dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We would like to thank the  GO Consortium  for funding the renal ontology development workshop. We thank the following people for their advice and comments on newly created GO terms:  Professor Helen Skaer ,  Dr. Barry Denholm  and  Dr. Stephanie Bunt  for comments relating to renal development in insects;  Professor Peter Scambler ,  Professor Sanjay Jain  and  Professor Jamie Davies  for comments relating to mammalian renal development,  Professor Peter Vize  and  Dr. Elizabeth Jones  for comments relating to Xenopus renal development. We would also like to thank  Professor Hans Baelde  for allowing us to use his original DN differential gene expression dataset for reanalysis with  GO-Elite and Ontologizer  and for providing feedback on the reanalysis results.
ASW is supported by  MRC  grant  [MR/L2744/1 ]. CJM was supported by the Director,  Office of Science, Office of Basic Energy Sciences , of the  U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. SRT was supported by  VPH NoE [EU FP7 , Grant  23920 ]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
, were from November 18 th 2009 and those for creating Figures 2, 3 and 4 were from March 19 th 2012. Gene Association Files (GO annotation datasets) were down- loaded from ftp://ftp. ebi. ac. uk/pub/databases/GO/goa/old/ HUMAN/and ftp://ftp. ebi. ac. uk/pub/contrib/goa/ ReferenceGenome/20120319/. File versions used for the 2012, 'post-annotation dataset' analysis reported in Tables S2-S9 in File S1 and Tables 2 and 3 were Gene Ontology revision 4. 1180 (March 20 th 2012) and annotation file 'gene_association. goa_human. gz' (March 19 th 2012). File versions used for the 2009, 'pre-annotation dataset' analysis reported in Tables S2-S9 in File S1 and Table 3, were Gene Ontology revision 4. 548 (March 5 th 2009) and annotation file 'gene_association. goa_human. 72. gz' (March 5 th 2009).
In parallel, the working parties performed a systematic literature search of their topic with the appropriate key words using Medline/Pubmed and the Cochrane database, as well as their own files. The evidence level (EL) was graded according to the 2011 Oxford Centre for Evidence-Based Medicine (http://www. cebm. net/index. aspx?o=5653). Provisional update guideline statements were then posted on a weblog. Discussions and exchange of the literature evidence among the working party members was then performed on the weblog. The working parties then met in Lille on the 15th-16th of November 2012 to agree on the statements. Consensus was defined as agreement by N 80% of participants, termed a Consensus Statement and numbered for convenience in the document. This paper is the product of work by gastroenterologists, infectious disease experts and pediatricians.
Risk factors for developing an opportunistic infection Predisposing factors not only lower the patient's resistance to opportunistic infection, but enable the infection to develop and progress to an extent that is not otherwise seen. 3 Only few data are available regarding risks factors for developing an opportunistic infection. In a recent study infection-related hospitalisations were independently associated with age, co-morbidity, malnutrition, total parenteral nutrition, and bowel surgery. 6 Information was therefore also collected from patients with rheumatological disease and from the general population. We have defined two categories of risk: those that are external to the patient (immunomodulator therapy, exposure to pathogens, or geographic clustering) and those that are inherent to the patient (age, comorbidity and malnutrition).
8 Corticosteroids are dose dependently linked to increased risk of both serious 9,10 and non-serious infections (NSI) in rheumatoid arthritis (RA). 11 Relative risks for NSIs in one study were 1. 10 and 1. 85 with prednisolone dosed at b 5 mg/d and N 20 mg/d respectively. 11 In the CORRONA registry the RR for all infectious events was increased with N 10 mg/d (RR 1. 30) whilst any dose increased the risk of opportunistic infection (RR 1. 67). 12 There are no precise data in the IBD population that identify a dose associated with increased risk of infection. The risk of post-operative infections has been linked to concurrent use of corticosteroids in IBD patients undergoing elective surgery. 13 Both corticosteroids and anti-TNF therapy independently, and more significantly ECCO Statement OI 2A Patients with IBD should not be routinely considered to have altered immunocompetence [EL5] per se, despite evidence of impaired innate mucosal immunity.
