Search Results (1,696)

Sonneratia alba Sm. and Sonneratia caseolaris (L.) Engl. are two ecologically important components of mangrove communities in Thailand. However, their population genetic patterns in Thailand remain poorly understood. Here, we assessed the genetic diversity and population structure of 107 S. alba and 131 S. caseolaris individuals sampled across their full coastal range in Thailand using single-nucleotide polymorphism (SNP) markers. Population structure analyses consistently revealed strong genetic subdivision associated with geography: S. alba formed three clusters (including one admixed group), whereas S. caseolaris formed two clusters. In both species, populations were clearly separated between the Andaman Sea and the Gulf of Thailand, reflecting the isolating influence of the Malay Peninsula. Genetic differentiation between clusters was high (F_ST = 0.364 in S. alba and 0.321 in S. caseolaris). Genetic differentiation increased with geographic distance in S. caseolaris, whereas no such relationship was detected in S. alba. Both species exhibited low levels of genetic diversity (H_o = 0.173; H_e = 0.223 in S. alba and H_o = 0.290; H_e = 0.406 in S. caseolaris). Together, these results reveal pronounced spatial genetic structure and limited evolutionary connectivity between coastal regions, providing genome-wide insights into mangrove population differentiation with important implications for conservation and restoration. Full article

Quercus texana seeds are recalcitrant and thus highly sensitive to desiccation, which makes storage difficult. For practical seed handling, it is important to define their safe water content and to understand how water is distributed during dehydration. The present study utilized magnetic resonance imaging (MRI) and nuclear magnetic resonance (NMR) technologies to investigate the migration and phases of water, respectively, revealing the underlying reasons for the recalcitrance of Q. texana seeds. The water content of fresh Q. texana seeds was found to be 39.6% and the germination percentage was 93.3%. As the water content decreased, the germination percentage decreased continuously, reaching 0% at a water content of 13.0%. At 20.0% water content, the germination percentage was 71.7%. MRI showed that water was primarily stored in the embryo axis and cotyledon center in fresh Q. texana seeds. Water loss occurs in the following order during seed dehydration: embryo axis, cotyledon center, cotyledon periphery, and cotyledon end. However, water in the radicle region persisted until seed water content decreased to 15.0%, at which point no signal was detected. The NMR T₂ relaxation spectrum indicated the presence of bound water (T₂₁ = 0.01–5.44 ms) and free water (T₂₂ = 7.19–1401.93 ms) in the seeds. During the dehydration process, most of the water was lost as free water, and the T₂₂ shifted to longer times. Concurrently, the bound water shifted to shorter T₂₁ times. Overall, for practical purposes, seed water should be maintained at or above 20.0%. MRI further showed that water loss from the radicle plays a decisive role in the decline of seed germination, and that protecting the region of radicle and the cupule scar can effectively retard water loss. Furthermore, the bound-water content is positively correlated with seed germination. Full article

Wild ginkgo, as an endangered species, holds significant value for genetic resource conservation, yet its practical applications face numerous challenges. Traditional field surveys are inefficient in mountainous mixed forests, while satellite remote sensing is limited by spatial resolution. Current deep learning approaches relying on single-source data or merely simple multi-source fusion fail to fully exploit information, leading to suboptimal recognition performance. This study presents a multimodal ginkgo crown dataset, comprising RGB and multispectral images acquired by an UAV platform. To achieve precise crown segmentation with this data, we propose a novel dual-branch dynamic weighting fusion network, termed dual-branch cross-modal attention-enhanced UNet (DCA-UNet). We design a dual-branch encoder (DBE) with a two-stream architecture for independent feature extraction from each modality. We further develop a cross-modal interaction fusion module (CIF), employing cross-modal attention and learnable dynamic weights to boost multi-source information fusion. Additionally, we introduce an attention-enhanced decoder (AED) that combines progressive upsampling with a hybrid channel-spatial attention mechanism, thereby effectively utilizing multi-scale features and enhancing boundary semantic consistency. Evaluation on the ginkgo dataset demonstrates that DCA-UNet achieves a segmentation performance of 93.42% IoU (Intersection over Union), 96.82% PA (Pixel Accuracy), 96.38% Precision, and 96.60% F1-score. These results outperform differential feature attention fusion network (DFAFNet) by 12.19%, 6.37%, 4.62%, and 6.95%, respectively, and surpasses the single-modality baselines (RGB or multispectral) in all metrics. Superior performance on cross-flight-altitude data further validates the model’s strong generalization capability and robustness in complex scenarios. These results demonstrate the superiority of DCA-UNet in UAV-based multimodal ginkgo crown recognition, offering a reliable and efficient solution for monitoring wild endangered tree species. Full article

