Bipartite Network Analysis Research Articles

Deciphering Eurasian otter (Lutra lutra L.) and seal (Phoca vitulina L.; Halichoerus grypus F.) diet: Metabarcoding tailored for fresh and saltwater fish species.

Long-lived top predators shape biodiversity structure in their ecosystems and predator-prey interactions are critical in decoding how communities function. Studies on the foraging ecology of seals and Eurasian otters in Western Europe are outdated and most studies solely performed traditional hard part analysis. Molecular metabarcoding can be used as an innovative noninvasive diet analysis tool, which has proven efficient and complementary to hard part analysis, however, lacking application in the wider North Sea area. In this study, DNA from digesta, collected between 2014-2020, were used to identify fish species in the diet of 47 Eurasian otters, 54 harbour seals and 21 grey seals by applying a next-generation metabarcoding approach. A newly designed 16S rRNA primer, providing the best coverage of >130 local marine and freshwater fish species, was used to amplify prey DNA from seal scats and otter gut content sampled from the North Sea and regional freshwater bodies. Frequent fish species included tench, ninespine stickleback and white bream in otters; hooknose and common roach in grey seals and Pleuronectidae and sand gobies in harbour seals. Bipartite network analysis showed a strong overlap of harbour and grey seal diets. Otter diet intersected with both seal species in terms of freshwater species. This study provides new knowledge about dietary composition and community assemblage of fish prey in otters and seals in the North Sea and regional freshwaters, and a new molecular tool to elucidate predator-prey interactions and interspecies competition in complex and changing ecosystems under pressure from anthropogenic activities.

University–Industry Technology Transfer: Empirical Findings from Chinese Industrial Firms

The knowledge and innovation generated by researchers at universities is transferred to industries through patent licensing, leading to the commercialization of academic output. In order to investigate the development of Chinese university–industry technology transfer and whether this kind of collaboration may affect a firm’s innovation output, we collected approximately 6400 license contracts made between more than 4000 Chinese firms and 300 Chinese universities for the period between 2009 and 2014. This is the first study on Chinese university–industry knowledge transfer using a bipartite social network analysis (SNA) method, which emphasizes centrality estimates. We are able to investigate empirically how patent license transfer behavior may affect each firm’s innovative output by allocating a centrality score to each firm in the university–firm technology transfer network. We elucidate the academic–industry knowledge by visualizing flow patterns for different regions with the SNA tool, Gephi. We find that innovation capabilities, R&D resources, and technology transfer performance all vary across China, and that patent licensing networks present clear small-world phenomena. We also highlight the Bipartite Graph Reinforcement Model (BGRM) and BiRank centrality in the bipartite network. Our empirical results reveal that firms with high BGRM and BiRank centrality scores, long history, and fewer employees have greater innovative output.

GgClusterNet: An R package for microbiome network analysis and modularity-based multiple network layouts.

The network analysis has attracted increasing attention and interest from ecological academics, thus it is of great necessity to develop more convenient and powerful tools. For that reason, we have developed an R package, named "ggClusterNet,"to complete and display the network analysis in an easier manner. In that package, ten network layout algorithms are designed to better display the modules of microbiome network (randomClusterG, PolygonClusterG, PolygonRrClusterG, ArtifCluster, randSNEClusterG, PolygonModsquareG, PolyRdmNotdCirG, model_Gephi.2, model_igraph, and model_maptree). For the convenience of the users, many functions related to microbial network analysis, such as corMicor(), net_properties(), node_properties(), ZiPiPlot(), random_Net_compate(), are integrated to complete the network mining. Furthermore, the pipeline function named network.2() and corBionetwork() are also added for the quick achievement of the network or bipartite network analysis as well as their in-depth mining. The ggClusterNet is publicly available via GitHub (https://github.com/taowenmicro/ggClusterNet/) or Gitee (https://gitee.com/wentaomicro/ggClusterNet) for users' access. A complete description of the usages can be found on the manuscript's GitHub page (https://github.com/taowenmicro/ggClusterNet/wiki).

