Search Results (3,871)

The bacterial diversity of soils cultivated with avocado (Persea americana M.) is influenced by different factors, perhaps the most decisive being the type of agronomic management used by farmers. In conventional agronomic management (CM), high doses of agrochemicals are applied, in contrast to organic agronomic management (OM), where organic fertilizers are used. This alters the diversity and abundance of soil microorganism populations, which in turn affects crop health. This study aimed to isolate and morphologically characterize rhizospheric bacteria from avocado trees under different agronomic management systems (CM and OM). For the bacterial isolates, their ability to promote plant growth in vitro was determined through biochemical tests for phosphorus and calcium solubilization and nitrogen fixation. In addition, their in vivo effect on tomato (S. lycopersicum) growth was evaluated, and their antagonistic capacity against Fusarium sp. was assessed. The results showed differences in the quantity, diversity, and morphologies of bacterial isolates depending on the type of agronomic management. A higher Shannon diversity index was found in OM (2.44) compared to CM (1.75). A total of 35 bacterial isolates were obtained from both management types. A greater number of isolates from OM soils exhibited in vitro PGP activity; notably, eight isolates from OM plots showed phosphate-solubilizing activity, compared to only one from CM plots. Furthermore, although all isolates demonstrated nitrogen fixing capacity, those from OM orchards produced significantly higher nitrate levels than the control (Azospirillum vinelandii). On the other hand, inoculation of tomato plants with bacterial isolates from OM soils increased plant height, root length, and total fresh and dry biomass compared to isolates from CM soils. Likewise, OM isolates exhibited greater antagonistic activity against Fusarium sp. These findings demonstrate the impact of agronomic management on soil bacterial populations and its effect on plant growth and protection against pathogens. Full article

Capsicum is one of the most economically significant vegetable crops worldwide, owing to its high content of bioactive compounds with nutritional, pharmacological, and industrial relevance. However, research has focused on C. annuum, often disregarding local diversity and secondary gene pools, which may contain hidden variation for quality traits. Therefore, this study evaluated the genetic and phenotypic diversity of 283 accessions from the Colombian germplasm collection in the agrobiodiversity hotspot of northwest South America, representing all five domesticated species of the genus. A total of 18 morphological, physicochemical, and biochemical fruit traits were assessed, including texture, color, capsaicinoid, and carotenoid content. The phenotypic data were integrated with genomic information obtained through genotyping-by-sequencing (GBS) using the C. annuum reference genome and a multispecies pangenome. Fixed-and-Random-Model-Circulating-Probability-Unification (FarmCPU) and Bayesian-information-and-Linkage-disequilibrium-Iteratively-Nested-Keyway (BLINK) genome-wide association studies (GWAS) were performed on both alignments, respectively, leading to the identification of complex polygenic architectures with 144 and 150 single nucleotide polymorphisms (SNPs) significantly associated with key fruit quality traits. Candidate genes involved in capsaicinoid biosynthesis were identified within associated genomic regions, terpenoid and sterol pathways, and cell wall modifiers. These findings highlight the potential of integrating pangenomic resources with multi-omics approaches to accelerate Capsicum improvement programs and facilitate the development of cultivars with enhanced quality traits and increased agro-industrial value. Full article

Microscopy is a fundamental tool in biological research. However, conventional microscopes require manual operation and depend on user and equipment availability, limiting their suitability for continuous observation. Moreover, their size and complexity make them impractical for in situ experimentation. In this work, we present a novel, compact, affordable, and portable microscope that enables continuous in situ monitoring by being placed directly on biological samples. This chip-sized lensless holographic microscope (CLHM) is specifically designed to overcome the limitations of traditional microscopy. The device consists solely of an ultra-compact, state-of-the-art micro-LED display and a CMOS sensor, all enclosed within a 3D-printed housing. This unique light source enables a size that is markedly smaller than any comparable technology, allowing a resolution of 2.19 μm within a 7 mm distance between the light source and the camera. This paper demonstrates the CLHM’s versatility by monitoring in vitro models and performing whole-organism morphological analyses of small specimens. These experiments underscore its potential as an on-platform sensing device for continuous, in situ biological monitoring across diverse models. Full article