Acknowledgement The following National Representatives and Additional Reviewers who participated in the 2nd online voting round of the  OI Update: National Representatives: Austria: Novacek Gottfried Czech Republic: Bortlik Martin, Douda Tomas Denmark: Dahlerup Jens F. Finland: Manninen Pia Germany: Kucharzik Torsten Sturm Andreas Greece: Epameinondas Tsianos Israel: Odes Selwyn Italy: Gionchetti Paolo Latvia: Pokrotnieks, Juris Norway: Prytz Berset Ingrid Romania: Diculescu Mihai Mircea Second European consensus on prevention, diagnosis and management of infections in IBD Serbia: Jojic Njegica Sweden: Strid Hans Turkey: Celik Aykut Ferhat UK: Irving Peter Additional Reviewers: Austria: Papay Pavol Germany: Rogler, Gerhard Germany: Baumgart Daniel Israel: Kopylov Uri Italy: Guidi Luisa Italy: Papa Alfredo Spain: Lopez-Sanroman Antonio Ukraine: Golovchenko Oleksandr UK: Sebastian, Shaji UK: Gupta Arun.
This latter encompasses many aspects such as making the timeline easier to read, identifying correlations between events or producing conclusions from knowledge contained in the timeline (Criterion 3 in Table  1 ). For the majority of existing approaches, solutions are provided to automatically extract events and construct the timeline. Chen et al. (2003)  introduced a set of automated extractors to collect events and store them in a canonical database, which allows to quickly generate a temporal ordered sequence of events. These automatic extractors, a widely used concept, can also generate the timeline  (Olsson and Boldt, 2009; Gudhjonsson, 2010; Hargreaves and Patterson, 2012). However, current tools extract data in its raw form without a good understanding of the meaning of footprints, which makes their analysis more difficult.
log2timeline uses various sources including Windows logs, history of various web browsers, log files of other software resources, etc. Multiple heterogeneous sources require a consistent representation of the events in order to process them. The majority of approaches propose their own model for event representation. The most commonly used event features include the date and time of the event (an instant or an interval when the duration of events is included) and information about the nature of the event. Regarding the timeline analysis, few solutions have been proposed. The FORE approach attempts to identify event correlations by connecting events with causal or effect links. In  Gladyshev and Patel (2004) , the authors try to perform the event reconstruction by representing the system behaviour as a finite state machine.
Unlike conventional approaches, the SADFC approach uses techniques from knowledge management, semantic web and data mining at various phases of the investigation process. Issues related to knowledge management and ontology are two central elements of this approach as they deal with a large part of the mentioned challenges. According to  Gruber (1993) , "an ontology is an explicit, formal specification of a shared conceptualization". Thus, ontology allows to represent knowledge generated during an investigation (knowledge about footprints, events, objects etc. ). Ontology provides several advantages such as the possibility to use automatic processes to reason on knowledge thanks to its formal and explicit nature, the availability of rich semantics to represent knowledge (richer than databases due to its sophisticated semantic concepts  (Martinez-Cruz et al.
Subject, object, event and footprint A crime scene is a space where a set of events E ¼ {e 1 ,e 2 ,…,e i } takes place. An event is a single action occurring at a given time and lasting a certain duration. An event may be the drafting of a document, the reading of a webpage or a conversation via instant messaging software. Subject. During its life cycle, an event involves subjects. Let S be the set containing subjects covering human actors and processes (e. g. Firefox web browser, Windows operating system, etc. ), a subject x2S corresponds to an entity involved in one or more events e2E and is defined by x ¼ {a2A s jx a s a} where: A s is a set containing all the attributes which can be used to describe a subject. A subject attribute may be the first name and the last name of a person, the identifier of a web session, the name of a Windows session, etc.