Elite Pipelines: How Elite School Ties Are Reflected in Interfirm Employee Migration

Recent research has pointed to pipelines, or sequenced hiring of individuals from the same source organizations over time, as a common practice employed by firms to reduce labor market imperfections and create human capital resource advantages. We extend this literature by considering how pipelines may emerge not just out of strategic intent but also out of informal social processes reflecting network transitivity and homophily. Using bipartite social network analysis techniques in a sample of 110 public U.S. electronics component manufacturers, we argue and find evidence for transitive effects between firms’ employee-based ties to elite educational institutions and the formation of interfirm employee pipelines. We demonstrate that pipelines are more likely to form between firms that both hire educational elites, especially when those firms share ties to the same elite schools and when those shared elite schools are executives’ alma mater(s). We also find that these effects tend to be more pronounced when the focal firm has higher growth potential, encouraging mobility by attracting potential elite candidates and facilitating the two-way matching process in labor markets. Our theory and findings contribute to a better understanding of informal processes underlying pipeline formation and specifically demonstrate how hiring tendencies may lead to closed recruiting networks characterized by elite replication.

364 Identification of Symptom-Based Phenotypes in PASC Patients through Bipartite Network Analysis: Implications for Patient Triage and Precision Treatment Strategies

OBJECTIVES/GOALS: Approximately 10% of COVID-19 patients experience multiple symptoms weeks and months after the acute phase of infection. Our goal was to use advanced machine learning methods to identify PASC phenotypes based on their symptom profiles, and their association with critical adverse outcomes, with the goal of designing future targeted interventions. METHODS/STUDY POPULATION: Data. All COVID-19 outpatients from 12 University of Minnesota hospitals and 60 clinics. Independent variables consisted of 20 CDC-defined PASC symptoms extracted from clinical notes using NLP. Covariates included demographics, and outcomes included New Psychological Diagnostic Evaluation, and Number of PASC Hospital Visits (>=5). Cases (n=3235) consisted of patients with at least one symptom, and controls (n=3034) consisted of patients with no symptoms. Method. (1) Used bipartite network analysis and modularity maximization to identify patient-symptom biclusters. (2) Used multivariable logistic regression (adjusted for demographics and corrected through Bonferroni) to measure the odds ratio of each patient bicluster to adverse outcomes, compared to controls, and to each of the other biclusters. RESULTS/ANTICIPATED RESULTS: The analysis identified 6 PASC phenotypes (http://www.skbhavnani.com/DIVA/Images/Fig-1-PASC-Network.jpg), which was statistically significant compared to 1000 random permutations of the data (PASC=.31, Random Median=.27, z=11, P<.01). Three of the clusters (Cluster-1, Cluster-4, and Cluster-5 encircled with ovals in Fig. 1) contained CNS-related symptoms, which had statistically significant risk for one or both of the adverse outcomes. For example, Cluster-1 with critical CNS symptoms (depression, insomnia, anxiety, brain-fog/difficulty-thinking), had a significantly higher OR compared to the controls for New Psychological Diagnostic Evaluation (OR=6.6, CI=4.9-9.1, P-corr<.001), in addition to having a significantly higher ORs for the same outcome compared to all the other clusters. DISCUSSION/SIGNIFICANCE: The results identified distinct PASC phenotypes based on symptom profiles, with three of them related to CNS symptoms, each of which had significantly higher risk for specific adverse outcomes compared to controls. We will test whether these phenotypes replicate in the N3C data, and explore their translation into triage and treatment strategies.

Applying Bipartite Network Analysis and Ordination Technique to Evaluate Long-Term Data from Veterinary-Sanitary Examination of Slaughtered Pigs.

