A microengineered Brain-Chip to model neuroinflammation in humans.

Species differences in brain and blood-brain barrier (BBB) biology hamper the translation of findings from animal models to humans, impeding the development of therapeutics for brain diseases. Here, we present a human organotypic microphysiological system (MPS) that includes endothelial-like cells, pericytes, glia, and cortical neurons and maintains BBB permeability at relevant levels. This human Brain-Chip engineered to recapitulate critical aspects of the complex interactions that mediate neuroinflammation and demonstrates significant improvements in clinical mimicry compared to previously reported similar MPS.

Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie cytometry data.

Background: Time-lapse microscopy live-cell imaging is essential for studying the evolution of bacterial communities at single-cell resolution. It allows capturing detailed information about the morphology, gene expression, and spatial characteristics of individual cells at every time instance of the imaging experiment. The image analysis of bacterial "single-cell movies" (videos) generates big data in the form of multidimensional time series of measured bacterial attributes.

Modeling alpha-synuclein pathology in a human brain-chip to assess blood-brain barrier disruption.

Parkinson's disease and related synucleinopathies are characterized by the abnormal accumulation of alpha-synuclein aggregates, loss of dopaminergic neurons, and gliosis of the substantia nigra. Although clinical evidence and in vitro studies indicate disruption of the Blood-Brain Barrier in Parkinson's disease, the mechanisms mediating the endothelial dysfunction is not well understood. Here we leveraged the Organs-on-Chips technology to develop a human Brain-Chip representative of the substantia nigra area of the brain containing dopaminergic neurons, astrocytes, microglia, pericytes, and microvascular brain endothelial cells, cultured under fluid flow.

A Novel Microphysiological Colon Platform to Decipher Mechanisms Driving Human Intestinal Permeability.

Background & Aims: The limited availability of organoid systems that mimic the molecular signatures and architecture of human intestinal epithelium has been an impediment to allowing them to be harnessed for the development of therapeutics as well as physiological insights. We developed a microphysiological Organ-on-Chip (Emulate, Inc, Boston, MA) platform designed to mimic properties of human intestinal epithelium leading to insights into barrier integrity.

Methods: We combined the human biopsy-derived leucine-rich repeat-containing G-protein-coupled receptor 5-positive organoids and Organ-on-Chip technologies to establish a micro-engineered human Colon Intestine-Chip (Emulate, Inc, Boston, MA).

An information-theoretic approach for measuring the distance of organ tissue samples using their transcriptomic signatures.

Motivation: Recapitulating aspects of human organ functions using in vitro (e.g. plates, transwells, etc.

Heterogeneity of single cell inactivation: Assessment of the individual cell time to death and implications in population behavior.

A direct microscopic time-lapse method, using appropriate staining for cell viability in a confocal scanning laser microscope, was used for the direct assessment of Salmonella Agona individual cell inactivation in small two-dimensional colonies exposed to osmotic stress. Individual cell inactivation times were fitted to a variety of continuous distributions using @Risk software. The best fitted distribution (LogLogistic) was further used to predict the inactivation of Salmonella populations of various initial levels using Monte Carlo simulation.

Multi-criteria protein structure comparison and structural similarities analysis using pyMCPSC.

Protein Structure Comparison (PSC) is a well developed field of computational proteomics with active interest from the research community, since it is widely used in structural biology and drug discovery. With new PSC methods continuously emerging and no clear method of choice, Multi-Criteria Protein Structure Comparison (MCPSC) is commonly employed to combine methods and generate consensus structural similarity scores. We present pyMCPSC, a Python based utility we developed to allow users to perform MCPSC efficiently, by exploiting the parallelism afforded by the multi-core CPUs of today's desktop computers.

Image analysis driven single-cell analytics for systems microbiology.

Background: Time-lapse microscopy is an essential tool for capturing and correlating bacterial morphology and gene expression dynamics at single-cell resolution. However state-of-the-art computational methods are limited in terms of the complexity of cell movies that they can analyze and lack of automation. The proposed Bacterial image analysis driven Single Cell Analytics (BaSCA) computational pipeline addresses these limitations thus enabling high throughput systems microbiology.

Tracking single-cells in overcrowded bacterial colonies.

Cell tracking enables data extraction from timelapse "cell movies" and promotes modeling biological processes at the single-cell level. We introduce a new fully automated computational strategy to track accurately cells across frames in time-lapse movies. Our method is based on a dynamic neighborhoods formation and matching approach, inspired by motion estimation algorithms for video compression.

Efficient Multicriteria Protein Structure Comparison on Modern Processor Architectures.