Understanding the spatial structure of large mammals is critical for conservation planning, especially under increasing habitat fragmentation. This study applies an integrated spatial analysis combining the DBSCAN density-based clustering algorithm and the α-hull method to delineate non-convex geographic ranges of the maned wolf (Chrysocyon brachyurus) across South America. Using 454 occurrence records filtered for ecological reliability, we identified 11 geographically isolated α-populations distributed across five countries and multiple biomes, including the Cerrado, Chaco, and Atlantic Forest. The sensitivity analysis of the α parameter demonstrated that values below 2 failed to generate viable polygons, while α = 2 provided the best balance between geometric detail and ecological plausibility. Our results reveal a highly fragmented distribution, with α-populations varying in area from 43,077 km² to 566,154.7 km² and separated by distances up to 994.755 km. Smaller and peripheral α-populations are likely more vulnerable to stochastic processes, genetic drift, and inbreeding, while larger clusters remain functionally isolated due to anthropogenic barriers. We propose the concept of ‘α-population’ as an operational unit to describe geographically and functionally isolated groups identified through combined spatial clustering and non-convex hull analysis. This approach offers a reproducible and biologically meaningful framework for refining range estimates, identifying conservation units, and guiding targeted management actions. Overall, integrating α-hulls with density-based clustering improves our understanding of the species’ fragmented spatial structure and supports evidence-based conservation strategies aimed at maintaining habitat connectivity and long-term viability of C. brachyurus populations. Full article

The Soil and Plant Analyzer Development (SPAD) value is a reliable proxy for chlorophyll, yet conventional field measurement remains labor-intensive and spatially limited. Current remote sensing inversion models typically depend on costly multispectral sensors and rarely account for phenological changes, restricting their applicability across orchards and seasons. To overcome these limitations, this study introduces a stage-aware and low-cost SPAD inversion framework for jujube trees, integrating multi-source data fusion and an optimized ensemble model. A two-year experiment (2023–2024) combined UAV multispectral vegetation indices (VI) with RGB-derived color indices (CI) across leaf expansion, flowering, and fruit-setting stages. Rather than using static features, stage-specific predictors were systematically identified through a hybrid selection mechanism combining Random Forest Cumulative Feature Importance (RF-CFI), Recursive Feature Elimination (RFE), and F-tests. Building on these tailored features, XGBoost, decision tree (DT), CatBoost, and an Optimized Integrated Architecture (OIA) were developed, with all hyperparameters globally tuned using a genetic algorithm (GA). The RFI-CFI-OIA-GA model delivered superior accuracy (R² = 0.758–0.828; MSE = 0.214–2.593; MAPE = 0.01–0.045 in 2024) in the training dataset, and robust cross-year transferability (R² = 0.541–0.608; MSE = 0.698–5.139; MAPE = 0.015–0.058 in 2023). These results demonstrate that incorporating phenological perception into multi-source data fusion substantially reduces interference and enhances generalizability, providing a scalable and reusable strategy for precision orchard management and spatiotemporal SPAD mapping. Full article

