Non-linear dimensionality reduction of signaling networks

<p>Abstract</p> <p>Background</p> <p>Systems wide modeling and analysis of signaling networks is essential for understanding complex cellular behaviors, such as the biphasic responses to different combinations of cytokines and growth factors. For example, tumor necrosis factor (TNF) can act as a proapoptotic or prosurvival factor depending on its concentration, the current state of signaling network and the presence of other cytokines. To understand combinatorial regulation in such systems, new computational approaches are required that can take into account non-linear interactions in signaling networks and provide tools for clustering, visualization and predictive modeling.</p> <p>Results</p> <p>Here we extended and applied an unsupervised non-linear dimensionality reduction approach, Isomap, to find clusters of similar treatment conditions in two cell signaling networks: (I) apoptosis signaling network in human epithelial cancer cells treated with different combinations of TNF, epidermal growth factor (EGF) and insulin and (II) combination of signal transduction pathways stimulated by 21 different ligands based on AfCS double ligand screen data. For the analysis of the apoptosis signaling network we used the Cytokine compendium dataset where activity and concentration of 19 intracellular signaling molecules were measured to characterise apoptotic response to TNF, EGF and insulin. By projecting the original 19-dimensional space of intracellular signals into a low-dimensional space, Isomap was able to reconstruct clusters corresponding to different cytokine treatments that were identified with graph-based clustering. In comparison, Principal Component Analysis (PCA) and Partial Least Squares – Discriminant analysis (PLS-DA) were unable to find biologically meaningful clusters. We also showed that by using Isomap components for supervised classification with k-nearest neighbor (k-NN) and quadratic discriminant analysis (QDA), apoptosis intensity can be predicted for different combinations of TNF, EGF and insulin. Prediction accuracy was highest when early activation time points in the apoptosis signaling network were used to predict apoptosis rates at later time points. Extended Isomap also outperformed PCA on the AfCS double ligand screen data. Isomap identified more functionally coherent clusters than PCA and captured more information in the first two-components. The Isomap projection performs slightly worse when more signaling networks are analyzed; suggesting that the mapping function between cues and responses becomes increasingly non-linear when large signaling pathways are considered.</p> <p>Conclusion</p> <p>We developed and applied extended Isomap approach for the analysis of cell signaling networks. Potential biological applications of this method include characterization, visualization and clustering of different treatment conditions (i.e. low and high doses of TNF) in terms of changes in intracellular signaling they induce.</p

Discrimination of Individual Tigers (\u3cem\u3ePanthera tigris\u3c/em\u3e) from Long Distance Roars

This paper investigates the extent of tiger (Panthera tigris) vocal individuality through both qualitative and quantitative approaches using long distance roars from six individual tigers at Omaha\u27s Henry Doorly Zoo in Omaha, NE. The framework for comparison across individuals includes statistical and discriminant function analysis across whole vocalization measures and statistical pattern classification using a hidden Markov model (HMM) with frame-based spectral features comprised of Greenwood frequency cepstral coefficients. Individual discrimination accuracy is evaluated as a function of spectral model complexity, represented by the number of mixtures in the underlying Gaussian mixture model (GMM), and temporal model complexity, represented by the number of sequential states in the HMM. Results indicate that the temporal pattern of the vocalization is the most significant factor in accurate discrimination. Overall baseline discrimination accuracy for this data set is about 70% using high level features without complex spectral or temporal models. Accuracy increases to about 80% when more complex spectral models (multiple mixture GMMs) are incorporated, and increases to a final accuracy of 90% when more detailed temporal models (10-state HMMs) are used. Classification accuracy is stable across a relatively wide range of configurations in terms of spectral and temporal model resolution

Synaptome.db: A Bioconductor package for synaptic proteomics data

SUMMARY: The neuronal synapse is underpinned by a large and diverse proteome but the molecular evidence is spread across many primary datasets. These data were recently curated into a single dataset describing a landscape of ∼8000 proteins found in studies of mammalian synapses. Here, we describe programmatic access to the dataset via the R/Bioconductor package Synaptome.db, which enables convenient and in-depth data analysis from within the Bioconductor environment. Synaptome.db allows users to obtain the respective gene information, e.g. subcellular localization, brain region, gene ontology, disease association and construct custom protein–protein interaction network models for gene sets and entire subcellular compartments. AVAILABILITY AND IMPLEMENTATION: The package Synaptome.db is part of Bioconductor since release 3.14, https://bioconductor.org/packages/release/data/annotation/html/synaptome.db.html, it is open source and available under the Artistic license 2.0. The development version is maintained on GitHub (https://github.com/lptolik/synaptome.db). Full documentation including examples is provided in the form of vignettes on the package webpage. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online

Virtual Fly Brain: An ontology-linked schema of the Drosophila Brain

Drosophila neuro-anatomical data is scattered across a large, diverse literature dating back over 75 years and a growing number of community databases. Lack of a standardized nomenclature for neuro-anatomy makes comparison and searching this growing data-set extremely arduous. &#xd;&#xa;&#xd;&#xa;A recent standardization effort (BrainName; Manuscript in preparation) has produced a segmented, 3D model of the Drosophila brain annotated with a controlled vocabulary. We are formalizing these developments to produce a web-based ontology-linked atlas in which gross brain anatomy is defined, in part, by labeled volumes in a standard reference brain.&#xd;&#xa;&#xd;&#xa;We have developed new relations that allow us to use this well-defined gross anatomy as a substrate to define neuronal types according to where they fasciculate and innervate as well as to record the neurotransmitters they release, their lineage and functions. The resulting ontology will provide a vocabulary for annotation and a means for integrative queries of neurobiological data.&#xd;&#xa;&#xd;&#xa;The ontology and associated images, queries and annotations will be integrated into the Virtual Fly Brain website. This will provide a resource that biologists can use to browse annotated images of Drosophila neuro-anatomy and to get answers to questions about that anatomy and related data, without any need for ontology expertise.&#xd;&#xa

