Cosmology in the Presence of Non-Gaussianity

Modern observational cosmology relies on statistical inference, which models measurable quantities (including their systematic and statistical uncertainties) as random variates, examples are model parameters (`cosmological parameters') to be estimated via regression, as well as the observable data itself. In various contexts, these exhibit non-Gaussian distribution properties, e.g., the Bayesian joint posterior distribution of cosmological parameters from different data sets, or the random fields affected by late-time nonlinear structure formation like the convergence of weak gravitational lensing or the galaxy density contrast. Gaussianisation provides us with a powerful toolbox to model this non-Gaussian structure: a non-linear transformation from the original non-Gaussian random variate to an auxiliary random variate with (approximately) Gaussian distribution allows one to capture the full distribution structure in the first and second moments of the auxiliary. We consider parametric families of non-linear transformations, in particular Box-Cox transformations and generalisations thereof. We develop a framework that allows us to choose the optimally-Gaussianising transformation by optimising a loss function, and propose methods to assess the quality of the optimal transform a posteriori. First, we apply our maximum-likelihood framework to the posterior distribution of Planck data, and demonstrate how to reproduce the contours of credible regions without bias - our method significantly outperforms the current gold standard, kernel density estimation. Next, we use Gaussianisation to compute the model evidence for a combination of CFHTLenS and BOSS data, and compare to standard techniques. Third, we find Gaussianising transformations for simulated weak lensing convergence maps. This increases the information content accessible to two-point statistics (e.g., the power spectrum) and potentially allows for rapid production of independent mock maps with non-Gaussian correlation structure. With these examples, we demonstrate how Gaussianisation expands our current inference toolbox, and permits us to accurately extract information from non-Gaussian contexts

Quantitative in vivo assessment of radiation injury of the liver using Gd-EOB-DTPA enhanced MRI: tolerance dose of small liver volumes

<p>Abstract</p> <p>Backround</p> <p>Hepatic radiation toxicity restricts irradiation of liver malignancies. Better knowledge of hepatic tolerance dose is favourable to gain higher safety and to optimize radiation regimes in radiotherapy of the liver. In this study we sought to determine the hepatic tolerance dose to small volume single fraction high dose rate irradiation.</p> <p>Materials and methods</p> <p>23 liver metastases were treated by CT-guided interstitial brachytherapy. MRI was performed 3 days, 6, 12 and 24 weeks after therapy. MR-sequences were conducted with T1-w GRE enhanced by hepatocyte-targeted Gd-EOB-DTPA. All MRI data sets were merged with 3D-dosimetry data. The reviewer indicated the border of hypointensity on T1-w images (loss of hepatocyte function) or hyperintensity on T2-w images (edema). Based on the volume data, a dose-volume-histogram was calculated. We estimated the threshold dose for edema or function loss as the D₉₀, i.e. the dose achieved in at least 90% of the pseudolesion volume.</p> <p>Results</p> <p>At six weeks post brachytherapy, the hepatocyte function loss reached its maximum extending to the former 9.4Gy isosurface in median (i.e., ≥9.4Gy dose exposure led to hepatocyte dysfunction). After 12 and 24 weeks, the dysfunctional volume had decreased significantly to a median of 11.4Gy and 14Gy isosurface, respectively, as a result of repair mechanisms. Development of edema was maximal at six weeks post brachytherapy (9.2Gy isosurface in median), and regeneration led to a decrease of the isosurface to a median of 11.3Gy between 6 and 12 weeks. The dose exposure leading to hepatocyte dysfunction was not significantly different from the dose provoking edema.</p> <p>Conclusion</p> <p>Hepatic injury peaked 6 weeks after small volume irradiation. Ongoing repair was observed up to 6 months. Individual dose sensitivity may differ as demonstrated by a relatively high standard deviation of threshold values in our own as well as all other published data.</p

