood events is critical for sustainable floodplain management, yet causal evidence linking flooding to vegetation recovery remains limited. This study investigated the causal impact of the 2022 flood on ecosystem recovery in the Chi River Basin, Thailand, using 232,172 spatial points with multi-temporal VCI data (2020–2024). We employed Propensity Score Matching (PSM) to control for confounding factors and Difference-in-Differences (DiD) to estimate the causal effect. XGBoost with SHAP analysis identified predictors of Slow Recovery Hotspots, and a four-tier policy zoning framework was developed for sustainable land use planning. The DiD estimate of −0.0734 (95% CI: −0.0802, −0.0666) in standardized VCI units (equivalent to a raw VCI reduction of 2.47 units on the 0–100 scale) suggests that flooding caused a significant reduction in vegetation recovery, with effects persisting for at least two years. Slow Recovery Hotspots—defined as locations where two-year post-flood VCI remained below pre-flood levels—comprised 51.1% of the study area (118,643 points), with estimated annual economic losses of 226.2 Million Baht (95% CI: 203.6–248.8 Million Baht). SHAP analysis revealed that pre-flood VCI (48.6%), elevation (21.6%), and pre-flood NDVI (13.5%) were the dominant predictors. A critical elevation threshold of 145–155 m was identified, with flood frequency showing cumulative effects (≥2 events out of 5 observation years significantly increasing risk). A four-tier policy zoning framework (Red: 4.5%, Yellow: 46.6%, Orange: 10.0%, Green: 38.9%) was developed, providing actionable recommendations including no-build zones, land buyouts, building codes, early warning systems, and drainage improvements. Scenario-based analysis suggests that implementation could reduce economic losses by 30–40% (67.9–90.5 Million Baht/year). The approach demonstrates the value of combining causal inference with machine learning for evidence-based floodplain management. This framework contributes to SDGs 2, 6, 11, 13, and 15 and is transferable to other flood-prone regions in Southeast Asia. Full article

Common bean (Phaseolus vulgaris L.) is an economically important grain legume for the human diet and sustainable agricultural development. Snap bean, a vegetable-type common bean cultivated for edible immature pods, serves as a crucial dietary source of essential vitamins, minerals and proteins. However, its evolutionary and domestication history remains poorly characterized. Here, we assembled and characterized the complete chloroplast genome of snap bean using PacBio HiFi sequencing technology. The chloroplast genome formed a single circular molecule of 150,248 bp, consisting of a pair of inverted repeat regions, a large single-copy region, and a small single-copy region. A total of 75 protein-coding genes, 37 tRNA genes, and 8 rRNA genes were annotated. Phylogenomics revealed that snap bean has the closest relationship with dry common bean, grouping with lima bean; Phaseolus is phylogenetically nearest to Vigna. Comparative plastid genomics identified variants in accD, rpoC2, matK, and other coding genes. Nucleotide diversity analysis detected several highly variable hotspot regions, including accD, trnY-psbD, ndhH, and ycf1-rps15, which are promising informative plastid markers for phylogeny and population genetics. These findings deepen our understanding of legume chloroplast evolution and lay genomic foundations for common bean phylogenetic research and genetic breeding improvement. Full article

FK506-binding protein 4 (FKBP4), also known as FKBP52, is an HSP90 co-chaperone associated with poor outcomes in lung adenocarcinoma (LUAD). We evaluated FKBP4 using TCGA-LUAD, cBioPortal, CellMiner, and H1299 cell data. Among 535 primary tumors, the 20 displayed FKBP4 correlations from each of the HALLMARK_APOPTOSIS and HALLMARK_PEROXISOME sets remained significant after set-wise Bonferroni correction. cBioPortal identified two FKBP4 coding alterations among 511 tumors (0.39%), without a recurrent hotspot. No CellMiner association remained significant after Benjamini–Hochberg correction across 263 activity profiles. Three siRNAs reduced FKBP4 transcript abundance in an archived qPCR screen, although independent transfections could not be confirmed. Only si-FKBP4-3 was assessed by CCK-8, and FKBP4 protein depletion was not confirmed during the assay window. In one archived experiment, mean OD450 values were higher at 48 and 72 h across five technical wells. Because CCK-8 reflects WST-8 reduction, this observation does not establish proliferation, survival, or another defined phenotype. The study therefore identifies tumor-level co-expression associations and a preliminary cellular observation requiring independent and orthogonal validation. Full article