Simple SummaryVeterinary inspections in abattoirs are important in the surveillance of zoonotic diseases. This study is based on veterinary inspection reports providing data about the diseases and welfare issues of 231 million pigs slaughtered in 16 regions of Poland between 2009 and 2019. Over 80 million slaughtered pigs were found with pathological changes that could pose a risk to human health. The most frequently observed changes were abscesses, soiling, faecal or other contaminations, and congestions, together accounting for 77.6% of the total infections. Statistical approaches conducted with the use of long-term data can help identify the most problematic health and welfare issues in slaughterhouses. The results of this study showed that changes related to poor animal welfare (purulent foci, contamination, congestion) and parasitic diseases accounted for the majority of the identified cases of condemnation.Animal and meat inspections in abattoirs are important in the surveillance of zoonotic diseases. Veterinary inspections in abattoirs can provide useful data for the management of health and welfare issues of humans and animals. Using the network analysis and ordination technique, in this study, we analyzed the data from 11 years of veterinary inspections in pig slaughterhouses from 16 regions in Poland. Based on the huge data set of 80,187,639 cases of diseases and welfare issues of pigs, the most frequent livestock diseases were identified to be abscesses, soiling, faecal or other contaminations, and congestions, which together accounted for 77.6% of the total condemnations. Spatial and temporal differences in swine diseases between the Polish regions were recognized using the above-mentioned statistical approaches. Moreover, with the use of a quite novel method, not used yet in preventive veterinary medicine, called a heatmap, the most problematic disease and welfare issues in each region in Poland were identified. The use of statistical approaches such as network analysis and ordination technique allow for identification of the health and welfare issues in slaughterhouses when dealing with long-term inspection data based on a very large number of cases, and then have to be adopted in current veterinary medicine.

More than meets the eye: decrypting diversity reveals hidden interaction specificity between frogs and frog‐biting midges

1. Female frog‐biting midges (Diptera: Corethrellidae) eavesdrop on the nocturnal mating calls of their blood hosts – male frogs. Available data suggest variable degrees of specialisation among Corethrella‐host associations, with limited information on the mechanisms involved in host selection and partitioning on a community level.2. Our study provides a first comprehensive analysis of host interactions for a neotropical community of frog‐biting midges, based on both morphological and molecular genetic species delimitation. We used quantitative bipartite interaction networks to investigate host specificity among the midge‐frog community of La Gamba, Pacific lowland Costa Rica.3. Midges that were collected directly from frog hosts (16 frog species) showed more pronounced levels of specificity (network‐wide degree of specialisation: H2′ = 0.3) than those caught with acoustic traps broadcasting their calls (12 frog species; H2′ = 0.08). This indicates that, despite a rather generalist acoustic foraging behaviour, frog‐biting midges discriminate between potential hosts by using additional close‐range recognition cues.4. Based on COI and ITS2 sequencing data, we identified considerable levels of cryptic diversity within our five Corethrella morphotypes, with at least 17 distinct MOTUs of Corethrella in La Gamba. Including these MOTUs in bipartite network analyses produced higher resolution in species interactions, and increased estimators of network specificity (H2′ = 0.42).

Endogenous bacteria inhabiting the Ophiocordyceps highlandensis during fruiting body development

BackgroundThe genus Ophiocordyceps, which includes Ophiocordyceps sinensis, has been demonstrated to be one of the most valuable medicinal taxa. The low rate of larval infection and slow development that characterize the cultivation of this genus should be urgently addressed. To identify potential bioinoculants that stimulate the growth of Ophiocordyceps, O. highlandensis was selected as a model system, and a total of 72 samples were collected to systematically compare the microbial communities present during fruiting body development. By applying high-throughput 16S and ITS2 amplicon sequencing technology, the bacterial and fungal communities were identified in O. highlandensis and its surrounding soil, and the functional dynamics of the bacteria were explored.ResultsThe results indicate that the most abundant bacteria across all the samples from O. highlandensis were Proteobacteria, Firmicutes and Bacteroidetes, while members of Ascomycota were detected among the fungi. The pathways enriched in the developmental stages were associated with carbohydrate degradation, nucleotides and pyridoxal biosynthesis, and the TCA cycle. Compared with that in the fungal community, an unexpectedly high taxonomic and functional fluctuation was discovered in the bacterial community during the maturation of O. highlandensis. Furthermore, bipartite network analysis identified four potential supercore OTUs associated with O. highlandensis growth.ConclusionsAll the findings of this study suggest unexpectedly high taxonomic and functional fluctuations in the bacterial community of O. highlandensis during its maturation. O. highlandensis may recruit different endogenous bacteria across its life cycle to enhance growth and support rapid infection. These results may facilitate Ophiocordyceps cultivation and improve the development of strategies for the identification of potential bioinoculant resources.

