Explore the vast range of titles available.

Background Microbial interactions shape the structure and function of microbial communities; microbial co-occurrence networks in specific environments have been widely developed to explore these complex systems, but their interconnection pattern across microbiomes in various environments at the global scale remains unexplored. Here, we have inferred an Earth microbial co-occurrence network from a communal catalog with 23,595 samples and 12,646 exact sequence variants from 14 environments in the Earth Microbiome Project dataset. Results This non-random scale-free Earth microbial co-occurrence network consisted of 8 taxonomy distinct modules linked with different environments, which featured environment specific microbial co-occurrence relationships. Different topological features of subnetworks inferred from datasets trimmed into uniform size indicate distinct co-occurrence patterns in the microbiomes of various environments. The high number of specialist edges highlights that environmental specific co-occurrence relationships are essential features across microbiomes. The microbiomes of various environments were clustered into two groups, which were mainly bridged by the microbiomes of plant and animal surface. Acidobacteria Gp2 and Nisaea were identified as hubs in most of subnetworks. Negative edges proportions ranged from 1.9% in the soil subnetwork to 48.9% the non-saline surface subnetwork, suggesting various environments experience distinct intensities of competition or niche differentiation. 2NmSq7Q5noeLTtkrbKwa4L Video abstract Conclusion This investigation highlights the interconnection patterns across microbiomes in various environments and emphasizes the importance of understanding co-occurrence feature of microbiomes from a network perspective.

Questions: Are liana–host interactions structured at the community level? Do liana–host interactions differ between species, growth form guilds or habitats? Location: Otari-Wilton's Bush, on the southern tip of North Island, New Zealand. The forest contains 75 ha of mature and regenerating conifer–broadleaf forest. Methods: Nine liana species were quantified among 217 trees to test for negative co-occurrence patterns. We also conducted additional analyses within and among compartments embedded in the community-level matrix. Liana and host abundance distributions were assessed across two contrasting habitats. Results: Community-level analyses revealed negative co-occurrence patterns. Positive, neutral and negative co-occurrence patterns were found among compartments within the community-level matrix. Host species compartments were consistent with randomized expectations, while positive co-occurrence patterns were found within the host species matrix. Negative co-occurrence patterns were found inconsistently among lianas that share the same region of host space, and those that do not. Conclusions: Overall, results indicate the liana community is structured non-randomly. Liana–host interactions appear to follow an opportunistic growth strategy and interactions are due mostly to habitat partitioning.

Negative co-occurrence patterns are intriguing because they may reflect the outcome of interspecific interactions and therefore signal how competition shapes communities. However, other factors also contribute to these patterns. For example, theoretical studies as well as two survey-based studies have all suggested that dispersal may also impact these patterns. While natural communities commonly have nonrandom patterns of negative co-occurrence, understanding how different processes drive these patterns requires further research. We tested the influence of dispersal on co-occurrence patterns using a zooplankton mesocosm experiment with four different dispersal treatments varying in the number of dispersers delivered into mesocosms on regular intervals. Our dispersal treatments were intended to adjust the relative importance of dispersal and competition experienced within mesocosms (i.e., high dispersal results in a relatively low influence of competition on species composition and vice versa). Higher dispersal translated into increased zooplankton species richness and inter-mesocosm compositional similarity, and also changed species occupancy patterns such that species occurrences were more even across mesocosms in higher-dispersal treatments. Dispersal treatments also differed markedly in species co-occurrence patterns. Negative co-occurrence patterns were significant for all but the lowest-dispersal treatment, peaked in the intermediate-dispersal treatments, and declined in the highest-dispersal treatment. Stability analyses illustrate that co-occurrence differences are robust to the exclusion of any single mesocosm in null model analyses. Dispersal treatments did not significantly differ with respect to abiotic variation, which has been recognized as a potential driver of negative co-occurrence patterns. These results suggest that not only can dispersal influence patterns of negative co-occurrence via changes to species richness and distribution (occupancy patterns among mesocosms), but the degree to which they do so varies nonlinearly with the strength of dispersal. Critically, because negative co-occurrence patterns were nonsignificant when the contribution of dispersal was lowest, it is possible that dispersal contributes strongly to many observed patterns of negative co-occurrence. Consequently, great care should be taken prior to interpreting significant co-occurrence tests as a product of species interactions.