Driven by the Sustainable Development Goals (SDGs) and digital transformation, digital governance of megaprojects has become a key means of advancing sustainability objectives. However, the existing scholarship lacks a holistic understanding of digital governance in megaprojects, and the sustainability-oriented research landscape has yet to be systematically mapped. This study takes the Web of Science Core Collection as the primary data source, complemented by citation tracking, to identify and screen 127 publications published between 2016 and 5 March 2026. Bibliometric analysis is employed to delineate the developmental characteristics and research hotspots of the field. Subsequently, a theoretical analytical framework—Megaproject Sustainable Digital Governance (MSDG)—is constructed, integrating three core dimensions: the project lifecycle, sustainability, and digital governance. Guided by this framework, a coding scheme is developed for the content analysis of the 127 selected papers. The findings reveal that digital technology governance permeates the entire project lifecycle and garners the most scholarly attention, whereas digital derivatives governance is conspicuously understudied across all lifecycle stages. In terms of sustainability, the economic dimension dominates the discourse, while environmental issues are insufficiently represented. This paper aims to map the full landscape of lifecycle-oriented research on sustainable digital governance of megaprojects and proposes two future research agendas to facilitate the transition toward sustainable digital governance. Full article

Tourism is carbon-intensive, with transport and mobility driving much of its greenhouse-gas emissions, yet destination-level evidence on carbon hotspots and decarbonization from a supply-chain perspective remains scarce, especially in developing regions. This study examines low-carbon tourism supply chain management in seven purposively selected provinces of Northeastern Thailand (Isan) using a qualitative-dominant convergent mixed-methods design that integrates demand- and supply-side evidence. A visitor survey yielded 74 open-ended responses (60 complete questionnaires), alongside seven semi-structured key-informant interviews with tourism supply chain operators across the seven provinces. Quantitative data were analyzed with descriptive statistics, and qualitative data with thematic analysis using qualitative data analysis software, drawing on code-frequency and co-occurrence analysis; the two strands were triangulated in joint displays. Visitors reported moderate satisfaction (grand mean 3.91 on a five-point scale) but rated environmental management and safety lowest, engaging with sustainability through visible service cues rather than emissions. Key informants identified perceived carbon hotspots, carrying capacity, and seasonality as dominant concerns, attributing the destination’s footprint chiefly to transport dependence (informant-reported estimates ranging from approximately 80% to nearly 100% private-car arrivals, amid limited public transport) and accommodation energy; these hotspots reflect stakeholder perceptions rather than measured emissions, as no carbon accounting, environmentally extended input–output analysis, or life-cycle assessment was conducted. Integration revealed a demand–supply perception gap and five proposed, interdependent low-carbon pathways: coordinated mobility, accommodation energy efficiency, strengthened local procurement, carrying-capacity and waste management, and multi-stakeholder governance. The findings offer a developing-region, supply-chain-oriented basis for future tourism decarbonization research and practice. Full article

City image links perceptions of urban form and environmental quality to place identity and sociocultural sustainability. However, city-scale research rarely integrates visible streetscape characteristics with visitor narratives. Using Wuhan, China, as a case study, this study integrates panoramic streetscape imagery with online visitor reviews. Semantic segmentation was employed to quantify nine streetscape visual indicators, such as building interfaces, visible greenery and water, color characteristics, and visual disturbances. High-frequency term extraction and thematic coding identified four dimensions of visitor perception: cultural, aesthetic, leisure, and scientific–educational perceptions. Statistical and spatial analyses were subsequently conducted to examine the relationships between the visual indicators and these perceptual dimensions. Leisure and aesthetic perceptions emerged as the most prominent dimensions. Color harmony, water visibility, and visible greenery were positively associated with cultural, aesthetic, and leisure perceptions. Dense building interfaces and visual disturbances were negatively associated with aesthetic and leisure perceptions but positively associated with scientific–educational perception. Hotspots of cultural, aesthetic, and leisure narrative intensity were concentrated in core Wuchang and the East Lake area, whereas scientific–educational perception showed weaker overall spatial clustering, with localized hotspots in central urban areas. These findings suggest that integrating street-view imagery with visitor narratives can help identify spatially differentiated relationships between visible urban environments and city image. The proposed framework may facilitate the identification of sustainability-relevant visual resources and provide diagnostic evidence that may inform urban design and public-space management. Full article

