Search Results (237)

Machine learning and statistical classification methods are widely applied to microbiome data for diagnostic, prognostic, and phenotypic insights. However, the complex, multivariate nature of microbiome communities makes it difficult to assess the relative performance of these methods. Most comparisons rely on a small number of published datasets, without considering their underlying ecological properties or how these properties may, in turn, influence classification performance. We introduced a coenocline-based simulation framework to generate synthetic microbiome datasets that incorporate realistic ecological variation arising from species’ responses to host-associated gradients such as disease severity. To evaluate the ecological fidelity of these simulations, we compared synthetic datasets to five widely used real-world microbiome datasets: Cirrhosis, Colorectal Cancer (CRC), Type 2 Diabetes (Chinese and Women cohorts), and the Human Microbiome Project (HMP). Comparisons across α-diversity (species richness), β-diversity (species composition and turnover), and abundance distributions demonstrated that coenocline simulations closely recapitulate the key ecological structures of empirical data. Synthetic datasets exhibited similar richness and abundance patterns to disease-associated microbiomes, with realistic distributions of few dominant and many rare taxa. Moreover, community composition analyses (Bray–Curtis index) revealed that the simulated datasets captured natural levels of compositional dissimilarity among samples, spanning the same variability range observed in real data. When compared against 100 independently simulated datasets, the coenocline model consistently reproduced empirical ranges of species diversity, relative abundance, and between-group compositional differences (ANOSIM-R values), confirming the model’s robustness and reproducibility. This coenocline-based simulation framework provides a novel, flexible, and ecologically grounded approach for generating synthetic microbiome data with controlled complexity. By reproducing realistic ecological gradients and community structures, the framework supplies the controlled test beds needed for systematic future benchmarking of machine learning and statistical classification methods across diverse and biologically meaningful scenarios. In doing so, it will help bridge the gap between ecological realism and computational modeling, thereby supporting more reliable and generalizable inference from microbiome data. Full article

Background: Recent evidence suggests that gut microbiome plays a role in the development of pancreatic ductal adenocarcinoma (PDAC) and influences treatment response. However, the association of tumor location and preoperative biliary stenting (PBS) with gut microbial composition and diversity remains poorly understood. Methods: Preoperative stool specimens were prospectively collected from patients with PDAC undergoing surgery between March 2020 and July 2021 at the Department of Surgery, Heidelberg University Hospital, Germany. Whole-genome shotgun metagenomic sequencing was performed. Microbial diversity was assessed using the Shannon index and Bray–Curtis dissimilarity with principal coordinates analysis. Results: A total of 63 preoperative stool samples were analyzed from 40 patients with pancreatic head (63.5%) and 23 with body/tail tumors (36.5%). Baseline characteristics were comparable between groups. Microbial community composition differed significantly between tumor locations (Bray–Curtis, p = 0.005), with enrichment of Ruminococcus bromii in body/tail tumors. Among patients with pancreatic head tumors, PBS was associated with reduced alpha diversity (Shannon index, p = 0.04) and depletion of taxa including Eubacteriales and Clostridiales taxa, and members of the genera Raoultella and Prevotella. PBS was associated with a higher rate of major complications > 3a according to the Clavien–Dindo classification (28.6% vs. 3.8%; p = 0.04). Conclusions: PBS was associated with reduced microbial diversity and distinct taxonomic alterations of the gut microbiome. These findings suggest that biliary stenting is associated with microbiome alterations that may be relevant for perioperative risk stratification and warrant further investigation. Full article

Nitraria tangutorum Bobr. fruits are rich in bioactive polysaccharides (QBC) with high nutritional and developmental potential. This study optimized the ultrasonic-assisted extraction QBC via RSM, determining the optimal parameters to be 45 min, 59 °C, 19:1 mL/g, and 50% (150 W eq.), yielding 15.13%. Structurally, QBC exhibited a Mw of 1.997 × 10⁵, Mw/Mn of 3.727, Rz of 15.6, and a monosaccharide molar ratio of rhamnose:arabinose:mannose:glucose:galactose = 1:1.12:1.66:7.3:1.86. In this study, the effects of QBC on oxidative stress markers and pro-inflammatory cytokines were evaluated via in vivo experiments. The results demonstrated that QBC significantly improved the thymic index and restored immune function. Specifically, QBC upregulated the expression of pro-inflammatory cytokines such as IL-6, TNF-α, and IFN-γ. The analysis of α/β diversity via 16S rRNA sequencing revealed that the CTX group induced a reduction in microbial richness, a more dispersed community structure, and increased intergroup dissimilarities. KEGG functional prediction indicated that polysaccharides regulated the ‘Infectious disease: parasitic’ and ‘Nitrotoluene degradation’ pathways, potentially by exerting their effects through metabolite-mediated immune regulation. Full article

