dataesr/scientific-paragraphs-categorization huggingface dataset:
Fully hadronic channel The fully hadronic channel requires that at least two jets satisfy kinematic and t tagging selection criteria. The data were collected online with a trigger requiring the scalar sum of the AK4 jet energies (H T ) to be larger than 800 GeV. The trigger selection has an efficiency of above 95%, as measured in simulation, for events that satisfy the offline requirement H T > 1000 GeV. The event reconstruction is performed using only AK8 jets. The two leading jets are required to have p T > 500 GeV, rapidity |y| < 2. 4, and both are required to be t tagged. A back-to-back topology is selected by requiring the azimuthal separation of the two leading jets to satisfy |∆φ| > 2. 1. Events are further categorized into six regions based on two criteria: the rapidity difference (∆y) between the two AK8 jets and the number of jets with at least one b-tagged subjet for the two highest p T jets.
The remaining contamination from tt is removed by subtracting the distribution of tt events in simulation. The t mistag rate is measured separately for the muon and electron channels, in data and simulation. The resulting values, together with the data-to-simulation SFs, are shown in Table  1. As the SFs for the muon and electron channels are consistent, the weighted average is used: SF = 0. 79 ± 0. 15. hypothesis to data  [66]. Distributions defined in samples dominated by various backgrounds are used simultaneously in a binned maximum likelihood fit to constrain the different uncertainties in the background model using the data. The reconstructed M tt distribution is used in regions dominated by tt and W+jets, and the dimuon invariant mass is used in a region dominated by Z+jets. The tt-dominated region is defined by M tt < 2 TeV and χ 2 min < 30.
The anti-tag jet is required to satisfy 110 < M SD < 210 GeV and τ 32 > 0. 69. This "anti-tag and probe" method yields a per-jet t mistag rate parameterized as a function of jet momentum (which is more closely tied to the radiation within the jet than is the p T ) and is measured separately for events falling into each of the six b tag and |∆y| categories (Fig. 3 ). The anti-tag requirement is designed to select a sample in data dominated by NTMJ events. A small number of genuine tt events survive this selection. This contamination is removed by subtracting the distributions measured in tt simulation from those measured in the anti-tag and probe selection in data. Once the t mistag rate has been determined from the NTMJ control sample, it is used to estimate the normalization and shape of NTMJ events passing the final event selection.
An uncertainty of 2% is assigned to the efficiency of the electron trigger selection, and is determined from a complementary measurement of the e+jets trigger efficiency in a dilepton (eµ) control region. The uncertainties in the data-to-simulation corrections for jet energy scale and jet energy resolution are evaluated by varying these corrections within their uncertainties, as functions of the jet p T and η. Both systematic variations are also propagated to the measurement of p miss T and the jet mass. A SF is applied to account for differing efficiencies and misidentification rates of the b tagging selection between data and simulation. Uncertainties in the SFs are measured as functions of the jet p T and treated as uncorrelated. The data-to-simulation correction for the subjet b tagging algorithm efficiency is also included as an independent uncertainty and is evaluated by varying the correction within its uncertainties, as a function of jet p T and η.
The best fit values obtained from this maximum likelihood evaluation are used to correct the distributions of background and signal processes. A Bayesian statistical method  [66, 74]  is used to extract the upper limits at 95% confidence level (CL) on the product of the cross section and branching fraction, i. e. σ(pp → X) B(X → tt), for heavy resonances decaying to a tt pair. In order to maximize the expected sensitivity of the search, twelve exclusive categories are employed simultaneously in the statistical analysis, as described above. For each category, the observable used in the limit-setting procedure is M tt. A template-based shape analysis is performed using the Theta software package  [66]  for these M tt distributions. The systematic uncertainties listed in Table  2  are introduced as individual nuisance parameters in the limit calculation.
