dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: The authors would like to thank the entire  AETIONOMY consortium  for valuable discussions. They would also like to thank  David de Graaf ,  William Hayes  and  Natalie Catlett  ( Selventa Inc. , Cambridge, MA, USA ),  Magali Haas  ( Orion Bionetworks, New York, NY, USA ),  Tim Clark  ( Harvard University, Cambridge, MA, USA ) and  Keith Elliston  (CEO of the tranSMART foundation, http://transmartfoundation. org) for inspiring discussions. This work has received support from the  EU/EFPIA Innovative Medicines Initiative Joint Undertaking AETIONOMY  Grant No. 115568.
Decentralized openness SmartKeepers is designed to support the full range of real life situations that emerge from the paradigm shift introduced by coworking. For this purpose, the network architecture of SmartKeepers is highly flexible and supports various network topologies. Communication between users is supported by the communication between the Keep-Boxes. Each user is identified by the URI of his FOAF profile. Each user is hosted on a single box. However a box can host several users. This model enables the decoupling from the user and the hosting of her data. SmartKeepers grants elasticity in two ways: User relation: Relations between users evolve over time. This is a standard feature of social networks. In traditional centralized networks such a Facebook, this relation is represented by a value in a database.
RWW defines performatives to access and modify data over the Web. SmartKeepers utilizes notifications in order to keep users updated of the news from their network. Notifications are implemented upon the Pingback protocol and its related ontology. Data: Every service of SmartKeepers, such as calendar, group management, message board, file exchange, has an underlying data format defined by a standard ontology. FOAF is used to describe a person. Message stream uses the SIOC ontology to describe users' posts and comments. Calendar is based upon NCAL  4  , contact management is backed by NCO  5. Security: Security is a key service of SmartKeepers. WebID and Web Access Control are the two standards implemented to harness security. We detail the usage of these standards to implement secure services in the next section.
This central thread holds together the showcase of three different aspects of the SmartKeepers system as follows. Signing up and adding friends: At the first stage, we showcase the creation of a new user named Paul on a KeepBox. This includes the browser-side generation and storage of the user certificate. Paul is able to use enterprise-ready services (calendar, mail,. ) but also to search for new friends in the decentralized on-site architecture. He ultimately adds Alice (on-site box) and Carol (remote box) as friends. Collaborative agenda and pingbacks: Users then enter a collaborative task with their friends. Paul suggest a meeting with both Alice and Carol, which are notified using the pingback ontology and its service. In their calendar module, Alice, Paul and Carol view the newly created event.
Background: The aim was to describe a strategy for recruitment of healthy volunteers (HV) to a randomized trial that assessed the efficacy of different telephone techniques to assist HV in performing cardiac massage for vital emergency. Participation in the randomized trial was not financially compensated, however HV were offered emergency first-aid training. We also studied factors associated with HV motivation and satisfaction regarding participation in the trial. Methods: Strategy for recruitment of 210 HV aged 18 to 60 years was based on: (1) the updated records of all telephone number since January 2000 of HV registered in the Rouen Clinical Investigation Centre HV database, (2) a communication campaign for the general public focussing on posters and media advertisements. Data on the recruitment, socio-demographics, motivation and satisfaction of the 210 HV were collected by anonymous self-administered questionnaire.
Results: Of the 210 HV included, 63. 3% (n = 133) were recruited from the HV database and 36. 7% (n = 77) by the communication campaign. On the one hand, the HV database enabled screening of 1315 HV, 54. 8% (n = 721) of whom were reached by phone, 55. 2% (n = 398) of these latter accepted to participate in the study and 10. 1% of the initial screening (n = 133) were finally included. One the other hand, for the 77 HV not recruited from the HV database, word-of-mouth (56. 1%) was the main means of recruitment. The male/female ratio of the 210 HV was 0. 5 and mean age 43. 5 years (Standard Deviation = 12. 4). The main motivations given for participating in the trial were to support research (87. 6%) and receive emergency first-aid training (85. 7%). Overall satisfaction with the welcome process was significantly higher for older HV (46-60 years) (adjusted odds ratio (AOR): 3.
