dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples and curation of freely available cancer data Informed consent was obtained from all subjects. Collection and use of patient samples were approved by the appropriate IRB of each Institution. In addition to newly generated data, we curated freely available somatic mutations from three other sources: -The data portal of The Cancer Genome Atlas (TCGA) -The data portal of the International Cancer Genome Consortium (ICGC) -Previously published data in peer-review journals, see additional references  6, 23,.
Factors that influence extraction of mutational signatures Recently, using simulated and real data, we described in detail the factors that influence the extraction of mutational signatures  5. These included the number of available samples, the mutation prevalence in samples, the number of mutations contributed by different mutational signatures, the similarity between the signatures of mutational processes operative in cancer samples, as well as the limitations of our computational approach. Here, we examined datasets with varying sizes from 30 different cancer types and we have taken great care to report only validated mutational signatures. However, our approach identified two similar patterns most likely representing the same biological process, viz. , Signature 1A and 1B. The reasons for this is, for some cancer types, we have sufficient numbers of samples and/or mutations ( i.
"Other" indicates mutational signatures for which we were not able to perform validation or for which validation failed (Supplementary Figs 24 to 28). Prevalence in cancer samples indicates the percentage of samples from our dataset of 7,042 cancers in which the signature contributed significant number of somatic mutations. For most signatures, significant number of mutations in a sample is defined as more than 100 substitutions or more than 25% of all mutations in that sample.
Experimental Crystal data  Hydrogen-bond geometry (A ˚, ). (i) x; y À 1; z; (ii) Àx þ 1; y þ 1 2 ; Àz þ 1; (iii) Àx þ 2; y À 1 2 ; Àz þ 1. Data collection: APEX2  (Bruker, 2012) ; cell refinement: SAINT  (Bruker, 2012) ; data reduction: SAINT; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and PLATON  (Spek, 2009) ; software used to prepare material for publication: publCIF  (Westrip, 2010). Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: KP2453).
Computing details Data collection: APEX2  (Bruker, 2012) ; cell refinement: SAINT  (Bruker, 2012) ; data reduction: SAINT (Bruker, 2012); program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and PLATON  (Spek, 2009) ; software used to prepare material for publication: publCIF  (Westrip, 2010). where P = (F o 2 + 2F c 2 )/3 (Δ/σ) max < 0. 001 Δρ max = 0. 13 e Å -3 Δρ min = -0. 19 e Å -3 Absolute structure: The absolute configuration was assigned to agree with that of its precusor at the chiral center C2.
In parallel with transit studies, in the next decade, direct imaging is expected to provide insight about hot, young planets at large separations from their parent stari. e. gaseous planets newly formed in the outer regions of their planetary disc and not (yet?) migrated in-or planets formed closer in and then kicked out through dynamical interactions with the other planets in the disc. The first spectrum of a hot, giant planet, at a projected separation of 38 AU from its host star, was observed with the ESO Very Large Telescope (VLT)/NACO by  Janson et al. (2010). Photometric data in the NIR were obtained for a couple of similar planets  (Currie et al. 2011; Barman et al. 2011). Spectroscopy in the wavelength range of YJHK-band will start soon with dedicated instruments on VLT (SPHERE) and Gemini (GPI).
2005)  and Hubble NICMOS  (Swain et al. 2009b). The spectral simulations and data interpretations, were performed by different teams, using radiative-transfer models and spectral retrieval methods. All the teams agree that water vapour and the presence of a stratosphere are a good baseline to fit the data. Models including data from NICMOS, conclude the best fit is obtainable with a combination of H 2 O, CH 4 , CO and CO 2. Top left: best fit by  Burrows et al. (2007). Top right: best solutions retrieved by  Griffith and Tinetti (Griffith and Tinetti 2010; Swain et al. 2009b). Bottom: best solutions retrieved by  Madhusudhan and Seager (2009)  2009b;  Madhusudhan and Seager 2009; Lee et al. 2012; Line et al. 2012). In Fig. 7b , we show the photometric and spectroscopic data relative to the planet HD209458b collected by different teams using, Spitzer IRAC/IRS/MIPS  (Knutson et al.