Simple Neurite Tracer: open source software for reconstruction, visualization and analysis of neuronal processes

Motivation: Advances in techniques to sparsely label neurons unlock the potential to reconstruct connectivity from 3D image stacks acquired by light microscopy. We present an application for semi-automated tracing of neurons to quickly annotate noisy datasets and construct complex neuronal topologies, which we call the Simple Neurite Tracer. Availability: Simple Neurite Tracer is open source software, licensed under the GNU General Public Licence (GPL) and based on the public domain image processing software ImageJ. The software and further documentation are available via http://fiji.sc/Simple_Neurite_Tracer as part of the package Fiji, and can be used on Windows, Mac OS and Linux. Documentation and introductory screencasts are available at the same URL. Contact: [email protected]; [email protected]

Improved prediction of hiking speeds using a data driven approach

Hikers and hillwalkers typically use the gradient in the direction of travel (walking slope) as the main variable in established methods for predicting walking time (via the walking speed) along a route. Research into fell-running has suggested further variables which may improve speed algorithms in this context; the gradient of the terrain (hill slope) and the level of terrain obstruction. Recent improvements in data availability, as well as widespread use of GPS tracking now make it possible to explore these variables in a walking speed model at a sufficient scale to test statistical significance. We tested various established models used to predict walking speed against public GPS data from almost 88,000 km of UK walking / hiking tracks. Tracks were filtered to remove breaks and non-walking sections. A new generalised linear model (GLM) was then used to predict walking speeds. Key differences between the GLM and established rules were that the GLM considered the gradient of the terrain (hill slope) irrespective of walking slope, as well as the terrain type and level of terrain obstruction in off-road travel. All of these factors were shown to be highly significant, and this is supported by a lower root-mean-square-error compared to existing functions. We also observed an increase in RMSE between the GLM and established methods as hill slope increases, further supporting the importance of this variable

Metamorphosis of the Mushroom Bodies; Large-Scale Rearrangements of the Neural Substrates for Associative Learning and Memory in Drosophila

Paired brain centers known as mushroom bodies are key features of the circuitry for insect associative learning, especially when evoked by olfactory cues. Mushroom bodies have an embryonic origin, and unlike most other brain structures they exhibit developmental continuity, being prominent components of both the larval and the adult CNS. Here, we use cell-type-specific markers, provided by the P{GAL4} enhancer trap system, to follow specific subsets of mushroom body intrinsic and extrinsic neurons from the larval to the adult stage. We find marked structural differences between the larval and adult mushroom bodies, arising as the consequence of large-scale reorganization during metamorphosis. Extensive, though incomplete, degradation of the larval structure is followed by establishment of adult specific α and β lobes. Kenyon cells of embryonic origin, by contrast, were found to project selectively to the adult γ lobe. We propose that the γ lobe stores information of relevance to both developmental stages, whereas the α and β lobes have uniquely adult roles

Pyrrole Syntheses Based on Titanium-Catalyzed Hydroamination of Diynes

Titanium-catalyzed hydroamination of 1,4- and 1,5-diynes by primary amines leads to imino-alkynes that undergo in situ 5-endo dig and 5-exo dig cyclization reactions, respectively. The products are 1,2,5-trisubstituted pyrroles accessed directly from readily available diyne starting materials

Pyrrole Syntheses Based on Titanium-Catalyzed Hydroamination of Diynes

Titanium-catalyzed hydroamination of 1,4- and 1,5-diynes by primary amines leads to imino-alkynes that undergo in situ 5-endo dig and 5-exo dig cyclization reactions, respectively. The products are 1,2,5-trisubstituted pyrroles accessed directly from readily available diyne starting materials

Improved prediction of hiking speeds using a data driven approach

Hikers and hillwalkers typically use the gradient in the direction of travel (walking slope) as the main variable in established methods for predicting walking time (via the walking speed) along a route. Research into fell-running has suggested further variables which may improve speed algorithms in this context; the gradient of the terrain (hill slope) and the level of terrain obstruction. Recent improvements in data availability, as well as widespread use of GPS tracking now make it possible to explore these variables in a walking speed model at a sufficient scale to test statistical significance. We tested various established models used to predict walking speed against public GPS data from almost 88,000 km of UK walking / hiking tracks. Tracks were filtered to remove breaks and non-walking sections. A new generalised linear model (GLM) was then used to predict walking speeds. Key differences between the GLM and established rules were that the GLM considered the gradient of the terrain (hill slope) irrespective of walking slope, as well as the terrain type and level of terrain obstruction in off-road travel. All of these factors were shown to be highly significant, and this is supported by a lower root-mean-square-error compared to existing functions. We also observed an increase in RMSE between the GLM and established methods as hill slope increases, further supporting the importance of this variable.Comment: 37 pages, 13 figure