44; 95% confidence interval (95% CI): 1. 48-7. 99), and for HV in management jobs (AOR: 4. 26; 95% CI: 1. 22-14. 87). Satisfaction with protocol management was higher for women (AOR: 2. 33; 95% CI: 1. 18-4. 60) and for older HV (46-60 years) (AOR: 4. 76; 95% CI: 1. 97-11. 52). Conclusions: Recruitment of non-compensated HV required broad screening with a primary HV database alongside word-of-mouth communication which seemed more efficient than media advertising. To enhance HV recruitment to randomized trials without financial compensation it seems crucial to provide them not only with a direct interest but also to ensure their satisfaction.
Recruitment The first stage in the recruitment process of HV was the use of the Rouen CIC database: "Logic CIC", (Oriam® eTM@V3). Logic CIC is a software programme which is specially designed by the French National Institute of Health and Medical Research (INSERM) to assist CICs in France in managing their own HV database. Healthy volunteers wishing to participate in medical research protocols are registered in this database with their demographic characteristics (last name, first name, gender, date of birth, place of birth, profession), life-style characteristics (contraception, smoking, alcoholism) and their contacts (address, telephone numbers). Our registration of HV began in January 2000 and included an overall 1500 HV at the moment of extraction. The database is updated when HVs self report any changes and when participating in a study.
The database was declared to the National Commission on Informatics and Liberty (Commission Nationale Informatique et Libertés N°1383369). Logic CIC allows selection of HVs according to their demographic and life-style characteristics, and contacts. Recruitment for the baseline randomized trial was based on extraction of the mobile and land line telephone numbers of all HV aged 18 to 60 years registered in Logic CIC on August 27, 2013. We attempted to contact by telephone all HV extracted from Logic CIC and proposed participation in the study to those HV successfully contacted. The randomized trial was presented as medical research on emergency first aid, with an emergency first-aid training session. If participation in the study was declined, the reason for refusal was requested by an open-ended question, the answer to which was retrospectively categorized as "HV not available", "no financial compensation", "other" or "reason not given".
Data collection HV were included in the study after interview at the CIC, verification of selection criteria and signature of consent. They then participated in the trial on the same day (one visit: information, inclusion, intervention). After the intervention (cardiac massage), the HV completed an anonymous self-administered questionnaire. Demographics were collected on gender, age and socio-professional category, classified as follows: without professional activity (unemployed, inactive or retired), student, farmer, bluecollar worker, employee, middle-level activity (mid-level profession, shop keeper, craftsman or business owner) and manager/intellectual profession. Information's source of the study's conduct were collected: word-of-mouth, posters, Rouen University Hospital website, newspapers, radio, social networks, and others (several answers possible).
The HV not registered in Logic CIC database were those recruited by these other communication strategies. Participation in other clinical trials and motivation to participate in the present study were: to support research, learn about emergency first-aid, update knowledge of emergency first-aid, curiosity, interest in medical research results, protocol without risk (no needle, no drugs etc. ), and other choices (several answers possible). Prospects for HV participation were also collected: participation in a new medical research study, recommendations for relatives to participate in a new medical research study, interest in possibility of consulting the research protocols proposed by the CIC on the Internet and to register directly online (5-item Likert scale: "strongly disagree" to "completely agree").
Statistical analysis Age was categorized into three groups for analyses (18-30, 31-45 and 46-60 years). The age cut-offs were chosen to form approximately equal periods of time (about 15 years for each age group). In our study, the median was high for the satisfaction scores related to overall welcome and protocol management. Satisfaction scores were dichotomized into two classes according to the median: not fully satisfied HV (score below or equal to the median) and fully satisfied HV (score above the median). Percentages were used to describe HV responses. There were some missing data (i. e. no response to items in the self-administered questionnaire by the 210 HV): 7 on gender, 8 on age, 10 on socio-professional category, 4 on overall satisfaction with the welcome process and 10 on satisfaction with protocol management.
Chi-square tests were conducted to determine differences in categorical data and chi-square tests for linear trend (extended Mantel-Haenszel) to analyse the variables according to age. We performed two multivariate analyses to identify factors associated with full satisfaction using logistic regression for each of the two satisfaction scores (Overall welcome satisfaction and Protocol management satisfaction) as dependant variables (two classes: not fully satisfied or fully satisfied HV). Factors with a p value lower than 0. 30 (gender, age, socio-professional category and registration status in Logic CIC database) were included in the multivariate analysis and a p value lower than 0. 05 was considered to be significant. Statistical analyses were performed using StatCalc Epi Info® 7.
