Nimit Dhulekar

Lung necrosis and neutrophils reflect common pathways of susceptibility to Mycobacterium tuberculosis in genetically diverse, immune-competent mice.

ABSTRACT Pulmonary tuberculosis (TB) is caused by Mycobacterium tuberculosis in susceptible humans. Here, we infected Diversity Outbred (DO) mice with ∼100 bacilli by aerosol to model responses in a highly heterogeneous population. Following infection, ‘supersusceptible’, ‘susceptible’ and ‘resistant’ phenotypes emerged. TB disease (reduced survival, weight loss, high bacterial load) correlated strongly with neutrophils, neutrophil chemokines, tumor necrosis factor (TNF) and cell death. By contrast, immune cytokines were weak correlates of disease. We next applied statistical and machine learning approaches to our dataset of cytokines and chemokines from lungs and blood. Six molecules from the lung: TNF, CXCL1, CXCL2, CXCL5, interferon-γ (IFN-γ), interleukin 12 (IL-12); and two molecules from blood – IL-2 and TNF – were identified as being important by applying both statistical and machine learning methods. Using molecular features to generate tree classifiers, CXCL1, CXCL2 and CXCL5 distinguished four classes (supersusceptible, susceptible, resistant and non-infected) from each other with approximately 77% accuracy using completely independent experimental data. By contrast, models based on other molecules were less accurate. Low to no IFN-γ, IL-12, IL-2 and IL-10 successfully discriminated non-infected mice from infected mice but failed to discriminate disease status amongst supersusceptible, susceptible and resistant M.-tuberculosis-infected DO mice. Additional analyses identified CXCL1 as a promising peripheral biomarker of disease and of CXCL1 production in the lungs. From these results, we conclude that: (1) DO mice respond variably to M. tuberculosis infection and will be useful to identify pathways involving necrosis and neutrophils; (2) data from DO mice is suited for machine learning methods to build, validate and test models with independent data based solely on molecular biomarkers; (3) low levels of immunological cytokines best indicate a lack of exposure to M. tuberculosis but cannot distinguish infection from disease. Summary: Molecular biomarkers of tuberculosis are identified and used to classify disease status of Diversity Outbred mice that have been infected with Mycobacterium tuberculosis.

Cell-Based Multi-Parametric Model of Cleft Progression during Submandibular Salivary Gland Branching Morphogenesis

Cleft formation during submandibular salivary gland branching morphogenesis is the critical step initiating the growth and development of the complex adult organ. Previous experimental studies indicated requirements for several epithelial cellular processes, such as proliferation, migration, cell-cell adhesion, cell-extracellular matrix (matrix) adhesion, and cellular contraction in cleft formation; however, the relative contribution of each of these processes is not fully understood since it is not possible to experimentally manipulate each factor independently. We present here a comprehensive analysis of several cellular parameters regulating cleft progression during branching morphogenesis in the epithelial tissue of an early embryonic salivary gland at a local scale using an on lattice Monte-Carlo simulation model, the Glazier-Graner-Hogeweg model. We utilized measurements from time-lapse images of mouse submandibular gland organ explants to construct a temporally and spatially relevant cell-based 2D model. Our model simulates the effect of cellular proliferation, actomyosin contractility, cell-cell and cell-matrix adhesions on cleft progression, and it was used to test specific hypotheses regarding the function of these parameters in branching morphogenesis. We use innovative features capturing several aspects of cleft morphology and quantitatively analyze clefts formed during functional modification of the cellular parameters. Our simulations predict that a low epithelial mitosis rate and moderate level of actomyosin contractility in the cleft cells promote cleft progression. Raising or lowering levels of contractility and mitosis rate resulted in non-progressive clefts. We also show that lowered cell-cell adhesion in the cleft region and increased cleft cell-matrix adhesions are required for cleft progression. Using a classifier-based analysis, the relative importance of these four contributing cellular factors for effective cleft progression was determined as follows: cleft cell contractility, cleft region cell-cell adhesion strength, epithelial cell mitosis rate, and cell-matrix adhesion strength.

Prediction of Growth Factor-Dependent Cleft Formation During Branching Morphogenesis Using A Dynamic Graph-Based Growth Model

This study considers the problem of describing and predicting cleft formation during the early stages of branching morphogenesis in mouse submandibular salivary glands (SMG) under the influence of varied concentrations of epidermal growth factors (EGF). Given a time-lapse video of a growing SMG, first we build a descriptive model that captures the underlying biological process and quantifies the ground truth. Tissue-scale (global) and morphological features related to regions of interest (local features) are used to characterize the biological ground truth. Second, we devise a predictive growth model that simulates EGF-modulated branching morphogenesis using a dynamic graph algorithm, which is driven by biological parameters such as EGF concentration, mitosis rate, and cleft progression rate. Given the initial configuration of the SMG, the evolution of the dynamic graph predicts the cleft formation, while maintaining the local structural characteristics of the SMG. We determined that higher EGF concentrations cause the formation of higher number of buds and comparatively shallow cleft depths. Third, we compared the prediction accuracy of our model to the Glazier-Graner-Hogeweg (GGH) model, an on-lattice Monte-Carlo simulation model, under a specific energy function parameter set that allows new rounds of de novo cleft formation. The results demonstrate that the dynamic graph model yields comparable simulations of gland growth to that of the GGH model with a significantly lower computational complexity. Fourth, we enhanced this model to predict the SMG morphology for an EGF concentration without the assistance of a ground truth time-lapse biological video data; this is a substantial benefit of our model over other similar models that are guided and terminated by information regarding the final SMG morphology. Hence, our model is suitable for testing the impact of different biological parameters involved with the process of branching morphogenesis in silico, while reducing the requirement of in vivo experiments.