Acute lymphoblastic leukemia (ALL) is a genetically heterogeneous disease where current clinical practice guidelines remain focused on traditional cytogenetic markers. Despite recent advances demonstrating excellent diagnostic accuracy for gene expression signatures, a discontinuity exists between biomarker validation and clinical implementation. This study aimed to develop and validate a multiparametric gene expression signature using digital PCR (dPCR) to accurately diagnose pediatric ALL, with potential utility for monitoring measurable residual disease (MRD). We analyzed 130 bone marrow aspirates from pediatric patients from four clinical groups: non-leukemia, MRD-negative, MRD-positive and leukemia characterized by immunophenotype. Gene expression of an 8-gene panel (JUP, MYC, NT5C3B, GATA3, PTK7, CNP, ICOSLG, and SNAI1) was quantified by dPCR. The diagnostic performance of individual markers was assessed, and a Random Forest machine learning model was trained to classify active disease. The model was validated using a 5-fold stratified cross-validation approach. Individual markers, particularly JUP, MYC, and NT5C3B, showed good diagnostic accuracy for distinguishing leukemia from non-leukemia. However, integrating all eight markers into a multivariate Random Forest model significantly enhanced performance. The model achieved a mean cross-validated area under the curve (AUC) of 0.908 (±0.041) on receiver operator characteristic (ROC) analysis and 0.961 (±0.019) on Precision–Recall (PR) analysis, demonstrating high reliability and a favorable balance between sensitivity and precision. The integrated model achieved high sensitivity (88.9%) for detecting active disease, particularly at initial diagnosis. Although specificity was moderate (65.0%), the high positive predictive value (PPV 85.1%) and accuracy (81.5%) confirm the clinical utility of a positive result. While the panel showed promising performance for distinguishing MRD-positive from MRD-negative samples, the limited MRD-positive cohort size (n = 11) indicates that validation in larger MRD-focused studies is required before clinical implementation for treatment monitoring. This dPCR-based platform provides accessible, quantitative detection without requiring knowledge of clonal shifts or specific genomic landscape, offering potential advantages for resource-limited settings such as those represented in our Mexican pediatric cohort. Full article

This study employed machine learning to investigate the mechanical behavior of one-part geopolymer (OPG)-stabilized soil subjected to acid erosion. Based on the unconfined compressive strength (UCS) data of acid-eroded OPG-stabilized soil, eight machine learning models, namely, Adaptive Boosting (AdaBoost), Decision Tree (DT), Extra Trees (ET), Gradient Boosting (GB), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), along with hyper-parameter optimization by Genetic Algorithm (GA), were used to predict the degradation of the UCS of OPG-stabilized soils under different durations of acid erosion. The results showed that GA-SVM (R² = 0.9960, MAE = 0.0289) and GA-XGBoost (R² = 0.9961, MAE = 0.0282) achieved the highest prediction accuracy. SHAP analysis further revealed that solution pH was the dominant factor influencing UCS, followed by the FA/GGBFS ratio, acid-erosion duration, and finally, acid type. The 2D PDP combined with SEM images showed that the microstructure of samples eroded by HNO₃ was marginally denser than that of samples eroded by H₂SO₄, yielding a slightly higher UCS. At an FA/GGBFS ratio of 0.25, abundant silica and hydration products formed a dense matrix and markedly improved acid resistance. Further increases in FA content reduced hydration products and caused a sharp drop in UCS. Extending the erosion period from 0 to 120 days and decreasing the pH from 4 to 2 enlarged the pore network and diminished hydration products, resulting in the greatest UCS reduction. The results of the study provide a new idea for applying the ML model in geoengineering to predict the UCS performance of geopolymer-stabilized soils under acidic erosion. Full article

The genus Pinus (~115 species) represents a cornerstone of boreal and temperate forests and plays a central role in global forestry, industrial applications, and carbon sequestration. Their distinctive biology—including exceptionally large genomes, guaiacyl-rich lignin, tracheid-based xylem, and pronounced seasonal growth regulation—makes pines both scientifically compelling and technically challenging to study. Recent advances in genomics and transcriptomics, supported by emerging multi-omics and computational frameworks, have significantly advanced our understanding of the molecular architecture of wood formation, including key processes such as NAC–MYB regulatory cascades, lignin biosynthesis pathways, and adaptive processes such as compression wood development. Yet functional studies remain limited by low transformation efficiency, regeneration difficulties, and a scarcity of conifer-optimized genetic tools. This review highlights recent breakthroughs in single-cell and spatial transcriptomics, CRISPR-based genome editing, synthetic promoter design, and machine learning-driven regulatory network prediction and comprehensively examines translational applications in biomass improvement, lignin engineering, stress resilience, and industrial biotechnology. By expanding the research frontiers of Pinus, we aim to connect molecular discovery with applied forestry and climate mitigation strategies. Full article