Forest planning and operational execution still often rely on separate office-based workflows, field inventories, and analogue tree-marking procedures, limiting direct communication between planners and field personnel. Mixed reality (MR) interfaces and forest digital twin concepts may help link planning decisions with field-level visualization and interaction. This study presents an initial proof-of-concept prototype for MR-based virtual tree tagging and field–office communication in forestry. The prototype integrates a Unity GE-based Forest Planner System, a Microsoft HoloLens 2-based Forest Worker System, and a locally hosted MagicOnion server for bidirectional client–server communication. Virtual trees with predefined stem diameters were created in Unity GE, and torus-shaped markers were implemented to support tree selection, color-coded designation, marker deletion, and visualization of predefined diameter-related attributes. Under controlled indoor conditions using a 5G mobile hotspot, both clients connected to the server and exchanged marker-related events. Marker creation, color assignment, deletion, and shared marker-state updates initiated from either client were reproduced in the corresponding system. The prototype demonstrates the functional feasibility of the core communication workflow but remains limited to a simulated environment. Full article

Residential building retrofit requires reliable building energy simulation and efficient performance prediction. This research presents an adaptable workflow for establishing two residential base model templates, namely corner space (CS) and intermediate space (IS). The models were assessed through National Construction Code of Australia-based verification, comparison with a documented Melbourne residential reference model, and Morris index convergence analysis. These models were coupled with an AI-assisted three-stage prediction framework and evaluated across seven additional Australian climates, with Melbourne as the primary case, to examine cross-climate applicability. Stage 1 developed a two-variable analytical model to predict annual energy consumption. Stage 2 extended this to a three-variable relation and compared its energy predictions with support vector machine (SVM). Stage 3 examined multivariable cases using four machine learning (ML) models—artificial neural network (ANN), SVM, random forest (RF), and decision tree (DT)—to predict performance metrics and identify the best model. For the primary Melbourne case, the two-variable relation reproduced the simulated response with mean absolute percentage errors (MAPEs) of 0.27% and 0.62% for CS and IS, respectively. In Stage 2, the analytical model achieved a MAPE of 8.38%, while SVM reduced the error to 0.75%. Cross-climate results confirmed that the staged workflow remained robust across different climates. In Stage 3, SVM was robust with limited data, while the ANN and RF became more competitive with larger samples. DT was prone to overfitting. The proposed framework supports early-stage residential retrofit assessment by enabling retrofit hotspot screening and suitable surrogate model selection before detailed optimization. Full article

Prior to this study, knowledge on the evolutionary lineage of Garra remained inadequate, as previous phylogenetic investigations were primarily based on partial gene sequences. Although several mitogenomes of Garra species have been reported, their structural organization and comprehensive genomic characteristics have not been thoroughly evaluated. In this study, Garra manipurensis, endemic to the Indo-Burma biodiversity hotspot, was identified based on its detailed morphology and meristic counts. The circular mitogenome of G. manipurensis is 16,776 bp in length and contains the canonical set of 37 genes, along with duplicated control regions separated by tRNA-Proline. The comparative assessments across Garra species indicate predominantly conserved GTG start codons, occasional alternative ATA initiation codons, and incomplete stop codons. The selection pressure examinations within Garrini taxa reveal a purifying selection across all protein-coding genes. The control region comprises four conserved sequence blocks and species-specific tandem repeats, reflecting a balance between functional constraint and lineage-dependent evolutionary dynamics. The phylogenetic inference supports the monophyly of Garra and places G. manipurensis in close affinity with Garra flavatra, which is native to the western slope of Rakhine Yoma in Myanmar and Mizoram State in northeastern India. The genetic diversity analyses revealed haplotype differentiation, with shallow intraspecific genetic distances (0.000–0.011) observed samples between two distinct drainage systems in Manipur and Mizoram, northeastern India. The observed pattern of haplotype divergence in G. manipurensis may reflect the historical or seasonal hydrological connectivity among the western-slope drainages of the Chin Hills, with the subsequent geographic isolation potentially contributing to the emergence of distinct genetic lineages. Nevertheless, the extent and evolutionary significance of this differentiation remain uncertain and warrant further investigation through expanded geographic sampling and the incorporation of additional molecular data. Collectively, these findings provide in-depth insights into the mitogenomic architecture, comparative gene arrangements, phylogenetic patterns, and matrilineal evolutionary history of G. manipurensis and other congeners, thereby improving our understanding of the systematics and genetic diversity of this important cyprinid fish lineage. Full article

