Data-driven prediction of colonization outcomes for complex microbial communities.

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities.

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations.

Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally.

Identifying keystone species in microbial communities using deep learning.

Previous studies suggested that microbial communities can harbour keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. Here we propose a data-driven keystone species identification (DKI) framework based on deep learning to resolve this challenge.

Nutritional redundancy in the human diet and its application in phenotype association studies.

Studying human dietary intake may help us identify effective measures to treat or prevent many chronic diseases whose natural histories are influenced by nutritional factors. Here, by examining five cohorts with dietary intake data collected on different time scales, we show that the food intake profile varies substantially across individuals and over time, while the nutritional intake profile appears fairly stable. We refer to this phenomenon as 'nutritional redundancy' and attribute it to the nested structure of the food-nutrient network.

Data-driven prediction of colonization outcomes for complex microbial communities.

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities.

Identifying chronic obstructive pulmonary disease from integrative omics and clustering in lung tissue.

Background: Chronic obstructive pulmonary disease (COPD) is a highly morbid and heterogenous disease. While COPD is defined by spirometry, many COPD characteristics are seen in cigarette smokers with normal spirometry. The extent to which COPD and COPD heterogeneity is captured in omics of lung tissue is not known.

Identifying keystone species in microbial communities using deep learning.

Previous studies suggested that microbial communities harbor keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. This is mainly due to our limited knowledge of microbial dynamics and the experimental and ethical difficulties of manipulating microbial communities.

Assessment of community efforts to advance network-based prediction of protein-protein interactions.

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs.

Ecological dynamics imposes fundamental challenges in community-based microbial source tracking.

Quantifying the contributions of possible environmental sources ("sources") to a specific microbial community ("sink") is a classical problem in microbiology known as microbial source tracking (MST). Solving the MST problem will not only help us understand how microbial communities were formed, but also have far-reaching applications in pollution control, public health, and forensics. MST methods generally fall into two categories: target-based methods (focusing on the detection of source-specific indicator species or chemicals); and community-based methods (using community structure to measure similarity between sink samples and potential source environments).

A statistical physics approach for disease module detection.

Extensive evidence indicates that the pathobiological processes of a complex disease are associated with perturbation in specific neighborhoods of the human protein-protein interaction (PPI) network (also known as the interactome), often referred to as the disease module. Many computational methods have been developed to integrate the interactome and omics profiles to extract context-dependent disease modules. Yet, existing methods all have fundamental limitations in terms of rigor and/or efficiency.

Predicting microbiome compositions from species assemblages through deep learning.

Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment or their hosts' well-being. Rationally managing these microbial communities requires improving our ability to predict how different species assemblages affect the final species composition of the community. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics.

Discovery of a novel nonsteroidal selective glucocorticoid receptor modulator by virtual screening and bioassays.

Synthetic glucocorticoids (GCs) have been widely used in the treatment of a broad range of inflammatory diseases, but their clinic use is limited by undesired side effects such as metabolic disorders, osteoporosis, skin and muscle atrophies, mood disorders and hypothalamic-pituitary-adrenal (HPA) axis suppression. Selective glucocorticoid receptor modulators (SGRMs) are expected to have promising anti-inflammatory efficacy but with fewer side effects caused by GCs. Here, we reported HT-15, a prospective SGRM discovered by structure-based virtual screening (VS) and bioassays.

Integrating gut microbiome and host immune markers to understand the pathogenesis of infection.

() infection is the most common cause of healthcare-associated infection and an important cause of morbidity and mortality among hospitalized patients. A comprehensive understanding of infection (CDI) pathogenesis is crucial for disease diagnosis, treatment, and prevention. Here, we characterized gut microbial compositions and a broad panel of innate and adaptive immunological markers in 243 well-characterized human subjects (including 187 subjects with both microbiota and immune marker data), who were divided into four phenotype groups: CDI, Asymptomatic Carriage, Non-CDI Diarrhea, and Control.

Deciphering functional redundancy in the human microbiome.

Although the taxonomic composition of the human microbiome varies tremendously across individuals, its gene composition or functional capacity is highly conserved - implying an ecological property known as functional redundancy. Such functional redundancy has been hypothesized to underlie the stability and resilience of the human microbiome, but this hypothesis has never been quantitatively tested. The origin of functional redundancy is still elusive.

Link Prediction through Deep Generative Model.

Inferring missing links based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine, e-commerce, social media, and criminal intelligence. Numerous methods have been proposed to solve the link prediction problem. Yet, many of these methods are designed for undirected networks only and based on domain-specific heuristics.

Comparative study of classifiers for human microbiome data.

Accumulated evidence has shown that commensal microorganisms play key roles in human physiology and diseases. Dysbiosis of the human-associated microbial communities, often referred to as the human microbiome, has been associated with many diseases. Applying supervised classification analysis to the human microbiome data can help us identify subsets of microorganisms that are highly discriminative and hence build prediction models that can accurately classify unlabeled samples.

Identifying concepts from medical images via transfer learning and image retrieval.

Automatically identifying semantic concepts from medical images provides multimodal insights for clinical research. To study the effectiveness of concept detection on large scale medical images, we reconstructed over 230,000 medical image-concepts pairs collected from the ImageCLEFcaption 2018 evaluation task. A transfer learning-based multi-label classification model was used to predict multiple high-frequency concepts for medical images.

Control energy of complex networks towards distinct mixture states.

Controlling complex networked systems is a real-world puzzle that remains largely unsolved. Despite recent progress in understanding the structural characteristics of network control energy, target state and system dynamics have not been explored. We examine how varying the final state mixture affects the control energy of canonical and conformity-incorporated dynamical systems.

Repeat HIV Testing and Incident Rates among Individuals Attending Voluntary Counseling and Testing Clinics in Wuxi, China: A Retrospective Study.

Objective: We aimed to elucidate the rates of repeat HIV testing and incident HIV diagnosis, and baseline CD4+ T cell count among individuals attending HIV voluntary counseling and testing (VCT) clinics in Wuxi, China.

Methods: A repeat HIV testing within 12 months was recorded if individuals had their first test with negative results, during 2013-2014 and retested within 12 months. An incident HIV diagnosis was recorded if individuals had their first test with negative results, during 2013-2015 and had a subsequent positive result at any point by the end of 2015.

Effect of correlations on controllability transition in network control.

The network control problem has recently attracted an increasing amount of attention, owing to concerns including the avoidance of cascading failures of power-grids and the management of ecological networks. It has been proven that numerical control can be achieved if the number of control inputs exceeds a certain transition point. In the present study, we investigate the effect of degree correlation on the numerical controllability in networks whose topological structures are reconstructed from both real and modeling systems, and we find that the transition point of the number of control inputs depends strongly on the degree correlation in both undirected and directed networks with moderately sparse links.

[Study on the prevalence rate and immunity of hepatitis B virus infection among urban adults aged over 20 years in Wuxi, Jiangsu province].

Objective: To study the relationship on the prevalence rate of hepatitis B virus (HBV) infection and hepatitis B vaccination in urban citizens aged over 20 years old which would led to the development of strategies on HBV control.

Methods: A total of 3744 subjects from general population were randomly selected in this study. Both ELISA and radio immunoassay were used to test five items of HBV infection, including HBsAg, anti-HBs, HBeAg, anti-HBe and anti-HBc.