Diet and trophic niche overlap among a native waterbird and two non-native herbivores in Pampas grasslands

The use of areas by native and non-native herbivores in sympatry has frequently led to conflicts between farmers and native animals. Dietary studies integrating methodologies usually provide a better quantified understanding of resource sharing and dietary overlap. This study aims to evaluate diet and niche overlap among three species that occur in sympatry in southern Brazil: a native herbivorous bird in South America, the southern screamer (Chauna torquata), domestic sheep (Ovis aries) and cattle (Bos taurus). Three complementary methodologies were used: microhistology and metabarcoding of faeces and stable isotope analysis (SIA) in faeces and whole blood. Bipartite networks were constructed for the three dietary assessments. No significant isotopic niche overlap was found between the native bird and the domestic herbivores by SIA in blood, while the three methods applied to faecal samples showed limited overlap between the bird and the non-native ungulates. Conversely, SIA in faecal samples revealed similarity in the feeding habits and niches of domestic herbivores. The complementary use of the three different techniques allowed the evaluation of the herbivore diet from different perspectives, according to strengths and limitations of each method: while metabarcoding indicated the use of a larger number of plant groups in different proportions and identified several of the components at the species level, Cyperaceae species were only identified by microhistology. Conversely, metabarcoding performed better than microhistology in the detection of some species of Onagraceae and Poaceae. SIA in blood elucidated different proportions of resources assimilated by herbivores, while faecal SIA revealed plants with similar photosynthetic cycles in the diet of the non-native herbivores. Through bipartite network analysis, modules emerged (e.g. SIA = 0.2, 95% IC = 0.1–0.19), confirming the differential use of some groups of resources. The combination of the three techniques, enhanced by comparative bipartite network analysis, was critical for obtaining a detailed understanding of the vast range of potential plants used and interactions among herbivores. The detected patterns of dietary partitioning also confirm that the southern screamer is not competing with cattle and sheep and can even provide ecosystem services through the biological control of aquatic weeds.

Modeling Multi-Year Customers’ Considerations and Choices in China’s Auto Market Using Two-Stage Bipartite Network Analysis

Choice modeling is important in transportation planning, marketing and engineering design, as it can quantify the influence of product attributes and customer demographics on customers’ choice behaviors. Consumer studies suggest that customers’ choice-making process often consists of two different stages: customers first consider subsets of available products on the market, and then make the final choice from the subsets. As existing preference modeling is mostly focused on the choice stage, there is a need to develop methods for understanding customer preferences at both stages, and investigate how customer preferences change from “consideration” to “choice”, and whether such changes will be consistent over time. In this paper, we study customers’ consideration and purchase behaviors in China’s auto market using multi-year survey datasets. We demonstrate how descriptive network analysis and analytic network models (bipartite Exponential Random Graph Model (ERGM)) capture the change of customers’ preferences from the consideration stage to the choice stage in multiple consecutive years. Our results show that factors such as fuel consumption per unit power, car make origin, and place of production influence customers’ considerations and final purchase decisions in different ways, and this difference between consideration and purchase is consistent over time. The main contribution of this study is that we validate the two-stage network-based modeling approach and its utility in preference elicitation using multiple-year dataset, which sheds lights on understanding the trend of customers’ consideration and choice behaviors across years. Our study also contributes to a refined interpretation of the ERGM results with categorization of continuous variables into ranges, which shows that customer choice decisions may be more qualitatively influenced by product attributes rather than quantitatively. Our approach is generic and thus can be applied to solving broader choice modeling problems, such as the transportation mode selection and the adoption of clean technology (e.g., electric vehicles).

