Transkingdom Analysis of the Female Reproductive Tract Reveals Bacteriophages Form Communities

The female reproductive tract (FRT) microbiome plays an important role in maintaining vaginal health. Viruses play a key role in regulating other microbial ecosystems, but little is known about how the FRT viruses (virome), particularly bacteriophages, impacts FRT health and dysbiosis. We hypothesize that bacterial vaginosis is associated with alterations in the FRT virome, and these changes correlate with bacteriome shifts. We conducted a retrospective, longitudinal analysis of vaginal swabs collected from 54 bacterial vaginosis (BV)-positive and 46 BV-negative South African women. Bacteriome analysis revealed samples clustered into five distinct bacterial community groups (CG). Bacterial alpha diversity was significantly associated with BV. Virome analysis on a subset of baseline samples showed FRT bacteriophages clustering into novel viral state types (VSTs), a viral community clustering system based on virome composition and abundance. Distinct BV bacteriophage signatures included increased alpha diversity along with Bacillus, Burkholderia and Escherichia bacteriophages. Discriminate bacteriophage-bacteria transkingdom associations were also identified between Bacillus and Burkholderia viruses and BV-associated bacteria, providing key insight for future studies elucidating transkingdom interactions driving BV-associated microbiome perturbations. In this cohort, bacteriophage-bacterial associations suggest complex interactions which may play a role in the establishment and maintenance of BV.

Effects of Xylanase in Corn- or Wheat-Based Diets on Cecal Microbiota of Broilers

Xylanase has been demonstrated to improve growth performance of broilers fed wheat- or corn-based diets due to its ability to degrade arabinoxylans (AX). However, content and structure of AX in corn and wheat are different, comparing effects of xylanase on cecal microbiota of broilers fed corn- or wheat-based diets could further elaborate the mechanism of the specificity of xylanase for different cereal grains. Thus, a total of 192 one-day-old broilers were randomly allotted into four dietary treatments, including wheat-soybean basal diet, wheat-soybean basal diet with 4,000U/kg xylanase, corn-soybean basal diet, and corn-soybean basal diet with 4,000U/kg xylanase to evaluate interactive effects of xylanase in corn- or wheat-based diets on broilers cecal microbiota during a 6-week production period. The results indicated that bacterial community clustering was mainly due to cereal grains rather than xylanase supplementation. Compared with broilers fed wheat-based diets, corn-based diets increased alpha-diversity and separated from wheat-based diets (p<0.05). Xylanase modulated the abundance of specific bacteria without changing overall microbial structure. In broilers fed wheat-based diets, xylanase increased the abundance of Lactobacillus, Bifidobacterium, and some butyrate-producing bacteria, and decreased the abundance of non-starch polysaccharides-degrading (NSP) bacteria, such as Ruminococcaceae and Bacteroidetes (p<0.05). In broilers fed corn-based diets, xylanase decreased the abundance of harmful bacteria (such as genus Faecalitalea and Escherichia-Shigella) and promoted the abundance of beneficial bacteria (such as Anaerofustis and Lachnospiraceae_UCG_010) in the cecum (p<0.05). Overall, xylanase supplementation to wheat- or corn-based diets improved broilers performance and cecal microbiota composition. Xylanase supplementation to wheat-based diets increased the abundance of butyrate-producing bacteria and decreased the abundance of NSP-degrading bacteria. Moreover, positive effects of xylanase on cecal microbiota of broilers fed corn-based diets were mostly related to the inhibition of potentially pathogenic bacteria, and xylanase supplementation to corn-based diets slightly affected the abundance of butyrate-producing bacteria and NSP-degrading bacterium, the difference might be related to lower content of AX in corn compared to wheat.

Causes of death after biannual azithromycin treatment: A community-level randomized clinical trial

