Flow in temporally and spatially varying porous media: a model for transport of interstitial fluid in the brain.

Flow in a porous medium can be driven by the deformations of the boundaries of the porous domain. Such boundary deformations locally change the volume fraction accessible by the fluid, creating non-uniform porosity and permeability throughout the medium. In this work, we construct a deformation-driven porous medium transport model with spatially and temporally varying porosity and permeability that are dependent on the boundary deformations imposed on the medium. We use this model to study the transport of interstitial fluid along the basement membranes in the arterial walls of the brain. The basement membrane is modeled as a deforming annular porous channel with the compressible pore space filled with an incompressible, Newtonian fluid. The role of a forward propagating peristaltic heart pulse wave and a reverse smooth muscle contraction wave on the flow within the basement membranes is investigated. Our results identify combinations of wave amplitudes that can induce either forward or reverse transport along these transport pathways in the brain. The magnitude and direction of fluid transport predicted by our model can help in understanding the clearance of fluids and solutes along the Intramural Periarterial Drainage route and the pathology of cerebral amyloid angiopathy.

Boundary waves in a microfluidic device as a model for intramural periarterial drainage.

The failure to clear amyloid-Beta from an aging brain leads to its accumulation within the walls of arteries and potentially to Alzheimer's disease. However, the clearance mechanism through the intramural periarterial pathway is not well understood. We previously proposed a hydrodynamic reverse transport model for the cerebral arterial basement membrane pathway. In our model, solute transport results from fluidic forcing driven by the superposition of forward and reverse propagating boundary waves. The aim of this study is to experimentally validate this hydrodynamic reverse transport mechanism in a microfluidic device where reverse transport in a rectangular conduit is driven by applying waveforms along its boundaries. Our results support our theory that while the superimposed boundary waves propagate in the forward direction, a reverse flow in the rectangular conduit can be induced by boundary wave reflections. We quantified the fluid transport velocity and direction under various boundary conditions and analyzed numerical simulations that support our experimental findings. We identified a set of boundary wave parameters that achieved reverse transport, which could be responsible for intramural periarterial drainage of cerebral metabolic waste.

Automated analysis of food-borne pathogens using a novel microbial cell culture, sensing and classification system.

We hereby report the design and implementation of an Autonomous Microbial Cell Culture and Classification (AMC(3)) system for rapid detection of food pathogens. Traditional food testing methods require multistep procedures and long incubation period, and are thus prone to human error. AMC(3) introduces a "one click approach" to the detection and classification of pathogenic bacteria. Once the cultured materials are prepared, all operations are automatic. AMC(3) is an integrated sensor array platform in a microbial fuel cell system composed of a multi-potentiostat, an automated data collection system (Python program, Yocto Maxi-coupler electromechanical relay module) and a powerful classification program. The classification scheme consists of Probabilistic Neural Network (PNN), Support Vector Machines (SVM) and General Regression Neural Network (GRNN) oracle-based system. Differential Pulse Voltammetry (DPV) is performed on standard samples or unknown samples. Then, using preset feature extractions and quality control, accepted data are analyzed by the intelligent classification system. In a typical use, thirty-two extracted features were analyzed to correctly classify the following pathogens: Escherichia coli ATCC#25922, Escherichia coli ATCC#11775, and Staphylococcus epidermidis ATCC#12228. 85.4% accuracy range was recorded for unknown samples, and within a shorter time period than the industry standard of 24 hours.

Perturbation and candidate analysis to combat overfitting of gene expression microarray data.

Analysis of gene expression microarray datasets presents the high risk of over-fitting (spurious patterns) because of their feature-rich but case-poor nature. This paper describes our ongoing efforts to develop a method to combat over-fitting and determine the strongest signal in the dataset. A GA-SVM hybrid along with Gaussian noise (manual noise gain) is used to discover feature sets of minimal size that accurately classifies the cases under cross-validation. Initial results on a colorectal cancer dataset shows that the strongest signal (modest number of candidates) can be found by a binary search.

Taste-specific cell assemblies in a biologically informed model of the nucleus of the solitary tract.

Although the cellular organization of many primary sensory nuclei has been well characterized, questions remain about the functional architecture of the first central relay for gustation, the rostral nucleus of the solitary tract (NTS). Here we used electrophysiological data recorded from single cells in the NTS to inform a network model of taste processing. Previous studies showed that electrical stimulation of the chorda tympani (CT) nerve initiates two types of inhibitory influences with different time courses in separate groups of NTS cells. Each type of inhibition targeted cells with distinct taste response properties. Further analyses of these data identified three NTS cell types differentiated by their latency of evoked response, time course of CT evoked inhibition, and degree of selectivity across taste qualities. Based on these results, we designed a model of the NTS consisting of discrete, reciprocally connected, stimulus-specific "cell" assemblies. Input to the network of integrate-and-fire model neurons was based on electrophysiological recordings from the CT nerve. Following successful simulation of paired-pulse CT stimulation, the network was tested for its ability to discriminate between two "taste" stimuli. Network dynamics of the model produced biologically plausible responses from each unit type and enhanced discrimination between taste qualities. We propose that an interactive network of taste quality specific cell assemblies, similar to our model, may account for the coherence in across-neuron patterns of NTS responses between similar tastants.