Existing image super-resolution methods still suffer from limitations in edge-structure restoration, high-frequency texture preservation, and artifact suppression, which may lead to blurred contours and unnatural textures. To address these issues, this paper proposes an image super-resolution method based on hierarchical feature aggregation and Laplacian high-frequency compensation. First, a Hierarchical Feature Aggregation Attention Block (HFAB) is designed in the generator to progressively extract image features at different levels through multiple convolutional layers. A High-Frequency Variance Adaptive Channel Attention Block (HFVB) is further introduced to adaptively enhance key texture and edge information. Second, a Laplacian Adaptive Upsampling (LAU) module is developed to combine low-frequency content reconstruction with high-frequency detail compensation, thereby strengthening edge contours, preserving fine textures, and reducing artifacts. Finally, a Dissimilarity Structural Similarity Index Measure (DSSIM) loss is incorporated into the loss function to constrain local structural consistency and further improve the structural preservation and perceptual quality of reconstructed images. Experimental results on Set5, Set14, BSD100, and Urban100 show that, compared with SRGAN, the proposed method improves PSNR by 0.31 dB, 0.18 dB, 0.14 dB, and 0.14 dB, respectively, while reducing LPIPS by 0.0234, 0.0183, 0.0222, and 0.0222. These results indicate that the proposed method provides consistent improvements over SRGAN and achieves modest, metric-dependent gains over ESRGAN, suggesting an incremental enhancement in reconstruction accuracy and perceptual quality on both natural image benchmarks and complex urban scene datasets. Full article

Coastal lagoons are key wintering habitats for waders, yet long-term changes in their community structure remain poorly understood in Mediterranean systems. We analyzed a 30-year dataset (1993–2022, excluding 2021) of wintering waders in the Venice Lagoon to assess trends in abundance, community structure, thermal composition and spatial patterns. Total abundance increased significantly (+3.5% yr⁻¹), while species richness ranged between 12 and 21 species per winter and increased over time. Community structure changed markedly, but the assemblage remained highly dominated by Dunlin Calidris alpina without evidence of increasing dominance or declining evenness. Instead, richness, Shannon diversity and Pielou’s evenness increased, whereas Berger–Parker dominance declined slightly but significantly. Species-level analyses showed a prevalence of increasing trends: ten of the 19 species analyzed increased significantly, three declined, one was stable, and five showed uncertain trends. Multivariate analyses based on Bray–Curtis dissimilarities showed significant compositional differences among approximately decadal periods, both including and excluding Dunlin, indicating that long-term assemblage reorganization was not solely attributable to the dominant species. The Community Temperature Index (CTI) increased significantly (p = 0.001), but this abundance-weighted signal was weak in biological magnitude and contrasted with a declining presence–absence CTI; moreover, this pattern was not robust to the exclusion of Dunlin, indicating dominance-driven dynamics. Spatial analyses revealed a strong increase in the proportion of counted birds recorded in the open lagoon (p < 0.001) and a decline in fish farms (p < 0.001), but this pattern disappeared after excluding Dunlin, suggesting that the apparent spatial redistribution was largely driven by this species. Overall, the assemblage is increasing and compositionally reorganized, while remaining strongly influenced by Dunlin dominance, highlighting the need to integrate species- and community-level approaches when interpreting ecological indicators. Full article

The intricate interaction of a host’s microbiome, the microbiomes of other hosts, and environmental microbial populations significantly impacts host health, given the essential physiological functions the microbiome performs within the organism. The oral microbiome of domesticated animals is also influenced by a variety of host and environmental factors. This study investigated the characteristics of the oral microbiome of dogs and cats under comparable and disparate living conditions, emphasizing the description of diversity patterns, taxonomic composition, and predicted functional profiles. Oral buccal swabs were collected from four groups of companion animals (n = 5 per group): dogs housed alone in single-pet households (Group A), dogs cohabiting with cats in multi-pet households (Group B), cats cohabiting with dogs from the same households (Group C), and cats housed alone in single-pet households (Group D). The cohabiting groups were derived from five multi-pet households, with one dog and one cat sampled from each household. Amplicon sequence variations (ASVs) were used for downstream analysis after 16S rRNA gene sequencing. Rarefaction curve behavior indicated proper sequencing depth. Alpha diversity varied by group (Shannon index, p = 0.045), with Groups C and D having larger diversity. A Beta diversity study revealed community composition differences (Bray–Curtis dissimilarity, R² = 0.257, p = 0.001), with some overlap between groupings. In all samples, Proteobacteria, Firmicutes, Bacteroidota, and Fusobacteriota dominated the microbiome. The relative abundance of Fusobacterium, Porphyromonas, and Pasteurella varied across groups. Core microbiome analysis identified limited overlap of core ASVs between groups, with most taxa being group-specific. Functional prediction using PICRUSt2 suggested differences in predicted metabolic and cellular pathways. Overall, these exploratory findings suggest that the oral microbiome of companion animals may be influenced by host species and cohabitation conditions. Although limited by the small sample size, the study provides preliminary insights into microbial diversity, community structure, and predicted functional profiles that may inform future One Health-oriented investigations. Full article

