Classifying migraine using PET compressive big data analytics of brain's -opioid and D2/D3 dopamine neurotransmission.

Migraine is a common and debilitating pain disorder associated with dysfunction of the central nervous system. Advanced magnetic resonance imaging (MRI) studies have reported relevant pathophysiologic states in migraine. However, its molecular mechanistic processes are still poorly understood .

DataSifterText: Partially Synthetic Text Generation for Sensitive Clinical Notes.

Petabytes of health data are collected annually across the globe in electronic health records (EHR), including significant information stored as unstructured free text. However, the lack of effective mechanisms to securely share clinical text has inhibited its full utilization. We propose a new method, DataSifterText, to generate partially synthetic clinical free-text that can be safely shared between stakeholders (e.

DataSifter II: Partially synthetic data sharing of sensitive information containing time-varying correlated observations.

There is a significant public demand for rapid data-driven scientific investigations using aggregated sensitive information. However, many technical challenges and regulatory policies hinder efficient data sharing. In this study, we describe a partially synthetic data generation technique for creating anonymized data archives whose joint distributions closely resemble those of the original (sensitive) data.

Compressive Big Data Analytics: An ensemble meta-algorithm for high-dimensional multisource datasets.

Health advances are contingent on continuous development of new methods and approaches to foster data-driven discovery in the biomedical and clinical sciences. Open-science and team-based scientific discovery offer hope for tackling some of the difficult challenges associated with managing, modeling, and interpreting of large, complex, and multisource data. Translating raw observations into useful information and actionable knowledge depends on effective domain-independent reproducibility, area-specific replicability, data curation, analysis protocols, organization, management and sharing of health-related digital objects.

The Role of Dimensionality in Understanding Granuloma Formation.

Within the first 2-3 months of a (Mtb) infection, 2-4 mm spherical structures called granulomas develop in the lungs of the infected hosts. These are the hallmark of tuberculosis (TB) infection in humans and non-human primates. A cascade of immunological events occurs in the first 3 months of granuloma formation that likely shapes the outcome of the infection.

Predictive Big Data Analytics using the UK Biobank Data.

The UK Biobank is a rich national health resource that provides enormous opportunities for international researchers to examine, model, and analyze census-like multisource healthcare data. The archive presents several challenges related to aggregation and harmonization of complex data elements, feature heterogeneity and salience, and health analytics. Using 7,614 imaging, clinical, and phenotypic features of 9,914 subjects we performed deep computed phenotyping using unsupervised clustering and derived two distinct sub-cohorts.

Controlled feature selection and compressive big data analytics: Applications to biomedical and health studies.

The theoretical foundations of Big Data Science are not fully developed, yet. This study proposes a new scalable framework for Big Data representation, high-throughput analytics (variable selection and noise reduction), and model-free inference. Specifically, we explore the core principles of distribution-free and model-agnostic methods for scientific inference based on Big Data sets.

Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology.

Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long-term control of infection while also preventing an over-zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro- and anti-inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback.

HDDA: DataSifter: statistical obfuscation of electronic health records and other sensitive datasets.

There are no practical and effective mechanisms to share high-dimensional data including sensitive information in various fields like health financial intelligence or socioeconomics without compromising either the utility of the data or exposing private personal or secure organizational information. Excessive scrambling or encoding of the information makes it less useful for modelling or analytical processing. Insufficient preprocessing may compromise sensitive information and introduce a substantial risk for re-identification of individuals by various stratification techniques.

A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment.

Tuberculosis (TB) is an ancient and deadly disease characterized by complex host-pathogen dynamics playing out over multiple time and length scales and physiological compartments. Computational modeling can be used to integrate various types of experimental data and suggest new hypotheses, mechanisms, and therapeutic approaches to TB. Here, we offer a first-time comprehensive review of work on within-host TB models that describe the immune response of the host to infection, including the formation of lung granulomas.

Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome.

Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions.

A Multi-Compartment Hybrid Computational Model Predicts Key Roles for Dendritic Cells in Tuberculosis Infection.

Unlabelled: Tuberculosis (TB) is a world-wide health problem with approximately 2 billion people infected with (Mtb, the causative bacterium of TB). The pathologic hallmark of Mtb infection in humans and Non-Human Primates (NHPs) is the formation of spherical structures, primarily in lungs, called granulomas. Infection occurs after inhalation of bacteria into lungs, where resident antigen-presenting cells (APCs), take up bacteria and initiate the immune response to Mtb infection.

An anthropologically based model of the impact of asymptomatic cases on the spread of Neisseria gonorrhoeae.

Neisseria gonorrhoeae (GC) remains a serious burden in many high-sexual-activity, undertreated populations. Using empirical data from a 2009 study of GC burden among pastoralists in Kaokoveld, Namibia, we expand the standard gonorrhoea transmission model by using locally derived sexual contact data to explore transmission dynamics in a population with high rates of partner exchange and low treatment-seeking behaviour. We use the model to generate ball-park estimates for transmission probabilities and other parameter values for low-level (i.

Variability in tuberculosis granuloma T cell responses exists, but a balance of pro- and anti-inflammatory cytokines is associated with sterilization.