Recruitment process The telephone numbers of 1315 HV aged 18 to 60 years were extracted from Logic CIC database. The male/female ratio was 1. 3. The number of HV at each stage of recruitment is shown in the flow chart (Figure  1 ). Of the 1315 HV, 133 were finally recruited for the randomized trial (10. 1%, 95% CI: 8. 5 -11. 7). Table  1  shows the demographics of HV registered in Logic CIC database at the three main stages of recruitment. Greater age of HV was associated with easier contact by telephone (358/776, 46. 1% of under 30 year old HV; 112/180, 62. 2% of 31 to 45 year old HV and 250/354, 70. 6% of 46 to 60 year old HV; p < 0. 001). Overall, 133 of the 210 HV (63. 3%) included in the intervention study were recruited by this process. Five HV did not report their registration in the anonymous self-administered questionnaire.
Of the 82 HV who had declared they were not registered in Logic CIC database, the main source of information was word-of-mouth (46/82 HV, 56. 1%). Sources of information according to gender and age are reported in Table  2. Word-of-mouth was most frequently reported by younger HV (22/29, 75. 9% of under 30 year old HV; 9/23, 39. 1% of 31 to 45 year old HV and 13/27, 48. 2% of 46 to 60 year old HV; p = 0. 02).
Satisfaction of HV included Reponses for each satisfaction item on the self-administered questionnaire are shown in Figure  2. Median, minimum, and maximum satisfaction scores for the overall welcome were 15, 10 and 16 (out of 16) respectively. This satisfaction score differed significantly according to age (p = 0. 001) but not to gender or registration in Logic CIC database (Table  3 ). After multivariate analysis (Table  4 ), HV aged 46-60 years and managers/intellectual professions were significantly more satisfied with the overall welcome, with respectively AOR: 3. 44, 95% CI [1. 48 -7. 99] and AOR: 4. 26, 95% CI  [1. 22 -14. 87 ]. Median, minimum, and maximum satisfaction scores for protocol management were 19, 11 and 20 (ouf of 20) respectively. This satisfaction score differed significantly according to gender (p = 0.
Recruitment process of HV The HV database of the CIC of Rouen (Logic CIC) was large and allowed extraction of telephone numbers of 1315 HV aged 18 to 60 years. The rate of recruitment by this HV database was 10. 1% and represented 63. 3% of the total sample required demonstrating the value of such a database for recruitment of HV. Greater age of HV was associated with easier contact by telephone. An assumption may be that young HV more likely had a mobile telephone, providing more difficulties to reach them than with a land line, because of number changes or technical problems such as regional coverage. Pitrou et al. [35]  found that French people using only mobile telephones were under 40 years and that study participants  contacted on their mobile telephone appeared to be less easily reachable than by land line.
This possible confounder is minimized because the telephone numbers provided by HV to register on Logic CIC database were the numbers which were the easiest reachable. Furthermore in our study, the main reason given by HV who had been successfully contacted for their refusal to participate was lack of availability during the proposed time slots. Logistical issues appeared as a major obstacle in recruitment, such as inability to take extra time off work for research appointments  [17]. Moreover the appointment reliability of HV was also problematic: in our study, 35. 1% of HV with an appointment did not attend and some without advance warning. Thus the study which was initially planned to last 5 days, finally required 7 days. Word-of-mouth was the main source of information for HV not recruited by database, highlighting the importance of satisfied HW to encourage their relatives to participate in the current study and so enhancing of the recruitment.
Conclusions The main strength of our study was the large sample size of HV recruited, despite absence of financial compensation. The recruitment of HV aged 18 to 60 years without financial compensation required broad screening with an essential updated HV database. Word-of-mouth appeared to be a more efficient source of information on the study than media advertising. One of the main motivations for participating was to benefit from a brief emergency first-aid training session at the end of the trial. Thus recruitment strategy of HV in randomized studies without financial compensation should be enhanced by providing direct interest to HV and ensuring HV satisfaction.