Environmental and Anthropogenic Factors Shape Major Bacterial Community Types Across the Complex Mountain Landscape of Switzerland.

Mountain areas harbor large climatic and geographic gradients and form numerous habitats that promote high overall biodiversity. Compared to macroorganisms, knowledge about drivers of biodiversity and distribution of soil bacteria in mountain regions is still scarce but a prerequisite for conservation of bacterial functions in soils. An important question is, whether soil bacterial communities with similar structures share environmental preferences. Using metabarcoding of the 16S rRNA gene marker, we assessed soil bacterial communities at 255 sites of a regular grid covering the mountainous landscape of Switzerland, which is characterized by close location of biogeographic regions that harbor different land-use types. Distribution of bacterial communities was mainly shaped by environmental selection, as revealed by 47.9% variance explained by environmental factors, with pH (29%) being most important. Little additional variance was explained by biogeographic regions (2.8%) and land-use types (3.3%). Cluster analysis of bacterial community structures revealed six bacterial community types (BCTs), which were associated to several biogeographic regions and land-use types but overall differed mainly in their preference for soil pH. BCT I and II occurred at neutral pH, showed distinct preferences for biogeographic regions mainly differing in elevation and nutrient availability. BCT III and IV differed only in their preferred soil pH. BCT VI occurred in most acidic soils (pH 3.6) and almost exclusively at forest sites. BCT V occurred in soils with a mean pH of 4 and differed from BCT VI in preference for lower values of organic C, total nitrogen and their ratio. Indicator species and bipartite network analyses revealed 3,998 OTUs associating to different levels of environmental factors and BCTs. Taxonomic classification revealed opposing associations of taxa deriving from the same phyla. The results revealed that pH, land-use type, biogeographic region, and nutrient availability were the main factors shaping bacterial communities across Switzerland. Indicator species and bipartite network analyses revealed environmental preferences of bacterial taxa. Combining information of environmental factors and BCTs yielded increased resolution of the factors shaping soil bacterial communities and provided an improved biodiversity framework. OTUs exclusively associated to BCTs provide a novel resource to identify unassessed environmental drivers.

Bipartite network analysis of ant-task associations reveals task groups and absence of colonial daily activity.

Social insects are one of the best examples of complex self-organized systems exhibiting task allocation. How task allocation is achieved is the most fascinating question in behavioural ecology and complex systems science. However, it is difficult to comprehensively characterize task allocation patterns due to behavioural complexity, such as the individual variation, context dependency and chronological variation. Thus, it is imperative to quantify individual behaviours and integrate them into colony levels. Here, we applied bipartite network analyses to characterize individual-behaviour relationships. We recorded the behaviours of all individuals with verified age in ant colonies and analysed the individual-behaviour relationship at the individual, module and network levels. Bipartite network analysis successfully detected the module structures, illustrating that certain individuals performed a subset of behaviours (i.e. task groups). We confirmed age polyethism by comparing age between modules. Additionally, to test the daily rhythm of the executed tasks, the data were partitioned between daytime and nighttime, and a bipartite network was re-constructed. This analysis supported that there was no daily rhythm in the tasks performed. These findings suggested that bipartite network analyses could untangle complex task allocation patterns and provide insights into understanding the division of labour.

Amplicon Sequencing-Based Bipartite Network Analysis Confirms a High Degree of Specialization and Modularity for Fungi and Prokaryotes in Deadwood.

