Search Results (605)

Background/Objectives: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Emerging evidence highlights the role of the gut microbiota in the development and progression of CRC. Microbial dysbiosis is hypothesized to contribute to chronic inflammation through a variety of mechanisms, such as the production of free radicals, which induce mutagenesis and immune dysregulation in the host, ultimately leading to diseases such as cancer. Methods: Tumor tissue samples or healthy mucosa tissue were collected for bacterial DNA extraction. The V3–V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq platform. Bioinformatics analysis was performed with QIIME2, including quality control, DADA2 denoising, alpha and beta diversity calculation, and taxonomic classification using the SILVA database. Results: Differences in microbial composition were observed between groups. The healthy controls exhibited high relative abundances of beneficial genera such as Faecalibacterium, Bacteroides, and Asteroleplasma, whereas the patients with CRC showed enrichment of atypical genera including Novosphingobium, Bradyrhizobium, and Undibacterium. Alpha diversity was lower in the CRC group, and clear clustering by group was observed in the beta diversity analysis. LEfSe analysis identified potential bacterial biomarkers associated with CRC at both the species and genus levels. Conclusions: The findings of this study support the hypothesis that colorectal cancer is associated with distinct alterations in gut microbiota composition, such as an increase in the Novosphingobium genus and a decrease in the Bacteroides genus. An exploratory description of these microbial profiles may aid in the development of microbiome-based diagnostic and therapeutic strategies and contribute to current knowledge of the role of the gut microbiota in CRC in southern Peru. Full article

A comprehensive annotated checklist of the members of the phytophagous ladybird beetle tribe Epilachnini (Coccinellinae) in China is compiled based on existing published sources and incorporates the latest taxonomic and nomenclatural updates. The checklist documents 176 extant species across 10 genera and provides analyses of regional species richness, distribution, and host plant associations. Regarding regional species richness, Yunnan Province is home to the highest number of species (76), followed by Taiwan (50), Sichuan (48), Guizhou (48), Guangxi (43), Tibet (43), Guangdong (25), Hainan (17), Hubei (17), Hunan (13), Shaanxi (13), Fujian (12), Henan (10), Jiangsu (10), Anhui (7), Shandong (7), Zhejiang (7), Jiangxi (5), Hong Kong (5), Gansu (5), Beijing (4), Hebei (4), Liaoning (3), Shanxi (2), and Chongqing, Jilin, Heilongjiang, Ningxia, and Xinjiang (each with one species). Among the recognized genera, Epilachna Chevrolat, 1837, is currently the most species-rich genera, with 59 species, followed by Afissa Dieke, 1947 (34), Uniparodentata Wang & Cao, 1993 (28), Henosepilachna Li, 1961 (29), Afidentula Kapur, 1958 (10), Diekeana Tomaszewska & Szawaryn, 2015 (9), and Epiverta Dieke, 1947 (4). Additionally, Afidenta Dieke, 1947, Cynegetis Chevrolat, 1837, and Subcoccinella Agassiz & Erichson, 1845 are each represented by a single species. Host plant data are currently available for only 72 species (approximately 41% of the species recorded in China), which are associated with 177 plant species across 34 families. The most frequently recorded host plant families are Solanaceae (43 species), Cucurbitaceae (32), Urticaceae (15), Fabaceae (14), Asteraceae (14), and Poaceae (10), whereas each of the remaining 28 families comprises fewer than 10 host species. For 104 species (59% of the Chinese members of the tribe), host plant associations remain unknown, highlighting a substantial gap in our understanding of their feeding habits. Full article

Two new species, Aprostocetus eucalyptus Zheng & Yefremova sp. nov. (Hymenoptera: Eulophidae) and Megastigmus bipolaris Zheng & Yefremova sp. nov. (Hymenoptera: Megastigmidae), were discovered on populations of the invasive gall wasp Ophelimus bipolaris (Hymenoptera: Eulophidae) infesting Eucalyptus in Guangxi, China. An integrative taxonomic approach combining morphological characterization and 28S rRNA-based phylogenetic analysis was used for species identification and classification. Detailed morphological descriptions, illustrations, and an identification key for both sexes are provided. Field parasitism data confirm their potential as native natural enemies, supporting their utility for the biological control of this economically important pest. Full article