Identification and Analysis of Co-Occurrence Networks with NetCutter

BACKGROUND: Co-occurrence analysis is a technique often applied in text mining, comparative genomics, and promoter analysis. The methodologies and statistical models used to evaluate the significance of association between co-occurring entities are quite diverse, however. METHODOLOGY/PRINCIPAL FINDINGS: We present a general framework for co-occurrence analysis based on a bipartite graph representation of the data, a novel co-occurrence statistic, and software performing co-occurrence analysis as well as generation and analysis of co-occurrence networks. We show that the overall stringency of co-occurrence analysis depends critically on the choice of the null-model used to evaluate the significance of co-occurrence and find that random sampling from a complete permutation set of the bipartite graph permits co-occurrence analysis with optimal stringency. We show that the Poisson-binomial distribution is the most natural co-occurrence probability distribution when vertex degrees of the bipartite graph are variable, which is usually the case. Calculation of Poisson-binomial P-values is difficult, however. Therefore, we propose a fast bi-binomial approximation for calculation of P-values and show that this statistic is superior to other measures of association such as the Jaccard coefficient and the uncertainty coefficient. Furthermore, co-occurrence analysis of more than two entities can be performed using the same statistical model, which leads to increased signal-to-noise ratios, robustness towards noise, and the identification of implicit relationships between co-occurring entities. Using NetCutter, we identify a novel protein biosynthesis related set of genes that are frequently coordinately deregulated in human cancer related gene expression studies. NetCutter is available at http://bio.ifom-ieo-campus.it/NetCutter/). CONCLUSION: Our approach can be applied to any set of categorical data where co-occurrence analysis might reveal functional relationships such as clinical parameters associated with cancer subtypes or SNPs associated with disease phenotypes. The stringency of our approach is expected to offer an advantage in a variety of applications

Facilitating the development of controlled vocabularies for metabolomics technologies with text mining

BACKGROUND: Many bioinformatics applications rely on controlled vocabularies or ontologies to consistently interpret and seamlessly integrate information scattered across public resources. Experimental data sets from metabolomics studies need to be integrated with one another, but also with data produced by other types of omics studies in the spirit of systems biology, hence the pressing need for vocabularies and ontologies in metabolomics. However, it is time-consuming and non trivial to construct these resources manually. RESULTS: We describe a methodology for rapid development of controlled vocabularies, a study originally motivated by the needs for vocabularies describing metabolomics technologies. We present case studies involving two controlled vocabularies (for nuclear magnetic resonance spectroscopy and gas chromatography) whose development is currently underway as part of the Metabolomics Standards Initiative. The initial vocabularies were compiled manually, providing a total of 243 and 152 terms. A total of 5,699 and 2,612 new terms were acquired automatically from the literature. The analysis of the results showed that full-text articles (especially the Materials and Methods sections) are the major source of technology-specific terms as opposed to paper abstracts. CONCLUSIONS: We suggest a text mining method for efficient corpus-based term acquisition as a way of rapidly expanding a set of controlled vocabularies with the terms used in the scientific literature. We adopted an integrative approach, combining relatively generic software and data resources for time- and cost-effective development of a text mining tool for expansion of controlled vocabularies across various domains, as a practical alternative to both manual term collection and tailor-made named entity recognition methods

Role of PACAP and VIP Signalling in Regulation of Chondrogenesis and Osteogenesis

Pituitary adenylate cyclase activating polypeptide (PACAP) and vasoactive intestinal peptide (VIP) are multifunctional proteins that can regulate diverse physiological processes. These are also regarded as neurotrophic and anti-inflammatory substances in the CNS, and PACAP is reported to prevent harmful effects of oxidative stress. In the last decade more and more data accumulated on the similar function of PACAP in various tissues, but its cartilage- and bone-related presence and functions have not been widely investigated yet. In this summary we plan to verify the presence and function of PACAP and VIP signalling tool kit during cartilage differentiation and bone formation. We give evidence about the protective function of PACAP in cartilage regeneration with oxidative or mechanically stress and also with the modulation of PACAP signalling in vitro in osteogenic cells. Our observations imply the therapeutic perspective that PACAP might be applicable as a natural agent exerting protecting effect during joint inflammation and/or may promote cartilage regeneration during degenerative diseases of articular cartilage