Aminoglycosides (AGs) in combination with β-lactams play an important role in antimicrobial therapy in severe infections. Pan-resistance to clinically relevant AGs increasingly arises from the production of 16S rRNA methylases (RMTases) that are mostly encoded by plasmids in Gram-negative bacteria. The recent emergence and spread of isolates encoding RMTases is worrisome, considering that they often co-produce extended-spectrum β-lactamases (ESBLs) or carbapenemases. Our study aimed to retrospectively analyze and characterize the association of carbapenem- and aminoglycoside-resistant clinical isolates in Switzerland during a 3.5-year period between January 2017 and June 2020. A total of 103 pan-aminoglycoside- and carbapenem-resistant clinical isolates were recovered at the NARA (Swiss National Reference Center for Emerging Antibiotic Resistance) during the 2017–2020 period. Carbapenemase and RMTase determinants were identified by PCR and sequencing. The characterization of plasmids bearing resistance determinants was performed by a mating-out assay followed by PCR-based replicon typing (PBRT). Clonality of the isolates was investigated by multilocus sequence typing (MLST). Over the 991 Enterobacterales collected at the NARA during this period, 103 (10.4%) of them were resistant to both carbapenems and all aminoglycosides. Among these 103 isolates, 35 isolates produced NDM-like carbapenemases, followed by OXA-48-like (n = 23), KPC-like (n = 21), or no carbapenemase (n = 13), OXA-48-like and NDM-like co-production (n = 7), and VIM-like enzymes (n = 4). The RMTases ArmA, RmtB, RmtC, RmtF, RmtG, and RmtB + RmtF were identified among 51.4%, 13.6%, 4.9%, 24.3%, 1%, and 1%, respectively. Plasmid co-localization of the carbapenemase and the RMTase encoding genes was found among ca. 20% of the isolates. A high diversity was identified in terms of the nature of associations between RMTase and carbapenemase-encoding genes, of incompatibility groups of the corresponding plasmids, and of strain genetic backgrounds, highlighting heterogeneous importations rather than clonal dissemination.

Background The gut microbiota impacts on a range of host biological processes, and the imbalances in its composition are associated with pathology. Though the understanding of contribution of the many factors, e.g. gender, diet and age, in the development of gut microbiota has been well established, the dynamic changes of the phylogenetic composition and the interaction networks along with the age remain unclear in pigs. Results Here we applied 16S ribosomal RNA gene sequencing, enterotype-like clustering (Classification of the gut microbiome into distinct types) and phylogenetic co-occurrence network to explore the dynamic changes of pig gut microbiome following the ages with a successive investigation at four ages in a cohort of 953 pigs. We found that Firmicutes and Bacteroidetes are two predominant phyla throughout the experimental period. The richness of gut microbiota was significantly increased from 25 to 240 days of age. Principal coordinates analysis showed a clear difference in the gut microbial community compositions between pre-weaning piglets and the pigs at the other three age groups. The gut microbiota of pre-weaning piglets was clearly classified into two enterotypes, which were dominated by Fusobacterium and p-75-a5 , respectively. However, Prevotella and Treponema were the main drivers of the enterotypes for pigs at the age of 80, 120 and 240 days. Besides the piglets, even some adult pigs switched putative enterotypes between ages. We confirmed that the topological features of phylogenetic co-occurrence networks, including scale, stability and complexity were increased along with the age. The biological significance for modules in the network of piglets were mainly associated with the utilization of simple carbohydrate and lactose, whereas the sub-networks identified at the ages of 80, 120 and 240 days may be involved in the digestion of complex dietary polysaccharide. The modules related to the metabolism of protein and amino acids could be identified in the networks at 120 and 240 days. This dynamic change of the functional capacities of gut microbiome was further supported by functional prediction analysis. Conclusions The present study provided meaningful biological insights into the age-based dynamic shifts of ecological community of porcine gut microbiota.

Background Feed shortage is an important factor limiting livestock production in the world. To effectively utilize natural woody plant resources, we used wilting and microbial additives to prepare an anaerobic fermentation feed of mulberry, and used PacBio single-molecule real-time (SMRT) sequencing technology to analyse the “enzyme–bacteria synergy” and fermentation mechanism. Results The fresh branches and leaves of mulberry have high levels of moisture and nutrients, and also contain a diverse range of epiphytic microorganisms. After ensiling, the microbial diversity decreased markedly, and the dominant bacteria rapidly shifted from Gram-negative Proteobacteria to Gram-positive Firmicutes . Lactic acid bacteria (LAB) emerged as the dominant microbial population, resulting in increased in the proportion of the carbohydrate metabolism and decreased in the proportion of the amino acid and “global and overview map” (GOM) metabolism categories. The combination of cellulase and LAB exhibited a synergistic effect, through which cellulases such as glycanase, pectinase, and carboxymethyl cellulase decomposed cellulose and hemicellulose into sugars. LAB converted these sugars into lactic acid through the glycolytic pathway, thereby improving the microbial community structure, metabolism and fermentation quality of mulberry silage. The GOM, carbohydrate metabolism, and amino acid metabolism were the main microbial metabolic categories during ensiling. The presence of LAB had an important effect on the microbial community and metabolic pathways during silage fermentation. A “co-occurrence microbial network” formed with LAB, effectively inhibiting the growth of harmful microorganisms, and dominating the anaerobic fermentation process. Conclusions In summary, PacBio SMRT was used to accurately analyse the microbial network information and regulatory mechanism of anaerobic fermentation, which provided a scientific basis for the study of woody silage fermentation theory. This study reveals for the first time the main principle of the enzyme–bacteria synergy in a woody silage fermentation system, which provides technical support for the development and utilization of woody feed resources, and achieves sustainable livestock production. Highlights Mulberry is potential feed resource as a natural woody plant. Wilting method inhibits anaerobic fermentation of clostridia during ensiling. Bacteria–enzyme synergy action regulates silage fermentation of woody plant. Microbial community and metabolism regulate the woody silage fermentation. Microbial sequencing provides accurate microbial network and fermentation information.