As in the case of Titan or the giant planets in our Solar System, haze opacities may warm up the atmospheric layer where they absorb the stellar photons and induce the formation of a stratosphere. Vertical temperature inversion has been proposed by  Burrows et al. (2007)  and then by other teams  (Swain et al. 2009a (Swain et al. , 2009b;; Madhusudhan and Seager 2009; Lee et al. 2012 ) to explain the available data for HD209458b. However, data at higher spectral resolution are needed to confirm this interpretation. Thermal inversions have been proposed for other planets, for which only a handful of secondary eclipse, IR, photometric data points are available. In these other cases, the claims are unsupported by the observations, which allow for a large number of degenerate solutions. Fortney et al.
Status of current databases for hot temperatures The modelling of planetary and exoplanetary atmospheres requires extensive opacity databases. High-temperature spectroscopic data cannot be extrapolated from low temperature atmospheric database such as the most currently used HITRAN  (Rothman et al. 2013) and GEISA (Jacquinet-Husson et al. 2011). Such extrapolations fail to reproduce high J rovibrational transitions and hot band transitions involving highly excited vibrational levels. The question of the opacity calculations at high temperature is discussed, in particular, by  Sharp and Burrows (2007) ,  Tinetti et al. (2012a)  and  Tennyson and Yurchenko (2012). Several databases already provide transitions line lists at high temperature (including positions and absorption The dashed vertical lines indicate the positions of maximum absorption for the different molecules.
We used high temperature line lists BT2  (Barber et al. 2006)  for water, BYTe  (Yurchenko et al. 2011)  for ammonia,  Neale et al. (1996)  coefficients for H + 3 and HITEMP  (Rothman et al. 2010)  for CO and CO 2. The other molecules were simulated using HITRAN 2008  (Rothman et al. 2013 ) line list cross sections) of a series of molecules of astrophysical and planetological relevance (BT2 for water  (Barber et al. 2006; Fig. 13 ), CDSD-4000 for CO 2  (Tashkun and Perevalov 2011) , as well as some hot spectra for NH 3  (Hargreaves et al. 2011; Yurchenko et al. 2011; Zobov et al. 2011; Fig. 13) , HCN  (Harris et al. 2008; Mellau 2011 ) and CH 4  (Nassar and Bernath 2003; Thiévin et al. 2008; Hargreaves et al. 2012; Yurchenko et al. , in prep. ) ). Several of these line lists are assembled in the databases EXOMOL  (Tennyson and Yurchenko 2012)  and HITEMP  (Rothman et al.
2010). Most of these databases will be soon available through the common portal built within the Framework of the VAMDC (Virtual Atomic and Molecular Data Centre) European Network  (Dubernet et al. 2010). At present, accurate molecular models are still missing to generate complete high temperature line lists for polyatomic absorbers, such as small hydrocarbons as CH 4 , C 2 H 2 , C 2 H 4 or C 2 H 6 and the C-containing transient species (C 2 H, CH 2 , C 3 , C 4 ) which dominate the opacity of Brown Dwarfs, exoplanets and AGBs and play a primary role in the physical-chemistry of their outer atmospheres. We list them here: CH 4 -After water, methane is one of the most important opacity sources in the layers of exoplanets  (Sudarsky et al. 2000; Swain et al. 2008 Swain et al. , 2009a Swain et al.
Introduction Citation is the reference text and recognition of article information. Generally, each article has a reference section at the end including all references cited in that article. Each reference is called a citation. The frequency of citations to an especial article by other articles means citation count. In addition, citation index as a type of bibliographic database traces the references in a published article. Citation index shows that how many times an article has been cited with other articles. In other words, one can find that which later article cites which earlier articles. Citations are applied to measure the importance of information contained in an article and the effect of a journal in related area of research activity and publications. The first citation index for published articles in scientific journals is Science Citation Index (SCI), which is founded by Eugene Garfield's Institute for Scientific Information (ISI, previously known as Eugene Garfield Associates Inc.