Microplastic pollution in the Chesapeake Bay is of critical concern as estuaries serve as habitats and nurseries for diverse aquatic organisms and offer vital ecological services. However, quantitative analysis of microplastics, especially those smaller than 300 µm, in the natural aquatic environment is very challenging due to a lack of efficient sampling methods. This study takes a novel approach to quantify the abundance, size distribution, and morphological characteristics of microplastics, as small as 20 µm, in the surface waters of the Chesapeake Bay. Water samples (10 L) were collected monthly from July 2023 to October 2023 at four locations along the Chesapeake Bay. The samples were digested with a 10% potassium hydroxide solution and subjected to density separation using sodium chloride (ρ = 1.2 g/cc). Microplastic particles were examined using a Shimadzu AIM–9000 FTIR microscope for enumeration and chemical identification. Overall, the mean microplastic concentration observed was 766.16 ± 302.59 MP/L, significantly higher than previously estimated in the Chesapeake Bay. Microplastic abundance exhibited a significant (p = 0.02) spatial variation across the four sampling locations. Most abundant were particles less than 100 µm (60.65%), followed by particles between 100 µm and 300 µm (23.19%), and particles exceeding 300 µm (16.16%). Morphological analysis identified fragments as the dominant shape (86.02%), followed by fibers (11.87%), and beads (2.10%). This study underscores the importance of standard and efficient sampling methods in microplastics research. By sampling microplastics as small as 20 µm, this research demonstrated that the abundance of microplastics in the Chesapeake Bay is significantly higher than previously estimated and dominated by smaller–sized particles. These small microplastics are more likely to enter the food web where human exposure may occur. Therefore, microplastic pollution in the Chesapeake Bay ecosystem has the potential to impose environmental and public health risks. Full article

Accurate mapping of seafloor morphological features, such as pockmarks, is essential for marine spatial planning, geological hazard assessment, and environmental monitoring. Traditional manual delineation methods are often subjective and inefficient when applied to large, high-resolution bathymetric datasets. This study presents a semi-automated workflow based on the CoMMa (Confined Morphologies Mapping) toolbox to classify pockmarks in Flensburg Fjord, Germany–Denmark. Initial detection employed the Bathymetric Position Index (BPI) with intentionally permissive parameters to ensure high recall of morphologically diverse features. Morphometric descriptors were then extracted and used to train a Random Forest classifier, enabling noise reduction and refinement of overinclusive delineations. Validation against expert-derived mappings showed that the model achieved an overall classification accuracy of 86.16%, demonstrating strong performance across the validation area. These findings highlight how integrating a GIS-based geomorphometry toolbox with machine learning yields a reproducible, objective, and scalable approach to seabed mapping, supporting decision-making processes and advancing standardized methodologies in marine geomorphology. Full article

Accurate and timely detection of brain tumors from magnetic resonance imaging (MRI) scans is critical for improving patient outcomes and informing therapeutic decision-making. However, the complex heterogeneity of tumor morphology, scarcity of annotated medical data, and computational demands of deep learning models present substantial challenges for developing reliable automated diagnostic systems. In this study, we propose a robust and scalable deep learning framework for brain tumor detection and classification, built upon an enhanced YOLO-v11 architecture combined with a two-stage transfer learning strategy. The first stage involves training a base model on a large, diverse MRI dataset. Upon achieving a mean Average Precision (mAP) exceeding 90%, this model is designated as the Brain Tumor Detection Model (BTDM). In the second stage, the BTDM is fine-tuned on a structurally similar but smaller dataset to form Brain Tumor Detection and Segmentation (BTDS), effectively leveraging domain transfer to maintain performance despite limited data. The model is further optimized through domain-specific data augmentation—including geometric transformations—to improve generalization and robustness. Experimental evaluations on publicly available datasets show that the framework achieves high mAP@0.5 scores (up to 93.5% for the BTDM and 91% for BTDS) and consistently outperforms existing state-of-the-art methods across multiple tumor types, including glioma, meningioma, and pituitary tumors. In addition, a post-processing module enhances interpretability by generating segmentation masks and extracting clinically relevant metrics such as tumor size and severity level. These results underscore the potential of our approach as a high-performance, interpretable, and deployable clinical decision-support tool, contributing to the advancement of intelligent real-time neuro-oncological diagnostics. Full article