Hard ticks are important vectors for several human and zoonotic pathogens, transmitting diseases such as Crimean–Congo hemorrhagic fever, Lyme disease, Kyasanur forest disease, Powassan virus disease, Tick-borne encephalitis, Rickettsiosis, and Anaplasmosis. Morphological identification of ticks relies on taxonomic keys but is often challenging due to damaged, engorged, or immature specimens and requires expertise. Molecular taxonomy can be a supplement to species identification and usually requires nucleotide sequencing of the genetic markers. PCR-RFLP is an important tool for tick identification and can be supplemented to the classical taxonomy. The current study focused on the morphological identification of important hard tick vectors from India, their phylogenetic positioning, and developing a PCR-RFLP based diagnostic tool for easy identification of hard tick vectors. The primer sets were designed to amplify the ITS-2 region from important tick vectors causing human and zoonotic diseases in India. These ticks were morphologically identified with taxonomical keys, and the extracted genomic DNA were used for ITS-2 based PCR amplification. The nucleotide sequences from each vector were used for their phylogenetic positioning. We obtained variable sizes of ITS-2 amplicons from each species and utilized the sequence for RFLP assays design. We have successfully shown PCR-RFLP based assays with two different restriction enzymes (Hae III & Rsa I) with specific restriction sites on the amplified regions. The PCR-RFLP tool showed different DNA fragment patterns on the agarose gel, specific for each hard tick vector. This study presents the phylogenetic positioning of Indian tick vectors and demonstrates the development and applicability of a molecular tool for their identification. Full article

Biotechnological tools have emerged as key alternatives for the protection, improvement, and sustainable use of forest species. This paper analyzes the main biotechnological strategies applied to the European chestnut, a species of significant ecological, economic, and cultural importance in many temperate regions. However, in recent decades, it has been seriously threatened by various factors, including devastating diseases such as chestnut blight and ink disease, as well as the impacts of climate change. First, classical and assisted breeding techniques are discussed, including controlled hybridization and the use of molecular markers to accelerate the selection of genotypes of interest. In the field of molecular biotechnology, studies related to the identification of key genes, the development of genetic markers (e.g., SSRs and SNPs), and the omics characterization of chestnut are reviewed. The use of micropropagation techniques for the clonal multiplication of elite individuals is also included. Furthermore, advances in genetic modifications are explored, highlighting the introduction of resistance genes through transgenic and cisgenic approaches, as well as emerging technologies such as CRISPR/Cas9. In the future, the integration of classical breeding with advanced genomics will enable the precise selection and accelerated development of European chestnut varieties, combining traditional trait improvement with genomic tools such as marker-assisted selection, genomic prediction, and gene editing to enhance disease resistance and climate resilience. Full article

Tree bark is a chemically rich but underexploited forest byproduct that can support circular bioeconomy strategies. This study investigates how provenance and drying temperature influence the structural and chemical composition of Larix decidua Mill. bark, aiming to support genotype selection and biomass valorization. The experimental design included bark collected from seven distinct provenances and subjected exclusively to controlled drying at three temperatures (60 °C, 80 °C, and 100 °C), enabling a focused assessment of thermally induced chemostructural variation. Bark samples from seven Romanian provenances were exposed to four drying treatments (control, 60 °C, 80 °C, 100 °C) and examined using FT-IR and GC–MS. FT-IR spectra revealed temperature-dependent shifts in O–H, C–H, and C=O regions, indicating subtle rearrangements in lignin, cellulose, and hemicellulose structures. GC–MS profiling identified major terpenoid, ester, amide, and diterpenoid/triterpenoid derivatives whose concentrations varied significantly across both thermal regimes and genetic origins. Moderate heating (60–80 °C) enhanced the release or stabilization of α-pinene, larixol, and several esterified or diterpenoid compounds, whereas 100 °C promoted oxidative transformations, increasing lipid-derived amides and resin-oxidation products such as caryophyllene oxide. Provenances from cooler, mid-altitude regions showed higher terpenoid abundance and greater thermochemical stability, while southern provenances accumulated more oxidative derivatives under high-temperature exposure. The strong provenance × temperature interactions highlight genetically driven variation in thermochemical plasticity. These findings provide a basis for identifying elite genotypes suitable for resin-oriented breeding and for optimizing temperature-controlled bark processing within sustainable biomass valorization frameworks. Full article

