Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.</p

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.</p

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Directed Neural Differentiation of Mouse Embryonic Stem Cells Is a Sensitive System for the Identification of Novel Hox Gene Effectors

The evolutionarily conserved Hox family of homeodomain transcription factors plays fundamental roles in regulating cell specification along the anterior posterior axis during development of all bilaterian animals by controlling cell fate choices in a highly localized, extracellular signal and cell context dependent manner. Some studies have established downstream target genes in specific systems but their identification is insufficient to explain either the ability of Hox genes to direct homeotic transformations or the breadth of their patterning potential. To begin delineating Hox gene function in neural development we used a mouse ES cell based system that combines efficient neural differentiation with inducible Hoxb1 expression. Gene expression profiling suggested that Hoxb1 acted as both activator and repressor in the short term but predominantly as a repressor in the long run. Activated and repressed genes segregated in distinct processes suggesting that, in the context examined, Hoxb1 blocked differentiation while activating genes related to early developmental processes, wnt and cell surface receptor linked signal transduction and cell-to-cell communication. To further elucidate aspects of Hoxb1 function we used loss and gain of function approaches in the mouse and chick embryos. We show that Hoxb1 acts as an activator to establish the full expression domain of CRABPI and II in rhombomere 4 and as a repressor to restrict expression of Lhx5 and Lhx9. Thus the Hoxb1 patterning activity includes the regulation of the cellular response to retinoic acid and the delay of the expression of genes that commit cells to neural differentiation. The results of this study show that ES neural differentiation and inducible Hox gene expression can be used as a sensitive model system to systematically identify Hox novel target genes, delineate their interactions with signaling pathways in dictating cell fate and define the extent of functional overlap among different Hox genes

Neurocognitive Dysfunction in Systemic Lupus Erythematosus: Association with Antiphospholipid Antibodies, Disease Activity and Chronic Damage

Introduction: Systemic lupus erythematosus (SLE) is characterized by frequent neuropsychiatric involvement, which includes cognitive impairment (CI). We aimed at assessing CI in a cohort of Italian SLE patients by using a wide range of neurocognitive tests specifically designed to evaluate the fronto-subcortical dysfunction. Furthermore, we aimed at testing whether CI in SLE is associated with serum autoantibodies, disease activity and chronic damage. Methods: Fifty-eight consecutive patients were enrolled. Study protocol included data collection, evaluation of serum level

2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

S

Measurement of the t¯tZ and t¯tW cross sections in proton-proton collisions at √s=13 TeV with the ATLAS detector

A measurement of the associated production of a top-quark pair (t¯t) with a vector boson (W, Z) in proton-proton collisions at a center-of-mass energy of 13 TeV is presented, using 36.1  fb−1 of integrated luminosity collected by the ATLAS detector at the Large Hadron Collider. Events are selected in channels with two same- or opposite-sign leptons (electrons or muons), three leptons or four leptons, and each channel is further divided into multiple regions to maximize the sensitivity of the measurement. The t¯tZ and t¯tW production cross sections are simultaneously measured using a combined fit to all regions. The best-fit values of the production cross sections are σt¯tZ=0.95±0.08stat±0.10syst pb and σt¯tW=0.87±0.13stat±0.14syst pb in agreement with the Standard Model predictions. The measurement of the t¯tZ cross section is used to set constraints on effective field theory operators which modify the t¯tZ vertex

Measurement of single top-quark production in association with a W boson in the single-lepton channel at \sqrt{s} = 8\,\text {TeV} with the ATLAS detector

The production cross-section of a top quark in association with a W boson is measured using proton–proton collisions at \sqrt{s} = 8\,\text {TeV}. The dataset corresponds to an integrated luminosity of 20.2\,\text {fb}^{-1}, and was collected in 2012 by the ATLAS detector at the Large Hadron Collider at CERN. The analysis is performed in the single-lepton channel. Events are selected by requiring one isolated lepton (electron or muon) and at least three jets. A neural network is trained to separate the tW signal from the dominant t{\bar{t}} background. The cross-section is extracted from a binned profile maximum-likelihood fit to a two-dimensional discriminant built from the neural-network output and the invariant mass of the hadronically decaying W boson. The measured cross-section is \sigma _{tW} = 26 \pm 7\,\text {pb}, in good agreement with the Standard Model expectation