Particle identification using artificial neural networks with the ALICE transition radiation detector

Der ALICE Übergangsstrahlungsdetektor (TRD) wurde als Tracking-Detektor, als Trigger-Detektor für Elektronen und für die Identifikation von Elektronen konzipiert. Das Konstruktionsziel für den Übergangsstrahlungsdetektor war eine Pioneneffizienz von 1% bei einer Elektroneneffizienz von 90% zu erreichen. Das Signal das im TRD zur Teilchenidentifikation benutzt wird besteht aus zwei Komponenten. Geladene Teilchen, die den Übergangsstrahlungsdetektor durchqueren, deponieren Energie durch Stoßionisation. Zusätzliche dazu produtieren Elektronen Übergangsstrahlung die früh im Driftbereich des TRD absorbiert wird. In dieser Arbeit wurde die Anwendung von neuronalen Netzen für die Teilchenidentifikation mit dem TRD untersucht. Als Eingangsgröße für die Netze wurde das Signal in mehrere Abschnitte unterteilt. Die Ergebnisse sowohl von Teststrahl-Experimenten als auch von Simulationen zeigen, dass mit neuronalen Netzen eine bessere Teilchenidentifikation erreichbar ist, als mit anderen Methoden

Learning Layer-wise Equivariances Automatically using Gradients

Convolutions encode equivariance symmetries into neural networks leading to better generalisation performance. However, symmetries provide fixed hard constraints on the functions a network can represent, need to be specified in advance, and can not be adapted. Our goal is to allow flexible symmetry constraints that can automatically be learned from data using gradients. Learning symmetry and associated weight connectivity structures from scratch is difficult for two reasons. First, it requires efficient and flexible parameterisations of layer-wise equivariances. Secondly, symmetries act as constraints and are therefore not encouraged by training losses measuring data fit. To overcome these challenges, we improve parameterisations of soft equivariance and learn the amount of equivariance in layers by optimising the marginal likelihood, estimated using differentiable Laplace approximations. The objective balances data fit and model complexity enabling layer-wise symmetry discovery in deep networks. We demonstrate the ability to automatically learn layer-wise equivariances on image classification tasks, achieving equivalent or improved performance over baselines with hard-coded symmetry

VarSight: prioritizing clinically reported variants with binary classification algorithms.

BackgroundWhen applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance.MethodsWe tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network.ResultsWe treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20.ConclusionsWe demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets

The Bose-Einstein correlation function C2(Q)C_2(Q) from a Quantum Field Theory point of view

We show that a recently proposed derivation of Bose-Einstein correlations (BEC) by means of a specific version of thermal Quantum Field Theory (QFT), supplemented by operator-field evolution of the Langevin type, allows for a deeper understanding of the possible coherent behaviour of the emitting source and a clear identification of the origin of the observed shape of the BEC function $C_2(Q)$. Previous conjectures in this matter obtained by other approaches are confirmed and have received complementary explanation.Comment: Some misprints corrected. To be publishe in Phys. Rev.

Scaling violations: Connections between elastic and inelastic hadron scattering in a geometrical approach

Starting from a short range expansion of the inelastic overlap function, capable of describing quite well the elastic pp and $\bar{p}p$ scattering data, we obtain extensions to the inelastic channel, through unitarity and an impact parameter approach. Based on geometrical arguments we infer some characteristics of the elementary hadronic process and this allows an excellent description of the inclusive multiplicity distributions in $pp$ and $\bar{p}p$ collisions. With this approach we quantitatively correlate the violations of both geometrical and KNO scaling in an analytical way. The physical picture from both channels is that the geometrical evolution of the hadronic constituents is principally reponsible for the energy dependence of the physical quantities rather than the dynamical (elementary) interaction itself.Comment: 16 pages, aps-revtex, 11 figure

Occurrence, function and evolutionary origins of ‘2A-like’ sequences in virus genomes

2A is an oligopeptide sequence mediating a ribosome ‘skipping’ effect, producing an apparent ‘cleavage’ of polyproteins. First identified and characterized in picornaviruses, ‘2A-like’ sequences are found in other mammalian viruses and a wide range of insect viruses. Databases were analysed using a motif conserved amongst 2A/2A-like sequences. The newly identified 2A-like sequences (30 aa) were inserted into a reporter polyprotein to determine their cleavage activity. Our analyses showed that these sequences fall into two categories. The majority mediated very high (complete) cleavage to separate proteins and a few sequences mediated cleavage with lower efficiency, generating appreciable levels of the uncleaved form. Phylogenetic analyses of 2A-like sequences and RNA-dependent RNA polymerases (RdRps) indicated multiple, independent, acquisitions of these sequences at different stages during virus evolution. Within a virus family, 2A sequences are (probably) homologous, but diverge due to other evolutionary pressures. Amongst different families, however, 2A/2A-like sequences appear to be homoplasic

Large-scale genome-wide association studies and meta-analyses of longitudinal change in adult lung function.