The ‘Tenuifoliae irises’ are a distinctive group of beardless, rhizomatous perennial irises, which are characterised by their somewhat vertical rhizomes, typically clothed at the apex with long maroon-brown, sharp fibrous remains of leaf sheaths; perianth tube long, filiform to scapiform; stigma bilobed; capsules often trigonous to six-ribbed, apically beaked; and seeds angulose to subcubic or pyriform, lacking fleshy appendages, and with testa hard, irregularly wrinkled. The representatives of the aggregate are mostly native to the dry steppes and grasslands from lowland to high mountain habitats of Central and Eastern Asia, extending westwards to the Black Sea and Caspian regions. Morphological classification of the ‘Tenuifoliae irises’ recognises about ten to eleven species, which are arranged into two genera, Sclerosiphon and Cryptobasis. Diverse molecular research recovered members of the ‘Tenuifoliae irises’ in contrasting placements within the ‘Iris-flower clade’. Sometimes, Sclerosiphon was sister to Eremiris, but Cryptobasis aligned with the ‘Spuria irises’ (Chamaeiris) and the ‘Spanish irises’ (Xiphion and related genera); in other cases, both Sclerosiphon and Cryptobasis formed a clade sister to Chamaeiris, or Cryptobasis alone was identified as the basal member of the Iris s.l. clade, positioned immediately after Siphonostylis. To examine these taxonomic discrepancies within a rigorous molecular systematic framework and using 12 reliably authenticated specimens, we generated 24 sequences of the matK gene (12) and the trnL (UAA)–trnF (GAA) loci (12) from members of the ‘Tenuifoliae irises’. These sequences were subsequently incorporated into a comprehensive dataset of the ‘Iris-flower clade’, enabling a broader analytical assessment. The obtained three-taxon statement hierarchy of patterns and maximum likelihood phylogenetic trees both recover the ‘Tenuifoliae irises’ as monophyletic and sister to Chamaeiris, and in turn to the ‘Xiphion s.l. clade’. We also found Sclerosiphon and Cryptobasis as sister genera. The morphological and karyological data supporting those relationships are discussed, which allow getting back to Rodionenko’s sources and recovering Sclerosiphon in his original sense, alongside Cryptobasis. Furthermore, the molecular results allow us to expand Sclerosiphon to include the Eastern Chinese members of the aggregate. In consequence, five new combinations (one series and four species) are established in the genus, one lectotype is designated, and data on nomenclature, distribution and ecology of the accepted species are reported. Full article

Lodgepole pine (Pinus contorta Dougl.) exhibits pronounced morphological variation across its range, historically attributed to allopatric differentiation during the Wisconsin glaciation. However, whether genetic divergence aligns with morphological differentiation—a fundamental prediction of allopatric speciation theory—remains untested. We conducted a comprehensive phylogeographic analysis of chloroplast DNA (trnL intron and trnL/trnF spacer) and mitochondrial DNA (nad1 b/c intron) across 31 populations representing all four recognized subspecies to test hypotheses of refugial isolation and to evaluate the genetic basis of current taxonomic classification. Contrary to predictions of allopatric divergence, both organellar genomes showed striking genetic uniformity (π = 0.000178–0.000186; intersubspecific genetic distances: 1.06 × 10⁻⁴ to 3.96 × 10⁻⁴) with no phylogenetic structure corresponding to morphological boundaries. Significant negative neutrality test values (Tajima’s D = −2.26, p < 0.02; Fu and Li’s D* = −4.52, p < 0.02) suggest recent demographic expansion rather than equilibrium divergence. A distinctive 5 bp indel in coastal populations provides molecular evidence for a northern Pacific refugium, and its occurrence in interior populations is consistent with post-glacial pollen-mediated gene flow, though this directionality remains inferential pending nuclear genomic confirmation. These findings suggest that morphological divergence reflects rapid adaptive evolution in heterogeneous environments rather than deep phylogenetic divisions. This pattern exemplifies gene flow-selection balance, in which divergent selection maintains local adaptation despite extensive gene flow—supporting an ecotypic rather than a phylogenetic interpretation of intraspecific diversity. The persistence of morphological variation despite genetic homogeneity indicates strong selection on ecologically important traits, likely driven by variation in fire regimes, differential seed predation, and climate gradients. These results have critical implications for understanding adaptive evolution rates in widespread conifers and for developing conservation strategies that emphasize adaptive processes over taxonomic categories. Full article

