A Weighting Method for Feature Dimension by Semisupervised Learning With Entropy.

In this article, a semisupervised weighting method for feature dimension based on entropy is proposed for classification, dimension reduction, and correlation analysis. For real-world data, different feature dimensions usually show different importance. Generally, data in the same class are supposed to be similar, so their entropy should be small; and those in different classes are supposed to be dissimilar, so their entropy should be large. According to this, we propose a way to construct the weights of feature dimensions with the whole entropy and the innerclass entropies. The weights indicate the contribution of their corresponding feature dimensions in classification. They can be used to improve the performance of classification by giving a weighted distance metric and can be applied to dimension reduction and correlation analysis as well. Some numerical experiments are given to test the proposed method by comparing it with some other representative methods. They demonstrate that the proposed method is feasible and efficient in classification, dimension reduction, and correlation analysis.

Albuca Bracteata Polysaccharides Attenuate AOM/DSS Induced Colon Tumorigenesis via Regulating Oxidative Stress, Inflammation and Gut Microbiota in Mice.

Inflammation is an important risk factor in the development of inflammatory bowel disease (IBD) and colitis-associated colorectal cancer (CAC). Accumulating evidence indicates that some phytochemicals have anti-cancer properties. Polysaccharides extracted from Albuca bracteata (AB) have been reported to possess anti-neoplastic activities on colorectal cancer (CRC) models. However, it is still unclear whether they exert therapeutic effects on colorectal cancer. In this study, we investigate the properties of polysaccharides of A. bracteate, named ABP. The average molecular weight of ABP was 18.3 kDa and ABP consisted of glucose, mannose, galactose, xylose, galacturonic acid, glucuronic acid at a molar ratio of 37.8:8:2.5:1.7:1:1. An Azoxymethane/Dextran sodium sulfate (AOM/DSS) induced CAC mouse model was established. The CAC mice treated with ABP showed smaller tumor size and lower tumor incidence than untreated ones. ABP increased anti-inflammatory cytokine IL-10, inhibited secretion of pro-inflammatory cytokines (IL-6, IFN-γ, and TNF-α), mitigated oxidative stress by increasing GSH and decreasing MDA levels, suppressed the activation of STAT3 and expressions of its related genes c-Myc and cyclin D1. Moreover, ABP treatment increased the relative abundance of beneficial bacteria (f_Ruminococcaceae, g_Roseburia, g_Odoribacter, g_Oscillospira, and g_Akkermansia) and the levels of fecal short-chain fatty acid (SCFA) in CAC model mice. In summary, our data suggest that ABP could be a potential therapeutic agent for treating CAC.

Bacteria are better predictive biomarkers of environmental estrogen transmission than fungi.

The heavy reliance on estrogens in the food industry worldwide greatly contributes to the environmental release of these compounds, begetting serious public concern of their fate. Various microorganisms capable of estrogen degradation, and their catabolic pathways, have been isolated, suggesting that they can eliminate estrogens in both engineered and natural environments. Nonetheless, it remains little understood as to how potential estrogen-degrading microorganisms are distributed within those habitats. An estrogen transmission chain from swine manure to compost, compost-amended soil, and neighboring agricultural soil was investigated in five suburban areas of Beijing, China. The concentrations of major estrogen classes decreased by > 90% from manure to soils, which did not co-vary with environmental antibiotics and heavy metal concentrations. Many bacterial taxa, such as Lactobacillus and Bacteroides, could serve as potential biomarkers of estrogen concentrations, while fungi were only occasionally accurate. To explain this phenomenon, stochasticity was found to be dominant in shaping the fungal communities across all samples, while deterministic selection, arising from biotic interactions, was important for bacterial communities. Metabolic genes involved in oxidizing phenol and catalyzing oxidative ring cleavage of catechol were detected, co-varying with estrogen concentrations. These findings are important as identifying microbial biomarkers of estrogen dynamics, spanning the levels of both taxonomy and functional genes, provides valuable information for assessing estrogen bioavailability and biomarking of estrogen fate in the environment.