BACKGROUND: Genome-wide association studies (GWAS) have identified numerous loci influencing cross-sectional lung function, but less is known about genes influencing longitudinal change in lung function. METHODS: We performed GWAS of the rate of change in forced expiratory volume in the first second (FEV1) in 14 longitudinal, population-based cohort studies comprising 27,249 adults of European ancestry using linear mixed effects model and combined cohort-specific results using fixed effect meta-analysis to identify novel genetic loci associated with longitudinal change in lung function. Gene expression analyses were subsequently performed for identified genetic loci. As a secondary aim, we estimated the mean rate of decline in FEV1 by smoking pattern, irrespective of genotypes, across these 14 studies using meta-analysis. RESULTS: The overall meta-analysis produced suggestive evidence for association at the novel IL16/STARD5/TMC3 locus on chromosome 15 (P  =  5.71 × 10(-7)). In addition, meta-analysis using the five cohorts with ≥3 FEV1 measurements per participant identified the novel ME3 locus on chromosome 11 (P  =  2.18 × 10(-8)) at genome-wide significance. Neither locus was associated with FEV1 decline in two additional cohort studies. We confirmed gene expression of IL16, STARD5, and ME3 in multiple lung tissues. Publicly available microarray data confirmed differential expression of all three genes in lung samples from COPD patients compared with controls. Irrespective of genotypes, the combined estimate for FEV1 decline was 26.9, 29.2 and 35.7 mL/year in never, former, and persistent smokers, respectively. CONCLUSIONS: In this large-scale GWAS, we identified two novel genetic loci in association with the rate of change in FEV1 that harbor candidate genes with biologically plausible functional links to lung function

Measurement of charm production at central rapidity in proton-proton collisions at s=2.76\sqrt{s} = 2.76 TeV

The $p_{\rm T}$-differential production cross sections of the prompt (B feed-down subtracted) charmed mesons D$^0$, D$^+$, and D$^{*+}$ in the rapidity range $|y|<0.5$, and for transverse momentum $1< p_{\rm T} <12$ GeV/$c$, were measured in proton-proton collisions at $\sqrt{s} = 2.76$ TeV with the ALICE detector at the Large Hadron Collider. The analysis exploited the hadronic decays D$^0 \rightarrow$K$\pi$, D$^+ \rightarrow$K$\pi\pi$, D$^{*+} \rightarrow$D$^0\pi$, and their charge conjugates, and was performed on a $L_{\rm int} = 1.1$ nb$^{-1}$ event sample collected in 2011 with a minimum-bias trigger. The total charm production cross section at $\sqrt{s} = 2.76$ TeV and at 7 TeV was evaluated by extrapolating to the full phase space the $p_{\rm T}$-differential production cross sections at $\sqrt{s} = 2.76$ TeV and our previous measurements at $\sqrt{s} = 7$ TeV. The results were compared to existing measurements and to perturbative-QCD calculations. The fraction of cdbar D mesons produced in a vector state was also determined.Comment: 20 pages, 5 captioned figures, 4 tables, authors from page 15, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/307