Lung granulomas are the pathologic hallmark of tuberculosis (TB). T cells are a major cellular component of TB lung granulomas and are known to play an important role in containment of Mycobacterium tuberculosis (Mtb) infection. We used cynomolgus macaques, a non-human primate model that recapitulates human TB with clinically active disease, latent infection or early infection, to understand functional characteristics and dynamics of T cells in individual granulomas.

Computational modeling predicts IL-10 control of lesion sterilization by balancing early host immunity-mediated antimicrobial responses with caseation during mycobacterium tuberculosis infection.

Although almost a third of the world's population is infected with the bacterial pathogen Mycobacterium tuberculosis, our understanding of the functions of many immune factors involved in fighting infection is limited. Determining the role of the immunosuppressive cytokine IL-10 at the level of the granuloma has proven difficult because of lesional heterogeneity and the limitations of animal models. In this study, we take an in silico approach and, through a series of virtual experiments, we predict several novel roles for IL-10 in tuberculosis granulomas: 1) decreased levels of IL-10 lead to increased numbers of sterile lesions, but at the cost of early increased caseation; 2) small increases in early antimicrobial activity cause this increased lesion sterility; 3) IL-10 produced by activated macrophages is a major mediator of early antimicrobial activity and early host-induced caseation; and 4) increasing levels of infected macrophage derived IL-10 promotes bacterial persistence by limiting the early antimicrobial response and preventing lesion sterilization.

Macrophage polarization drives granuloma outcome during Mycobacterium tuberculosis infection.

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), induces formation of granulomas, structures in which immune cells and bacteria colocalize. Macrophages are among the most abundant cell types in granulomas and have been shown to serve as both critical bactericidal cells and targets for M. tuberculosis infection and proliferation throughout the course of infection.

Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models.

The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g.

Phase variation and host immunity against high molecular weight (HMW) adhesins shape population dynamics of nontypeable Haemophilus influenzae within human hosts.

Nontypeable Haemophilus influenzae (NTHi) is a bacterium that resides within the human pharynx. Because NTHi is human-restricted, its long-term survival is dependent upon its ability to successfully colonize new hosts. Adherence to host epithelium, mediated by bacterial adhesins, is one of the first steps in NTHi colonization.

Mathematical modeling of primary succession of murine intestinal microbiota.

Understanding the nature of interpopulation interactions in host-associated microbial communities is critical to understanding gut colonization, responses to perturbations, and transitions between health and disease. Characterizing these interactions is complicated by the complexity of these communities and the observation that even if populations can be cultured, their in vitro and in vivo phenotypes differ significantly. Dynamic models are the cornerstone of computational systems biology and a key objective of computational systems biologists is the reconstruction of biological networks (i.

Microenvironments in tuberculous granulomas are delineated by distinct populations of macrophage subsets and expression of nitric oxide synthase and arginase isoforms.

Macrophages in granulomas are both antimycobacterial effector and host cell for Mycobacterium tuberculosis, yet basic aspects of macrophage diversity and function within the complex structures of granulomas remain poorly understood. To address this, we examined myeloid cell phenotypes and expression of enzymes correlated with host defense in macaque and human granulomas. Macaque granulomas had upregulated inducible and endothelial NO synthase (iNOS and eNOS) and arginase (Arg1 and Arg2) expression and enzyme activity compared with nongranulomatous tissue.

Inoculation dose of Mycobacterium tuberculosis does not influence priming of T cell responses in lymph nodes.

The effect of Mycobacterium tuberculosis inocula size on T cell priming in the lymph node and effector T cells in the lung remains controversial. In this study, we used a naive mouse model, without the transfer of transgenic T cells, in conjunction with mathematical model to test whether infection with higher aerosolized inocula would lead to increased priming of M. tuberculosis-specific T cells in the lung-draining lymph node.

Intracellular bacillary burden reflects a burst size for Mycobacterium tuberculosis in vivo.

We previously reported that Mycobacterium tuberculosis triggers macrophage necrosis in vitro at a threshold intracellular load of ~25 bacilli. This suggests a model for tuberculosis where bacilli invading lung macrophages at low multiplicity of infection proliferate to burst size and spread to naïve phagocytes for repeated cycles of replication and cytolysis. The current study evaluated that model in vivo, an environment significantly more complex than in vitro culture.

A hybrid multi-compartment model of granuloma formation and T cell priming in tuberculosis.

Tuberculosis is a worldwide health problem with 2 billion people infected with Mycobacterium tuberculosis (Mtb, the bacteria causing TB). The hallmark of infection is the emergence of organized structures of immune cells forming primarily in the lung in response to infection. Granulomas physically contain and immunologically restrain bacteria that cannot be cleared.

Multiscale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation.

Multiple immune factors control host responses to Mycobacterium tuberculosis infection, including the formation of granulomas, which are aggregates of immune cells whose function may reflect success or failure of the host to contain infection. One such factor is TNF-α. TNF-α has been experimentally characterized to have the following activities in M.

A multifaceted approach to modeling the immune response in tuberculosis.

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacterium tuberculosis (Mtb). No available vaccine is reliable and, although treatment exists, approximately 2 million people still die each year. The hallmark of TB infection is the granuloma, a self-organizing structure of immune cells forming in the lung and lymph nodes in response to bacterial invasion.