The intestinal microbiome is increasingly recognized as an important determinant of canine gastrointestinal health. However, interpreting microbiome sequencing data remains challenging because most analytical approaches rely on taxonomic descriptions, alpha diversity indices, or dysbiosis indices derived generally from a limited number of microbial ecological interpretation targets. While shotgun metagenomic approaches increasingly allow the identification of microbial communities, such analyses remain costly and are not yet widely accessible in routine veterinary settings. The objective of this study was to develop an integrative interpretation framework based on widely accessible biomarkers combining fecal calprotectin and 16S rRNA gene sequencing data. These data enabled the generation of complementary ecological dimensions of gut microbiome organization: biological inflammation assessed through fecal calprotectin, microbiological inflammatory pressure estimated through a Microbiological Inflammatory Score (MIS), and microbiome stability measured by a Microbiome Resilience Score (MRS) derived from alpha diversity, functional balance, and dominance structure. Fecal microbiome profiles obtained by 16S rRNA gene sequencing were analyzed in a real-life cohort of privately owned dogs. Alpha diversity, taxonomic weighting, abundance-dependent dominance rules, beta diversity based on Bray–Curtis dissimilarity, distance to a reference microbiome core, and a 16S-derived dysbiosis score were integrated into a multidimensional interpretation model. Strong ecological associations were observed between resilience, microbial diversity, and dysbiosis-related metrics. Microbiome resilience strongly correlated with Shannon diversity (Spearman ρ = 0.98, p < 0.001), while the reconstructed 16S-derived dysbiosis score showed a more moderate positive correlation with MIS (Spearman ρ = 0.41, p = 0.004), supporting the partially independent ecological dimensions captured by the framework. The results revealed a continuum ranging from stable microbiomes to inflammatory dysbiosis. Most dogs clustered near a reference microbiome core characterized by low microbiological inflammatory pressure and high resilience, whereas a subset of microbiomes showed elevated MIS values, reduced resilience, increased compositional distance from the reference core, and higher dysbiosis index values. These findings support the value of a multidimensional experimental framework integrating inflammation, dysbiosis, and resilience to improve interpretation of canine microbiome profiles under real-life conditions. Full article

Reducing building energy demand is essential for achieving carbon neutrality and advancing sustainable urban development. This study examines the associations between building-related and topographical characteristics and summer electricity consumption using large-scale empirical data from Seoul, South Korea. A Gamma regression model was employed, with geometric, scale, system, and topographical variables as predictors and building electricity consumption as the dependent variable. The results indicate that compact building forms are significantly associated with lower electricity consumption, suggesting their relevance for energy-efficient and low-carbon building design. In contrast, horizontal expansion appears to increase energy use more strongly than vertical, tower-type configurations. The findings further show that larger building scale amplifies energy demand even under similar geometric conditions. Among topographical variables, slope exhibits a relatively strong association with energy consumption, whereas elevation shows a weaker relationship. These results highlight the importance of integrating building form, scale, and site conditions into early-stage design decision-making. The study provides empirical evidence for sustainable built-environment strategies by linking architectural form, urban spatial context, and energy-demand reduction. Full article

