Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease

The joint analysis of biomedical data in Alzheimer's Disease (AD) is important for better clinical diagnosis and to understand the relationship between biomarkers. However, jointly accounting for heterogeneous measures poses important challenges related to the modeling of the variability and the interpretability of the results. These issues are here addressed by proposing a novel multi-channel stochastic generative model. We assume that a latent variable generates the data observed through different channels (e.g., clinical scores, imaging, ...) and describe an efficient way to estimate jointly the distribution of both latent variable and data generative process. Experiments on synthetic data show that the multi-channel formulation allows superior data reconstruction as opposed to the single channel one. Moreover, the derived lower bound of the model evidence represents a promising model selection criterion. Experiments on AD data show that the model parameters can be used for unsupervised patient stratification and for the joint interpretation of the heterogeneous observations. Because of its general and flexible formulation, we believe that the proposed method can find important applications as a general data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with MICCAI 2018, September 20, Granada, Spai

Simple integrative preprocessing preserves what is shared in data sources

<p>Abstract</p> <p>Background</p> <p>Bioinformatics data analysis toolbox needs general-purpose, fast and easily interpretable preprocessing tools that perform data integration during exploratory data analysis. Our focus is on vector-valued data sources, each consisting of measurements of the same entity but on different variables, and on tasks where source-specific variation is considered noisy or not interesting. Principal components analysis of all sources combined together is an obvious choice if it is not important to distinguish between data source-specific and shared variation. Canonical Correlation Analysis (CCA) focuses on mutual dependencies and discards source-specific "noise" but it produces a separate set of components for each source.</p> <p>Results</p> <p>It turns out that components given by CCA can be combined easily to produce a linear and hence fast and easily interpretable feature extraction method. The method fuses together several sources, such that the properties they share are preserved. Source-specific variation is discarded as uninteresting. We give the details and implement them in a software tool. The method is demonstrated on gene expression measurements in three case studies: classification of cell cycle regulated genes in yeast, identification of differentially expressed genes in leukemia, and defining stress response in yeast. The software package is available at <url>http://www.cis.hut.fi/projects/mi/software/drCCA/</url>.</p> <p>Conclusion</p> <p>We introduced a method for the task of data fusion for exploratory data analysis, when statistical dependencies between the sources and not within a source are interesting. The method uses canonical correlation analysis in a new way for dimensionality reduction, and inherits its good properties of being simple, fast, and easily interpretable as a linear projection.</p

Cosmopolitan Species As Models for Ecophysiological Responses to Global Change: The Common Reed \u3cem\u3ePhragmites australis\u3c/em\u3e

Phragmites australis is a cosmopolitan grass and often the dominant species in the ecosystems it inhabits. Due to high intraspecific diversity and phenotypic plasticity, P. australis has an extensive ecological amplitude and a great capacity to acclimate to adverse environmental conditions; it can therefore offer valuable insights into plant responses to global change. Here we review the ecology and ecophysiology of prominent P. australis lineages and their responses to multiple forms of global change. Key findings of our review are that: (1) P. australis lineages are well-adapted to regions of their phylogeographic origin and therefore respond differently to changes in climatic conditions such as temperature or atmospheric CO2; (2) each lineage consists of populations that may occur in geographically different habitats and contain multiple genotypes; (3) the phenotypic plasticity of functional and fitness-related traits of a genotype determine the responses to global change factors; (4) genotypes with high plasticity to environmental drivers may acclimate or even vastly expand their ranges, genotypes of medium plasticity must acclimate or experience range-shifts, and those with low plasticity may face local extinction; (5) responses to ancillary types of global change, like shifting levels of soil salinity, flooding, and drought, are not consistent within lineages and depend on adaptation of individual genotypes. These patterns suggest that the diverse lineages of P. australis will undergo intense selective pressure in the face of global change such that the distributions and interactions of co-occurring lineages, as well as those of genotypes within-lineages, are very likely to be altered. We propose that the strong latitudinal clines within and between P. australis lineages can be a useful tool for predicting plant responses to climate change in general and present a conceptual framework for using P. australis lineages to predict plant responses to global change and its consequences

Cosmopolitan Species as Models for Ecophysiological Responses to Global Change: The Common Reed Phragmites australis