Genetic Variants Associated With Cancer Therapy-Induced Cardiomyopathy

BACKGROUND: Cancer therapy-induced cardiomyopathy (CCM) is associated with cumulative drug exposures and preexisting cardiovascular disorders. These parameters incompletely account for substantial interindividual susceptibility to CCM. We hypothesized that rare variants in cardiomyopathy genes contribute to CCM. METHODS: We studied 213 patients with CCM from 3 cohorts: retrospectively recruited adults with diverse cancers (n=99), prospectively phenotyped adults with breast cancer (n=73), and prospectively phenotyped children with acute myeloid leukemia (n=41). Cardiomyopathy genes, including 9 prespecified genes, were sequenced. The prevalence of rare variants was compared between CCM cohorts and The Cancer Genome Atlas participants (n=2053), healthy volunteers (n=445), and an ancestry-matched reference population. Clinical characteristics and outcomes were assessed and stratified by genotypes. A prevalent CCM genotype was modeled in anthracycline-treated mice. RESULTS: CCM was diagnosed 0.4 to 9 years after chemotherapy; 90% of these patients received anthracyclines. Adult patients with CCM had cardiovascular risk factors similar to the US population. Among 9 prioritized genes, patients with CCM had more rare protein-altering variants than comparative cohorts ( P≤1.98e-04). Titin-truncating variants (TTNtvs) predominated, occurring in 7.5% of patients with CCM versus 1.1% of The Cancer Genome Atlas participants ( P=7.36e-08), 0.7% of healthy volunteers ( P=3.42e-06), and 0.6% of the reference population ( P=5.87e-14). Adult patients who had CCM with TTNtvs experienced more heart failure and atrial fibrillation ( P=0.003) and impaired myocardial recovery ( P=0.03) than those without. Consistent with human data, anthracycline-treated TTNtv mice and isolated TTNtv cardiomyocytes showed sustained contractile dysfunction unlike wild-type ( P=0.0004 and P<0.002, respectively). CONCLUSIONS: Unrecognized rare variants in cardiomyopathy-associated genes, particularly TTNtvs, increased the risk for CCM in children and adults, and adverse cardiac events in adults. Genotype, along with cumulative chemotherapy dosage and traditional cardiovascular risk factors, improves the identification of patients who have cancer at highest risk for CCM. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov . Unique identifiers: NCT01173341; AAML1031; NCT01371981.This work was supported in part by grants from the Instituto de Salud Carlos III (ISCIII) (PI15/01551, PI17/01941 and CB16/11/00432 to P.G-P. and L.A-P.), the Spanish Ministry of Economy and Competitiveness (SAF2015-71863-REDT to P.G-P.), the John S. LaDue Memorial Fellowship at Harvard Medical School (Y.K.), Wellcome Trust (107469/Z/15/Z to J.S.W.), Medical Research Council (intramural awards to S.A.C. and J.S.W; MR/M003191/1 to U.T), National Institute for Health Research Biomedical Research Unit at the Royal Brompton and Harefield National Health Service Foundation Trust and Imperial College London (P.J.B., S.A.C., J.S.W.), National Institute for Health Research Biomedical Research Centre at Imperial College London Healthcare National Health Service Trust and Imperial College London (D.O.R., S.A.C., S.P., J.S.W.), Sir Henry Wellcome Postdoctoral Fellowship (C.N.T.), Rosetrees and Stoneygate Imperial College Research Fellowship (N.W.), Fondation Leducq (S.A.C., C.E.S., J.G.S.), Health Innovation Challenge Fund award from the Wellcome Trust and Department of Health (UK; HICF-R6-373; S.A.C., P.J.B., J.S. W.), the British Heart Foundation (NH/17/1/32725 to D.O.R.; SP/10/10/28431 to S.A.C), Alex’s Lemonade Stand Foundation (K.G.), National Institutes of Health (R.A.: U01CA097452, R01CA133881, and U01CA097452; Z.A.: R01 HL126797; B.K.: R01 HL118018 and K23-HL095661; J.G.S. and C.E.S.: 5R01HL080494, R01HL084553), and the Howard Hughes Medical Institute (C.E.S.). The Universitario Puerta de Hierro and Virgen de la Arrixaca Hospitals are members of the European Reference Network on Rare and Complex Diseases of the Heart (Guard-Heart; http://guard-heart.ern-net.eu). This publication includes independent research commissioned by the Health Innovation Challenge Fund (HICF), a parallel funding partnership between the Department of Health and Wellcome Trust. The Centro Nacional de Investigaciones Cardiovasculares (CNIC) is supported by the Ministry of Economy, Industry and Competitiveness and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505). Grants from ISCIII and the Spanish Ministry of Economy and Competitiveness are supported by the Plan Estatal de I+D+I 2013-2016 – European Regional Development Fund (FEDER) “A way of making Europe”.S

Genome-wide association analysis identifies six new loci associated with forced vital capacity

Forced vital capacity (FVC), a spirometric measure of pulmonary function, reflects lung volume and is used to diagnose and monitor lung diseases. We performed genome-wide association study meta-analysis of FVC in 52,253 individuals from 26 studies and followed up the top associations in 32,917 additional individuals of European ancestry. We found six new regions associated at genome-wide significance (P < 5 × 10−8) with FVC in or near EFEMP1, BMP6, MIR129-2–HSD17B12, PRDM11, WWOX and KCNJ2. Two loci previously associated with spirometric measures (GSTCD and PTCH1) were related to FVC. Newly implicated regions were followed up in samples from African-American, Korean, Chinese and Hispanic individuals. We detected transcripts for all six newly implicated genes in human lung tissue. The new loci may inform mechanisms involved in lung development and the pathogenesis of restrictive lung disease