Alzheimer’s disease (AD) is characterized by progressive cognitive decline and the accumulation of amyloid-β (Aβ) plaques and tau neurofibrillary tangles. Beyond genetic and proteostatic mechanisms, infection- and dysbiosis-based models of AD have gained renewed attention, including the antimicrobial protection hypothesis, in which Aβ may participate in innate immune defense. Here, we reanalyzed ribosomal depleted (Ribo-Zero) RNA-seq data from dorsolateral prefrontal cortex (DLPFC) samples from the Mount Sinai Brain Bank cohort (GSE53697) to screen for non-human transcripts. Reads underwent quality control and adapter trimming, taxonomic classification with Kraken2, abundance re-estimation with Bracken, and differential abundance testing with edgeR. Across 17 samples (9 advanced AD and 8 controls), we detected low-biomass microbial signals, with Acinetobacter radioresistens showing enrichment in the AD group (FDR = 0.018). Several additional taxa showed suggestive group differences but did not remain significant after multiple testing correction, including Lactobacillus iners (FDR = 0.051). We also performed an exploratory in silico analysis of an A. radioresistens biofilm-associated protein homolog, identifying predicted amyloidogenic motifs and surface-exposed regions that may be relevant to cross-seeding hypotheses, although no mechanistic inference can be drawn without experimental validation. Given the technical challenges of inferring microbial signals from post-mortem brain RNA-seq data, including contamination risk, low microbial biomass, and overwhelming host background, these findings should be interpreted as hypothesis-generating and warrant orthogonal validation in larger, microbiome-aware cohorts. Full article

Background: Blood-derived circulating cell-free microbial DNA (cfmDNA) has emerged as a potential non-invasive biomarker source for cancer detection. However, low biomass and high susceptibility to analytical variability raise concerns regarding the stability and interpretability of inferred microbial signatures. This study aimed to evaluate how different taxonomic profiling strategies influence downstream machine learning-based classification and feature interpretation in lung cancer. Methods: cfDNA sequencing data from 168 individuals (80 lung cancer patients and 88 non-cancer controls) were analyzed using two taxonomic profiling workflows: a Bracken-based abundance estimation approach and a BLAST-refined alignment-based strategy. Microbial profiles derived from each pipeline were evaluated using supervised machine learning models within a nested cross-validation framework. Feature stability and fold-change trends were compared across profiling strategies. Results: A Random Forest model achieved robust classification performance under both workflows (AUC 0.852 for Bracken-derived data and 0.906 for BLAST-derived data). However, substantial pipeline-dependent variation was observed in feature selection patterns and quantitative fold-change directionality. Although 13 genera were consistently selected across cross-validation folds in both workflows, the magnitude and direction of abundance differences were not uniformly concordant. Conclusions: Blood-derived microbial DNA profiles can support machine learning-based lung cancer classification; however, feature-level interpretation remains sensitive to taxonomic assignment strategy. These findings underscore the importance of pipeline-aware interpretation and methodological transparency in low-biomass blood microbiome research. Full article

Accurate identification of disease vectors is crucial for public health, yet distinguishing morphologically similar species demands significant taxonomic expertise and data resources. This study proposes MVP-Net, an AI-driven framework designed to extract a minimal sufficient set of diagnostic anatomical views from multi-view imagery for efficient identification. The framework was evaluated on regionally collected datasets of Calyptratae (8 views) and Culicidae (11 views) from routine surveillance in Shanghai. Under all-view fusion, MVP-Net achieved Top-1 accuracies of 87.04% for Calyptratae and 100% for Culicidae. After Pareto-based view optimization, the required input was reduced to 5 views for Calyptratae and 2 views for Culicidae, lowering computational cost by 37.49% and 81.82%, respectively, while retaining comparable classification performance (86.11% for the recommended Calyptratae configuration and 100% for the recommended Culicidae configuration). These results show that MVP-Net can reduce view redundancy while preserving comparable identification performance within the current Shanghai surveillance setting, providing a practical approach for optimizing regional multi-view auxiliary identification workflows. Full article