) in 1960  (Nigam & Nigam, 2012. ). Then, it was developed to produce the Arts and Humanities Citation Index (AHCI) and the Social Sciences Citation Index (SSCI). The SSCI is one of the first databases developed on the Dialog system in 1972. Small (1973)  develops his efforts on Co-Citation analysis as a self-organizing classification mechanism namely "Research Reviews". Garner, Lunin, and Baker (1967)  describes that the worldwide citation network has graphical nature. Giles, Bollacker, and Lawrence (1998)  introduce the autonomous citation indexing, which can provide the automatic algorithmic extraction and classify the citations for any digital scientific and academic articles. Subsequently, several citation indexing services are created to automate citation indexing including Google Scholar, EBSCOhost, Institute for Scientific Information (ISI) and Elsevier (Scopus).
Among all citation indexing services, ISI, has dominated the citation indexing career. ISI is a part of Thomson Reuters, which publishes the citation indexes in form of compact discs and print format. One can access to the products of ISI through the website with the name 'Web of Science' (WOS). It is possible to access seven databases in WOS including: Social Sciences Citation Index (SSCI), Index Chemicus, Science Citation Index (SCI), Arts & Humanities Citation Index (A&HCI), Current Chemical Reactions, Conference Proceedings Citation Index: Science and Conference Proceedings Citation Index: Social Science and Humanities.
2) The coverage of database is not complete since Thomson Scientific excludes some specific types of sources from the denominator. As an example, books (as a source for citations) are excluded from the database. Citations from journals which are not indexed in the ISI are not considered in IF calculation  (Khan & Hegde, 2009). Falagas, Kouranos, Arencibia-Jorge, and Karageorgopoulos (2008)  assert that one of the major shortcomings of journal IF is considering only "citable" papers mainly original papers and reviews. 3) IF could not be an appropriate measure because it is an arithmetic mean of number of citations to each paper and the arithmetic mean is not an appropriate measure (Joint Committee on Quantitative Assessment of Research 2008). IF does not consider the actual quality of research articles, their impressiveness, or the long-term impact of journals.
8) IF is not relevant in some disciplines such as engineering, where the main scientific outputs are technical reports, conference proceedings and patents. Regarding the literature, IF is not a relevant measure because books constitute the most important publications in literature, which cite other books. 9) The number of citations from journals in less universal languages and less-developed countries is understated, since only ISI database are utilized to define the IF. It is argued that the simple methodology applied in the calculation of IF provides editors with the opportunity to use different practices in order to inflate the impact factor of their journals  (Garfield, 1996; Hemmingsson, Skjennald, & Edgren, 2002; The PLoS Medicine Editors, 2006). Reviewers may request from the authors to expand the article's citation before publishing the articles.
It should be independent from the length and number of the papers published in the journal. In addition, it should consider essential advances in the area over short term or incremental contributions. IF should have relative stability in a year to year basis. The most important is that IF should evaluate the impact of a journal during a long period since some articles are still cited after 10, 20, or even 50 years from the date of publication. In addition, we should consider the warnings by Garfield against using to compare different IF and scientific field. Beside the IF we can consider other internet databases to calculate the IF of journals or articles which is collaborating with Elsevier. The journal IF is an indicator of journal performance, and as such many of the suggestions for performance indicators could equally be used to the application of journal IF  (Greenwood, 2007).
Investigating the TAR/peptide interactions Fluorescence monitoring of the peptide binding to cTAR Equilibrium binding experiments aimed at determining the apparent association constant, K obs , of the complexes between cTAR and Tat peptides were performed by adding small aliquots (2. 5-5 ml) of the peptide stock solution to 10 nM cTAR-3 0 -Fl. Steady-state fluorescence anisotropy (with excitation at 480 nm) of each data point was monitored at 20 C using a T-format SLM 8000 spectrofluorometer equipped with a thermostated cell holder. A home-made device ensured the automatic rotation of the excitation polarizer. The light emitted by the reaction mixture was monitored through high-pass filters (Kodak, 520 nm). The best estimate of K obs was obtained by fitting the fluorescence data to Equation (1)  (30) , which assumes that one molecule of cTAR can bind n molecules of peptide on independent and equivalent binding sites: S ¼ S 0 + S t À S 0 nN t Â 1+ P t +nN t ð Þ K obs À ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1+ P t +nN t ð Þ K obs ð Þ 2 À4P t nN t K 2 obs q 2K obs 0 @ 1 A ð1Þ where S o and S t are the initial and final anisotropy, respectively, N t the oligonucleotide concentration and P t the protein concentration.