Albuca Bracteate Polysaccharides Synergistically Enhance the Anti-Tumor Efficacy of 5-Fluorouracil Against Colorectal Cancer by Modulating ß-Catenin Signaling and Intestinal Flora.

The first-line treatment for colorectal cancer (CRC) is 5-fluorouracil (5-FU). However, the efficacy of this treatment is sometimes limited owing to chemoresistance as well as treatment-associated intestinal mucositis and other adverse events. Growing evidence suggests that certain phytochemicals have therapeutic and cancer-preventing properties. Further, the synergistic interactions between many such plant-derived products and chemotherapeutic drugs have been linked to improved therapeutic efficacy. Polysaccharides extracted from Albuca bracteata (Thunb.) J.C.Manning and Goldblatt (ABP) have been reported to exhibit anti-oxidant, anti-inflammatory, and anti-tumor properties. In this study, murine CRC cells (CT26) and a murine model of CRC were used to examine the anti-tumor properties of ABP and explore the mechanism underlying the synergistic interactions between ABP and 5-FU. Our results revealed that ABP could inhibit tumor cell proliferation, invasion, and migratory activity in vitro and inhibited tumor progression in vivo by suppressing ß-catenin signaling. Additionally, treatment with a combination of ABP and 5-FU resulted in better outcomes than treatment with either agent alone. Moreover, this combination therapy resulted in the specific enrichment of Ruminococcus, Anaerostipes, and Oscillospira in the intestinal microbiota and increased fecal short-chain fatty acid (SCFA) levels (acetic acid, propionic acid, and butyric acid). The improvement in the intestinal microbiota and the increase in beneficial SCFAs contributed to enhanced therapeutic outcomes and reduced the adverse effects of 5-FU. Together, these data suggest that ABP exhibits anti-neoplastic activity and can effectively enhance the efficacy of 5-FU in CRC treatment. Therefore, further research on the application of ABP in the development of novel anti-tumor drugs and adjuvant compounds is warranted and could improve the outcomes of CRC patients.

The microbial network property as a bio-indicator of antibiotic transmission in the environment.

Interspecies interaction is an essential mechanism for bacterial communities to develop antibiotic resistance via horizontal gene transfer. Nonetheless, how bacterial interactions vary along the environmental transmission of antibiotics and the underpinnings remain unclear. To address it, we explore potential microbial associations by analyzing bacterial networks generated from 16S rRNA gene sequences and functional networks containing a large number of antibiotic-resistance genes (ARGs). Antibiotic concentration decreased by more than 4000-fold along the environmental transmission chain from manure samples of swine farms to aerobic compost, compost-amended agricultural soils, and neighboring agricultural soils. Both bacterial and functional networks became larger in nodes and links with decreasing antibiotic concentrations, likely resulting from lower antibiotics stress. Nonetheless, bacterial networks became less clustered with decreasing antibiotic concentrations, while functional networks became more clustered. Modularity, a key topological property that enhances system resilience to antibiotic stress, remained high for functional networks, but the modularity values of bacterial networks were the lowest when antibiotic concentrations were intermediate. To explain it, we identified a clear shift from deterministic processes, particularly variable selection, to stochastic processes at intermediate antibiotic concentrations as the dominant mechanism in shaping bacterial communities. Collectively, our results revealed microbial network dynamics and suggest that the modularity value of association networks could serve as an important indicator of antibiotic concentrations in the environment.

A Novel Neural Model With Lateral Interaction for Learning Tasks.

We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some neurons with lateral interaction, and the neurons in different fields are connected by the rules of synaptic plasticity. The model is established on the current research of cognition and neuroscience, making it more transparent and biologically explainable. Our proposed model is applied to data classification and clustering. The corresponding algorithms share similar processes without requiring any parameter tuning and optimization processes. Numerical experiments validate that the proposed model is feasible in different learning tasks and superior to some state-of-the-art methods, especially in small sample learning, one-shot learning, and clustering.