The rabbit industry urgently needs natural alternatives to maintain gut health. This need is growing due to the higher incidence of intestinal problems in antibiotic-free production systems. Pfaffia glomerata (Brazilian ginseng) is a medicinal plant rich in bioactive compounds. This study evaluated the effects of dietary inclusion of P. glomerata leaf powder on growth performance, immune function, and gut microbiota in New Zealand rabbits. A total of 100 New Zealand rabbits (35 days old) were randomly assigned to five groups (n = 20 each). The groups comprised a blank control group (CON), an immunosuppressed group (CTX), and three treatment groups receiving low (L), medium (M), and high (H) doses of P. glomerata leaf powder at 0.5%, 1%, and 2% of the basal diet, respectively. Growth performance was assessed by average daily gain (ADG). Intestinal morphology was evaluated by measuring villus height and crypt depth in the duodenum, jejunum, and ileum. Immune parameters included thymus and spleen weight, serum immunoglobulin levels, and leukocyte counts. Cecal microbiota diversity and composition were analyzed. Compared to CON, Pfaffia supplementation improved growth performance: the L and H groups had significantly higher ADG and a lower feed conversion ratio (p < 0.05). In intestinal histology, groups L and M had reduced crypt depth in the duodenum (p < 0.05), and group L had an increased villus height to crypt depth ratio. In the jejunum and ileum, groups L and H exhibited increased villus height and villus height to crypt depth ratio (p < 0.05), indicating an enhanced absorptive surface. In the immunosuppressed model, rabbits in all Pfaffia groups showed significantly increased thymus and spleen weight compared to the CTX group (p < 0.05) and demonstrated elevated serum immunoglobulins and leukocyte counts (monocytes, lymphocytes, and neutrophils) (p < 0.05). Pfaffia supplementation also enhanced cecal microbiota diversity and increased the abundance of beneficial bacteria. In summary, dietary P. glomerata leaf powder enhanced growth performance, immune organ development, and healthy gut microbiota in growing rabbits. The 0.5% supplementation level (L group) yielded the most consistent benefits. These results suggest that P. glomerata is a promising natural feed additive to promote rabbit health and production in antibiotic-free systems. Full article

High-Andean Creole cattle represent a genetic resource with significant adaptive value in high-altitude ecosystems. In this study, 151 Creole cows from the provinces of Canas and Quispicanchis (Cusco, Peru) were evaluated to characterize their morphometry, calculate zoometric indices, and establish functional biotypes. Ten morphometric parameters were measured, and eight structural indices were calculated. Four differentiated biotypes were identified through the multivariate analysis and hierarchical clustering of quantitative variables (BI, BII, BIII, and BIV). Morphological differences were statistically significant (p < 0.05) and associated with different productive orientations. Biotype I showed greater thoracic development and a compact structure, which is compatible with intermediate beefaptitude; Biotype II exhibited more elongated proportions and a lower weight, indicating a dairy tendency; Biotype III stood out for its muscle mass, thoracic depth, and pelvic width, evidencing a clear beef orientation; and Biotype IV combined a higher weight, rectangular conformation, and wide pelvis, showing potential for beef production and ease of calving. Functional differentiation was supported by Principal Coordinates Analysis (PCoA) and PERMANOVA tests. Correlations between morphometric variables and zoometric indices revealed anatomical patterns consistent with the productive functions proposed. The results highlight the morpho-functional diversity of Andean Creole cattle and their potential for conservation and improvement programs in extensive highland systems. Full article

The present study applied a GIS-based methodology for assessing soil diversity in a protected mountain area of Italy. Using QGIS, morphological (i.e., altitude and slope), lithological, climatic, and land use layers were intersected to delineate 16 land units (LUs), each representing relatively homogeneous conditions for soil formation, according to Jenny’s equation. To obtain the soil map units, a total of 112 soil profiles were analyzed, including 79 from previous studies and 33 that were newly excavated during 2023–2024 to fill gaps in underrepresented LU types. Most soils were classified as Inceptisols/Cambisols, occurring in both Dystric and Eutric variants, mainly in relation to lithology (i.e., arenaceous or pelitic facies). Alfisols, Umbrisols, and hydromorphic soils were also identified. The physicochemical properties showed marked variability among LUs, with sand content ranging from 39 to 798 g kg⁻¹, pH from 4.4 to 7.9, and organic carbon content from 1.6 to 6.1%. This LU-based framework allowed efficient field sampling, if compared to grid-based surveys, while retaining information on fine-scale pedodiversity. No quantitative accuracy assessment (e.g., boundary precision, internal homogeneity metrics) was conducted, even if the spatial coherence of the delineated LUs was supported by the distribution of soil profiles, which provided empirical validation of the LU framework. Full article