Acknowledgments Belgium); the  Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium) ; the  Ministry of Education, Youth and Sports (MEYS)  of the Czech Republic; the  Council of Science and Industrial Research, India ; the  HOMING PLUS programme of the Foundation for Polish Science , cofinanced from  European Union, Regional Development Fund , the  Mobility Plus programme of the Ministry of Science and Higher Education , the  National Science Center (Poland) , contracts Harmonia  2014/14/M/ST2/00428 ,  Opus 2013/11/B/ST2/04202 ,  2014/13/B/ST2/02543  and  2014/15/B/ST2/03998 , Sonata-bis  2012/07/E/ST2/01406 ; the Thalis and Aristeia programmes cofinanced by  EU-ESF  and the  Greek NSRF ; the  National Priorities Research Program  by  Qatar National Research Fund ; the  Programa Clarín-COFUND del Principado de Asturias ; the  Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University  and the  Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; and the  Welch Foundation , contract  C-1845.
San Diego, CA). Read alignment to the reference genome (NCBI human genome assembly build 36; Ensembl core database release 50_361)  [Hubbard et al. , 2009]  was performed using the Burrows-Wheeler Alignment (BWA) tool  [Li and Durbin 2009] , and single nucleotide variants (SNVs) and small insertion/deletions (indels) were identified using SAMtools  [Li et al. , 2009]. PCR duplicates were removed using the Picard software. We also performed local realignment and base call quality recalibration using GATK  [McKenna et al. , 2010]. Using the PhenoDB Variant Analysis Tool  [Sobreira et al. , 2015] , we analyzed the WES data by applying a filter designed to prioritize rare (Minor Allele Frequency <1 percent) functional variants (missense, nonsense, splice site variants, and indels) that were homozygous or compound heterozygous in the proband and the affected sister but absent or heterozygous in the unaffected brother.
SH3 domain and the C1 functional domains are frequently components of signaling proteins  [Reinholt et al. , 2013]  and STAC3 encodes a novel component of excitation-contraction (EC) coupling in skeletal muscle. The affected sisters in family 1 harbor the STAC3 homozygous c. 851G>C (p. Trp284Ser) missense variant that was also reported as the genetic cause of NAM in 5 Lumbee Native American families  [Horstick et al. , 2013]. This p. Trp284Ser variant is in the first SH3 (Src homology three) domain of STAC3 and the tryptophan in this position is highly conserved across SH3 domains. It is present in the ExAC database as heterozygous in 13 alleles out of 121350 alleles (12 from African population and 1 from Latino population); no homozygotes are described. The affected siblings in Family 2 were compound heterozygous for the c.
Acknowledgments We thank the families for participating in this research. We also thank the  Moebius Syndrome Foundation  for their dedication and support. A subset of the data in this study was generated by the  Moebius Syndrome Research Consortium , which was funded and supported by  NICHD ,  NHGRI ,  NEI ,  NIDCR ,  NINDS ,  NIDCD ,  NIMH ,  NIH Clinical Center ,  NISC  ( U01HD079068 ), and  NHGRI  ( 1U54HG006542 ).
Bioinformatic Analysis on Potential Target Genes (mRNAs) of miR-425-5p Examination for potential target mRNAs of miR-425-5p revealed 282 entries using TargetScan  [14]  and 224 mRNAs were predicted with mirDataBase (miRDb,  [15] ). Both tools identified 69 mRNAs and were forwarded to PANTHER  [16, 17]. The number of genes regulating macromolecular metabolic processes (p = 2 × 10 -5 ) and gene expression (p = 2. 6 × 10 -4 ) showed approximately two-fold overrepresentation. These changes were significantly associated with an about two-fold overrepresentation in the number of genes coding for intracellular membrane bound organelles (p = 4. 5 × 10 -6 ). Secondly, we screened our microarray data and from the potential 69 mRNAs we selected those target genes that were differentially expressed (≥two-fold difference) among both HARS groups.
We found that miR-425-5p appeared promising with respect to normal tissue response to radiation in our examination of healthy baboons. Accordingly, this miRNA has been previously associated with chemoand radiosensitivity  [20, 21] , cell proliferation  [22, 23] , and cell death  [20]. Our results pointed to understanding potential mRNA target genes of miR-425-5p. We used two different databases, for which 69 mRNA species were identified and PANTHER indicated genes that were involved in regulating macromolecular metabolic processes, gene expression, and coding for intracellular membrane bound organelles. Searching for differentially expressed mRNAs, 43 out of the 69 potential target mRNAs were identified taking advantage of our whole genome screening approach using microarrays. These genes coded for biological processes such as proliferation, apoptosis, protein degradation, cell signaling, DNA damage response/repair, and radiation sensitivity (Figure  3 ).
