dataesr/scientific-paragraphs-categorization huggingface dataset:
J/y Elliptic Flow The expected absolute statistical error of the elliptic flow (v 2 ) measurement is shown in Figure  2. 38 (left panel) for the Muon Spectrometer data. The measurement with 1 nb 1 data will improve significantly the present situation, but it is ultimately the upgrade data that will provide a precision measurement. The J/y v 2 could be measured with At mid-rapidity, the measurement of elliptic flow of J/y is only possible with the 10 nb 1 Pb-Pb data expected with the upgrade. As shown in Figure  2. 38 (right panel), a measurement with a good significance over a broad range in p T (up to 5-6 GeV/c) can be achieved only with the usage of the TRD electron identification. In this case, a direct measurement of elliptic flow of J/y from B hadrons could become also feasible.
The measurement of the J/y polarization parameters in Pb-Pb collisions at low-p T is a challenging measurement that ALICE will perform with the envisaged luminosity of 10 nb 1. It is expected that statistical errors on l q (see Figure  2. 39) of about 0. 02 will be reached in case of the Muon Spectrometer data for such integrated luminosities. A comparable, albeit slightly worse, accuracy is expected for the Central Barrel data. (GeV/c) T p 0 1 2 3.
The large combinatorial background prevents also a straight-forward online trigger scheme. Consequently, high data rates need to be handled by the detectors and the readout systems. These experimental requirements will be fulfilled after the luminosity increase of the LHC, providing Pb-Pb collisions at 50 kHz, and a substantial upgrade of the ALICE detector and readout systems. Specifically, running the TPC in continuous mode will allow to record about 2. 5 • 10 9 central (0-10%) Pb-Pb collisions, corresponding to about R Ldt = 3 nb 1 , within a typical running period dedicated for the low-mass dilepton measurement, where the ALICE solenoid will be operated at a reduced magnetic field of B = 0. 2 T (see below). This is to be compared to 2. 5 • 10 7 central Pb-Pb collisions without TPC upgrade.
We note that ALICE has the unique capability to measure p 0 and h down to low p T via conversions  [108]  and can therefore constrain the uncertainty on the hadronic cocktail by data from the same experiment. For the subtraction of the charm contribution a relative systematic uncertainty of 20% is assumed. Also here we note that the yield of correlated dilepton pairs from charm decays can be well constrained by inversion of the DCA cuts, which enhances the charm contribution, and by the exclusive measurement of charmed hadrons at low p T with complementary methods in ALICE.
We estimate the further reduction potential by these steps to be on the order of two. Based on online detector calibration and full event reconstruction, a more aggressive scenario could be envisaged. Most of the parts of what is traditionally called offline processing would then be done online. This would offer the exciting possibility to have as output of the online systems a dataset which is comparable to the output of the traditional first physics reconstruction pass of the data. This would only require the addition of a modest amount of software and computing resources to the online systems. The subsequent offline processing would be reduced to the creation of the analysis datasets, which would then significantly reduce the time between data-taking and physics analysis as compared to the present system.
Detector Control System The present Detector Control System (DCS) of ALICE will also need to be upgraded. The DCS will have to integrate new detectors and interfaces to new classes of equipment. New electronics that will be adopted by the detectors for the upgrade will require the development and implementation of new interface standards. The current plan is to keep the same SCADA (supervisory control and data acquisition) system and migrate to its next generation, the WinCC Open Architecture. Also all operating systems will be upgraded and the cluster will be upgraded. The benefits of this migration will be the following: -Improved performance due to parallel processing and load balancing. -Enhanced availability trough a better fault tolerance. -Object-oriented databases and graphics -Easier import and export of data This migration will require a substantial adaptation or redesign of many software components.
Acknowledgements The Collaboration wishes to thank the following persons for their contribution to the preparation of this LOI:  M. He ,  R. J. Fries  and  R. Rapp ,  Cyclotron Institute  and  Department of Physics and Astronomy, Texas A&M University ;  M. Cacciari ,  LPTHE Paris  and CNRS;  L. Ropelewski ,  E. Oliveri  and  R. Veenhof ,  CERN (PH-DT) ;  H. Mugnier ,  J. Rousset , et  P. Chalmet  of  MIND Microtechnologies in Archamps, and the Departement de la Haute Savoie  for supporting MIND to collaborate on monolithic detectors;.
Data management A dedicated database was created for the study which was tested out and validated before any data was entered. This dedicated database will be developed with the aid of Capture System (CLINSIGHT), which is a software package designed for the global management of clinical studies and meeting the study's regulatory requirements. A data validation plan will be drawn up and will describe in detail the checks to be performed for each variable together with the list of the blatant errors for which correction is authorised. The observation books will be checked when they are collected and then the data will be entered followed by post-checking (data entered/hard copy data). This data will be checked once again by the team in charge of the data management via the error messages received from the validation programmes.