Phragmites australis is a cosmopolitan grass and often the dominant species in the ecosystems it inhabits. Due to high intraspecific diversity and phenotypic plasticity, P. australis has an extensive ecological amplitude and a great capacity to acclimate to adverse environmental conditions; it can therefore offer valuable insights into plant responses to global change. Here we review the ecology and ecophysiology of prominent P. australis lineages and their responses to multiple forms of global change. Key findings of our review are that: (1) P. australis lineages are well-adapted to regions of their phylogeographic origin and therefore respond differently to changes in climatic conditions such as temperature or atmospheric CO2; (2) each lineage consists of populations that may occur in geographically different habitats and contain multiple genotypes; (3) the phenotypic plasticity of functional and fitness-related traits of a genotype determine the responses to global change factors; (4) genotypes with high plasticity to environmental drivers may acclimate or even vastly expand their ranges, genotypes of medium plasticity must acclimate or experience range-shifts, and those with low plasticity may face local extinction; (5) responses to ancillary types of global change, like shifting levels of soil salinity, flooding, and drought, are not consistent within lineages and depend on adaptation of individual genotypes. These patterns suggest that the diverse lineages of P. australis will undergo intense selective pressure in the face of global change such that the distributions and interactions of co-occurring lineages, as well as those of genotypes within-lineages, are very likely to be altered. We propose that the strong latitudinal clines within and between P. australis lineages can be a useful tool for predicting plant responses to climate change in general and present a conceptual framework for using P. australis lineages to predict plant responses to global change and its consequences

Timing of Favorable Conditions, Competition and Fertility Interact to Govern Recruitment of Invasive Chinese Tallow Tree in Stressful Environments

The rate of new exotic recruitment following removal of adult invaders (reinvasion pressure) influences restoration outcomes and costs but is highly variable and poorly understood. We hypothesize that broad variation in average reinvasion pressure of Triadica sebifera (Chinese tallow tree, a major invader) arises from differences among habitats in spatiotemporal availability of realized recruitment windows. These windows are periods of variable duration long enough to permit establishment given local environmental conditions. We tested this hypothesis via a greenhouse mesocosm experiment that quantified how the duration of favorable moisture conditions prior to flood or drought stress (window duration), competition and nutrient availability influenced Triadica success in high stress environments. Window duration influenced pre-stress seedling abundance and size, growth during stress and final abundance; it interacted with other factors to affect final biomass and germination during stress. Stress type and competition impacted final size and biomass, plus germination, mortality and changes in size during stress. Final abundance also depended on competition and the interaction of window duration, stress type and competition. Fertilization interacted with competition and stress to influence biomass and changes in height, respectively, but did not affect Triadica abundance. Overall, longer window durations promoted Triadica establishment, competition and drought (relative to flood) suppressed establishment, and fertilization had weak effects. Interactions among factors frequently produced different effects in specific contexts. Results support our ‘outgrow the stress’ hypothesis and show that temporal availability of abiotic windows and factors that influence growth rates govern Triadica recruitment in stressful environments. These findings suggest that native seed addition can effectively suppress superior competitors in stressful environments. We also describe environmental scenarios where specific management methods may be more or less effective. Our results enable better niche-based estimates of local reinvasion pressure, which can improve restoration efficacy and efficiency by informing site selection and optimal Management

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection.

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data

Understanding Group Structures and Properties in Social Media

Abstract. The rapid growth of social networking sites enables people to connect to each other more conveniently than ever. With easy-to-use social media, people contribute and consume contents, leading to a new form of human interaction and the emergence of online collective behav-ior. In this chapter, we aim to understand group structures and proper-ties by extracting and profiling communities in social media. We present some challenges of community detection in social media. A prominent one is that networks in social media are often heterogeneous. We intro-duce two types of heterogeneity presented in online social networks and elaborate corresponding community detection approaches for each type, respectively. Social media provides not only interaction information but also textual and tag data. This variety of data can be exploited to profile individual groups in understanding group formation and relationships. We also suggest some future work in understanding group structures and properties. Key words: social media, community detection, group profiling, het-erogeneous networks, multi-mode networks, multi-dimensional networks

Preparation of new substituted alkylamide derivatives of teicoplanin as antibacterials

The title compds. [I; R = H, protecting group; Y = NR1X1(XX2)p(TX3)qW; R1 = H, alkyl; T, X = O, (substituted) imino; X1, X2, X3 = C2-10 alkylene; W = OH, amino; p = 1-50; q = 0-12; A = H, N-acylated \u3b2-D-2-deoxy-2-aminoglucopyranosyl; B = H, N-acetyl-\u3b2-D-2-deoxy-2-aminoglucopyranosyl; M = H, \u3b1-D-mannopyranosyl; B = H only when both A, M = H], were prepd. Thus, teicoplanin A1 component 2 in ET3N/DMF was treated with PhCH2O2CCl in acetone to give 3c96% of the N-15 CBZ deriv. This was esterified with ClCH2CN in DMF/Et3N in 3c98% yield and the ester was treated with H2N(CH2)2NH(CH2)2NH2 in DMF followed by hydrogenolysis to give I [A = N-(8-methylnonanoyl)-\u3b2-D-2-deoxy-2-aminoglucopyranosyl, B = N-acetyl-\u3b2-D-2-deoxy-2-aminoglucopyranosyl, M = \u3b1-D-mannopyranosyl, Y = H2NCH2CH2NHCH2CH2NH, R = H] (II). II had an ED50 of 0.09 mg/kg s.c. against Streptomyces pyrogenes C203 in mice. Several I were active against multi-resistant Pseudomonas aeruginosa with MIC of 4-128 \u3bcg/mL