The main motivation was supporting medical research, reported by 87. 6% (184/210) of HV. Emergency first-aid training ("learning about emergency first-aid" or "updating knowledge on emergency first-aid") was reported by 85. 7% (180/210) of the HV. Supporting medical research was reported significantly more by women (54/68, 79. 4% of men and 123/135, 91. 1% of women; p = 0. 02) and by Logic CIC database registered HV (67/82, 81. 7% of unregistered HV and 117/128, 91. 4% of registered HV; p = 0. 04). Interest in medical research results was more frequently reported by older HV (18/70, 25. 7% of under 30 year old HV; 14/51, 27. 5% of 31 to 45 year old HV and 40/81, 49. 4% of 46 to 60 year old HV; p = 0. 003) and curiosity by younger HV (31/70, 44. 3% of under 30 year old HV; 14/51, 27. 5% of 31 to 45 year old HV and 21/81, 25.
87 0. 02 1. 47 0. 45 -4. 83 0. 53 Logic CIC database Unregistered  † 1. 00 1. 00 Registered 1. 35 0. 71 -2. 56 0. 36 1. 33 0. 70 -2. 53 0. 39 Missing data: 15 healthy volunteers for overall satisfaction with welcome process; 14 healthy volunteers for satisfaction with protocol management. *Adjusted Odds Ratio to others factors listed in the table, logistic regression. † Healthy volunteers unregistered in Logic CIC database were those recruited by the other communication strategies.
JASPAR (http://jaspar. genereg. net) is an openaccess database storing curated, non-redundant transcription factor (TF) binding profiles representing transcription factor binding preferences as position frequency matrices for multiple species in six taxonomic groups. For this 2016 release, we expanded the JASPAR CORE collection with 494 new TF binding profiles (315 in vertebrates, 11 in nematodes, 3 in insects, 1 in fungi and 164 in plants) and updated 59 profiles (58 in vertebrates and 1 in fungi). The introduced profiles represent an 83% expansion and 10% update when compared to the previous release. We updated the structural annotation of the TF DNA binding domains (DBDs) following a published hierarchical structural classification. In addition, we introduced 130 transcription factor flexible models trained on ChIP-seq data for vertebrates, which capture dinucleotide dependencies within TF binding sites.
The heart of the JASPAR database, the CORE collection, provides non-redundant and manually curated TF binding profiles described as PFMs and associated to TFs from species in six taxonomic groups (vertebrates, nematodes, insects, fungi, urochordates and plants). Position weight matrices (PWMs, also known as positionspecific scoring matrices) are derived from PFMs to predict TF binding sites (TFBSs) within a DNA sequence (see  (1)  for a review). These matrices represent an additive probabilistic model assuming independence between the TFBS nucleotides. A third generation of binding models, such as the transcription factor flexible models (TFFMs)  (2) , have been introduced to capture nucleotide interdependencies, which have been recurrently shown to occur within TFBSs  (3) (4) (5) (6) (7) (8).
The TFFMs represent a flexible representation of TFBSs and are based on hidden Markov models that capture dinucleotide dependencies and TFBS flexible length in a single framework  (2). TF binding models are widely used for genome analysis, and researchers benefit from a diverse array of databases that generate and/or aggregate TF binding models. Amongst the most widely used and longest maintained collections, the JASPAR database was created and persists with three guiding principles: (i) unfettered open-access for all, (ii) a manually curated non-redundant core collection and (iii) simplicity. In this report, we describe the extensive expansion and update of the CORE collection of the JASPAR database  (9) (10) (11) (12) (13). The new additions to the core collection of TF binding profiles represented as PFMs are predominantly derived from in vitro high-throughput experiments (PBM and HT-SELEX) from  (14) (15) (16).
The TF binding profiles introduced in the JASPAR 2016 release have been assessed by expert curators who have reconciled the high-throughput data with available literature support. The database provides nonredundant profiles (one profile per TF) with the exception of specific TFs which recognize TFBS in two or more distinct forms  (17) , either mediated by two distinct DBDs in the same TF or in a flexible spacing between protein-DNA contacts (e. g. SREBF1 or TFAP2A). Following the classification of TF DBDs from TFClass  (18) , we manually annotated the DBDs of the TFs stored in the JASPAR CORE collection. In addition to the core expansion, for the first time we introduce a third-generation model collection into JASPAR, featuring 130 TFFMs trained on ChIP-seq data. We accompany this release with a Ruby gem (a software module) for accessing and using JASPAR TF binding profiles, complementing our previous Perl, Python and R packages.