Fast increasing computational demand for all-to-all protein structures comparison (PSC) is a result of three confounding factors: rapidly expanding structural proteomics databases, high computational complexity of pairwise protein comparison algorithms, and the trend in the domain towards using multiple criteria for protein structures comparison (MCPSC) and combining results. We have developed a software framework that exploits many-core and multicore CPUs to implement efficient parallel MCPSC in modern processors based on three popular PSC methods, namely, TMalign, CE, and USM. We evaluate and compare the performance and efficiency of the two parallel MCPSC implementations using Intel's experimental many-core Single-Chip Cloud Computer (SCC) as well as Intel's Core i7 multicore processor.

A learning scheme for reach to grasp movements: on EMG-based interfaces using task specific motion decoding models.

A learning scheme based on random forests is used to discriminate between different reach to grasp movements in 3-D space, based on the myoelectric activity of human muscles of the upper-arm and the forearm. Task specificity for motion decoding is introduced in two different levels: Subspace to move toward and object to be grasped. The discrimination between the different reach to grasp strategies is accomplished with machine learning techniques for classification.

In silico modeling of the effects of alpha-synuclein oligomerization on dopaminergic neuronal homeostasis.

Background: Alpha-synuclein (ASYN) is central in Parkinson's disease (PD) pathogenesis. Converging pieces of evidence suggest that the levels of ASYN expression play a critical role in both familial and sporadic Parkinson's disease. ASYN fibrils are the main component of inclusions called Lewy Bodies (LBs) which are found mainly in the surviving neurons of the substantia nigra.

Visualizing meta-features in proteomic maps.

Background: The steps of a high-throughput proteomics experiment include the separation, differential expression and mass spectrometry-based identification of proteins. However, the last and more challenging step is inferring the biological role of the identified proteins through their association with interaction networks, biological pathways, analysis of the effect of post-translational modifications, and other protein-related information.

Results: In this paper, we present an integrative visualization methodology that allows combining experimentally produced proteomic features with protein meta-features, typically coming from meta-analysis tools and databases, in synthetic Proteomic Feature Maps.

Protein spot detection and quantification in 2-DE gel images using machine-learning methods.

Two-dimensional gel electrophoresis (2-DE) is the most established protein separation method used in expression proteomics. Despite the existence of sophisticated software tools, 2-DE gel image analysis still remains a serious bottleneck. The low accuracies of commercial software packages and the extensive manual calibration that they often require for acceptable results show that we are far from achieving the goal of a fully automated and reliable, high-throughput gel processing system.

Prediction of biological integrity based on environmental similarity--revealing the scale-dependent link between study area and top environmental predictors.

Indices of Biological integrity (IBI) are considered valid indicators of the overall health of a water body because the biological community is an endpoint within natural systems. However, prediction of biological integrity using information from multi-parameter environmental observations is a challenging problem due to the hierarchical organization of the natural environment, the existence of nonlinear inter-dependencies among variables as well as natural stochasticity and measurement noise. We present a method for predicting the Fish Index of Biological Integrity (IBI) using multiple environmental observations at the state-scale in Ohio.

Improving 2-DE gel image denoising using contourlets.

One of the most commonly used methods for protein separation is 2-DE. After 2-DE gel scanning, images with a plethora of spot features emerge that are usually contaminated by inherent noise. The objective of the denoising process is to remove noise to the extent that the true spots are recovered correctly and accurately i.

Proteomic feature maps: a new visualization approach in proteomics analysis.

The different steps of a proteomics analysis workflow generate a plethora of features for each extracted proteomic object (a protein spot in 2D gel electrophoresis (2-DE), or a peptide peak in liquid chromatography-mass spectrometry (LC-MS) analysis). Yet, the joint visualization of multiple object features on 2D gel-like maps is rather limited in currently available proteomics software packages. We introduce a new, simple, and intuitive visualization method that utilizes spheres to represent proteomic objects on proteomic feature maps, and exploits the spheres size and color to provide simultaneous visualization of user-selected feature pairs.

Linking indices of biotic integrity to environmental and land use variables: multimetric clustering and predictive models.

Advanced computerized methods and models of retrieving knowledge from large multiparameter data bases were used to analyze data on fish and macroinvertebrate composition (metrics), habitat, land use and water quality. The research focused on the north central and northeastern United States and involved thousands of sites monitored by the state agencies. The techniques and methodologies included supervised and unsupervised Artificial Neural Networks (ANN) modeling, Principal Component Analysis, Canonical Component Analysis (both linear and nonlinear), Multiple Regression Analyses, and analyses of variance by ANOVA.

Extracting knowledge on the links between the water body stressors and biotic integrity.

Multi-metric indices of biological integrity (IBIs) are most frequently created by examining single biological metrics along gradients of environmental degradation, and then combining multiple metrics using "best professional judgment" to characterize and calibrate stressor-response relationships. We aim to provide an efficient data analysis and visualization tool to assess the simultaneous effects of anthropogenic stressors on the fish population through the fish metrics and the associated Index of Biotic Integrity (IBI). Kohonen's self-organizing feature maps (SOM), unsupervised neural networks, are employed to pattern the sampling sites in the state of Ohio based on similar metrics characteristics.