Single-layer Analysis Research Articles

Multi-omics data integration reveals clinically meaningful molecular profiles of Alzheimer disease.

Alzheimer disease (AD) is a complex polygenic disease in which multiple molecular pathways and biological processes are affected in distinct cell-types of the brain. Increasingly, novel machine learning approaches have been applied in other areas of similarly high complexity (e.g., cancer) to integrate different modalities of "-omics" data and provide novel insights. We sought to apply high-throughput "multi-omics" and machine learning approaches to a large assembly of clinically and neuropathologically well-characterized brain samples to identify genes and biological pathways that may lead to new biomarkers and therapies for AD. We analyzed high-throughput transcriptomics, proteomics, metabolomics and lipidomics profiles of postmortem parietal cortex samples from the Knight ADRC (n=328) and the DIAN (n =21) participants including APP, PSEN1 and PSEN2 autosomal dominant AD mutation carriers (ADAD, n=28), sporadic AD (sAD, n=282), presymptomatic AD (preAD, n=14), and controls with minimal neuropathologic changes (n=25). We applied a Bayesian integrative clustering method (iClusterBayes), designed to identify disease subtypes, to cluster sAD samples on the basis of 2,676 transcripts, 1,067 proteins, 350 metabolites, and 277 lipids. Our analyses identified four different molecular signatures among sAD cases (Figure 1A). Cluster 4 was associated with higher Clinical Dementia Rating (CDR; p=2.2 x10-20) scores and shorter life span (p=5.6 x10-3, HR=1.6; Figure 1B). In particular, alpha-synuclein (SNCA) transcriptomic and proteomic levels were differentially expressed between samples in cluster 4 and others representing sAD, ADAD, preAD, and controls (Figure 1C) suggesting a unique role for SNCA in sAD cases with unfavorable outcomes. The lower levels of soluble SNCA protein in AD samples may be associated with insoluble aggregated SNCA (Lewy bodies) reported in 50-60% of sAD. By applying machine learning approaches to sAD samples with high-throughput multi-omic profiles, we identified novel molecular signatures missed by single layer analyses. These signatures are associated with clinical phenotypes and offer new insights into which molecules may wax or wane as cognition declines. The association of SNCA with poor outcomes suggests that concomitant Lewy bodies may be a negative prognostic factor in sAD. Currently, we are extending these analyses to incorporate replication cohorts and to perform downstream pathway and enrichment analyses.

A Monte-Carlo study on the fluorescent nuclear track detector (FNTD) response to fast neutrons: Which information can be obtained by single layer and 3D track reconstruction analyses?

Fluorescence Nuclear Track Detectors (FNTDs) are part of a new technology developed for particle detection and applicable to personal neutron dosimetry. The objective of this study is to simulate the FNTD fast neutron response to: (i) assess and understand the performance of the existing neutron dosimeter design (Landauer Inc.) and its associated single layer track-spots analysis; and (ii) evaluate the potential information that can be obtained by the analysis of the 3D reconstructed recoil proton trajectories. To achieve that, a FLUKA Monte Carlo (MC) model of the current FNTD design was developed and the FNTD response was investigated for mono-energetic neutrons and the 252Cf and 241AmBe neutron sources. The investigation of the recoil proton densities behind the different converters showed that the single layer analysis and dose calculation algorithm, based on the comparison and subtraction of the track densities behind the different converters, works properly only up to neutron energies ~13 MeV. Above this neutron energies, recoil protons generated in the detector housing (PE) have a range larger than the thickness of the PTFE and 6Li-enriched glass, reaching the FTND and, therefore, adding to the signal in these detection regions and influencing the secondary electron discrimination and the energy determination algorithm. MC simulations show that the FNTD 3D reconstructed recoil proton tracks can provide estimates of the irradiation angles and average neutron energy. The results show that the angle or displacement (dX/dZ or dY/dZ) distributions of the recoil proton tracks can be used to obtain information on irradiation angle; the angle with the detector's normal (polar angle), the most important because of its influences on the FTND sensitivity, can be determined in laboratory and for irradiation angles < 60° with an 4° uncertainty already for doses > 4.5 mSv in the case of a214AmBe neutron irradiation. The neutron field mean energy can also be determined for normal irradiation by analysing the depth distribution of the recoil proton tracks already for a minimum of 150 tracks, or 2.5 mSv for 241AmBe, assuming a scanned area is ~2.0 mm2. Therefore, the present study contributes to understanding the performance of the current FNTD design and analysis for neutron dosimetry and investigates a new detector evaluation approach to gain additional information on the irradiation conditions.