Soils are critical microbial habitats that support terrestrial ecosystem functioning and harbor numerous uncultured and functionally uncharacterized microbial groups. The black soil region in northeast China is a key agricultural ecosystem globally, yet the classification and functional understanding of its crucial microbial groups remain underexplored. In this study, we identified three high-completeness metagenome-assembled genomes (MAGs) from the Global Mollisols Genomic Atlas (GMGA). Phylogenetic and comparative genomic analyses identified these genomes as representing a novel evolutionary branch within the genus Flavobacterium, classified under the phylum Bacteroidota. Their novel taxonomic position is further supported by average nucleotide identity (ANI) and average amino acid identity (AAI) thresholds, demonstrating significant divergence from all known reference genomes. Functional annotation indicated that this species possesses strong plant polysaccharide degradation potential and a chemoheterotrophic lifestyle, together with environmental stress tolerance and a specialized nitrogen metabolic network adapted to agricultural inputs, thereby conferring a metabolic advantage in black soil environments characterized by high organic matter input and marked seasonal fluctuations. In addition, global distribution analysis showed that this lineage is widely distributed across diverse ecosystems and is significantly enriched in soil habitats, particularly in environments with fluctuating carbon sources and high organic matter inputs. The new species is most abundant in temperate soils, with the northeast black soil region of China emerging as a key hotspot. Based on these findings, and because no pure culture is currently available, we propose Candidatus Flavobacterium genomatis based on genome-resolved metagenomic evidence and in alignment with the International Code of Nomenclature of Prokaryotes rules for uncultivated prokaryotes. Our results expand the known species diversity of the genus Flavobacterium and suggest potential ecological roles of uncultured black-soil microbes in carbon and nitrogen cycling, including possible involvement in N₂O reduction under suitable environmental conditions. Full article

Background/Objectives: Colorectal cancer (CRC) represents a major public health problem in Mexico and is among the malignancies with the highest morbidity and mortality. Alterations in TP53 are frequent molecular events in tumors with chromosomal instability; however, information on TP53 variants in the Mexican population, particularly in exons 4-8, remains limited. Exons 4-8 comprise the main coding region of the p53 DNA-binding domain; therefore, this study aimed to identify TP53 variants in these regions and evaluate p53 protein expression by immunohistochemistry in sporadic CRC. Methods: Tumor samples from 142 patients who underwent surgical resection without neoadjuvant treatment were analyzed. DNA was extracted from tumor tissue. TP53 exons 4-8 were amplified by polymerase chain reaction (PCR), and variants were identified by Sanger sequencing. p53 immunohistochemistry was performed in 40 tumors and 36 adjacent tissues, and nuclear expression was assessed using the Immunoreactivity Score. Results: Forty-three heterozygous variants were identified in 106/142 patients, representing 75% of the cohort. Thirty-one patients carried oncogenic variants, mainly clustered within the DNA-binding domain and involving hotspot residues such as Arg175, Tyr220, Gly245, Arg248, Arg273, and Arg282. Nuclear p53 expression was observed in 9/40 tumors, whereas all adjacent tissues were negative. Conclusions: TP53 alterations in exons 4-8 are frequent and heterogeneous in this Mexican cohort. Integrating mutational profiling with p53 immunohistochemistry provides complementary information for the biological interpretation of these tumors, including variants of translational interest. Full article

This study investigates how different rural landscape types shape visual attention and physiological responses, with the aim of informing more targeted rural landscape renewal. Four typical rural landscape types in the suburbs of Hefei, China, were examined: Flat Farmland (FF), Hilly Forest (HF), Developed Plain (DP), and Water-network Lowland (WNL). All four study villages are project villages in the suburban area of Hefei where rural revitalization is currently being advanced. This study therefore treats them as empirical cases within the context of rural revitalization in China, using them to examine perceptual differences among rural landscape types and their implications for rural landscape renewal. A two-stage research design was adopted to balance field realism and laboratory control. In the first stage, 40 representative scene images were selected by combining field video records with fluctuations in on-site skin conductance response (SCR). In the second stage, laboratory experiments were conducted while participants viewed the selected images, during which eye-tracking, skin conductance, and heart rate data were recorded simultaneously. These measures were used to characterize visual attention allocation and autonomic physiological responses across different rural landscape types, rather than to directly measure landscape preference. For Area of Interest (AOI) analysis, each image was coded into six landscape element categories: vegetation, buildings, roads, sky, vernacular buildings, and water bodies. The results revealed significant typological differences in overall visual search patterns and autonomic responses. Gaze hotspots were concentrated on identifiable targets and boundary regions in the foreground and midground, whereas the sky attracted relatively limited attention. FF primarily emphasized vernacular buildings and farmland boundaries, HF emphasized settlement interfaces and spatial transition nodes, DP emphasized road junctions and facilities along routes, and WNL emphasized water bodies and water–land interface zones. These findings suggest that a two-stage multimodal design can provide supporting evidence for understanding type-specific perceptual responses and can support more targeted strategies for rural landscape renewal. Full article

