Modeling the cell-type-specific mesoscale murine connectome with anterograde tracing experiments.

The Allen Mouse Brain Connectivity Atlas consists of anterograde tracing experiments targeting diverse structures and classes of projecting neurons. Beyond regional anterograde tracing done in C57BL/6 wild-type mice, a large fraction of experiments are performed using transgenic Cre-lines. This allows access to cell-class-specific whole-brain connectivity information, with class defined by the transgenic lines. However, even though the number of experiments is large, it does not come close to covering all existing cell classes in every area where they exist. Here, we study how much we can fill in these gaps and estimate the cell-class-specific connectivity function given the simplifying assumptions that nearby voxels have smoothly varying projections, but that these projection tensors can change sharply depending on the region and class of the projecting cells. This paper describes the conversion of Cre-line tracer experiments into class-specific connectivity matrices representing the connection strengths between source and target structures. We introduce and validate a novel statistical model for creation of connectivity matrices. We extend the Nadaraya-Watson kernel learning method that we previously used to fill in spatial gaps to also fill in gaps in cell-class connectivity information. To do this, we construct a "cell-class space" based on class-specific averaged regionalized projections and combine smoothing in 3D space as well as in this abstract space to share information between similar neuron classes. Using this method, we construct a set of connectivity matrices using multiple levels of resolution at which discontinuities in connectivity are assumed. We show that the connectivities obtained from this model display expected cell-type- and structure-specific connectivities. We also show that the wild-type connectivity matrix can be factored using a sparse set of factors, and analyze the informativeness of this latent variable model.

Water-Accelerated Photooxidation of CH3NH3PbI3 Perovskite.

Halide perovskites have the potential to disrupt the photovoltaics market based on their high performance and low cost. However, the decomposition of perovskites under moisture, oxygen, and light raises concerns about service lifetime, especially because degradation mechanisms and the corresponding rate laws that fit the observed data have thus far eluded researchers. Here, we report a water-accelerated photooxidation mechanism dominating the degradation kinetics of archetypal perovskite CH3NH3PbI3 in air under >1% relative humidity at 25 °C. From this mechanism, we develop a kinetic model that quantitatively predicts the degradation rate as a function of temperature, ambient O2 and H2O levels, and illumination. Because water is a possible product of dry photooxidation, these results highlight the need for encapsulation schemes that rigorously block oxygen ingress, as product water may accumulate beneath the encapsulant and initiate the more rapid water-accelerated photooxidative decomposition.

Refinement: Measuring informativeness of ratings in the absence of a gold standard.

We propose a new metric for evaluating the informativeness of a set of ratings from a single rater on a given scale. Such evaluations are of interest when raters rate numerous comparable items on the same scale, as occurs in hiring, college admissions, and peer review. Our exposition takes the context of peer review, which involves univariate and multivariate cardinal ratings. We draw on this context to motivate an information-theoretic measure of the refinement of a set of ratings - entropic refinement - as well as two secondary measures. A mathematical analysis of the three measures reveals that only the first, which captures the information content of the ratings, possesses properties appropriate to a refinement metric. Finally, we analyse refinement in real-world grant-review data, finding evidence that overall merit scores are more refined than criterion scores.

Bayesian Non-Parametric Clustering of Ranking Data.

This paper studies the estimation of Dirichlet process mixtures over discrete incomplete rankings. The generative model for each mixture component is the generalized Mallows (GM) model, an exponential family model for permutations which extends seamlessly to top- t  rankings. While the GM  is remarkably tractable in comparison with other permutation models, its conjugate prior is not. Our main contribution is to derive the theory and algorithms for sampling from the desired posterior distributions under this DPM. We introduce a family of partially collapsed Gibbs samplers, containing as one extreme point an exact algorithm based on slice-sampling, and at the other a fast approximate sampler with superior mixing that is still very accurate in all but the lowest ranks. We empirically demonstrate the effectiveness of the approximation in reducing mixing time, the benefits of the Dirichlet process approach over alternative clustering techniques, and the applicability of the approach to exploring large real-world ranking datasets.

An overview of clustering applied to molecular biology.