Science-Driven Optimization of the LSST Observing Strategy

The Large Synoptic Survey Telescope is designed to provide an unprecedented optical imaging dataset that will support investigations of our Solar System, Galaxy and Universe, across half the sky and over ten years of repeated observation. However, exactly how the LSST observations will be taken (the observing strategy or "cadence") is not yet finalized. In this dynamically-evolving community white paper, we explore how the detailed performance of the anticipated science investigations is expected to depend on small changes to the LSST observing strategy. Using realistic simulations of the LSST schedule and observation properties, we design and compute diagnostic metrics and Figures of Merit that provide quantitative evaluations of different observing strategies, analyzing their impact on a wide range of proposed science projects. This is work in progress: we are using this white paper to communicate to each other the relative merits of the observing strategy choices that could be made, in an effort to maximize the scientific value of the survey. The investigation of some science cases leads to suggestions for new strategies that could be simulated and potentially adopted. Notably, we find motivation for exploring departures from a spatially uniform annual tiling of the sky: focusing instead on different parts of the survey area in different years in a "rolling cadence" is likely to have significant benefits for a number of time domain and moving object astronomy projects. The communal assembly of a suite of quantified and homogeneously coded metrics is the vital first step towards an automated, systematic, science-based assessment of any given cadence simulation, that will enable the scheduling of the LSST to be as well-informed as possible

Increased throughput and ultra-high mass resolution in DESI FT-ICR MS imaging through new-generation external data acquisition system and advanced data processing approaches

Desorption electrospray ionisation-mass spectrometry imaging (DESI-MSI) is a powerful imaging technique for the analysis of complex surfaces. However, the often highly complex nature of biological samples is particularly challenging for MSI approaches, as options to appropriately address mass spectral complexity are limited. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) offers superior mass accuracy and mass resolving power, but its moderate throughput inhibits broader application. Here we demonstrate the dramatic gains in mass resolution and/or throughput of DESI-MSI on an FT-ICR MS by developing and implementing a sophisticated data acquisition and data processing pipeline. The presented pipeline integrates, for the first time, parallel ion accumulation and detection, post-processing absorption mode Fourier transform and pixel-by-pixel internal re-calibration. To achieve that, first, we developed and coupled an external high-performance data acquisition system to an FT-ICR MS instrument to record the time-domain signals (transients) in parallel with the instrument’s built-in electronics. The recorded transients were then processed by the in-house developed computationally-efficient data processing and data analysis software. Importantly, the described pipeline is shown to be applicable even to extremely large, up to 1 TB, imaging datasets. Overall, this approach provides improved analytical figures of merits such as: (i) enhanced mass resolution at no cost in experimental time; and (ii) up to 4-fold higher throughput while maintaining a constant mass resolution. Using this approach, we not only demonstrate the record 1 million mass resolution for lipid imaging from brain tissue, but explicitly demonstrate such mass resolution is required to resolve the complexity of the lipidome

Gaussianization for fast and accurate inference from cosmological data

We present a method to transform multivariate unimodal non-Gaussian posterior probability densities into approximately Gaussian ones via non-linear mappings, such as Box–Cox transformations and generalizations thereof. This permits an analytical reconstruction of the posterior from a point sample, like a Markov chain, and simplifies the subsequent joint analysis with other experiments. This way, a multivariate posterior density can be reported efficiently, by compressing the information contained in Markov Chain Monte Carlo samples. Further, the model evidence integral (i.e. the marginal likelihood) can be computed analytically. This method is analogous to the search for normal parameters in the cosmic microwave background, but is more general. The search for the optimally Gaussianizing transformation is performed computationally through a maximum-likelihood formalism; its quality can be judged by how well the credible regions of the posterior are reproduced. We demonstrate that our method outperforms kernel density estimates in this objective. Further, we select marginal posterior samples from Planck data with several distinct strongly non-Gaussian features, and verify the reproduction of the marginal contours. To demonstrate evidence computation, we Gaussianize the joint distribution of data from weak lensing and baryon acoustic oscillations, for different cosmological models, and find a preference for flat Λcold dark matter. Comparing to values computed with the Savage–Dickey density ratio, and Population Monte Carlo, we find good agreement of our method within the spread of the other two