dataesr/scientific-paragraphs-categorization huggingface dataset:
Study populations and data Studies were conducted in six countries where the effect of screening programs has been small, if present at all, and showing substantial variability in CC incidence i. e. , high-risk populations in Africa (Morocco), Asia (India) and South America (Brazil, Peru), and intermediate-risk populations in Asia (Thailand and the Philippines). Eligible cases were residents in predefined study areas who had been admitted to local hospital(s) with incident, histologically confirmed CC. Controls were cytologically normal women who had been admitted to the same hospitals as CC cases. They were frequency-matched to cases by 5-year age group. Patients with gynecological diseases and other diseases potentially related to known risk factors for CC were not eligible as controls. Face-to-face interviews were conducted in the hospital by trained interviewers using a standardized questionnaire.
Background The evaluation of Connected Health Devices (CHD) raises questions with regard to both public health and the protection of individual privacy. The answers to these questions will, without doubt, have an impact on the industrial and economic success of the development of CHD. During a scientific symposium, held in France at the end of 2015, open to the general public, more than 500 participants including professionals from the pharmaceutical industry, the information technology industry and the healthcare system debated the need for a technological and ethical validation of CHD. These debates were chaired by the Vice President of the National Medical Council, and the Director of Public Relations and Research of the national commission for data protection (CNIL). This dual appraisal, involving society at large and the professional world, brought to light and pinpointed not only the immense potential of CHD, but also the obstacles to their development and solutions that could be implemented.
Going further on the way of a security policy analysis, we must also take into account the "worse scenario": an event which has "obviously" a low probability of occurring but whose consequences could be disastrous. For example, today some companies selling cars are developing connected devices which measure your alcohol level through breath-analysers and may prevent you to start the car if you have drunk too much. This could be effectively a good thing to reduce car accidents. Imagine that a new law on prohibition appears like between 1920 to 1933 in the US and that the connected breath-analysers already exist, people could have asked to have it permanently connected to know if they are consuming alcohol. Imagine what could have been the possibility offered to the Nazis to track people in 1939 through the database if they were already available.
However, CHD providers must be conscious that technological development in re-identification of deidentified patient data has become a major policy concern. As reported by Lee Tien  [17] , only in the past few years we have begun to realize how difficult it is to truly deidentify data, given the enormous amount of information about people that is publicly available to data-miners, including hospital discharge summary databases. Modern re-identification techniques do not depend on personally identifiable information -any information that distinguishes one person from another can be useful. Researchers recently used modern techniques to re-identify supposedly anonymized genetic samples -determining not just the names of some of the people who submitted the sample in the first place but also their entire families.
This auto-certification and a posteriori control process would constitute a guarantee while preserving the rapidity of authorization; it would require two essential elements: a standard and control measures. The standard is fundamental in that each CHD manufacturer must have full knowledge of what commitments he has to make. The good or poor behaviour of the manufacturer will be measured against this standard, which will be established by all of the actors in the field, by the enterprises, of course, but in collaboration with healthcare professionals and associations of users of the healthcare system. A focus group will be set up at the national level to lay the foundations for this work by drafting recommendations. These will constitute the minimal prerequisites that must be respected with regard to data processing by the CHD with healthcare applications.
The recommendations will then be submitted to the National Medical Council, and the national commission for data protection (CNIL) in France for approval. Contact will be made with European institutions to envisage their extension to different countries in the European Union. With regard to control measures and sanctions, they have already been provided for in the French law relative to the protection of personal data in the context of missions assigned to the CNIL, which is an administrative authority. In terms of personnel, the human resources of the CNIL are already insufficient to handle all of the tasks assigned to it, and the exponential development of CHD may overwhelm the departments that deliver the authorizations. The consequence would be an increase in delays, the proportion of which would become completely intolerable.
Histology Following sacrifice of mice, small intestines or lungs were removed and fixed immediately in 10% neutral-buffered formalin in PBS for 24 hours, embedded in paraffin and cut into 4 μm or 5 μm sections. After deparaffinization, sections were stained with hematoxylin and eosin (HE) before scanning with a light microscope (Nanozoomer2. 0, Hamamatsu). All images were obtained at X20 magnification and analyzed with NDPview software (Hamamatsu).
Antibodies and flow cytometry Single-cell suspensions were prepared from the lungs that were mechanically disrupted using collagenase IV (Sigma-Aldrich, St. Quentin Fallavier, France) as previously described  [39]. Red blood cells were lysed using ACK buffer (Gibco). Cell-surface molecule staining, transcription factor and cytokine intracellular staining were performed as previously described  [40]. Flow cytometry data were collected with the LSR Fortessa (BD Biosciences) and the results were analyzed with FlowJo software (v10; Tree Star).