g. a registry key is an object composed of several attributes such as its value and its key name. This registry key is also an attribute of the object representing the database containing all keys of the system. Event. Each event takes place in a time-interval defined by a start time and an end time. These boundaries define the life cycle of the event. The use of a time interval allows to represent the notion of uncertainty  (Liebig et al. , 1999). For example, when the start time of an event cannot be determined accurately, the use of a time-interval allows to approximate it. The use of intervals requires the introduction of a specific algebra (e. g. to order events). In our work, the Allen algebra, illustrated in the Table  2 , is used  (Allen, 1983). The authors of this paper are aware of the problems caused by temporal heterogeneity and anti-forensics techniques on the quality and the accuracy of timestamps (granularity, timestamps offset and alteration, timezone, etc).
a f is the relation used to link a footprint with the attributes of A f used to described it. Footprints are the only available information to define past events and can be used by investigators to reconstruct the events which happened during an incident. However, the imperfect and incomplete nature of the footprints can lead to produce approximate results. It is therefore not always possible to determine which event is associated with a given footprint. In addition, it is not always possible to fully reconstruct an event from a footprint. Thus, a footprint can be used to identify one or several features: The temporal features of an event. For example, a footprint extracted from the table moz_formhistory (database FormHistory of the web browser Firefox) can be used to establish the time at which a form field has been filled.
A relation between an event and an object. For example, a footprint extracted from the table moz_historyvisits (database Places of the web browser Firefox) can be used to link an event representing the visit of a webpage to this webpage. A relation between an event and a subject. For example, all footprints produced by the web browser Firefox are stored in a folder named with the profile name of the user. This allows to link each Firefox event to the user designated by this name. The features of an object. For example, a footprint extracted from the table moz_places (database Places) can be used to determine the URL and the title of a webpage. The features of a subject. For example, a footprint extracted from the table moz_historyvisits (database Places) can be used to determine the session identifier of a user.
CORRELATION BETWEEN TRAITS We looked at total and genetic correlations between the three traits: infection success, within-host growth, and sporulation capacity. We found a positive correlation between within-host growth and sporulation capacity (Table  3 -r = 0. 59, p = 2. 15 10 -14 ), but no significant correlation between infection success and sporulation capacity (r = -0. 09, p = 0. 295) or the growth within the host (r = 0. 0077, p = 0. 93). Surprisingly all total correlations implicating infection success turned out to be negative when calculated between strains (infection success vs. fungal fitness: r = -0. 38, p = 0. 13, infection success vs. sporulation capacity: r = -0. 56, p = 0. 02, and infection success vs. within-host growth: r = -0. 52, p = 0. 03). When the variety Maratelli was removed from the dataset (Table  S3 ) the correlations between infection success and sporulation capacity and between infection success and within-host growth turned to be negative as well (r = -0.
VARIETIES WITH CONTRASTED RESISTANCE LEVELS All three traits were significantly dependent on plant varieties, confirming that these three varieties have different levels of resistance to fungal infection (Figure  1 ). Interestingly, no variety performed better than the others for all measured traits. CO39 was better at controlling the fungal within-host growth and the sporulation capacity than Ariete and Maratelli, while Ariete was better at limiting infection success (Table  1 ). However, Maratelli was by far the less resistant on all traits. Two main conclusions Data used (log-transformed values) corresponded to the experimental units (pots) retained for measuring the sporulation capacity and fungal fitness and that had no missing data (136 points). Total, within-variety and between-strain correlations were calculated separately with "fungal strain" and "plant variety" as grouping variables.
CORRELATION BETWEEN TRAITS In this experiment, we measured three traits at different steps of the infection process, but are these traits independent or all correlated and just different ways to measure a more global trait? We found a positive correlation between within-host growth and sporulation capacity (Table  3 ). This shows that the bigger the lesion, the more spores are produced, an expected result already observed in previous studies (reviewed  in Lannou, 2012). It would be interesting to study this relationship in more details, and determine if the number of spores produced only depends on the lesion surface or if the number of spores produced per mm 2 can also vary. Unfortunately, in this dataset we only have average measures of lesion size and number of spores per lesion, estimating the number of spores per mm 2 would therefore potentially be subjected to multiple biases (confounding variables).