Winter warming rapidly increases carbon degradation capacities of fungal communities in tundra soil: Potential consequences on carbon stability.

High-latitude tundra ecosystems are increasingly affected by climate warming. As an important fraction of soil microorganisms, fungi play essential roles in carbon degradation, especially the old, chemically recalcitrant carbon. However, it remains obscure how fungi respond to climate warming and whether fungi, in turn, affect carbon stability of tundra. In a 2-year winter soil warming experiment of 2°C by snow fences, we investigated responses of fungal communities to warming in the active layer of an Alaskan tundra. Although fungal community composition, revealed by the 28S rRNA gene amplicon sequencing, remained unchanged (p > .05), fungal functional gene composition, revealed by a microarray named GeoChip, was altered (p < .05). Changes in functional gene composition were linked to winter soil temperature, thaw depth, soil moisture, and gross primary productivity (canonical correlation analysis, p < .05). Specifically, relative abundances of fungal genes encoding invertase, xylose reductase and vanillin dehydrogenase significantly increased (p < .05), indicating higher carbon degradation capacities of fungal communities under warming. Accordingly, we detected changes in fungal gene networks under warming, including higher average path distance, lower average clustering coefficient and lower percentage of negative links, indicating that warming potentially changed fungal interactions. Together, our study reveals higher carbon degradation capacities of fungal communities under short-term warming and highlights the potential impacts of fungal communities on tundra ecosystem respiration, and consequently future carbon stability of high-latitude tundra.

Progressive Microbial Community Networks with Incremental Organic Loading Rates Underlie Higher Anaerobic Digestion Performance.

Although biotic interactions among members of microbial communities have been conceived to be crucial for community assembly, it remains unclear how changes in environmental conditions affect microbial interaction and consequently system performance. Here, we adopted a random matrix theory-based network analysis to explore microbial interactions in triplicate anaerobic digestion (AD) systems, which is widely applied for organic pollutant treatments. The digesters were operated with incremental organic loading rates (OLRs) from 1.0 g volatile solids (VS)/liter/day to 1.3 g VS/liter/day and then to 1.5 g VS/liter/day, which increased VS removal and methane production proportionally. Higher resource availability led to networks with higher connectivity and shorter harmonic geodesic distance, suggestive of more intense microbial interactions and quicker responses to environmental changes. Strikingly, a number of topological properties of microbial network showed significant (P < 0.05) correlation with AD performance (i.e., methane production, biogas production, and VS removal). When controlling for environmental parameters (e.g., total ammonia, pH, and the VS load), node connectivity, especially that of the methanogenic archaeal network, still correlated with AD performance. Last, we identified the Methanothermus, Methanobacterium, Chlorobium, and Haloarcula taxa and an unclassified Thaumarchaeota taxon as keystone nodes of the network.IMPORTANCE AD is a biological process widely used for effective waste treatment throughout the world. Biotic interactions among microbes are critical to the assembly and functioning of the microbial community, but the response of microbial interactions to environmental changes and their influence on AD performance are still poorly understood. Using well-replicated time series data of 16S rRNA gene amplicons and functional gene arrays, we constructed random matrix theory-based association networks to characterize potential microbial interactions with incremental OLRs. We demonstrated striking linkage between network topological features of methanogenic archaea and AD functioning independent of environmental parameters. As the intricate balance of multiple microbial functional groups is responsible for methane production, our results suggest that microbial interaction may be an important, previously unrecognized mechanism in determining AD performance.

Environmental antibiotics drives the genetic functions of resistome dynamics.