The practical applications of breed identification are numerous and diverse, and they include breed conservation and breeding program design. However, distinguishing between breeds remains challenging and costly, especially for phenotypically similar chicken populations. Continued research is necessary to develop more accessible and optimized methodologies. To address these challenges, machine learning (ML) offers promising tools for analyzing complex genetic data. The capabilities of machine learning, especially the random forest (RF) model, to enhance various fields, including bioinformatics, have recently been demonstrated. In this study, microsatellite genotype data from 651 individuals across 30 chicken populations filtered from a larger initial dataset for consistency were used to classify breeds using an RF model. Cross-validation techniques, including 10-fold cross-validation and leave-one-out cross-validation, were employed to assess the performance of the model. The model performance was evaluated using metrics such as accuracy, Cohen’s Kappa, 95% confidence interval, and F1-score. Results showed that the RF model achieved a 95.38% accuracy on the testing dataset. Accuracies of 91.44% and 90.99% were observed for 10-fold cross-validation and leave-one-out cross-validation, respectively. It is believed that larger datasets will significantly improve outcomes for other breeds. Because of its generalizability, the trained model can serve as a straightforward and modern method for chicken breed determination using machine learning. This study demonstrates that ML, particularly automated approaches like AutoGluon, provides a robust and accessible framework for chicken breed identification using cost-effective microsatellite data. Full article

Early prediction of casing-running sticking is essential, as the mitigation of stuck-pipe incidents often incurs significant time and economic costs. Previous studies have largely relied on purely theoretical torque and drag models that are constrained by simplified assumptions, preventing them from fully leveraging available field data and often leading to insufficient prediction accuracy. To address this challenge, we developed a hybrid mechanistic-data-driven intelligent model for hook-load prediction and casing-sticking risk assessment. The model combines mechanical models with ensemble learning algorithms, incorporating both mechanically derived parameters (theoretical hook load, casing–borehole compatibility, casing-bottom deflection and tilt angle) as well as operational and casing structural features. To evaluate its cross-field generalizability, the proposed model was trained on 13,449 samples from 14 wells across three oilfields and tested on 3961 samples from an independent well in a separate Oilfield. Three ensemble algorithms (XGBoost, Random Forest, and LightGBM) were evaluated, among which XGBoost achieved the highest predictive accuracy (RMSE = 3.50, MAE = 2.51, R² = 0.97) and was selected for subsequent friction-factor-based casing sticking risk assessment. A genetic-algorithm-based optimization framework was further developed to minimize sticking risk by optimizing the centralizer configuration under a friction constraint. The proposed sticking-risk assessment and optimization strategy was validated through field implementation. This mechanistic-data-driven intelligent model outperforms traditional theoretical approaches in predictive accuracy, interpretability, and engineering applicability, providing a practical and explainable tool for casing-running risk mitigation and design optimization in complex three-dimensional wells. Full article

(1) Background: In the Sudano-Sahelian zone of Cameroon, which is affected by drought and forest decline, Vachellia nilotica leaves and seeds are fodder for livestock. (2) Methods: A provenance and progeny study on growth performance and heritability of V. nilotica was carried out to provide a reliable database for tree selection, improvement programs, and the creation of future forested areas in this region. Open-pollinated seeds from 120 mother trees (10 half-sib families per provenance) representing twelve provenances, 50–100 km apart, were used for a progeny trial near Maroua, the Far North region of Cameroon. The experimental design was a Fisher block. (3) Results: The results reveal significant differences among provenances only for the number of leaves, and the variability was marked by coefficients of variation ranging from 0.24−0.63. Narrow-sense heritability was measured, varying from 0.01 ± 0.009 to 0.74 ± 0.02, and genetic gain reached 21.83 at the selection intensity of 5% for the number of leaves per plant. The phenotypic coefficient of variation varied between 14% and 90%. Half-sib families were classified into three subgroups using hierarchical ascending classification, and provenances were grouped into five groups using principal component analysis. (4) Conclusions: These results could contribute to initiating tree selection, but more provenances, longer-term experiments, and molecular genetic testing are needed to complement these nursery-level observations. Full article

The Japanese cedar (Cryptomeria japonica), also known as sugi, is one of the most important trees in Japanese forests. It covers 44% of artificial forests, spanning approximately 4.5 million ha. It is cultivated in East Asia, the Azores archipelago, and some islands in the Indian Ocean. It is also grown worldwide as an ornamental tree in parks and gardens. The cultivation and use of sugi in Japan dates back centuries, and clonal forestry through cuttings has been practiced since the early 15th century. Its broad adaptability, genetic diversity, rapid growth, easy propagation, and precocious flowering—enabling early generational crosses—combined with their advanced genomic resources and efficient biotechnological tools, make sugi an outstanding conifer model. This review aims to provide an overview of the biotechnology and genetics of sugi for researchers and stakeholders. Full article