Rapid, standardised discrimination of soil taxonomic units remains challenging when relying solely on conventional field descriptions and laboratory analyses, particularly at high sampling densities. This study evaluated whether proximal spectroscopy and hyperspectral imaging can support the classification of Brazilian Soil Classification System (SiBCS) suborders and pedogenetic horizons when surface and subsurface spectra are treated separately. Six intact soil monoliths (0.12 × 1.60 m) were collected in Paraná State, southern Brazil, representing one Organossolo (Ooy), three Latossolos (LVd, LVd1, and LVd2) and two Argissolos (PVAd and PVd). For each monolith, 800 spectra were acquired per sensor with a non-imaging VIS–NIR–SWIR spectroradiometer (350–2500 nm), and 800 spectra per sensor per monolith were extracted from the SWIR hyperspectral images (1200–2450 nm). Principal component analysis (PCA) was used to summarise spectral variability, and supervised classification was performed via k-nearest neighbours, random forest, decision tree and gradient boosting for suborders (10-fold cross-validation), and a neural network was used for within-profile horizon classification. PCA indicated that most of the spectral variance was captured by a dominant axis, with clearer separation among suborders in the SWIR space than in the full VIS–NIR–SWIR range. With respect to suborder classification, subsurface spectra outperformed surface spectra, and SWIR outperformed VIS–NIR–SWIR: the best accuracies were 0.96 for subsurface SWIR (gradient boosting; AUC = 0.99; MCC = 0.95) and 0.89 for surface SWIR (k-nearest neighbours; AUC = 0.98; MCC = 0.87). Within-profile horizon classification via VIS–NIR–SWIR achieved accuracies of 0.84–0.97 with the Neural Network, with most misclassifications occurring between adjacent horizons. Overall, subsurface SWIR information provided the most reliable basis for taxonomic discrimination, whereas horizon classification was feasible but reflected gradual spectral transitions along the profile. Full article

Fungi of the Phallales order are globally distributed and are important in forest ecosystems, and many species have medicinal and edible value. However, despite the rich diversity, the information on this order is limited, and its taxonomic classification remains contentious. In this study, the mitogenomes of five species from the Phallales order were sequenced, assembled, annotated, and compared. All five assembled mitogenomes were circular, ranging in size from 41,465 bp to 99,150 bp. Introns and intergenic regions were the key factors for mitogenome size variation in the Phallales order. The arrangement of 15 protein-coding genes, 2 rRNA genes, and 24 tRNA genes was highly conserved among the Phallales species. The only variation observed was the presence of an additional copy of trnI, trnT, trnD, and trnF in some mitogenomes. Specifically, the mitogenomes of P. rugulosus, P. hadriani, P. rigidiindusiatus, and P. dongsun had an additional copy of trnI, trnT, trnD, and trnF, respectively. A phylogenetic analysis produced well-supported phylogenetic tree, indicating that the mitogenome was an effective molecular marker for inferring evolutionary relationships. The phylogenetic analysis showed that the Phallales and Gomphales species share a closer evolutionary relationship. Our results contribute to a better understanding of the evolutionary dynamics, genetic constitution, and systematic classification of this important fungal community. Full article