Interestingly, in these five studies (all performed on different biological systems), only two observed substantial and significant G×G interactions. Salvaudon et al. (2007)  observed G×G interactions between Hyaloperonospora arabidopsis and Arabidopsis thaliana on both traits they measured. Pariaud et al. (2012)  observed G×G interactions between latent period and spore production capacity in the wheat-Puccinia triticina system. Besides the fact that the existence of G×G interactions may obviously depend on the pathosystem considered, this also underlines that such interactions are very difficult to detect. This could be due to a lack of statistical power, to masking effects such as the presence of very susceptible hosts in the dataset, high migration rates or high levels of host local diversity (precluding parasite's specialization).
ACKNOWLEDGMENTS We thank  S. Fellous  and anonymous reviewers for fruitful discussion. This project was supported by grants from  Agropolis Fondation  (under the reference ID BIOFIS  1001-001 ),  SPE department of INRA and Comité Technique Permanent de la Sélection des Plantes Cultivées and Ministère de l'Agriculture et de la Pêche  (Contract:  C2008-27-riz Programme 227 -Sous Action 12 ).
Introduction In any form of communication it important to understand what others are talking about and in science it is essential for data to be reported in a form that allows others to repeat, verify and apply the determinations. Unfortunately, that has not always the case with enzyme activity and kinetic data, because insufficient experimental details have been provided. An idea of the nature of the difficulties can be obtained from enzyme properties and kinetics databases, such as BRENDA (http://www. brenda-enzymes. org) and SABIO-RK (http://sabio. villa-bosch. de) (Schomburg et al. , 2014; Wittig et al. , 2014). It is not uncommon to find that older values for activity were determined at 'room tempera ture' or in phosphate buffer, pH 7. 2, with no indication of the buffer concentration or the counter ion used.
The STRENDA commission The Standards for Reporting Enzyme Data (STRENDA) commission (http://www. beilstein-institut. de/en/projects/strenda) was set up in 2003, as a result of an initiative of the Beilstein-Institut (http://www. beilstein-institut. de;  Kettner and Hicks, 2005; Apweiler et al. , 2005) , in order to address these problems. A series of meetings on 'Experimental Standard Conditions of Enzyme Characterizations' (ESCEC) has been held at which experts discussed possibilities for improvement of reporting enzyme data. Their conclusions emphasised the urgent need for recommendations for the standardisation of data reporting in this area, and that such standards should be independent of the organism being studied and intended application of the data. The task of the STRENDA commission was to investigate how this could be achieved.
The present composition of the commission is listed on its website (http://www. beilstein-institut. de/en/projects/ strenda), where the proceedings of the previous ESCEC meet ings can also be found. Membership is open for additional scientists willing to help in the work and input from the scientific community is welcomed. The objective of the STRENDA Commission is to provide a framework for ensuring that enzyme functional data are recorded with adequate detail of the assay conditions and reliability. This aim is not to tell people how to assay enzymes or what conditions they must use but simply to ensure that they provide sufficient information.
The CAS (Chemical Abstracts Service) number of the compound should allow its identification through the free Common Chemistry utility (http://www. commonchemistry. org). Alternatively several databases provide alternative names that have been used for individual compounds together with their IUPAC names (http://pubchem. ncbi. nlm. nih. gov/; http://www. chemspider. com/; http://www. ebi. ac. uk/chebi/; http://www. genome. jp/kegg/). A common problem with com pounds that exist in more than one isomeric form is the failure to indicate which form was used. The question of whether the enzyme under study has been modified in any way is important since such modifications may affect its behaviour. It is common to find that proteolysed preparations are used, either by design or accident, with the assumption that if the enzyme preparation has activity, it must be satisfactory.