Background/Objectives: Patients receiving maintenance hemodialysis (HD) commonly exhibit chronic low-grade inflammation, nutritional disturbances, altered body composition, and metabolic imbalance. Gut dysbiosis may contribute to these abnormalities through the gut–kidney axis; however, the relationship between the gut microbiota composition and host phenotype in HD patients remains incompletely characterized. This study aimed to characterize the gut microbiota composition in maintenance HD patients and assess its cross-sectional associations with demographic, inflammatory, nutritional, dialysis-related, and bioimpedance-derived body composition parameters. Methods: This single-center cross-sectional study included 96 patients with end-stage kidney disease undergoing maintenance HD. The primary objective was to characterize the gut microbiota composition in maintenance HD patients. Secondary objectives were to assess cross-sectional associations with demographic factors (sex, age) and bioimpedance-derived body composition (specifically VAT). Clinical and laboratory data, inflammatory markers, nutritional indicators, malnutrition–inflammation score (MIS), dialysis-related variables, and bioimpedance-derived body composition parameters were collected. Stool samples were analyzed using full-length 16S rRNA sequencing. The gut microbiota composition was assessed using taxonomic profiling, alpha-diversity and beta-diversity analyses, subgroup comparisons, and exploratory distance-based analyses. Associations were interpreted within a descriptive and hypothesis-generating framework. Results: The gut microbiota composition showed marked inter-individual heterogeneity at the genus level, with dominant taxa including Blautia, Faecalibacterium, Streptococcus, Gemmiger, Ruminococcus, Escherichia-Shigella, and Enterococcus. Chao1 richness was higher in men than in women. Shannon entropy and Chao1 richness were positively associated with age and visceral adipose tissue (VAT), while Faith’s phylogenetic diversity increased with age. In contrast, the Gini index was negatively associated with age and VAT, indicating a more even microbial community structure in older individuals and in those with higher visceral adiposity. Beta-diversity analyses suggested modest differences in microbial community structure according to sex and selected body composition-related categories, particularly in sex-stratified analyses. Exploratory distance-based analysis showed a modest association between overall microbiota dissimilarity and host phenotype dissimilarity, although this finding was limited by reduced sample overlap. Conclusions: The gut microbiota composition in maintenance HD patients was highly heterogeneous and showed cross-sectional associations, mainly with sex, age, visceral adiposity, and broader host phenotype. These findings suggest that microbiota variation in HD reflects multidimensional demographic, inflammatory, nutritional, metabolic, and body composition-related factors rather than a single clinical determinant. Larger longitudinal studies integrating standardized dietary, medication, metabolic, and clinical outcome data are needed to determine the prognostic relevance of these microbiota patterns. Full article

In the current research, we introduce two operations of a “symmetric difference” type over three-dimensional extended index matrices, and investigate some of their basic properties. An example of the implementation of symmetric difference-type operations is presented in the field of relational databases, aiming to demonstrate the operations’ efficiency by comparing sets with dissimilar attributes. Full article

Hierarchical clustering is an unsupervised framework that organizes observations according to pairwise similarity relationships. In this study, an agglomerative hierarchical approach combined with Gower dissimilarity is employed to accommodate mixed-type customer data. To address data quality issues such as missing values and outliers, Multiple Imputation by Chained Equations (MICE) and Winsorization are incorporated into the preprocessing pipeline. To validate cluster stability and identify the optimal number of clusters, we employ silhouette analysis, the Davies–Bouldin Index (DBI), the Proportion of Ambiguous Clustering (PAC), and a subsampling-based consensus clustering framework. A consensus-based hierarchical tree derived from the consensus matrix is employed to assess the robustness of the segmentation structure. The resulting clusters are further evaluated through comparisons with baseline algorithms for mixed-type data, including Partitioning Around Medoids (PAM) based on Gower dissimilarity and the K-prototypes method, together with statistical tests confirming significant behavioral differences between the identified segments. From an application standpoint, these results provide a data-driven basis for customer targeting by identifying distinct behavioral patterns, thereby supporting more effective engagement strategies and optimized resource allocation. Full article

Hospital environments host diverse microbial communities that may contribute to nosocomial infections. Moisture-retaining surfaces such as cleaning sponges and faucet edges represent high-contact, under-investigated hygiene hotspots, particularly in wards caring for immunocompromised patients. Environmental samples were collected from cleaning sponges (n = 14) and faucet edges (n = 4) across multiple hospital rooms of a paediatric haematology–oncology unit, with domestic physician sponges as controls (n = 3). DNA was extracted and sequenced targeting the V3–V4 and V7–V9 hypervariable regions of the 16S rRNA gene on the Illumina MiSeq platform. Taxonomic composition and alpha/beta diversity were assessed using QIIME 2 and R. Sponge samples were dominated by Moraxellaceae, particularly Acinetobacter and Enhydrobacter, and showed significantly lower alpha diversity than faucet samples (Shannon index: Kruskal–Wallis H = 8.4, p = 0.01; Faith’s phylogenetic diversity: H = 9.17, p = 0.01). Faucet samples were enriched in human-associated genera including Staphylococcus, Streptococcus, and Chryseobacterium. Statistically significant beta-diversity differences were detected between sponge and faucet communities by PERMANOVA based on Bray–Curtis dissimilarity (p = 0.01), whereas no significant clustering by room or floor location was observed (p = 0.29). Potentially pathogenic taxa including Aeromonas, Pseudomonas, and Enterobacteriaceae were identified across both surface types. Domestic control sponges showed distinct microbiome profiles from hospital samples. Microbial communities differ significantly between hospital sponges and faucets, with surface type rather than location as the primary determinant of community structure. The presence of opportunistic pathogens on both surface types highlights the importance of enhanced hygiene protocols, inclusion of faucet edges and sink drains in routine decontamination schedules, and regular microbiological surveillance in clinical settings caring for immunocompromised patients. Full article