New TF binding profiles This sixth release of the JASPAR database provides a significant increase in the number of TF binding profiles available. As in previous releases, we manually curated profiles with independent publications for TFBSs or profiles consistent with the candidates, as described in  (12). The curated profiles were derived from PBM  (14, 16, 19, 20) , HT-SELEX  (15)  and ChIP-seq  (21)  experiments. Precisely, we introduced 553 TF binding profiles for TFs in the six taxonomic groups of the JASPAR CORE collection (Table  1 ). We provided 488 profiles for TFs which were not present in the previous release of the CORE collection. We introduced six profiles to complement profiles of TFs already present in JASPAR 2014 to address cases in which the TFs can recognize alternative sequences (e.
A TFFM-based third generation binding profile collection Classical second generation models, PWMs derived from PFMs, assume that the nucleotides within TFBSs are independent  (1). Even though such models perform well overall  (22) , it has been recurrently shown that some TFs significantly benefit from more complex models when predicting TFBSs  (2, 23, 24). We complemented the set of PFMs in the JASPAR CORE collection with TFFMs (2) which capture successive dinucleotide dependencies. The TFFMs were initialized with the JASPAR PFMs and trained on ChIP-seq data wherever possible (see Supplementary Text). Following the process used for PFMs derived from ChIPseq data in the previous JASPAR release  (13) , we curated the TFFMs by using a centrality P-value as described in  (25)  as one expects predicted TFBSs to be enriched at the position where the maximum amount of reads mapped in the ChIP-seq peaks.
We introduced 130 TFFMs in the database, corresponding to 25% of the vertebrate PFMs. For each TFFM, we provide the classical logo representation of the motifs along with the graphical representation of the motifs that convey properties of position interdependence as introduced in (2) (Figure  1 ). The centrality plot, which illustrates the enrichment for TFBSs at the ChIP-seq peak-max, is also provided (Figure  1 ). Finally, the TFFMs can be downloaded as XML files (at http://jaspar. genereg. net/html/DOWNLOAD/TFFM/) to be used through the TFFM web-application (http://cisreg. cmmt. ubc. ca/cgi-bin/ TFFM/TFFM webapp. py) or the TFFM framework API (http://cisreg. cmmt. ubc. ca/TFFM/doc/) (2).
New R/bioconductor data package and Ruby gem We provide a freely available R/Bioconductor  (27)  data package JASPAR2016 accessible at http://bioconductor. org/packages/JASPAR2016/ for data analysis using the JASPAR TF binding profiles. Moreover, the JASPAR database can be accessed through ture compares the DBD sequence of the given TF to those of homologous TFs stored in JASPAR, and infers the TF binding profiles from the best compared JASPAR homologous TFs as potentially recognized by the user's input protein sequence wherever possible (Figure  2 ).
CONCLUSIONS AND PERSPECTIVE The 2016 release of JASPAR maintains the long-term focus on providing high-quality, non-redundant TF binding profiles for the global research community. Consistent with past releases, we have (i) expanded the widely used JASPAR CORE collection, adding 494 profiles; (ii) enhanced usability, incorporating the TFClass structural classification and introducing an associated capacity to select profiles for not yet characterized TFs; (iii) expanded and updated programing tools, highlighted by a Ruby gem for JASPAR access and (iv) introduced a new collection, for the first time incorporating third generation binding profiles. Looking forward, the introduction of third generation methods may mark a significant transition for JASPAR. As TF binding data continues to expand, and we gain greater insight into each TF, advanced models that address specific TFs or TF-families may become the norm.
ACKNOWLEDGEMENTS The authors wish to thank the user community for useful input. We thank  Matthew T. Weirauch  and  Mihai Albu  for sharing the TF profile inference code implemented in the CIS-BP website, and  Roberto Solano  and  José Manuel Franco-Zorrilla  for providing their PBM data. We thank  Miroslav Hatas  for systems support and  Dora Pak  for management support to the  WWW lab.