Stage I Screening Whole genome screening for differentially expressed genes (protein coding mRNAs) was performed on 10 RNA samples (baboons with HARS and absence or presence of pancytopenia n = 2 × 5, Table  1 ). We used the Agilent oligo microarray GE 8 × 60 K (Agilent Technologies, Waldbronn, Germany) combined with a one-color based hybridization protocol of GeneSpring GX12 software for data analysis as described in detail elsewhere  [29]. We analyzed gene expression by quantile normalized log 2 -transformed probe signals as an outcome. We used the nonparametric Mann Whitney (MW) test to compare gene expression in the HARS with pancytopenia group considering the HARS without pancytopenia as the reference group. Only those gene transcripts that had a call "present" in at least 60% of RNA specimens were included in the analysis of gene expression and only genes with MW p-values ≤0.
05 and with a ≥two-fold gene expression difference among compared groups were considered to represent a candidate gene for validation in stage II. Due to the explorative nature of this study, the low sample size and the nonparametric statistic employed, we did not correct for multiple comparisons on the screening stage I of the study. We considered multiple comparisons in the bioinformatic approach as well as the validation stage II of our study where the numbers of hypothesis tested in parallel was reduced from about 20,000 (stage I) to 20 mRNAs and nine miRNAs in stage II (see below). Gene expression data presented in this publication have been deposited at the NCBI's Gene Expression Omnibus (GEO accession number GSE77254). Screening for differentially expressed miRNAs using qRT-PCR was performed on the same 10 RNA samples used for the whole genome mRNA screening.
Bioinformatics We identified potential target genes of miR-425-5p using the overlapping number of mRNAs detected with the mirDataBase (miRDb,  [15] ) and TargetScan  [14]. For these potential target genes we performed gene set enrichment analyses using PANTHER pathway software version 11. 1  [16, 17]. PANTHER groups genes with similar biological function based on their annotation (reference list was the current homo sapiens gene ontology database). The p-values were corrected for multiple testing employing the Bonferroni algorithm. In a second approach, we used our whole genome microarray data and examined whether the predicted target genes (mRNAs) were truly differentially expressed (≥two-fold and detected in ≥60% of the samples per HARS group). These mRNAs were finally grouped according to the biological processes for which they were coding.
Recent studies in eukaryotes have demonstrated the translation of alternative open reading frames (altORFs) in addition to annotated protein coding sequences (CDSs). We show that a large number of small proteins could in fact be coded by altORFs. The putative alternative proteins translated from altORFs have orthologs in many species and evolutionary patterns indicate that altORFs are particularly constrained in CDSs that evolve slowly. Thousands of predicted alternative proteins are detected in proteomic datasets by reanalysis using a database containing predicted alternative proteins. Protein domains and co-conservation analyses suggest a potential functional relationship between small and large proteins encoded in the same genes. This is illustrated with specific examples, including altMiD51, a 70 amino acid mitochondrial fission-promoting protein encoded in MiD51/Mief1/SMCR7L, a gene encoding an annotated protein promoting mitochondrial fission.
Introduction Current protein databases are cornerstones of modern biology but are based on a number of assumptions. In particular, a mature mRNA is predicted to contain a single CDS; yet, ribosomes can select more than one translation initiation site (TIS)  [1] [2] [3]  on any single mRNA. Also, minimum size limits are imposed on the length of CDSs, resulting in many RNAs being mistakenly classified as non-coding (ncRNAs)  [4] [5] [6] [7] [8] [9] [10] [11]. As a result of these assumptions, the size and complexity of most eukaryotic proteomes have probably been greatly underestimated  [12] [13] [14] [15]. In particular, few small proteins (defined as of 100 amino acids or less) are annotated in current databases. The absence of annotation of small proteins is a major bottleneck in the study of their function and their roles in health and disease.