All reported concentrations of the reagents correspond to those after mixing. Emission spectra and kinetic traces were recorded with a Fluoro-Max 3 spectrofluorometer (Jobin Yvon Instruments) equipped with a thermostated cell holder. All fluorescence intensities were corrected for buffer emission and lamp fluctuations. Non-linear least square fits were performed with the Levenberg-Marquardt algorithm using the Origin software (Microcal). Numerical resolution of the kinetic parameters was performed by fitting simultaneously the set of progress curves with the Dynafit software (BioKin Ltd. )  (48). Numerical tests (Kolmogorov, Durbin-Wattson, Tukey statistics) and the reduced 2 values were used to evaluate goodness of fit.
I 0 and I f stand for the fluorescence intensities of TMR-5 0 -cTAR-3 0 -Fl in its free form and in the final ED, respectively. The I f , I 0 and t 0 were determined independently. For most of the Tat(44-61) peptides, the fast component k obs1 linearly varies with [dTAR], whereas k obs2 shows a hyperbolic dependence on [dTAR]. This suggests that the various peptides likely promote the formation of an intermediate complex (IC) that further converts into the final ED, in a two-step reaction scheme (Scheme 1) similar to that of the native peptide  (31) : where k ass is the second-order association rate constant for the formation of IC, and k diss and k 2 are the first-order rate constants for the dissociation of IC and for its conversion into ED, respectively. The values of the different parameters (Table  2 ) were determined using the Dynafit numerical resolution software  (48) , which allows to simultaneously fit the experimental progress curves obtained at different dTAR concentrations.
The ability of these Tat proteins to restore RTion in HIV-1 Dtat viruses was evaluated in natural endogenous RTion assays, comparing HIV-1 viruses transcomplemented with Tat proteins mutated in the (44-57) domain relatively to viruses transcomplemented with the wild-type Tat protein. Interestingly, a perfect correlation was observed between the efficiency of Tat mutants to promote RTion and their ability to promote fast association and dissociation of the IC (Table  2 ). A dramatic decrease in RTion efficiency was seen with mutation of Tyr47 in Ala and replacement of pairs of basic residues by Ala, whereas in contrast, replacement of Gln54 with Ala or replacement of Tyr47 with Trp had no effect on Tatpromoted RTion. Taken together, our data show that the efficiency of the Tat peptides to promote cTAR/dTAR annealing is associated with their ability to favor highly dynamic interactions between the complementary oligonucleotides to nucleate the IC.
ACKNOWLEDGEMENTS The authors acknowledge  Anny Wund ,  Fanny Courte ,  Roman Storchak ,  Pauline Re´my  and  Florian Moser  for their technical help. FUNDING  Agence Nationale de la Recherche  [ ANR-10-BLAN-1529  to Y. M. ] and  Agence Nationale de Recherches sur le Sida et les he´patites virales  (to O. M. , J. G. and Y. M. ). Funding for open access charges:  Centre National de la Recherche Scientifique (CNRS)  and  University of Strasbourg.
Background: Drug-related adverse events remain an important cause of morbidity and mortality and impose huge burden on healthcare costs. Routinely collected electronic healthcare data give a good snapshot of how drugs are being used in 'real-world' settings. Objective: To describe a strategy that identifies potentially drug-induced acute myocardial infarction (AMI) from a large international healthcare data network. Methods: Post-marketing safety surveillance was conducted in seven population-based healthcare databases in three countries (Denmark, Italy, and the Netherlands) using anonymised demographic, clinical, and prescription/dispensing data representing 21,171,291 individuals with 154,474,063 person-years of follow-up in the period 1996-2010. Primary care physicians' medical records and administrative claims containing reimbursements for filled prescriptions, laboratory tests, and hospitalisations were evaluated using a three-tier triage system of detection, filtering, and substantiation that generated a list of drugs potentially associated with AMI.