With in situ imaging systems becoming more common, precise, and economically viable, use of these systems has grown dramatically, including both automated classification and biomass estimations. However, a rather large and overlooked portion of these efforts is reliable detection and classification of these organisms as they pass through the imaging device. This paper focuses on the development of an end-to-end classification CNN-based algorithm for marine zooplankton using the in situ Ichthyoplankton Imaging System (ISIIS-DPI) from Bellamare (La Jolla, CA, USA). Our novel approach considers many issues with automated segmentation and classification, including over-segmentation, noise segmentation, and organism size input. This allows for classifications in diverse water types, demonstrated by the comparison of three datasets created in conjunction with this project, each with very different water properties and zooplankton communities (Florida Gulf coast; Trondheimsfjord, Norway; Sargasso Sea). Our segmented image dataset contains 70,624 regions of interest (ROIs) across four organism classes—Chaetognath, Crustacean, Gelatinous, and Larvacean—with two classes dedicated to detritus. Four common network architectures—Resnet, Xception, GoogleNet, and Darknet—are trained on this dataset, with final test accuracies in the range of 95.94–96.09%. Following this initial training, a secondary level of classification is introduced. The base Gelatinous class is further divided into six groups. The same four CNN architectures are used once again, with final accuracies in the range of 86.12–90.40%, showing the ability to taxonomically classify down to the order level. The present work introduces a versatile, adaptable, scalable and autonomous segmentation and classification algorithm using niched networks mirroring taxonomy, and is fully contained in a publicly available MATLAB R2025a custom graphical user interface. Full article

Comprehensive soil surveys necessitate the integration of multidimensional pedological information, ranging from the morphological delineation of horizons and the taxonomic identification of soil groups to the quantitative assessment of soil organic matter (SOM). These attributes collectively constitute the basis for interpreting pedogenesis and guiding sustainable soil management. However, conventional methods are limited by the subjectivity of expert judgment for horizon and soil group identification, and the time-consuming nature of laboratory analyses for SOM quantification. We developed a novel multimodal deep learning framework based on an improved Mask R-CNN architecture that integrates soil profile images with auxiliary soil property data to concurrently delineate soil horizons, classify soil groups, and quantify SOM. The model was trained on high-resolution soil profile images from 451 soil survey sampling sites spanning ten soil groups across Anhui Province, China. Data augmentation and transfer learning with pre-training on large general image datasets were employed to address the dataset size limitations and improve model generalization. In addition to accurately delineating master horizons, we evaluated three schemes for classifying transitional horizons, which are often ambiguously determined by expert assessments: (i) assigning the transitional horizon to one adjacent master horizon; (ii) assigning it to both neighboring master horizons as an overlapping section; and (iii) treating the transitional horizon as an independent layer. Scheme (iii) achieved the best overall performance, e.g., horizon delineation with accuracy = 0.925, recall = 0.933, F1-score = 0.929, and segmentation mean average precision (seg-mAP) = 0.918, soil group classification accuracy = 0.717 and prediction of SOM with R² = 0.565. These results demonstrate that treating transitional horizons as independent layers yields superior segmentation. Consequently, this integrated framework provides a robust, automated solution for high-throughput soil resource assessment. Full article

The Atlantic sharpnose (Rhizoprionodon terraenovae), blacknose (Carcharhinus acronotus), blacktip (Carcharhinus limbatus), and bonnethead (Sphyrna tiburo) sharks are commonly encountered large mobile consumers found in the estuaries along the western North Atlantic coast. The bulk of the dietary data for these species has been coarsely recorded at a broad taxonomic level (e.g., “teleost fish”). Here, we used DNA metabarcoding of fecal DNA collected using non-lethal cloacal swabs to identify the species of prey contributing to the diets of these shark species and measure the degree of trophic overlap. Samples were collected from 24 Atlantic sharpnose, 33 blacknose, six blacktip, and 17 bonnethead sharks in the summer of 2020. Based on previous dietary research on these shark species, we targeted teleost fishes and crustaceans using two previously published primer sets. From the 80 sharks sampled off the coast of North Carolina, we identified 38 prey taxa, with 82% classified to the species level and all assigned to at least the genus and family levels. The most common prey taxa found in the diet of the bonnethead was Atlantic blue crab (Callinectes sapidus; 44.75%, based on percent of occurrence) followed by penaeid shrimp (Penaeus spp.; 24.41%), mantis shrimp (Squilla empusa; 20.34%), and spot (Leiostomus xanthurus; 4.75%). Atlantic sharpnose and blacknose sharks had the largest Levin’s niche overlap, with both species relying on the same two most frequently consumed prey taxa: penaeid shrimp (Atlantic sharpnose: 33.33%, percent of occurrence, and blacknose: 34.78%) and spot (Atlantic sharpnose: 32.70% and blacknose: 22.32%). Bonnetheads and blacktips had the least amount of overlap between all shark species, where blacktips primarily consumed menhaden (Brevoortia spp.; 58.62%) and penaeid shrimp (26.44%). Our findings highlight the value of DNA metabarcoding in refining our understanding of predator diets, moving beyond broad taxonomic classifications to identify species-level prey associations and trophic interactions. As coastal habitats undergo increasing alteration due to anthropogenic impacts, such information is crucial for fisheries management, helping to identify key prey dependencies and anticipate potential ecosystem shifts. Full article