Existing inverted index-based approximate nearest neighbor search methods are hindered by the necessity for manual adjustment of the quantity of inverted lists and the presence of negative similar results in the candidate set. These limitations inevitably restrict both search efficiency and generalization. To address these issues, we propose two ANNS methods based on adaptive hypersphere filtration, consisting of 3 steps: obtaining the candidate set, adaptive hypersphere filtration, and reranking. For this purpose, a hypersphere learning model is developed by adopting a fully connected neural network, which is independent of the vector dimension. Then, this model can be compatible with vectors of different dimensions without any architectural modifications. Each query vector is associated with an adaptive size hypersphere. In the procedure of obtaining the candidate set, only the inverted lists associated with the corresponding centroid located inside the hypersphere are employed to construct the candidate set. In the filtration stage, the hypersphere is employed to eliminate vectors dissimilar to the query vectors from the candidate set, thereby decreasing the number of candidate vectors taken into reranking. Experimental results on three public datasets demonstrate that two ANNS methods based on adaptive hypersphere filtration can effectively enhance the retrieval efficiency without weakening retrieval accuracy. Full article

Environmental changes and anthropogenic pressures significantly influence both the tree layer and natural regeneration within forest ecosystems. Protected areas represent essential territories for the maintenance and conservation of species within forest communities. In this context, the present study aims to develop a methodological framework for the integrated application of diversity, evenness, and dominance indices in the study of forest plant communities. Analyses were conducted at both α- and β-diversity levels, providing a methodological basis for characterizing local diversity and community differentiation. Species diversity was estimated using the Shannon–Wiener (H′) and Simpson (D) indices, while evenness and dominance were assessed using the Pielou (J′) and Berger–Parker (d) indices. Differences among communities were quantified using the Bray–Curtis dissimilarity index and its components, turnover and nestedness, and structural convergence of forest communities was analyzed through the ICF. The results indicate that α-diversity, estimated by H′, ranges from low to moderate, suggesting a relatively uniform distribution of species abundance. In certain microhabitats, processes of diversification and oligodominance are observed. At the β-diversity level, the analyzed communities are characterized by high dissimilarity, mainly driven by species turnover and, to a lesser extent, by nestedness associated with species loss. The ICF highlights that these forest communities exhibit relatively high structural uniformity, characteristic of mature stands in ecological equilibrium. Full article

The study investigated differences in ruminal and fecal microbiota composition, fermentation traits, and volatile organic compounds (VOC) in Simmental dairy cows classified as high (HME) or low (LME) methane emitters. Methane emissions from 48 cows were quantified using the Laser Methane Smart portable gas detector. The 12 animals with the highest and lowest emissions were selected and assigned to the HME and LME groups, respectively, balanced for body weight, days in milk, and body condition score. Rumen fluid and fecal samples were analyzed for pH, ammonia, volatile fatty acids (VFA), VOC, and microbiota composition. As expected, CH₄ emissions were significantly higher in HME than in LME cows (22.5 vs. 13.2 g/kg DMI; 16.9 vs. 8.4 g/kg FCM). The neutral detergent fiber digestibility was higher in HME cows (51.4% vs. 47.9%). The valeric acid concentration and the acetate-to-propionate ratio were significantly higher in HME cows (3.53 vs. 3.31). The VOC profiles significantly differed between groups in both feces and rumen fluid. The microbiota analysis revealed a significant difference between groups at the order and genus levels (Bray–Curtis dissimilarity). The Shannon index was higher in LME cows (2.08 vs. 1.95). HME cows exhibited a higher abundance of Methanosphaera and Methanobacteriales. Overall, the results indicate that re-shaping the rumen microbial community can play a key role in reducing methane emissions, strengthening the case for microbiome-driven approaches and offering insights that can support mitigation strategies across dairy production systems. Full article