In molecular biology, we are often interested in determining the group structure in, e.g., a population of cells or microarray gene expression data. Clustering methods identify groups of similar observations, but the results can depend on the chosen method's assumptions and starting parameter values. In this chapter, we give a broad overview of both attribute- and similarity-based clustering, describing both the methods and their performance. The parametric and nonparametric approaches presented vary in whether or not they require knowing the number of clusters in advance as well as the shapes of the estimated clusters. Additionally, we include a biclustering algorithm that incorporates variable selection into the clustering procedure. We finish with a discussion of some common methods for comparing two clustering solutions (possibly from different methods). The user is advised to devote time and attention to determining the appropriate clustering approach (and any corresponding parameter values) for the specific application prior to analysis.

IkappaB, NF-kappaB regulation model: simulation analysis of small number of molecules.

The regulation of IkappaB, NF-kappaB is of foremost interest in biology as the transcription factor NF-kappaB has multiple target genes. We have modeled a previously published model by Hoffmann et al. (2002) of IkappaB, NF-kappaB mathematically as discrete reaction systems. We have used stochastic algorithm to compare the results when there are large and small numbers of molecules available in a finite volume for each protein. Our results for small number of molecules show that with continuous presence of stimulation, nuclear NF-kappaB oscillates continuously in every individual cell rather than damping, which was observed in cell population results. This characteristic of the system is missed when averaged behavior is studied.

Mass spectrometry-based proteomics and its application to studies of Porphyromonas gingivalis invasion and pathogenicity.

Porphyromonas gingivalis is a Gram-negative anaerobe that populates the subgingival crevice of the mouth. It is known to undergo a transition from its commensal status in healthy individuals to a highly invasive intracellular pathogen in human patients suffering from periodontal disease, where it is often the dominant species of pathogenic bacteria. The application of mass spectrometry-based proteomics to the study of P. gingivalis interactions with model host cell systems, invasion and pathogenicity is reviewed. These studies have evolved from qualitative identifications of small numbers of secreted proteins, using traditional gel-based methods, to quantitative whole cell proteomic studies using multiple dimension capillary HPLC coupled with linear ion trap mass spectrometry. It has become possible to generate a differential readout of protein expression change over the entire P. gingivalis proteome, in a manner analogous to whole genome mRNA arrays. Different strategies have been employed for generating protein level expression ratios from mass spectrometry data, including stable isotope metabolic labeling and most recently, spectral counting methods. A global view of changes in protein modification status remains elusive due to the limitations of existing computational tools for database searching and data mining. Such a view would be desirable for purposes of making global assessments of changes in gene regulation in response to host interactions during the course of adhesion, invasion and internalization. With a complete data matrix consisting of changes in transcription, protein abundance and protein modification during the course of invasion, the search for new protein drug targets would benefit from a more comprehensive understanding of these processes than what could be achieved prior to the advent of systems biology.

Analysis of the stochastic variation in LTQ single scan mass spectra.

A better understanding of the scan-to-scan signal intensity variation can lead to more sophisticated algorithms for database searching and de novo peptide sequencing using single scan mass spectra. In this study, we systematically studied the variation in relative intensity of m/z values in the single scan product ion mass spectra (MS2) derived from five representative precursor ions (MS1) collected using an LTQ linear ion trap under constant flow direct infusion conditions with peptide concentrations held constant. We applied a matching algorithm based on a pair hidden Markov model to align the peaks from each scan belonging to the same m/z value prior to assessing the signal intensity variation. The most significant single contributor to scan-to-scan signal intensity variation for high abundance ions was centroider error. Our study also showed that the variation in signal intensity is higher than what would be expected if the ion statistics derived from the dual geometry electron multiplier detector followed a Poisson distribution.

Symbolic signatures for deformable shapes.

Recognizing classes of objects from their shape is an unsolved problem in machine vision that entails the ability of a computer system to represent and generalize complex geometrical information on the basis of a finite amount of prior data. A practical approach to this problem is particularly difficult to implement, not only because the shape variability of relevant object classes is generally large, but also because standard sensing devices used to capture the real world only provide a partial view of a scene, so there is partial information pertaining to the objects of interest. In this work, we develop an algorithmic framework for recognizing classes of deformable shapes from range data. The basic idea of our component-based approach is to generalize existing surface representations that have proven effective in recognizing specific 3D objects to the problem of object classes using our newly introduced symbolic-signature representation that is robust to deformations, as opposed to a numeric representation that is often tied to a specific shape. Based on this approach, we present a system that is capable of recognizing and classifying a variety of object shape classes from range data. We demonstrate our system in a series of large-scale experiments that were motivated by specific applications in scene analysis and medical diagnosis.