The genus Desmidorchis Ehrenb. (Apocynaceae) is a characteristic component of the succulent flora of the Arabian Peninsula, where high levels of endemism and increasing environmental pressures highlight the need for integrated genomic and conservation research. This study assessed the conservation status of three ethnomedicinally important endemics—D. adenensis, D. arabica, and D. awdeliana—and characterizes their complete plastomes to resolve their evolutionary and temporal history. Conservation assessments were conducted following IUCN Red List criteria, and complete plastomes were sequenced and compared within a dataset of 15 subtribe Stapeliinae taxa. Comparative analyses examined the genome structure, divergence hotspots, repetitive sequences, codon usage bias, and selection pressure, while divergence times were estimated using fossil-calibrated molecular clock analyses. All three species were classified as Near Threatened (NT), primarily due to anthropogenic and environmental pressures. Plastome analyses revealed a highly conserved genome structure; however, hypervariable regions, particularly ycf1 and clpP1, exhibited elevated sequence divergence and phylogenetic informativeness. Simple sequence repeats (SSRs) were also identified as potentially informative features at the genus level. Codon usage and Ka/Ks analyses further indicated that most plastid protein-coding genes are under strong purifying selection, whereas only a few loci, particularly clpP1, showed comparatively elevated evolutionary rates. Phylogenomic analyses supported the monophyly of Desmidorchis, with molecular dating indicating recent Pleistocene diversification (~0.34–1.51 Ma), potentially associated with Quaternary climatic oscillations. Overall, this study provides an important genomic foundation for future taxonomic, evolutionary, and conservation studies of rare Arabian taxa. Full article

Copy number variations (CNVs) are a major source of structural genomic diversity that influences adaptation, reproduction, and production traits in livestock. The Black Bengal goat, an economically important Indian breed known for its high fecundity, superior skin quality, and resilience to humid tropical climates, was studied to uncover its structural genomic landscape. We performed whole-genome CNV analysis using high-depth (10×) sequencing data from eight individuals. A total of 31,816 copy number variants (CNVs) were identified, predominantly duplications, with an average length of approximately 45 kb. These CNVs were combined into 8910 copy number variation regions (CNVRs) covering approximately 0.15 Gb (about 5.3% of the autosomal genome). CNVR hotspots were mainly located on chromosome 1. Gene annotation showed that regions overlapping with CNVs and CNVRs contained more than 1987 protein-coding genes involved in pathways related to immunity, reproduction, metabolism, and extracellular matrix (ECM) organization. The presence of CNVs involving genes such as GDF9 and BMPR1B on chromosomes 7 & 6, respectively, is important because it indicates that the breed has a high reproductive capacity due to dosage-sensitive duplications. Changes in the extracellular matrix and increased dermal strength have been linked to duplications of genes such as COL6A1, LAMC2, LAMB3, FMN1, and CLDN1. This helps explain the superior hide quality of the breed. This research offers a comprehensive map of CNVs and CNVRs within the genome of the Black Bengal goat. It demonstrates how these duplications lead to structural changes that enhance both reproductive performance and skin resilience. These findings provide a valuable genomic resource for future marker-assisted selection, comparative genomics, and conservation breeding programs aimed at preserving indigenous goat populations. Full article

Road safety studies commonly use machine learning to predict crashes or to estimate crash-based treatment effects. This study instead audits the modelled fatal-and-serious-injury (FSI) risk produced by the iRAP ViDA risk engine. We analyse 147,466 segments (100 m each) from 12 surveys grouped into four European reporting groups. In Stage 1, gradient-boosted trees reproduce the engine’s risk surface under road-grouped cross-validation(R² ≈ 0.92 with flows and survey identifiers), and Shapley-based attributions identify which coded attributes drive modelled risk at 396 hotspots (top-three segments per road). In Stage 2, a causal-forest double machine learning estimator adjusts for 38 covariates to estimate segment-level conditional contrasts between modelled risk and six retrofittable treatments across all eligible segments. Simple absolute and relative reduction thresholds translate these associations into 1170 association-based candidate upgrades. On 321 over-lapping hotspots, the candidate upgrades show moderate agreement with iRAP’s Safer Roads Investment Plan (Recall = 0.77; Precision = 0.66; Cohen’s κ = 0.40). All results are conditional associations on a calibrated risk engine whose totals are anchored to project- or network-level fatality totals or fatality estimates used in calibration, not causal effects on observed crashes. Full article