The MORDOR study, a masked, community-level randomized clinical trial conducted in Niger, Malawi and Tanzania (2015 to 2017), showed that biannual administration of single-dose azithromycin to preschool children reduced all-cause mortality. We sought to evaluate its impact on causes of death in children aged 1–59 months in Tanzania. A random sampling of 614 communities was conducted in Kilosa District, Tanzania, with simple random assignment of communities to receive either azithromycin or placebo. In these communities, a census was carried out every 6 months and children aged 1–59 months received biannual (every 6 months), single-dose azithromycin (~20mg/kg) or placebo depending on community assignment, over a 2-year period. Mortality was determined at the time of the biannual census. For child deaths, a verbal autopsy was performed to ascertain the cause using a standardized diagnostic classification. A total of 190- (0.58 /100 person-years) and 200 deaths (0.59/100 person-years) were reported in the azithromycin and placebo arms, respectively. Malaria, pneumonia and diarrhea, accounted for 71% and 68% of deaths in the respective arms. Overall, the mortality was not different by treatment arm, nor were the distribution of causes of death after adjusting for community clustering. The cause-specific mortality for diarrhea/pneumonia was no different over time. In children aged 1–5 months, 32 deaths occurred in the placebo arm and 25 deaths occurred in the azithromycin arm; 20 (62.5%) deaths in the placebo- and 10 (40%) in the azithromycin arm were attributed to diarrhea or pneumonia. Neither differences in the number of deaths nor the diarrhea/pneumonia attribution was statistically significant after adjusting for community clustering. In conclusion, azithromycin was not associated with a significant decline in deaths by specific causes compared to placebo. The non-significant lower rates of diarrhea or pneumonia in children <6 months who received azithromycin merit further investigation in high-mortality settings. Trial registration: NCT02048007.

Effective communication data transmission based on community clustering in opportunistic social networks in IoT system

With the rise of the Internet of Things, the opportunistic network of portable smart devices has become a new hot spot in academic research in recent years. The mobility of nodes in opportunistic networks makes the communication links between nodes unstable, so data forwarding is an important research content in opportunistic networks. However, the traditional opportunistic network algorithm only considers the transmission of information and does not consider the social relationship between people, resulting in a low transmission rate and high network overhead. Therefore, this paper proposes an efficient data transmission model based on community clustering. According to the user’s social relationship and the release location of the points of interest, the nodes with a high degree of interest relevance are divided into the same community. Weaken the concept of a central point in the community, and users can share information to solve the problem of excessive load on some nodes in the network and sizeable end-to-end delay.

Cross-Device Identity Resolution using Machine Learning: A Scalable Device Graph Approach

This paper outlines how Mediacorp, Singapore's public service broadcaster, addresses its cross-device identity challenges using a scalable device graph approach. Research in this area is relevant to the domain of advertising technology as it enables a holistic view of consumers that can be extended to use cases such as improving advertisement targeting, personalized recommendations and demographic predictions. Past research efforts were limited to high-level descriptions of the steps undertaken to create a one-off, static device graph based on data collected over a circumscribed time frame, thus limiting its use in larger-scale commercial applications. In this paper, we propose a scalable solution that enables continuous, incremental revisions of our device graph. We leverage behavioral data captured by Mediacorp across its sites and platforms to build a richer device graph that is updated weekly. First, we introduce additional features and explore various classifiers to improve pairwise probability scores between devices that are likely to belong to the same user. Then, we apply community clustering algorithms to uncover device communities to establish the final device graph. Extensive experiments showed that our additive approach has consistently delivered >90% precision and recall in real-world applications.

Two-Stage User Identification Based on User Topology Dynamic Community Clustering

In order to solve the problem of node information loss during user matching in the existing user identification method of fixed community across the social network based on user topological relationship, Two-Stage User Identification Based on User Topology Dynamic Community Clustering (UIUTDC) algorithm is proposed. Firstly, we perform community clustering on different social networks, calculate the similarity between different network communities, and screen out community pairs with greater similarity. Secondly, two-way marriage matching is carried out for users between pairs of communities with high similarity. Then, the dynamic community clustering was performed by resetting the different community clustering numbers. Finally, the iteration is repeated until no new matching user pairs are generated, or the set number of iterations is reached. Experiments conducted on real-world social networks Twitter-Foursquare datasets demonstrate that compared with the global user matching method and hidden label node method, the average accuracy of the proposed UIUTDC algorithm is improved by 33% and 26.8%, respectively. In the case of only user topology information, the proposed UIUTDC algorithm effectively improves the accuracy of identity recognition in practical applications.