In vitro studies were conducted in a series to investigate if the ruminal microbes are capable of degrading chlorpyrifos. This in vitro study presents the results from three experiments: Exp. I was conducted without feed, while Exp II and III were conducted with feed, either with or without methanol for dissolving chlorpyrifos, respectively. A basal diet comprising finger millet straw and concentrate was prepared. Incubation medium with feed but without chlorpyrifos served as the control. A total of six replicates each of control and chlorpyrifos spiked were used for the incubation. The pesticide concentration in the incubation medium before and after 24 h of incubation was analyzed using GC-MS/MS. The genomic DNA was isolated from the incubation fluid of the individual samples, and the shotgun metagenomic sequencing was performed. The clean reads were taxonomically classified using the Kraken2 database, and microbial classification at different taxonomic ranks was separated using Pavian v1.0. The microbial genes in the metagenome data were predicted and assigned functional roles using the MetaErg v1.2.3 pipeline. The assigned KEGG Orthology (KO), EC numbers (Enzyme Commission number), Gene Ontology (GO), and corresponding NCBI taxonomy information relevant to chlorpyrifos metabolism/degradation were retrieved. Results from the study revealed that the chlorpyrifos concentration was decreased from 5.78 to 1.64 ppm over 24 h of in vitro incubation with feed. Similar alpha and beta diversity indices between control and chlorpyrifos treatments revealed that the richness and the evenness of the microbial population were not affected by the presence of chlorpyrifos in the rumen fluid. There was no difference in the microbiota affiliated to the major phyla such as Bacteroidota, Fibrobacterota, Bacillota, and Pseudomonadota. The EC 3.1.8.1, EC 3.1.3.1, EC 1.14.13.-, and EC 1.1.1.- reported for chlorpyrifos degradation were not detected in the metagenome, and only EC 3.1.1.1 was identified, which demonstrated that degradation of chlorpyrifos was carried out by the affiliated enzyme carboxylesterase. The presence of GO:0004035, GO:0004364, GO:0019637, GO:0016791, and GO:0042178 in the metagenome strengthens that the chlorpyrifos degradation in the present study was primarily assigned to the rumen microbiota. This in vitro study provided insights into the rumen microbiota involved in the chlorpyrifos degradation and the initial clue that the rumen microbes are capable of degrading chlorpyrifos. Further, the animal studies in different species with the variable levels of chlorpyrifos are also warranted to confirm the efficacy of rumen microbes in mixed syntrophy and determine the threshold capabilities of the ruminal microbes. Full article

Disparities in root fungal endophyte (RFE) communities are well documented among plant species, yet differences among plant functional groups (PFGs) remain unclear. Given that RFE community structure is influenced by host plant abundance and species-specific root functional traits, and that PFGs exhibit divergent relative abundances and root traits, we hypothesize that PFGs harbor unique RFE communities, potentially aligned with their functional traits. We investigated RFE communities in 45 alpine meadow species representing four PFGs (grasses, legumes, dicot forbs, and monocot forbs), using high-throughput sequencing. Ascomycota dominated all groups (>50%) except monocot forbs (38.9%). Distinct differences in the RFE community species composition were found among PFGs. In particular, the differences were significant between dicot forbs and monocot forbs, and between monocot forbs and grasses, which contradicted with conventional PFG classification that combined monocot and dicot forbs as a single PFG. Moreover, marker operational taxonomic units (OTUs) with symbiotic lifestyles were more abundant in legumes, and their functional composition differed significantly from grasses. Roots’ nitrogen concentration was the strongest predictor of RFE variation, followed by root length, biomass, and species abundance. These results emphasize the importance of integrating microbial partners into understanding plants’ functional diversity and ecosystem resilience in alpine environments. Full article