Rice blast, caused by Magnaporthe oryzae, significantly threatens global rice production. Disease control is complicated by the pathogen’s high genetic diversity, which is driven by heterothallic recombination between opposite mating types that underlies variation. However, mechanisms governing sexual reproduction in this fungus remain poorly characterized, largely due to the absence of reliable methods for scalable ascospore progeny production. In this study, we established two novel mating methods, namely Conidial Mixing Mating (CMM) and Hyphal Segments Mixed Mating (HMM). Both methods employed optimized suspensions (5 × 10⁴ conidia/mL or equivalent hyphal density) mixed at 1:1 ratios, incubated under standardized conditions: 20 °C with a 12 h/12 h photoperiod. We characterized perithecia, asci, and ascospore morphology using fluorescence microscopy, paraffin sectioning, cryo-scanning electron microscopy, and transmission electron microscopy. Furthermore, both methods enabled phenotypic characterization of sexual reproduction-deficient mutants, including ΔMopmk1 and ΔMoopy2. In conclusion, we established two efficient methods for investigating M. oryzae sexual reproduction, providing foundational tools to advance studies of sexual mechanisms, pathogenicity evolution, and genetic variation. Full article

Background. Antimicrobial resistance (AMR) poses a significant global health threat, necessitating a deeper understanding of bacterial adaptation mechanisms. Introduction. This study investigates the genotypic and phenotypic evolutionary trajectories of Escherichia coli under meropenem and gentamicin selection, and it benchmarks these findings against florfenicol-evolved strains. Methodology. Utilizing a downsized, three-layer acrylic modified “Microbial Evolution and Growth Arena (MEGA-plate) system”—scaled to 40 × 50 cm for sterile handling and uniform 37 °C incubation—we tracked adaptation over 9–13 days, enabling real-time visualization of movement across antibiotic gradients. Results. Meropenem exposure elicited pronounced genetic heterogeneity and morphological remodeling (filamentous and circular forms), characteristic of SOS-mediated division arrest and DNA-damage response. In contrast, gentamicin exposure produced a uniform resistance gene profile and minimal shape changes, suggesting reliance on conserved defenses without major morphological adaptation. Comprehensive genomic analysis revealed a core resistome of 22 chromosomal loci shared across all three antibiotics, highlighting potential cross-resistance and the central roles of baeR, gadX, and marA in coordinating adaptive responses. Gene ontology enrichment underscored the positive regulation of gene expression and intracellular signaling as key themes in resistance evolution. Discussion. Our findings illustrate the multifaceted strategies E. coli employs—combining metabolic flexibility with sophisticated regulatory networks—to withstand diverse antibiotic pressures. This study underscores the utility of the MEGA-plate system in dissecting spatiotemporal AMR dynamics in a controlled yet ecologically relevant context. Conclusions. The divergent responses to meropenem and gentamicin highlight the complexity of resistance development and reinforce the need for integrated, One Health strategies. Targeting shared regulatory hubs may open new avenues for antimicrobial intervention and help preserve the efficacy of existing drugs. Full article

Accurate identification of cotton leaf pests and diseases is essential for sustainable cultivation but is challenged by complex backgrounds, diverse pest morphologies, and varied symptoms, where existing deep learning models often show insufficient robustness. To address these challenges, RDL-YOLO model is proposed in this study. In the proposed model, RepViT-Atrous Convolution (RepViT-A) is employed as the backbone network to enhance local–global interaction and improve the response intensity and extraction accuracy of key lesion features. In addition, the Dilated Dense Convolution (DDC) module is designed to achieve a dynamic multi-scale receptive field, enabling the network to adapt to lesion defects of different shapes and sizes. LDConv further optimizes the effect of feature fusion. Experimental results showed that the mean Average Precision (mAP) of the proposed model reached 77.1%, representing a 3.7% improvement over the baseline YOLOv11. Compared with leading detectors such as Real-Time Detection Transformer (RT-DETR), You Only Look Once version 11 (YOLOv11), DETRs as Fine-grained Distribution Refinement (D-FINE), and Spatial Transformer Network-YOLO (STN-YOLO). RDL-YOLO exhibits superior performance, enhanced reliability, and strong generalization capabilities in tests on the cotton leaf dataset and public datasets. This advancement offers a practical technical solution for improved agricultural pest and disease management. Full article