This is further supported by classical and recent examples of small proteins of functional importance, for instance many critical regulatory molecules such as the F0 subunit of the F0F1-ATPsynthase  16  , the sarcoplasmic reticulum calcium ATPase regulator phospholamban  17  , and the key regulator of iron homeostasis hepcidin  18. This limitation also impedes our understanding of the process of origin of new genes, which are thought to contribute to evolutionary innovations. Because these genes generally code for small proteins  [19] [20] [21] [22]  , they are difficult to unambiguously detect by proteomics and in fact are impossible to detect if they are not included in proteomics databases. Functional annotation of ORFs encoding small proteins is particularly challenging since an unknown fraction of small ORFs may occur by chance in the transcriptome, generating a significant level of noise  13.
However, given that many small proteins have important functions and are ultimately one of the most important sources of functional novelty, it is time to address the challenge of their functional annotations  13. We systematically reanalyzed several eukaryotic transcriptomes to annotate previously unannotated ORFs which we term alternative ORFs (altORFs), and we annotated the corresponding hidden proteome. Here, altORFs are defined as potential protein-coding ORFs in ncRNAs, in UTRs or in different reading frames from annotated CDSs in mRNAs (Figure  1a ). For clarity, predicted proteins translated from altORFs are termed alternative proteins and proteins translated from annotated CDSs are termed reference proteins. Our goal was to provide functional annotations of alternative proteins by (1) analyzing relative patterns of evolutionary conservation between alternative and reference proteins and their corresponding coding sequences; (2) estimating the prevalence of alternative proteins both by bioinformatics analysis and by detection in large experimental datasets; (3) detecting functional signatures in alternative proteins; and (4) predicting and testing functional cooperation between alternative and reference proteins.
Results Prediction of altORFs and alternative proteins. We predicted a total of 539,134 altORFs compared to 68,264 annotated CDSs in the human transcriptome (Figure  1b , Table  1 ). Because identical ORFs can be present in different RNA isoforms transcribed from the same genomic locus, the number of unique altORFs and CDSs becomes 183,191 and 54,498, respectively. AltORFs were also predicted in other organisms for comparison (Table  1 ). By convention, only reference proteins are annotated in current protein databases. As expected, altORFs are on average small, with a size ranging from 30 to 1480 codons. Accordingly, the median size of predicted human alternative proteins is 45 amino acids compared to 460 for reference proteins (Figure  1c ), and 92. 96 % of alternative proteins have less than 100 amino acids.
We observed a similar trend, with a 22fold for conservation at p<0. 0001, and a 24-fold for accelerated evolution at p<0. 0001 (Figure  4 -figure supplement 1). This is illustrated with three altORFs located within the CDS of NTNG1, RET and VTI1A genes (Figure  5 ). These three genes encode a protein promoting neurite outgrowth, the proto-oncogene tyrosine-protein kinase receptor Ret and a protein mediating vesicle transport to the cell surface, respectively. Two of these alternative proteins have been detected by ribosome profiling (RET, IP_182668. 1) or mass spectrometry (VTI1A, IP_188229. 1) (see below, Supplementary files 1 and 2). Evidence of expression of alternative proteins. We provide two lines of evidence indicating that thousands of altORFs are translated into proteins. First, we re-analyzed detected TISs in publicly available ribosome profiling data  28, 29  , and found 26,531 TISs mapping to annotated CDSs and 12,616 mapping to altORFs in these studies (Figure  6a ; Supplementary file 1).
Only a small fraction of TISs detected by ribosomal profiling mapped to altORFs  3'  even if those are more abundant than altORF  5'  relative to shuffled transcriptomes, likely reflecting a recently-resolved technical issue which prevented TIS detection in 3'UTRs  30. New methods to analyze ribosome profiling data are being developed and will likely uncover more translated altORFs  9. In agreement with the presence of functional altORFs  3'  , cap-independent translational sequences were recently discovered in human 3'UTRs  31. Second, we re-analyzed proteomic data using our composite database containing alternative proteins in addition to annotated reference proteins (Figure  6b ; Supplementary file 2). We selected four studies representing different experimental paradigms and proteomic applications: large-scale  32  and targeted  33  protein/protein interactions, post-translational modifications  34  , and a combination of bottom-up, shotgun and interactome proteomics  35.