Statistical analyses The data will be entered twice then exploited in SAS V9. 2 and SPSS v20. After the quality of the data has been checked and any corrections made, a description of the population included in the study will be given. A comparison of the main return-to-work criteria of the two groups drawn at random will be done (age, stage of development of the disease, occupational category). The reduction in the social inequalities of the return to work will be studied by comparing the deltas (difference in the return-to-work rates between the two sub-groups FS + and FS-) between the control group « B » and the intervention group« A » having the benefit of the APAPI. A rough comparison of the two deltas will be done with a Chi2 test. The taking into account of adjustment factors (age, secondary effects of the treatment …) will be done with the aid of multivariate regression models.
Nucleotide sequence-based hits The longest-established repositories of nucleotide sequences are GenBank  (Benson et al. , 2014) , the European Molecular Biology Laboratory Nucleotide Sequence Database (EMBL-Bank), which is part of the European Nucleotide Archive (ENA;  Pakseresht et al. , 2014) , and the DNA Data Bank of Japan (DDBJ)  (Kosuge et al. , 2014). These resources collaborate to share sequence information, so that all GenBank/EMBL/DDBJ entries can be retrieved from each of the host websites. The Reference Sequence (RefSeq) database  (Pruitt et al. , 2014)  aims to provide a single entry for each nucleic acid or protein molecule, making explicit the relationships between genes, transcripts, and proteins. GenBank/EMBL/DDBJ and RefSeq entries are stored in the Nucleotide database of the National Center for Biotechnology Information (NCBI; NCBI Resource  Coordinators, 2014) , making this an ideal home database for accessing nucleotide entries of these types.
The Ensembl resource  (Flicek et al. , 2014)  comprises sequences of genomic DNA, transcripts, and predicted polypeptide products, with data originating from genome sequencing projects, mostly from vertebrates. Updated versions are released on an approximately quarterly basis to incorporate genome reassemblies and the integration of new sequence and annotation data; this ensures that the database continues to improve in reliability, although one downside is that Ensembl IDs periodically become outdated and replaced. The main Ensembl resource is complemented by the Ensembl Genomes database  (Kersey et al. , 2014) , comprising entries from sequenced bacteria, fungi, plants, protists, and invertebrate Metazoa (Supplemental Table  S3 ). For species whose genomes have not been completely sequenced (e.
Protein hits For protein sequences, the UniProt resource (UniProt Consortium, 2014) comprises entries from the curated Swiss-Prot and the noncurated TrEMBL (translated EMBL nucleotide) databases. Entries from both use UniProt IDs (known as accession codes), which present a standard nomenclature used by the majority of protein-oriented programs and resources. Swiss-Prot, sometimes referred to as the gold-standard protein database, combines stable IDs with rich, expert-curated annotation relating to the protein's composition and biological functions. TrEMBL entries derive from automatically in silico-translated nucleotide entries from the ENA and Ensembl databases but lack functional annotation. With new versions being released approximately monthly, the UniProt resource manages to combine the best of both worlds: rich annotation together with regularly updated entries.
An additional advantage of UniProt is that for most entries, official gene symbols are included in the protein entry headings, providing a means for gene-as well as protein-oriented database resources to be directly accessed. Another protein resource of note is the NCBI's nonredundant (nr) database, compiled from entries originating from GenBank/ This review aims to contribute toward satisfying the desires of research biologists to explore the functions of protein-encoding genes and their transcripts and products. Ten questions are posed to guide the characterization of a given hit, each question being answered by the presentation of one or more Internet-based resources that provide reliable and relevant information, are freely accessible, and are described in peer-reviewed publications. Resources are included on the basis of quality, comprehensiveness, and usability.
Another important parameter is that hit-specific entries in these resources should be directly accessible via standard gene, transcript, or protein IDs. Each answer is accompanied by Web links to specific entries ("deep links") in relevant databases, presented in the accompanying supplemental tables. The utility of these resources is illustrated using a hypothetical data set of 20 factors that regulate aspects of cell proliferation. Of these, particular focus is drawn to DNA polymerase  (Kornberg, 1990) , the cyclin-dependent kinase Cdk1  (Dorée and Hunt, 2002) , and the tumor suppressor p53  (Lane et al. , 2010) , at least one being present in all organisms, enabling the outputs of various resources to be compared. Straightforward methods are also described for biologists to access these resources by generating one-click links from results spreadsheets directly to database entries and by supplementing results tables with information to annotate each hit.
These approaches provide an efficient and flexible means for biologists working with any genome-based technology and experimental species to retrieve reliable information to enhance biological discovery without the need for bioinformatic training, programming experience, or specialist software. QuESTION 1: WHAT IS THE SEQuENCE Of THE HIT, ANd WHAT ARE ITS gENOmIC ORIgINS? Hits from screens and analytical experiments may be in the form of genes (identified by unique codes, names, or symbols) or nucleotide or amino acid sequences (identified by unique IDs). In either case, it is often worthwhile to visit the relevant page of the home database, that is, the primary or official repository of information about that hit, before embarking on visits to online resources of more specific functional information.