Graph-theoretic analysis of epileptic seizures on scalp EEG recordings

This work presents a novel modeling of neuronal activity of the brain by capturing the synchronization of EEG signals along the scalp. The pair-wise correspondence between electrodes recording EEG signals are used to establish edges between these electrodes which then become the nodes of a synchronization graph. As EEG signals are recorded over time, we discretize the time axis into overlapping epochs, and build a series of time-evolving synchronization graphs for each epoch and for each traditional frequency band. We show that graph theory provides a rich set of graph features that can be used for mining and learning from the EEG signals to determine temporal and spatial localization of epileptic seizures. We present several techniques to capture the pair-wise synchronization and apply unsupervised learning algorithms, such as k-means clustering and multiway modeling of third-order tensors, to analyze the labeled clinical data in the feature domain to detect the onset and origin location of the seizure. We use k-means clustering on two-way feature matrices for detection of seizures, and Tucker3 tensor decomposition for localization of seizures. We conduct an extensive parametric search to determine the best configuration of the model parameters including epoch length, synchronization metrics, and frequency bands, to achieve the highest accuracy. Our results are promising: we are able to detect the onset of seizure with an accuracy of 88.24%, and localize the onset of the seizure with an accuracy of 76.47%.

A novel dynamic graph-based computational model for predicting salivary gland branching morphogenesis

In this paper, we introduce a biologically motivated dynamic graph-based growth model to describe and predict the stages of cleft formation during the process of branching morphogenesis in the submandibular mouse gland (SMG) from 3 hrs after embryonic day E12 to 8 hrs after embryonic day E12, which can be considered as E12.5. Branching morphogenesis is the process by which many mammalian exocrine and endocrine glands undergo significant morphological transformations, from a primary bud to an adult organ. Although many studies have investigated the cellular and molecular mechanisms driving branching morphogenesis, it is not clear how the shape changes that are inherent to establishing organ structure are produced. Using morphological features extracted from sequential images of SMG organ cultures we were able to develop a dynamic graph-based predictive model that is able to mimic the process of cleft formation and predict the final state. In addition, we compare our model to a state-of-the-art Glazier-Graner-Hogeweg (GGH) simulative tool, and demonstrate that the dynamic graph-based predictive model has comparable accuracy in modeling growth of clefts across SMG developmental stages, as well as faster convergence to the target SMG morphology.

Model coupling for predicting a developmental patterning process

Physics-based-theoretical models have been used to predict developmental patterning processes such as branching morphogenesis for over half a century. While such techniques are quite successful in understanding the patterning processes in organs such as the lung and the kidney, they are unable to accurately model the processes in other organs such as the submandibular salivary gland. One possible reason is the detachment of these models from data that describe the underlying biological process. This hypothesis coupled with the increasing availability of high quality data has made discrete, data-driven models attractive alternatives. These models are based on extracting features from data to describe the patterns and their time evolving multivariate statistics. These discrete models have low computational complexity and comparable or better accuracy than the continuous models. This paper presents a case study for coupling continuous-physics-based and discrete-empirical-models to address the prediction of cleft formation during the early stages of branching morphogenesis in mouse submandibular salivary glands (SMG). Given a time-lapse movie of a growing SMG, first we build a descriptive model that captures the underlying biological process and quantifies this ground truth. Tissue-scale (global) morphological features are used to characterize the biological ground truth. Second, we formulate a predictive model using the level-set method that simulates branching morphogenesis. This model successfully predicts the topological evolution, however, it is blind to the cellular organization, and cell-to-cell interactions occurring inside a gland; information that is available in the image data. Our primary objective via this study is to couple the continuous level set model with a discrete graph theory model that captures the cellular organization but ignores the forces that determine the evolution of the gland surface, i.e. formation of clefts and buds. We compared the prediction accuracy of our model to an on-lattice Monte-Carlo simulation model which has been used extensively for modeling morphogenesis and organogenesis. The results demonstrate that the coupled model yields comparable simulations of gland growth to that of the Monte-Carlo simulation model with a significantly lower computational complexity.

From Image to Translation: Processing the Endangered Nyushu Script

The lack of computational support has significantly slowed down automatic understanding of endangered languages. In this paper, we take Nyushu (simplified Chinese: 女书; literally: “women’s writing”) as a case study to present the first computational approach that combines Computer Vision and Natural Language Processing techniques to deeply understand an endangered language. We developed an end-to-end system to read a scanned hand-written Nyushu article, segment it into characters, link them to standard characters, and then translate the article into Mandarin Chinese. We propose several novel methods to address the new challenges introduced by noisy input and low resources, including Nyushu-specific feature selection for character segmentation and linking, and character linking lattice based Machine Translation. The end-to-end system performance indicates that the system is a promising approach and can serve as a standard benchmark.