Comparing the performance of forced alignersused in sociophonetic research

Forced aligners have revolutionized sociophonetics, but while there are several forced aligners available, there are few systematic comparisons of their performance. Here, we consider four major forced aligners used in sociophonetics today: MAUS, FAVE, LaBB-CAT and MFA. Through comparisons with human coders, we find that both aligner and phonological context affect the quality of automated alignments of vowels extracted from English sociolinguistic interview data. MFA and LaBB-CAT produce the highest quality alignments, in some cases not significantly different from human alignment, followed by FAVE, and then MAUS. Aligners are less accurate placing boundaries following a vowel than preceding it, and they vary in accuracy across manner of articulation, particularly for following boundaries. These observations allow us to make specific recommendations for manual correction of forced alignment

Comparing the performance of forced aligners used in sociophonetic research

Forced aligners have revolutionized sociophonetics, but while there are several forced aligners available, there are few systematic comparisons of their performance. Here, we consider four major forced aligners used in sociophonetics today: MAUS, FAVE, LaBB-CAT and MFA. Through comparisons with human coders, we find that both aligner and phonological context affect the quality of automated alignments of vowels extracted from English sociolinguistic interview data. MFA and LaBB-CAT produce the highest quality alignments, in some cases not significantly different from human alignment, followed by FAVE, and then MAUS. Aligners are less accurate placing boundaries following a vowel than preceding it, and they vary in accuracy across manner of articulation, particularly for following boundaries. These observations allow us to make specific recommendations for manual correction of forced alignment.We gratefully acknowledge support from the ARC Centre of Excellence for the Dynamics of Language, and funding from a Transdisciplinary & Innovation Grant (TIG952018). We thank Robert Fromont, Debbie Loakes, and the anonymous Linguistics Vanguard reviewers for valuable feedback on the paper, as well as Miriam Meyerhoff, Jim Stanford, and Hywel Stoakes for help in formulating the ideas presented here

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Search for squarks and gluinos in events with isolated leptons, jets and missing transverse momentum at s√=8 TeV with the ATLAS detector

The results of a search for supersymmetry in final states containing at least one isolated lepton (electron or muon), jets and large missing transverse momentum with the ATLAS detector at the Large Hadron Collider are reported. The search is based on proton-proton collision data at a centre-of-mass energy s√=8 TeV collected in 2012, corresponding to an integrated luminosity of 20 fb−1. No significant excess above the Standard Model expectation is observed. Limits are set on supersymmetric particle masses for various supersymmetric models. Depending on the model, the search excludes gluino masses up to 1.32 TeV and squark masses up to 840 GeV. Limits are also set on the parameters of a minimal universal extra dimension model, excluding a compactification radius of 1/R c = 950 GeV for a cut-off scale times radius (ΛR c) of approximately 30

Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector

Results of a search for H → τ τ decays are presented, based on the full set of proton-proton collision data recorded by the ATLAS experiment at the LHC during 2011 and 2012. The data correspond to integrated luminosities of 4.5 fb−1 and 20.3 fb−1 at centre-of-mass energies of √s = 7 TeV and √s = 8 TeV respectively. All combinations of leptonic (τ → `νν¯ with ` = e, µ) and hadronic (τ → hadrons ν) tau decays are considered. An excess of events over the expected background from other Standard Model processes is found with an observed (expected) significance of 4.5 (3.4) standard deviations. This excess provides evidence for the direct coupling of the recently discovered Higgs boson to fermions. The measured signal strength, normalised to the Standard Model expectation, of µ = 1.43 +0.43 −0.37 is consistent with the predicted Yukawa coupling strength in the Standard Model

Measurement of the top pair production cross section in 8 TeV proton-proton collisions using kinematic information in the lepton plus jets final state with ATLAS

A measurement is presented of the $t\bar{t}$ inclusive production cross-section in $pp$ collisions at a center-of-mass energy of $\sqrt{s}=8$ TeV using data collected by the ATLAS detector at the CERN Large Hadron Collider. The measurement was performed in the lepton+jets final state using a data set corresponding to an integrated luminosity of 20.3 fb$^{-1}$. The cross-section was obtained using a likelihood discriminant fit and $b$-jet identification was used to improve the signal-to-background ratio. The inclusive $t\bar{t}$ production cross-section was measured to be $260\pm 1{\textrm{(stat.)}} ^{+22}_{-23} {\textrm{(syst.)}}\pm 8{\textrm{(lumi.)}}\pm 4{\mathrm{(beam)}}$ pb assuming a top-quark mass of 172.5 GeV, in good agreement with the theoretical prediction of $253^{+13}_{-15}$ pb. The $t\bar{t}\to (e,\mu)+{\mathrm{jets}}$ production cross-section in the fiducial region determined by the detector acceptance is also reported.Comment: Published version, 19 pages plus author list (35 pages total), 3 figures, 2 tables, all figures including auxiliary figures are available at http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/TOPQ-2013-06