For gene-based experimental hits from model organisms, the home database would be the corresponding species-specific gene database (Supplemental Table  S1 ). Here gene pages can be accessed using species-specific gene nomenclature and codes, which may differ from those used by major sequence databases, with the majority also being directly accessible using sequence IDs of standard types. Species-specific gene databases allow rapid access to information about genotypes, phenotypes, and the availability of mutant strains and related resources, making these preferred first ports of call for gene-based studies. For sequence-based hits, the relevant home database would be the primary repository for the sequence (Supplemental Table  S2 ). Visiting such a resource allows the biologist to touch base with the experimental origins of the sequence and identify the research team or project from which the data originate.
These resources also allow rapid retrieval of nucleotide or amino acid sequences in FASTA and other standard formats, which, although unlikely to shed light on questions of gene or protein function, is useful for bioinformatic procedures for which direct linking is not possible. Which site is considered the home database depends on the source of the sequence information used in the analysis. However, this logic also works in reverse: for several analytical techniques, the research biologist (or the analyst, at the biologist's direction) has a choice of sequence include the following. For enzymes: substrates and products and molecules that modulate their activity, such as allosteric activators and cofactors; for structural proteins: the relevant cellular structures and partner molecules with which they collaborate to maintain the structure; for signaling molecules: upstream regulators and downstream targets, plus other components, such as scaffold proteins; and for gene expression modulators: members of protein complexes that modulate chromatin and affect transcription.
Entries in the NCBI Gene database (NCBI Resource Coordinators, 2014) contain, for better-characterized genes, a single-paragraph summary of the functions of the gene product in its physiological and (if relevant) pathological context. Similarly, the curated (Swiss-Prot) entries in UniProt have a General annotation (Comments) section in which functions, activity, subunit structure, and other properties are listed, broken down into categories, and well referenced. For human genes, more extensive expertcurated information is provided by the Online Mendelian Inheritance in Man (OMIM) resource  (Amberger et al. , 2011). Entries here contain well-referenced descriptions of the identification of the gene and its functions, allelic variants and association with disease, and the biochemical properties of the product.
, 2009) , although this comprehensive output often contains considerable EMBL/DDBJ, RefSeq, Swiss-Prot, and protein-structure databases. Entries, which are accessible from the NCBI protein portal (NCBI Resource Coordinators, 2014), all have two IDs: one from the database of origin, plus a GenInfo Identifier (GI) number. Combining entries in this way, nr has the advantage of being very comprehensive in its coverage, but it has disadvantages such as the rapid turnover of GI numbers, inconsistency in nomenclature between source databases, and poor functional annotation. A protein database that achieved popularity among proteomics researchers was the International Protein Index (IPI;  Kersey et al. , 2004) ; however, in 2011 this was discontinued and entries integrated into UniProt  (Griss et al.
Genomic context For hits of any type, users may wish to access information about the genomic contexts of the relevant genes, including chromosomal location, gene length and orientation, proximal genes, and relevant genomic features in the vicinity. Many species-specific gene databases contain an embedded genome viewer presenting concise genomic information, and often this is sufficient. However, when more detailed information is required, including multiple alignments with other sequences and features, direct access to the relevant locus within a specialized genome browser is desirable. Probably the most comprehensive cross-species tool for visualizing and aligning sequences in their genomic contexts is the UCSC Genome Browser  (Karolchik et al. , 2014) , which can be queried using all major gene, transcript, or protein IDs.
Here a diagram shows the gene's location within the relevant chromosome, and, below, a panel presents a graphical view of the relevant genomic region, with a multiple alignment of various sequences, including splice-variant mRNAs and expressed sequence tags, features such as single-nucleotide polymorphisms (SNPs) and repeats, and features derived from ENCODE project data  (Rosenbloom et al. , 2013). Also shown are multiple alignments between the gene of interest and orthologues from related species. A complementary functionality is provided by the genome browser within Ensembl, with flexible options for the export of genomic sequence data. Because navigation and exploration of genomes are not the themes of this review, for further information readers are directed to the Nature Genetics free online series of tutorials, "A user's guide to the human genome"  (Wolfsberg et al.
Project-specific databases Functional information more relevant to a particular biological process can in some cases be obtained from Web-based databases created to disseminate data generated by specific research projects (Supplemental Table  S7 ). Data from genome-scale knockout or knockdown projects are particularly relevant, as users can be almost certain to obtain some functional information relating to their genes of interest: at least whether they are essential for viability, plus obvious and more subtle loss-of-function phenotypes. A pioneering example of this is PhenoBank, which describes and shows movies of phenotypes obtained from a genome-wide RNAi-knockdown screen in Caenorhabditis elegans early embryos  (Sönnichsen et al. , 2005). Another such resource is the Schizosaccharomyces pombe gene database, PomBase  (Wood et al.