Forms and checklists Few people enjoy filling out forms. In fact some would prefer a visit to the dentist to having to do so. Nevertheless, it is important to collect the data in tabular form if they are to be made easily accessible and also to provide checklists for authors, and journals to ensure that the necessary data have been provided. A problem is that although it is relatively easy to list what data one would like to have, it becomes more convoluted and quasi-legalistic when put on a form in terms of information fields to be completed. The nastier and more complicated the form, the more the resistance one might expect from the user. The design of such a data deposition form has been a major preoccupation of the STRENDA Commission and it has undergone many revisions before the current on-line form that is that is planned to be released in the first half of 2014.
Currently, on the STRENDA website a prototype of the productive version is provided for further comments and suggestions for improvement (http://www. beilstein-institut. de/en/projects/strenda;  Apweiler et al. , 2010). Over 30 international journals (listed on the STRENDA website) have, so far, encouraged adherence to the STRENDA guidelines and it is hoped those working in the field will see the advantage of following them in reporting their own data. It is not the function of the STRENDA Commission to force scientists to use the form before their data can be published, rather it is to be hoped that they will come to appreciate the value of doing so.
Dissemination As well as collecting information, it is important to make it readily and freely accessible to everyone who may want to use it. That involves creating a database. This can be a real service because it is often difficult and time-consuming to find such data from the literature. Naturally, it is reasonable to ask whether we need yet another database. There are many databases that duplicate each other, with each claiming to offer some advantage over those already extant, although the only apparent advantage often appears to be that of allowing the publication of yet another database paper. Enzyme activity and kinetic data can be found elsewhere (http://www. brenda-enzymes. org; http:// sabio. villa-bosch. de/), but the uniqueness of this approach is that it intends to provide the data together with the conditions under which they were determined to allow others to duplicate or apply it.
Extensions Biochemists may have different reasons for determining enzyme data. Industrial enzymologists may be particularly interested in behaviour at elevated temperatures, whereas ease of assay may be a prime concern of others. This may involve using non-physiological substrates, working under conditions far removed from those occurring within the cell or adding 'unnatural' components to the assay mixture. Systems biologists would like the data to be collected under standard conditions that approximate to those pertaining in the tissue, cell or organelle they wish to model. However, even a brief survey of the literature will indicate that this has been far from the case. Even with what is apparently the same enzyme, different laboratories often assay under different conditions and the assay conditions used for different enzymes in the same metabolic pathway can differ markedly.
We develop an agent-based model of strategic link formation in Research and Development (R&D) networks. Empirical evidence has shown that the growth of these networks is driven by mechanisms which are both endogenous to the system (that is, depending on existing alliances patterns) and exogenous (that is, driven by an exploratory search for newcomer firms). Extant research to date has not investigated both mechanisms simultaneously in a comparative manner. To overcome this limitation, we develop a general modeling framework to shed light on the relative importance of these two mechanisms. We test our model against a comprehensive dataset, listing cross-country and cross-sectoral R&D alliances from 1984 to 2009. Our results show that by fitting only three macroscopic properties of the network topology, this framework is able to reproduce a number of micro-level measures, including the distributions of degree, local clustering, path length and component size, and the emergence of network clusters.
However, neither the network endogeneity nor the exogenous partner selection, taken independently, are able to explain the topology of observed R&D networks. Endogenous mechanisms alone would lead to more and more centralized network structures over time, which we do not observe in reality  6. On the other hand, exogenous mechanisms alone would lead to more regular networks topologies, which we do not observe neither. Inspired by these observations, here we introduce an agent-based model that includes and allows to modulate the weight of both endogenous and exogenous mechanisms for alliance formation. The model aims at reproducing the main global properties and a set of microscopic measures (including degree, local clustering and path length distributions) of real R&D networks. To this purpose, we test the model against one of the most comprehensive R&D alliance dataset available nowadays, consisting of a time-stamped list of global R&D alliances in the period 1984-2009.