The increasing prevalence of antibiotic-resistant microorganisms imposes a global threat to public health. The over reliant use of antibiotics in the food industry has contributed considerably to the dissemination of antibiotics into various environments, yet the mechanisms by which antibiotic dissemination influences the assembly of the microbial community continues to remain obscure. Here, we examine bacterial and fungal community assemblies in swine manure, compost, compost amended, and unamended agricultural soil in five suburban areas of Beijing, China. Total antibiotic concentration decreased by factors of 10-1000 from manure and compost to soils. The bacterial α-diversity was found to be low in manure and compost samples, while the fungal α-diversity was similar across all samples. We detected significantly (p < 0.05) higher relative abundances of well recognized pathogenic microbial taxa, virulence associated genes, and antibiotic resistance genes (ARGs) in manure and compost than those in agricultural soils, revealing the higher microbial capacity of pathogenicity, virulence and antibiotic resistance. Unexpectedly, the relative abundances of both bacterial and fungal taxa did not predict the antibiotic concentration. A possible explanation was that bacterial and fungal communities were mainly shaped by random assemblies. Rather, antibiotic concentration could be well predicted by relative abundances of antibiotic resistance, stress and virulence associated genes. Despite the weak interconnection between ARGs and the microbiome, we demonstrate that microbial genes should be the focal point in tracking the ecological effects of antibiotic dissemination by revealing microbial community patterns along the dissemination chain of antibiotics.

Genomic dynamics of full-scale temperature-phased anaerobic digestion treating waste activated sludge: Focusing on temperature differentiation.

A robust microbial community is essential for the overall stability and performance of the anaerobic digestion process. In this study, two digesters of a full-scale temperature-phased anaerobic digestion plant treating waste activated sludge were sampled for one year. The acidogenesis reactor (AR) was run at 45 ±â€¯2 °C for six months in Period I and was run at 38 ±â€¯2 °C for six months in Period II. While the methanogenesis reactor (MR) was run at 36 ±â€¯3 °C throughout the year. 16S rRNA amplicon sequencing and GeoChip 5.0 results showed that samples were clearly differentiated by reactors and periods. The elevated temperature in AR during Period I improved the effects of phase separation between the AR and MR. In AR, Fervidobacterium, assigned to Class Thermotogae, had a higher relative abundance of 8.9% in Period I. The abundance of genes involved with carbon degradation was significantly higher in Period I than Period II. In MR, the relative abundance of Methanosarcina increased from 19.8% in Period I to 30.6% in Period II. In addition, the influent characteristics, reactor performance, and operating parameters were determined as the key variables shaping the microbial community, contributing to a total of 76.3% and 69.5% of the variance of the AR and MR, respectively. Combined, this study enriches our understanding of genomic dynamics in full scale temperature-phased anaerobic digestion process.

Bacteriophage-prokaryote dynamics and interaction within anaerobic digestion processes across time and space.

BACKGROUND: Bacteriophage-prokaryote dynamics and interaction are believed to be important in governing microbiome composition and ecosystem functions, yet our limited knowledge of the spatial and temporal variation in phage and prokaryotic community compositions precludes accurate assessment of their roles and impacts. Anaerobic digesters are ideal model systems to examine phage-host interaction, owing to easy access, stable operation, nutrient-rich environment, and consequently enormous numbers of phages and prokaryotic cells. RESULTS: Equipped with high-throughput, cutting-edge environmental genomics techniques, we examined phage and prokaryotic community composition of four anaerobic digesters in full-scale wastewater treatment plants across China. Despite the relatively stable process performance in biogas production, phage and prokaryotic groups fluctuated monthly over a year of study period, showing significant correlations between those two groups at the α- and ß-diversity levels. Strikingly, phages explained 40.6% of total variations of the prokaryotic community composition, much higher than the explanatory power by abiotic factors (14.5%). Consequently, phages were significantly (P < 0.010) linked to parameters related to process performance including biogas production and volatile solid concentrations. Association network analyses showed phage-prokaryote pairs were shallowly conserved since they were detected only within small viral clades. CONCLUSIONS: Those results collectively demonstrate phages as a major biotic factor in controlling prokaryotic composition and process performance. Therefore, phages may play a larger role in shaping prokaryotic community dynamics and process performance of anaerobic digesters than currently appreciated.