Sashimi plots: Quantitative visualization of RNA sequencing read alignments

We introduce Sashimi plots, a quantitative multi-sample visualization of mRNA sequencing reads aligned to gene annotations. Sashimi plots are made using alignments (stored in the SAM/BAM format) and gene model annotations (in GFF format), which can be custom-made by the user or obtained from databases such as Ensembl or UCSC. We describe two implementations of Sashimi plots: (1) a stand-alone command line implementation aimed at making customizable publication quality figures, and (2) an implementation built into the Integrated Genome Viewer (IGV) browser, which enables rapid and dynamic creation of Sashimi plots for any genomic region of interest, suitable for exploratory analysis of alternatively spliced regions of the transcriptome. Isoform expression estimates outputted by the MISO program can be optionally plotted along with Sashimi plots. Sashimi plots can be used to quickly screen differentially spliced exons along genomic regions of interest and can be used in publication quality figures. The Sashimi plot software and documentation is available from: http://genes.mit.edu/burgelab/miso/docs/sashimi.htmlComment: 2 figure

Galaxy cluster rotation revealed in the MACSIS simulations with the kinetic Sunyaev-Zeldovich effect

The kinetic Sunyaev-Zeldovich (kSZ) effect has now become a clear target for ongoing and future studies of the cosmic microwave background (CMB) and cosmology. Aside from the bulk cluster motion, internal motions also lead to a kSZ signal. In this work, we study the rotational kSZ effect caused by coherent large-scale motions of the cluster medium using cluster hydrodynamic cosmological simulations. To utilise the rotational kSZ as a cosmological probe, simulations offer some of the most comprehensive data sets that can inform the modeling of this signal. In this work, we use the MACSIS data set to specifically investigate the rotational kSZ effect in massive clusters. Based on these models, we test stacking approaches and estimate the amplitude of the combined signal with varying mass, dynamical state, redshift and map-alignment geometry. We find that the dark matter, galaxy and gas spins are generally misaligned, an effect that can cause a sub-optimal estimation of the rotational kSZ effect when based on galaxy catalogues. Furthermore, we provide halo-spin-mass scaling relations that can be used to build a statistical model of the rotational kSZ. The rotational kSZ contribution, which is largest in massive unrelaxed clusters ($\gtrsim$100 $\mu$K), could be relevant to studies of higher-order CMB temperature signals, such as the moving lens effect. The limited mass range of the MACSIS sample strongly motivates an extended investigation of the rotational kSZ effect in large-volume simulations to refine the modelling, particularly towards lower mass and higher redshift, and provide forecasts for upcoming cosmological CMB experiments (e.g. Simons Observatory, SKA-2) and X-ray observations (e.g. \textit{Athena}/X-IFU).Comment: Submitted to Monthly Notices of the Royal Astronomical Society. Comments and discussions are welcome. Data and codes can be found at https://github.com/edoaltamura/macsis-cosmosi

Uncertainty quantification methods for neural networks pattern recognition

On-line monitoring techniques have attracted increasing attention as a promising strategy for improving safety, maintaining availability and reducing the cost of operation and maintenance. In particular, pattern recognition tools such as artificial neural networks are today largely adopted for sensor validation, plant component monitoring, system control, and fault-diagnostics based on the data acquired during operation. However, classic artificial neural networks do not provide an error context for the model response, whose robustness remains thus difficult to estimate. Indeed, experimental data generally exhibit a time/space-varying behaviour and are hence characterized by an intrinsic level of uncertainty that unavoidably affects the performance of the tools adopted and undermines the accuracy of the analysis. For this reason, the propagation of the uncertainty and the quantification of the so called margins of uncertainty in output are crucial in making risk-informed decision. The current study presents a comparison between two different approaches for the quantification of uncertainty in artificial neural networks. The first technique presented is based on the error estimation by a series association scheme, the second approach couples Bayesian model selection technique and model averaging into a unified framework. The efficiency of these two approaches are analysed in terms of their computational cost and predictive performance, through their application to a nuclear power plant fault diagnosis system

Strong anisotropy in surface kinetic roughening: analysis and experiments

We report an experimental assessment of surface kinetic roughening properties that are anisotropic in space. Working for two specific instances of silicon surfaces irradiated by ion-beam sputtering under diverse conditions (with and without concurrent metallic impurity codeposition), we verify the predictions and consistency of a recently proposed scaling Ansatz for surface observables like the two-dimensional (2D) height Power Spectral Density (PSD). In contrast with other formulations, this Ansatz is naturally tailored to the study of two-dimensional surfaces, and allows to readily explore the implications of anisotropic scaling for other observables, such as real-space correlation functions and PSD functions for 1D profiles of the surface. Our results confirm that there are indeed actual experimental systems whose kinetic roughening is strongly anisotropic, as consistently described by this scaling analysis. In the light of our work, some types of experimental measurements are seen to be more affected by issues like finite space resolution effects, etc. that may hinder a clear-cut assessment of strongly anisotropic scaling in the present and other practical contexts

New experimental limit on the Pauli Exclusion Principle violation by electrons

The Pauli Exclusion Principle (PEP) is one of the basic principles of modern physics and, even if there are no compelling reasons to doubt its validity, it is still debated today because an intuitive, elementary explanation is still missing, and because of its unique stand among the basic symmetries of physics. The present paper reports a new limit on the probability that PEP is violated by electrons, in a search for a shifted K$_\alpha$ line in copper: the presence of this line in the soft X-ray copper fluorescence would signal a transition to a ground state already occupied by 2 electrons. The obtained value, ${1/2} \beta^{2} \leq 4.5\times 10^{-28}$, improves the existing limit by almost two orders of magnitude.Comment: submitted to Phys. Lett.

On right-angled polygons in hyperbolic space

We study oriented right-angled polygons in hyperbolic spaces of arbitrary dimensions, that is, finite sequences (S0,S1,…,Sp−1) of oriented geodesics in the hyperbolic space HHn+2 such that consecutive sides are orthogonal. It was previously shown by Delgove and Retailleau (Ann Fac Sci Toulouse Math 23(5):1049–1061, 2014. https://doi.org/10.5802/afst.1435) that three quaternionic parameters define a right- angled hexagon in the 5-dimensional hyperbolic space. We generalise this method to right-angled polygons with an arbitrary number of sides p≥5 in a hyperbolic space of arbitrary dimension