, 2012) , which records phenotypes obtained from a genome-wide deletion screen  (Kim et al. , 2010; Hayles et al. , 2013). The MitoCheck database, based on human genes, shows movies of time-lapse fluorescence microscopy experiments, as well as inferred phenotypes, from genome-scale RNAi screens. Initially created to record the effects of human gene knockdowns on chromosome behavior during the cell cycle  (Neumann et al. , 2010) , this resource is being complemented by data sets from subsequent RNAi screens investigating additional cellular processes. Also included are data on the subcellular localization and protein interactions of gene products required for cell division  (Hutchins et al. , 2010). The MitoCheck database can be searched using human gene symbols, synonyms, or UniProt IDs, plus gene terms for orthologous nonhuman genes, making this a unique and valuable cross-species functional resource.
QuESTION 3: WHAT HOmOLOguES Of THIS gENE (OR PROTEIN) ARE kNOWN? HOW WELL HAS IT BEEN CONSERVEd THROugH EVOLuTION? There are several reasons for wanting to identify genes or proteins of closely related sequence to the one of interest. First, for a poorly annotated transcript or protein (e. g. , one with a sequence ID but no gene information) the issue may simply be one of identification: an identical (or virtually identical) sequence from the same species may provide sufficient information to allow gene identification and further exploration. Second, for a hit originating from a less well-annotated database, orthologues from closely related species may provide richer functional annotation and greater availability of research resources (including mutant strains, recombinant proteins, and antibodies) for follow-up studies.
QuESTION 4: HOW IS THIS gENE ExPRESSEd IN CELLS OR TISSuES, ANd HOW dOES THIS CHANgE uNdER ExPERImENTAL CONdITIONS? Valuable indications as to the potential involvement of a gene in a biological process of interest can be obtained from data relating to its pattern of expression within the organism and how this changes during development or in response to cell stress or drug treatment conditions. For experimental model organisms, summaries of gene expression in physiological contexts relevant to that species are most often included in the species-specific gene databases; those listed in Supplemental Table  S1  all have "expression" data sections, except for SGD (budding yeast), for which expression data are hosted by the SPELL database  (Hibbs et al. , 2007 ; Supplemental Table  S9 ). Because the major high-throughput gene expression technologies (microarray analysis and next-generation sequencing) are nucleic acid based, the majority of expression data are at the transcriptional level.
The two largest repositories of gene expression data obtained with such technologies are the NCBI's Gene Expression Omnibus (GEO;  Barrett et al. , 2013)  and the European Bioinformatics Institute (EBI)-hosted ArrayExpress  (Rustici et al. , 2013) , which also imports GEO entries. These resources provide public access to billions of expression data entries, together with corresponding experimental details. However, with so much data available, instead of accessing potentially hundreds of experimental records corresponding to a particular gene, it is almost always preferable to obtain an overview of the relevant data first. This facility is admirably provided by the EBI's Gene Expression Atlas  (Petryszak et al. , 2014) , a geneoriented database containing a curated subset of ArrayExpress data, accessible using virtually all gene, nucleotide, or protein IDs.
Subsections summarize expression of the selected gene in tissues (for some species accompanied by a diagram of the organism with expressing tissues highlighted), by cell types, cell lines, and disease states, and in response to drug and other experimental treatments. Results are provided with links to the original data at ArrayExpress. For expression detected at the protein level, The Human Protein Atlas  (Asplund et al. , 2012)  contains image-based data from immunohistochemical and immunocytochemical analyses for thousands of human gene products. Each gene page summarizes the subcellular location of its product and provides relative quantifications of antibody staining (from negative to strong) across a range of normal tissues and organs, cancer tissues, and cell lines. Clicking a summary result leads to a detailed page of staining data, and then to high-resolution micrograph images.
The MitoCheck database, searchable using human and nonhuman gene names, lists Ensembl-predicted paralogues and orthologues in a concise format, the latter being linked to species-specific gene databases where appropriate. HomoloGene (NCBI Resource Coordinators, 2014) is the NCBI's resource for automated retrieval of gene and protein homologues from 21 completely sequenced genomes. Querying by gene symbol or NCBI-based nucleotide or protein ID generates a list of gene orthologues, alongside each being a corresponding orthologous protein (all based on RefSeq entries), with a graphical representation of conserved domains. Links from this page include those to a multiple sequence alignment, a table of pairwise alignment scores, and literature references. An additional useful feature of HomoloGene is its statement on evolutionary conservation-for example, "gene conserved in fungi/metazoa group.