The objective of this study was to investigate the physicochemical properties of hybrid coatings with titanium nitride and boron nitride nanoparticles deposited on the TiAlV medical alloy via the sol–gel process. The developed layers were intended to impart bactericidal properties and provide protection against surgical abrasions during the implantation procedure. This study focused on evaluating the microstructure (SEM + EDS), structure (XRD, FTIR), and surface properties, including wettability, surface free energy, and roughness of the synthesized layers. Our results confirmed that it was feasible to produce hybrid layers with various microstructures and diverse layer morphologies. The FTIR and XRD structural analyses confirmed the presence of an organosilicon matrix incorporating the two aforementioned types of ceramic particles. Full article

Resolving the eukaryotic tree of life (eToL) remains a fundamental challenge in biology. Much of eukaryotic phylogenetic diversity is occupied by unicellular microbial eukaryotes (i.e., protists). Among these, the phylogenetic positions of a significant number of lineages remain unresolved due to limited data and ambiguous traits. To address this issue, we introduce the term “PUPAs” (protists with uncertain phylogenetic affiliations) to collectively describe these lineages, instead of using vague or inconsistent labels, such as incertae sedis or orphan taxa. Historically, protists were classified based solely on morphological features, and many with divergent cell structures were left unplaced in the eToL. With the advent of sequence-based approaches, the phylogenetic affiliations of some PUPAs have been clarified using molecular markers, such as small subunit ribosomal DNA. The combination of technological progress and continuous efforts to cultivate diverse protists, including PUPAs and novel protists, now enables phylogenetic analyses based on hundreds of proteins, providing their concrete placements in the eToL. For example, these advances have led to the discovery of new deep-branching lineages (e.g., Hemimastigophora), the resolution of relationships among major groups (e.g., Microheliella, which linked Cryptista and Archaeplastida), and insights into evolutionary innovations within specific clades (e.g., Glissandra). In this review, we summarize current consensus in eukaryotic phylogeny and highlight recent findings on PUPAs whose phylogenetic affiliations have been clarified. We also discuss a few lineages for which the phylogenetic homes remain unsettled, the evolutionary implications of these discoveries, and the remaining challenges in resolving the complete eToL. Full article

Snack foods (e.g., extruded flour-based products) are widely favored by consumers because of their convenience, affordability, and time-saving attributes. However, with the growing demand for high-quality snacks, several challenges have emerged that hinder industry development, such as relatively underdeveloped industrial standards, limited raw material diversity (primarily starch and soy protein), and, consequently, insufficient nutritional value. In this study, a novel type of puffed snack was developed using Alaskan pollock surimi and wheat flour using extrusion puffing technology. The effects of varying ratios of surimi to wheat flour (0:10, 1:9, 2:8, 3:7, and 4:6, which served as SFBC, SFB1, SFB2, SFB3, and SFB4, respectively), on the physicochemical properties, apparent morphology, microstructure, thermal stability, and protein structure of spicy strips were systematically investigated, and the interaction between extruded protein and flour mixtures was analyzed. The results indicated that increasing the proportion of surimi led to decreases in hardness, elasticity, and chewiness, whereas the moisture content and water solubility index increased. The maximum expansion rate (202.2%) was observed in the SFB1 sample. Morphological and microstructural observations further revealed that a higher surimi content resulted in a denser internal structure and a reduced degree of puffing. The protein distribution was relatively uniform, with large pores. Moreover, increased surimi content increased the proportion of immobilized water and improved the thermal stability. These findings provide valuable insights into starch–protein-complex-based extrusion puffing technologies and contribute to the development of innovative surimi-based puffed food products. Full article