Fungi and prokaryotes are dominant colonizers of wood and mediate its decomposition. Much progress has been achieved to unravel these communities and link them to specific wood properties. However, comparative studies considering both groups of organisms and assessing their relationships to wood resources are largely missing. Bipartite interaction networks provide an opportunity to investigate this colonizer-resource relationship more in detail and aim to directly compare results between different biotic groups. The main questions were as follows. Are network structures reflecting the trophic relationship between fungal and prokaryotic colonizers and their resources? If so, do they reflect the critical role of these groups, especially that of fungi, during decomposition? We used amplicon sequencing data to analyze fungal and prokaryotic interaction networks from deadwood of 13 temperate tree species at an early to middle stage of decomposition. Several diversity- and specialization-related indices were determined and the observed network structures were related to intrinsic wood traits. We hypothesized nonrandom bipartite networks for both groups and a higher degree of specialization for fungi, as they are the key players in wood decomposition. The results reveal highly modular and specialized interaction networks for both groups of organisms, demonstrating that many fungi and prokaryotes are resource-specific colonizers. However, as the level of specialization of fungi significantly surpassed that of prokaryotes, our findings reflect the strong association between fungi and their host. Our novel approach shows that the application of bipartite interaction networks is a useful tool to explore, quantify, and compare the deadwood-colonizers relationship based on sequencing data.IMPORTANCE Deadwood is important for our forest ecosystems. It feeds and houses many organisms, e.g., fungi and prokaryotes, with many different species contributing to its decomposition and nutrient cycling. The aim of this study was to explore and quantify the relationship between these two main wood-inhabiting organism groups and their corresponding host trees. Two independent DNA-based amplicon sequencing data sets (fungi and prokaryotes) were analyzed via bipartite interaction networks. The links in the networks represent the interactions between the deadwood colonizers and their deadwood hosts. The networks allowed us to analyze whether many colonizing species interact mostly with a restricted number of deadwood tree species, so-called specialization. Our results demonstrate that many prokaryotes and fungi are resource-specific colonizers. The direct comparison between both groups revealed significantly higher specialization values for fungi, emphasizing their strong association to respective host trees, which reflects their dominant role in exploiting this resource.

植物功能性状与森林生态系统服务的关系研究综述

植物功能性状途径是揭示生物多样性与生态系统服务关系的重要视角，尽管植物功能性状与生态系统服务的关系在单一地点的研究取得突出进展，但对于植物功能性状与生态系统服务的关系仍缺乏整体认识。以森林生态系统为对象，通过系统文献检索及筛选，收集了216篇文献，应用整合分析和二分网络分析等方法，探讨了植物功能性状对森林生态系统服务及其权衡与协同关系的影响。结果表明：植物叶片功能性状关注最多，占研究性状数量的48%，生态系统服务中关注最多是生物量、土壤肥力、病虫害控制和固碳服务；81.1%的植物功能性状与生态系统服务关系组表现为稳定的正向或负向关系，而关联植物功能性状多的生态系统服务（生物量、固碳服务、土壤水分、土壤肥力和病虫害控制）往往与植物功能性状表现为不稳定关系；森林生态系统中存在6组"植物功能性状-生态系统服务簇（简称"性状-服务簇"）：水循环相关的性状-服务簇、土壤保持相关的性状-服务簇、物质生产相关的性状-服务簇、灾害控制相关的性状-服务簇、养分循环相关的性状-服务簇和授粉相关的性状-服务簇，揭示了各性状-服务簇内生态系统服务的权衡或协同关系以及与各性状-服务簇关系密切的植物功能性状。该研究从总体上阐明了植物功能性状与森林生态系统服务关系的研究重点和进展、揭示了植物功能性状对森林生态系统服务影响效应的方向和强度，可为深化森林生态系统服务形成机制认识以及协调生态系统服务权衡关系提供科学依据。;Plant functional traits provide an effective way to investigate the complex relationship between biodiversity and ecosystem services. Many empirical studies have revealed the importance of plant functional traits for ecosystem service delivery, but there is still a lack of general understanding of the relationship between plant functional traits and ecosystem services. Here a systematic analysis was performed to investigate the effects of plant functional traits on forest ecosystem services and their trade-off/synergy relationships based on 216 references by using meta-analysis and bipartite network analysis. Among plant functional traits, leaf functional traits are the most concerned, accounting for 48.0% of the studied plant functional traits. Biomass, soil fertility, biocontrol, and carbon sequestration are the most in-depth studies in ecosystem services. About 81.8% of plant functional traits have stable relationships (positive or negative) with forest ecosystem services, and the unstable relationship is mainly concentrated in the ecosystem services associated with multiple plant functional traits, such as biomass, carbon sequestration, soil water content, soil fertility, and herbivory and biocontrol. In forest ecosystem, six trait-service clusters are determined, namely, water-cycle-related cluster, soil-conservation-related cluster, material-production-related cluster, disaster-control-related cluster, nutrient-cycle-related cluster, and pollination-related cluster, which reveal the relationships of trade-offs or synergies among forest ecosystem services. The key plant functional traits influencing these trait-service clusters were also identified. This study generally clarifies the research of the relationships between plant functional traits and forest ecosystem services, and reveals the influence direction and intensity of plant functional traits on forest ecosystems. The results can provide a scientific basis for understanding the formation mechanism of forest ecosystem services and coordinating the trade-off relationships of forest ecosystem services.