Older adults may be better able to regulate emotion responses to negative experiences than younger persons when provided instructions, but age group differences in spontaneous emotion responses are poorly understood. The current study determined age group differences in spontaneous reactivity and recovery in negative and positive affects, as well as the co-occurrence of negative and positive affects, following a laboratory mood induction. Younger (n = 71) and older adults (n = 44) rated negative and positive affects before and several times after a negative mood induction involving sad film clips. ANCOVA and multilevel longitudinal modeling in HLM were utilized to determine age group differences in spontaneous reactivity to and recovery from the mood induction, as well as age group differences in co-occurrence of negative and positive affects. Relative to younger adults, older adults reported greater negative affect reactivity to and recovery from the mood induction. Older adults also reported greater co-occurrence of negative and positive affects in response to the mood induction, as compared to younger adults. Thus, older adults reacted more strongly to sad film clips than younger persons, exhibited efficient recovery, and reported greater co-occurrence of negative and positive affects. A fruitful line of future research might determine whether affect co-occurrence facilitates effective emotion regulation.

The analysis of presence-absence matrices with \"null model\" randomization tests has been a major source of controversy in community ecology for over two decades. In this paper, I systematically compare the performance of nine null model algorithms and four co-occurrence indices with respect to Type I and Type II errors. The nine algorithms differ in whether rows and columns are treated as fixed sums, equiprobable, or proportional. The three models that maintain fixed row sums are invulnerable to Type I errors (false positives). One of these three is a modified version of the original algorithm of E. F. Connor and D. Simberloff. Of the four co-occurrence indices, the number of checkerboard combinations and the number of species combinations may be prone to Type II errors (false negatives), and may not reveal significant patterns in noisy data sets. L. Stone and A. Robert's checkerboard score has good power for detecting species pairs that do not co-occur together frequently, whereas D. Schluter's V ratio reveals nonrandom patterns in the row and column totals of the matrix. Degenerate matrices (matrices with empty rows or columns) do not greatly alter the outcome of null model analyses. The choice of an appropriate null model and index may depend on whether the data represent classic \"island lists\" of species in an archipelago or standardized \"sample lists\" of species collected with equal sampling effort. Systematic examination of a set of related null models can pinpoint how violation of the assumptions of the model contributes to nonrandom patterns.

The exponential growth of patent datasets poses a significant challenge in filtering relevant documents for research and innovation. Traditional semantic search methods based on keywords often fail to capture the complexity and variability in multidisciplinary terminology, leading to inefficiencies. This study addresses the problem by systematically evaluating supervised and unsupervised machine learning (ML) techniques for document relevance filtering across five technology domains: solid-state batteries, electric vehicle chargers, connected vehicles, electric vertical takeoff and landing aircraft, and light detecting and ranging (LiDAR) sensors. The contributions include benchmarking the performance of 10 classical models. These models include extreme gradient boosting, random forest, and support vector machines; a deep artificial neural network; and three natural language processing methods: latent Dirichlet allocation, non-negative matrix factorization, and k-means clustering of a manifold-learned reduced feature dimension. Applying these methods to more than 4200 patents filtered from a database of 9.6 million patents revealed that most supervised ML models outperform the unsupervised methods. An average of seven supervised ML models achieved significantly higher precision, recall, and F1-scores across all technology domains, while unsupervised methods show variability depending on domain characteristics. These results offer a practical framework for optimizing document relevance filtering, enabling researchers and practitioners to efficiently manage large datasets and enhance innovation.

Release of nanoceria (nCeO2) into the environment has caused much concern about its potential toxicity, which still remains poorly understood for soil microorganisms. In this study, nanoceria and cerium (III) nitrate at different doses (10, 100 and 500 mg/kg) were applied to bok choy (Brassica rapa subsp. chinensis), grown in potting soil, to investigate the responses of soil bacterial communities to nanoceria (NC) and ionic cerium (IC) applications. The results showed that bacterial richness was slightly increased in all cerium treatments relative to the negative control without cerium amendment (CK), but a significant increase was only found in IC500. The patterns of bacterial community composition, predicted functions and phenotypes of all NC treatments were significantly differentiated from IC and CK treatments, which was correlated with the contents of cerium, available potassium and phosphorus in soil. The co-occurrence network of bacterial taxa was more complex after exposure to ionic cerium than to nanoceria. The keystone taxa of the two networks were entirely different. Predicted functions analysis found that anaerobic and Gram-negative bacteria were enriched under nanoceria exposure. Our study implies that Proteobacteria and nitrifying bacteria were significantly enriched after exposure to nanoceria and could be potential biomarkers of soil environmental perturbation from nanoceria exposure.