List of abstracts

. Results obtained in the analysis of single layer and multilayered carbon stacks produced by PLD at low temperature will be presented. The mechanical properties of carbon single layers deposited at various laser pulse fluences, like hardness and elasticity, were determined by means of nano indentation measurements. It will be shown that a wide variety of mechanical properties can easily be adjusted by adapting the process parameters.

Comparison of whole retinal thickness and single layer analysis of optical coherence tomography in Multiple System Atrophy and Parkinson's Disease

Comparison of whole retinal thickness and single layer analysis of optical coherence tomography in Multiple System Atrophy and Parkinson's Disease

Seasonal and interannual variability of elemental carbon in the snowpack of Storglaciären, northern Sweden

AbstractWe studied the variability of elemental carbon (EC) over 3 years (2009–11) in the winter snowpack of Storglaciären, Sweden. The goal of this study was to relate the seasonal variation in EC to specific snow accumulation events in order to improve understanding of how different atmospheric circulation patterns control the deposition of EC. Specifically, we related meteorological parameters (e.g. wind direction, precipitation) to snow physical properties, EC content, stable-isotope δ18O ratios and anion concentrations in the snowpack. The distribution of EC in the snowpack varied between years. Low EC contents corresponded to a predominance of weather systems originating in the northwest, i.e. North Atlantic. Analysis of single layers within the snowpacks showed that snow layers enriched in heavy isotopes coincided predominantly with low EC contents but high chloride and sulfate concentration. Based on this isotopic and geochemical evidence, snow deposited during these events had a strong oceanic, i.e. North Atlantic, imprint. In contrast, snow layers with high EC content coincided with snow layers depleted in heavy isotopes but high anion concentrations, indicating a more continental source of air masses and origin of EC from industrial emissions.

Multilevel omic data integration in cancer cell lines: advanced annotation and emergent properties

BackgroundHigh-throughput (omic) data have become more widespread in both quantity and frequency of use, thanks to technological advances, lower costs and higher precision. Consequently, computational scientists are confronted by two parallel challenges: on one side, the design of efficient methods to interpret each of these data in their own right (gene expression signatures, protein markers, etc.) and, on the other side, realization of a novel, pressing request from the biological field to design methodologies that allow for these data to be interpreted as a whole, i.e. not only as the union of relevant molecules in each of these layers, but as a complex molecular signature containing proteins, mRNAs and miRNAs, all of which must be directly associated in the results of analyses that are able to capture inter-layers connections and complexity.ResultsWe address the latter of these two challenges by testing an integrated approach on a known cancer benchmark: the NCI-60 cell panel. Here, high-throughput screens for mRNA, miRNA and proteins are jointly analyzed using factor analysis, combined with linear discriminant analysis, to identify the molecular characteristics of cancer. Comparisons with separate (non-joint) analyses show that the proposed integrated approach can uncover deeper and more precise biological information. In particular, the integrated approach gives a more complete picture of the set of miRNAs identified and the Wnt pathway, which represents an important surrogate marker of melanoma progression. We further test the approach on a more challenging patient-dataset, for which we are able to identify clinically relevant markers.ConclusionsThe integration of multiple layers of omics can bring more information than analysis of single layers alone. Using and expanding the proposed integrated framework to integrate omic data from other molecular levels will allow researchers to uncover further systemic information. The application of this approach to a clinically challenging dataset shows its promising potential.

Numerical Approaches to the Nonlinear Stability Analysis of Single Layer Reticulated and Grid-Shell Structures

The paper illustrates the characteristics of a procedure for evaluating the buckling and post-buckling behaviour and the influence of imperfections on reticulated and grid-shells structures. The presentation is composed by two steps: firstly, a simple way to comprehend the effects of initial imperfections by means of an “equivalent” fictitious load vector, acting on the ideal design structure, is presented. The fictitious load's characteristics are evaluated so to qualitatively reproduce the behaviour of an unknown structure, affected by an arbitrary distribution of small geometry variations with respect to the design structure. The second step allows to detect exactly the position of critical points and to trace the secondary equilibrium paths by means of a perturbation technique. Numerical examples illustrate the characteristics and performances of the approaches and suggest practical applications.