Phylogenetic analysis of Harmonin homology domains

Background Harmonin Homogy Domains (HHD) are recently identified orphan domains of about 70 residues folded in a compact five alpha-helix bundle that proved to be versatile in terms of function, allowing for direct binding to a partner as well as regulating the affinity and specificity of adjacent domains for their own targets. Adding their small size and rather simple fold, HHDs appear as convenient modules to regulate protein–protein interactions in various biological contexts. Surprisingly, only nine HHDs have been detected in six proteins, mainly expressed in sensory neurons. Results Here, we built a profile Hidden Markov Model to screen the entire UniProtKB for new HHD-containing proteins. Every hit was manually annotated, using a clustering approach, confirming that only a few proteins contain HHDs. We report the phylogenetic coverage of each protein and build a phylogenetic tree to trace the evolution of HHDs. We suggest that a HHD ancestor is shared with Paired Amphipathic Helices (PAH) domains, a four-helix bundle partially sharing fold and functional properties. We characterized amino-acid sequences of the various HHDs using pairwise BLASTP scoring coupled with community clustering and manually assessed sequence features among each individual family. These sequence features were analyzed using reported structures as well as homology models to highlight structural motifs underlying HHDs fold. We show that functional divergence is carried out by subtle differences in sequences that automatized approaches failed to detect. Conclusions We provide the first HHD databases, including sequences and conservation, phylogenic trees and a list of HHD variants found in the auditory system, which are available for the community. This case study highlights surprising phylogenetic properties found in orphan domains and will assist further studies of HHDs. We unveil the implication of HHDs in their various binding interfaces using conservation across families and a new protein–protein surface predictor. Finally, we discussed the functional consequences of three identified pathogenic HHD variants involved in Hoyeraal-Hreidarsson syndrome and of three newly reported pathogenic variants identified in patients suffering from Usher Syndrome.

Generalized correlation-based dynamical network analysis: a new high-performance approach for identifying allosteric communications in molecular dynamics trajectories

Molecular interactions are essential for regulation of cellular processes, from the formation of multiprotein complexes, to the allosteric activation of enzymes. Identifying the essential residues and molecular features that regulate such interactions is paramount for understanding the biochemical process in question, allowing for suppression of a reaction through drug interventions, or optimization of a chemical process using bioengineered molecules. In order to identify important residues and information pathways within molecular complexes, the Dynamical Network Analysis method was developed and has since been broadly applied in the literature. However, in the dawn of exascale computing, this method is generally limited to relatively small biomolecular systems. In this work we provide an evolution of the method, application and interface. All data processing and analysis is conducted through Jupyter notebooks, providing automatic detection of important solvent and ion residues, an optimized and parallel generalized correlation implementation that is linear with respect to the number of nodes in the system, and subsequent community clustering, calculation of betweenness of contacts, and determination optimal paths. Using the popular visualization program VMD, high-quality renderings of the networks over the biomolecular structures can be produced. Our new implementation was employed to investigate three different systems, with up to 2.5 M atoms, namely the OMP-decarboxylase, the Leucyl-tRNA synthetase complexed with its cognate tRNA and adenylate, and the respiratory complex I in a membrane environment. Our enhanced and updated protocol provides the community with an intuitive and interactive interface, which can be easily applied to large macromolecular complexes.

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

Understanding how certain brain regions relate to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We propose BrainGNN, a graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers. Considering the special property of brain graphs, we design novel ROI-aware graph convolutional (Ra-GConv) layers that leverage the topological and functional information of fMRI. Motivated by the need for transparency in medical image analysis, our BrainGNN contains ROI-selection pooling layers (R-pool) that highlight salient ROIs (nodes in the graph), so that we can infer which ROIs are important for prediction. Furthermore, we propose regularization terms - unit loss, topK pooling (TPK) loss and group-level consistency (GLC) loss - on pooling results to encourage reasonable ROI-selection and provide flexibility to preserve either individual- or group-level patterns. We apply the BrainGNN framework on two independent fMRI datasets: Autism Spectral Disorder (ASD) fMRI dataset and Human Connectome Project (HCP) 900 Subject Release. We investigate different choices of the hyperparameters and show that BrainGNN outperforms the alternative fMRI image analysis methods in terms of four different evaluation metrics. The obtained community clustering and salient ROI detection results show high correspondence with the previous neuroimaging-derived evidence of biomarkers for ASD and specific task states decoded for HCP.

Structural features recapitulate collective dynamics of inhibitory networks

Inhibitory interneurons are ubiquitous through-out the central nervous system (CNS) and play an important role in organizing the excitatory neuronal populations into spatiotemporal patterns. These spatiotemporal patterns are believed to play a vital role in encoding sensory information. The olfactory system is a wellknown example where odor information is encoded in temporally evolving activity of the principal neurons and inhibitory interneurons play an important role in generating these patterns. In this work we study how inhibitory interactions generate such patterns in the con-text of odor encoding by simulating random biophysical models of mitral cells. Using the Newman community clustering algorithm we identify synchronously firing groups of neurons that switch in their activity. Our study presents a new method of inferring the dynamics of inhibitory networks from their structure.