Foliar mycoendophytome of an endemic plant of the Mediterranean biome (Myrtus communis) reveals the dominance of basidiomycete woody saprotrophs.

The true myrtle, Myrtus communis, is a small perennial evergreen tree that occurs in Europe, Africa, and Asia with a circum-Mediterranean geographic distribution. Unfortunately, the Mediterranean Forests, where M. communis occurs, are critically endangered and are currently restricted to small fragmented areas in protected conservation units. In the present work, we performed, for the first time, a metabarcoding study on the spatial variation of fungal community structure in the foliar endophytome of this endemic plant of the Mediterranean biome, using bipartite network analysis as a model. The local bipartite network of Myrtus communis individuals and their foliar endophytic fungi is very low connected, with low nestedness, and moderately high specialization and modularity. Similar network patterns were also retrieved in both culture-dependent and amplicon metagenomics of foliar endophytes in distinct arboreal hosts in varied biomes. Furthermore, the majority of putative fungal endophytes species were basidiomycete woody saprotrophs of the orders Polyporales, Agaricales, and Hymenochaetales. Altogether, these findings suggest a possible adaptation of these wood-decaying fungi to cope with moisture limitation and spatial scarcity of their primary substrate (dead wood), which are totally consistent with the predictions of the viaphytism hypothesis that wood-decomposing fungi inhabit the internal leaf tissue of forest trees in order to enhance dispersal to substrates on the forest floor, by using leaves as vectors and as refugia, during periods of environmental stress.

How High-Risk Comorbidities Co-Occur in Readmitted Patients With Hip Fracture: Big Data Visual Analytical Approach.

BackgroundWhen older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx.ObjectiveThis study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions.MethodsWe extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions.ResultsThe analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups.ConclusionsThe integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups.

Temporal variation and sharing of antibiotic resistance genes between water and wild fish gut in a peri-urban river

Antibiotic resistance genes (ARGs) as emergence contaminations have spread widely in the water environment. Wild fish may be recipients and communicators of ARGs in the water environment, however, the distribution and transmission of ARGs in the wild fish and relevant water environment were rarely reported. Here, we have profiled ARGs and bacterial communities in wild freshwater fish and relevant water in a peri-urban river using high-throughput qPCR and 16S rRNA gene sequence. A total of 80 and 220 unique ARG subtypes were identified in fish and water samples. Fish and water both showed significant ARG seasonal variations (P < 0.05). The highest absolute abundance of ARGs in fish and water occurred in summer (1.32 × 109 copies per g, on average) and autumn (9.04 × 106 copies per mL), respectively. In addition, the bipartite network analysis showed that 9 ARGs and 1 mobile genetic element continuously shared in fish and water. Furthermore, bacteria shared in fish and water were found to significantly correlate with shard ARGs. The findings demonstrate that bacteria and ARGs in fish and water could interconnect and ARGs might transfer between fish and water using bacteria as a spreading medium.

Understanding Missing Links in Bipartite Networks With MissBiN.