While the advantages of automated surveillance are obvious, there are growing concerns that such data mining may generate more signals than can be followed up effectively with currently available resources. This concern is not entirely unfounded, considering that the annual volume of reports received in spontaneous reporting systems (SRS), database systems primarily designed for signal detection, has become enormous and unmanageable. [10, 11]  The problem is likely to be worse with the use of EHR data which have been intended for other purposes and which can be mined for associations without routine human evaluation of potential alternative explanations. Detection of safety signals is only the initial step in the long and complex process of post-marketing safety surveillance. The evaluation of a signal may take years, from the earliest suspicion of a potential risk to an established mechanism of causation and fully understood phenomenon.
Data Sources Identification of 'prime suspects' was performed in seven databases of the EU-ADR network  [8]  for the period 1996-2010: (1) Health Search/CSD LPD (HSD, Italy); (2) Interdisciplinary Processing of Clinical Information (IPCI, Netherlands); (3) Pedianet (Italy); (4) PHARMO Network (PHARMO, Netherlands); (5) Aarhus University Hospital Database (Aarhus, Denmark); (6) Lombardy database (Lombardy, Italy); and (7) Tuscany database (Tuscany, Italy). HSD, IPCI, and Pedianet are primary care/general practitioner (GP) databases, where clinical information and drug prescriptions are recorded. Aarhus, PHARMO, Lombardy, and Tuscany are comprehensive record-linkage systems where drug dispensing data are linked to registries containing hospitalisation and other services. Table  1  provides an overview of the characteristics of each database.
All of the databases in EU-ADR have been widely used for pharmacoepidemiologic research, have well-developed safeguard mechanisms ensuring patient data protection, and have been validated for a variety of drug exposures and clinical outcomes. [17, 18, 19, 20, 21, 22, 23]  Most healthcare services, including pharmaceutical services, are provided for, or subsidised by, the state in Italy and Denmark and covered by obligatory health insurance in the Netherlands and turnover is low. In all of the countries with GP databases, GPs function as 'gatekeepers' of the healthcare system. A more detailed description of the database network can be found in earlier publications. [8, 24]  Healthcare data used in this study represent anonymised demographic and healthcare information from 21,171,291 individuals with 154,474,063 person-years of follow-up.
Distributed Data Processing A distributed database network approach was chosen in EU-ADR, allowing database custodians to maintain local control of their data, while reaching the goal of sharing data in a standardised manner. Input data files are created locally and are subsequently managed by purpose-built software called Jerboa ß , written entirely in Java TM to ensure that it will run in a wide variety of computational environments. The software queries patient-level data in the different databases, which are later aggregated, de-identified and sent in encrypted format to a central repository for evaluation and further analysis. This repository is managed by the Department of Medical Informatics at Erasmus Medical Center in the Netherlands, the project's coordinating centre.
Identification and Validation of Cases of Acute Myocardial Infarction Each of the databases in the EU-ADR network has unique characteristics depending on its primary objective and local function (i. e. administrative claims or medical records) and contains medical information coded according to different (natural) languages and disease terminologies. Potential cases of AMI were identified using search queries that utilised three disease coding terminologies: (1) International Classification of Primary Care (ICPC) for IPCI; (2) International Classification of Diseases 9th revision-Clinical Modification (ICD-9CM) for ARS, HSD, Lombardy, and PHARMO; and (3) ICD-10th revision for Aarhus. To extract the same event across databases, these different terminologies were mapped using the Unified Medical Language System, a biomedical terminology integration system handling more than 150 terminologies.
The mapping ensured that AMI was described using a common language. We identified AMI from the databases using an iterative process that included harmonising definitions based on clinical criteria established from literature, using diagnosis codes and free text as well as laboratory findings when available. We inspected differences in event ascertainment by comparing data queries and benchmarking age-specific and standardised incidence rates of the events (direct standardisation was carried out using the WHO World Standard Population). The incidence rates we obtained in EU-ADR are consistent with what has been cited in previous literature. The multi-step process of terminology mapping, harmonisation and benchmarking for the data extraction for AMI (and for four other events) has been described in more detail in earlier publications.