In the first dataset, we detected 3,957 predicted alternative proteins in the interactome of reference proteins  32  , providing a framework to uncover the function of these proteins. In a second proteomic dataset containing about 10,000 reference human proteins  35  , a total of 549 predicted alternative proteins were detected. Using a phosphoproteomic large data set  34  , we detected 384 alternative proteins. The biological function of these proteins is supported by the observation that some alternative proteins are specifically phosphorylated in cells stimulated by the epidermal growth factor, and others are specifically phosphorylated during mitosis (Figure  7 ; Supplementary file 3). We provide examples of spectra validation (Figure  7 -figure supplement 1). A fourth proteomic dataset contained 77 alternative proteins in the epidermal growth factor receptor interactome  33  (Figure  6b ).
Functional annotations of alternative proteins. An important goal of this study is to associate potential functions to alternative proteins, which we can do through annotations. Because the sequence similarities and the presence of particular signatures (families, domains, motifs, sites) are a good indicator of a protein's function, we analyzed the sequence of the predicted alternative proteins in several organisms with InterProScan, an analysis and classification tool for characterizing unknown protein sequences by predicting the presence of combined protein signatures from most main domain databases  36  (Figure  9 ; Figure  9 -figure supplement 1). We found 41,511 (23%) human alternative proteins with at least one InterPro signature (Figure  9b ). Of these, 37,739 (or 20. 6%) are classified as small proteins.
The most abundant class of predicted alternative proteins with at least one InterPro entry are C2H2 zinc finger proteins with 110 alternative proteins containing 187 C2H2-type/integrase DNA-binding domains, 91 C2H2 domains and 23 C2H2-like domains (Figure  11a ). Eighteen of these (17. 8%) were detected in public proteomic and ribosome profiling datasets, a percentage that is similar to reference zinc finger proteins (20. 1%) (Figure  6 , Table  3 ). Alternative proteins have between 1 and 23 zinc finger domains (Figure  11b ). Zinc fingers mediate protein-DNA, protein-RNA and protein-protein interactions  38. The linker sequence separating adjacent finger motifs matches or resembles the consensus TGEK sequence in nearly half the annotated zinc finger proteins  39. This linker confers high affinity DNA binding and switches from a flexible to a rigid conformation to stabilize DNA binding.
This study provides a unique opportunity to test if, in addition to possessing zinc finger domains, thus being functionally connected some pairs of reference and alternative proteins coded by the same gene also interact. We re-analyzed the MS data using our alternative protein sequence database to detect alternative proteins in this interactome (Supplementary file 5). Five alternative proteins (IP_168460. 1, IP_168527. 1, IP_270697. 1, IP_273983. 1, IP_279784. 1) were identified within the interactome of their reference zinc finger proteins. This number was higher than expected by chance (p<10 -6 ) based on 1 million binomial simulations of randomized interactomes. This result strongly supports the hypothesis of functional relationships between alternative and reference proteins coded by the same genes, and indicates that there are examples of physical interactions.
AltMiD51 is a 70 amino acid alternative protein conserved in vertebrates  42  and coconserved with its reference protein MiD51 from humans to zebrafish (Supplementary file 6). Its coding sequence is present in exon 2 of the MiD51/MIEF1/SMCR7L gene. This exon forms part of the 5'UTR for the canonical mRNA and is annotated as non-coding in current gene databases (Figure  13a ). Yet, altMiD51 is robustly detected by MS in several cell lines (Supplementary file 2: HEK293, HeLa Kyoto, HeLa S3, THP1 cells and gut tissue), and we validated some spectra using synthetic peptides (Figure  13 -figure supplement 1), and it is also detected by ribosome profiling (Supplementary file 1)  37, 42, 43. We confirmed co-expression of altMiD51 and MiD51 from the same transcript (Figure  13b ). Importantly, the tripeptide LYR motif predicted with InterProScan and located in the N-terminal domain of altMiD51 (Figure  13a ) is a signature of mitochondrial proteins localized in the mitochondrial matrix  44.