The analysis of bipartite networks is critical in a variety of application domains, such as exploring entity co-occurrences in intelligence analysis and investigating gene expression in bio-informatics. One important task is missing link prediction, which infers the existence of unseen links based on currently observed ones. In this article, we propose a visual analysis system, MissBiN, to involve analysts in the loop for making sense of link prediction results. MissBiN equips a novel method for link prediction in a bipartite network by leveraging the information of bi-cliques in the network. It also provides an interactive visualization for understanding the algorithm outputs. The design of MissBiN is based on three high-level analysis questions (what, why, and how) regarding missing links, which are distilled from the literature and expert interviews. We conducted quantitative experiments to assess the performance of the proposed link prediction algorithm, and interviewed two experts from different domains to demonstrate the effectiveness of MissBiN as a whole. We also provide a comprehensive usage scenario to illustrate the usefulness of the tool in an application of intelligence analysis.

Trophic niche similarities of sympatric Turdus thrushes determined by fecal contents, stable isotopes, and bipartite network approaches.

An ecological niche has been defined as an n‐dimensional hypervolume formed by conditions and resources that species need to survive, grow, and reproduce. In practice, such niche dimensions are measurable and describe how species share resources, which has been thought to be a crucial mechanism for coexistence and a major driver of broad biodiversity patterns. Here, we investigate resource partitioning and trophic interactions of three sympatric, phylogenetically related and morphologically similar species of thrushes (Turdus spp.). Based on one year of data collected in southern Brazil, we investigated niche partitioning using three approaches: diet and trophic niche assessed by fecal analysis, diet and niche estimated by stable isotopes in blood and mixing models, and bipartite network analysis derived from direct diet and mixing model outputs. Approaches revealed that the three sympatric thrushes are generalists that feed on similar diets, demonstrating high niche overlap. Fruits from C3 plants were one of the most important food items in their networks, with wide links connecting the three thrush species. Turdus amaurochalinus and T. albicollis had the greatest trophic and isotopic niche overlap, with 90% and 20% overlap, respectively. There was partitioning of key resources between these two species, with a shared preference for fig tree fruits—Ficus cestrifolia (T. amaurochalinus PSIRI% = 11.3 and T. albicollis = 11.5), which was not present in the diet of T. rufiventris. Results added a new approach to the network analysis based on values from the stable isotope mixing models, allowing comparisons between traditional dietary analysis and diet inferred by isotopic mixing models, which reflects food items effectively assimilated in consumer tissues. Both are visualized in bipartite networks and show food‐consumers link strengths. This approach could be useful to other studies using stable isotopes coupled to network analysis, particularly useful in sympatric species with similar niches.

Dietary impact of a plant-derived microRNA on the gut microbiome

BackgroundGlobal estimations of 4 billion people living on plant-based diets signify tremendous diversity in plant consumption and their assorted miRNAs, which presents a challenging model to experimentally address how plant-based miRNAs impact the microbiome. Here we establish baseline gut microbiome composition for a mouse model deficient in the specific mammalian miR-146a shown to alter gut microbiomes. We then asses the effect on the gut microbiome when miR-146a-deficient mice are fed a transgenic plant-based diet expressing the murine-derived miR-146a. Mice deficient in miR-146a were maintained either on a baseline diet until 7 weeks of age (day 0) and then fed either vector or miR-146a-expressing plant-based diets for 21 days. The gut microbiomes of mice were examined by comparing the V4 region of 16S rRNA gene sequences of DNA isolated from fecal samples at days 0 (baseline diet) and 21 (vector or miR-146a expressing plant-based diets).ResultsBeta-diversity analysis demonstrated that the transition from baseline chow to a plant-based diet resulted in significant longitudinal shifts in microbial community structure attributable to increased fiber intake. Bipartite network analysis suggests that miR-146a-deficient mice fed a plant diet rich in miR-146a have a microbiome population modestly different than mice fed an isogenic control plant diet deficient in miR-146a.ConclusionA mouse diet composed of a transgenic plant expressing a mouse miR-146a may fine tune microbial communities but does not appear to have global effects on microbiome structure and composition.