The paired-end reads were assembled with FLASH  [45]  (default parameters). Quality trimming and filtering (quality and length based) have been realized with QIIME (v1. 8)  [46]. Chimeric sequences have been predicted de novo with uchime  [47]  which is integrated in the usearch6. 1. QIIME pipeline version 1. 9, 97% de novo OTUs were picked using vsearch algorithm (version 2. 0. 3). RDP classifier trained on Green-Genes database (version 13. 8, august 2013)  [48]  was used for the taxonomic OTU annotation. A second level of sequence filtering based on OTU read counts (OTU read count> 0. 005% of total read count) was performed as recommended in  [49]. After quality filtering, a total 2,481,960 assembled reads, 31024 ± 8488 / sample, were included for downstream analyses. For alpha-and beta-diversity analyses, samples were rarefied to 15,000 sequences per sample.
Alpha-diversity (that measures diversity within samples) was assessed using rarefaction curves for richness (Observed OTUs), and evenness (Shannon index). Beta-diversity analysis (that measures diversity between samples) was performed on weighted Unifrac distances. Raw counts were normalized with the DESeq2 Bioconductor package normalization function  [50]. Normalized counts were converted to log2(CPM), counts per million (voom function, limma package)  [51]. Paired statistical analysis of the transformed data was performed with limma (block factor: mouse). Benjamini-Hochberg method  [52]  was used for p. value adjustment for multiple testing, (FDR threshold: 0. 05). An additional filter was used to filter significant OTU/taxa: at least 3 non-zero counts, in at least one of the groups considered in the contrast.
Statistical analysis Data were expressed as means ± S. D or means ± SEM. Student's t tests were used to analyze group differences. These analyses were conducted using PRISM 6. 04 (GraphPad). In addition, body weight and temperature kinetics (D1 to D10) were analyzed using linear mixed models for repeated measures adjusted by baseline value (D0) using SAS 9. 3. Values of p<0. 05 were considered significant.
Introduction In temperate latitudes, sheep have a seasonal reproductive behaviour. On an annual scale, they alternate between a period of sexual activity and a period of sexual rest, with no behavioural or ovarian activity in the ewe  (Hafez, 1952 ) † E-mail: balasse@mnhn. fr a Present address: Labex ARCHIMEDE, program IA-ANR-11-LABX-0032-01, UMR 5140, Archéologie des Sociétés Méditerranéennes, Université Paul Valéry, Montpellier 3, 390 avenue de Pérols, F-34970 Lattes, France. b Present address: Institut Català de Paleoecologia Humana i Evolució Social (IPHES), C/Marcel•lí Domingo s/n, Campus Sescelades URV (Edifici W3), 43007 Tarragona, Spain. Animal  (2017 ), 11:12, pp 2229 -2236 © The Animal Consortium 2017. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.
The modern reference sets The ROU is composed of the lower M2 from sheep from a Shetland cross, raised on a working farm on the island of Rousay in Orkney (Figure  2 ). They provide a reference for April/May births  (Balasse et al. , 2012b). The CAR is composed of five M2s from Préalpes du Sud ewes raised on an experimental farm in southern France (Digne, Alpes de Haute Provence, Figure  2 ). They provide references for late January/ early February births and September births  (Blaise and Balasse, 2011 ). An additional reference set was introduced to this study, composed of three sheep from a Ouessant × Landes de Bretagne cross, raised on the island of Kemenez (KMZ; Molène, Finistère) (Figure  2 ). Uncontrolled reproduction leads to lambing which occur almost exclusively in February. The specimens composing the data set were retrieved from the semi-articulated skeletons of individuals who had died of natural causes, collected on the shoreline of the island in 2013 and 2014.
Analytical protocol Enamel samples were drilled  (Balasse et al. , 2003) , on the posterior (M2), middle (lower M3) or anterior lobe (upper M3). The archaeological enamel powders were pre-treated for elimination of diagenetic carbonates following  Tornero et al. (2013). Isotope ratios were measured on a DeltaVAdvantage MS interfaced with a Kiel IV device (Thermo Scientific, Bremen, Germany). The analytical precision within each run, estimated from repeated analyses of our laboratory carbonate standard (Marbre LM, normalized to NBS19) was lower than 0. 06‰ (n = 8). The modelling procedure was applied to the data sets from KMZ, CTD, CR, HPWN, KH, SKB, PBORD, HVA and ALS (Table  1 ) following  Balasse et al. (2012b). The data set from Cheia (CHE)  (Tornero et al. , 2013)  was revised in order to correct the estimation of the location of the enamel root junction in teeth where mineralization was incomplete.
A difference of 6. 2 months (0. 52 in x 0 /X ratios, i. e. 52% of a year) is observed between the KMZ and the CAR sheep born 7 months apart; the KMZ sheep (February births) yielded slightly higher x 0 /X ratios than those measured in the CAR sheep (late January/early February). A slight disagreement Figure  3  Results from normalization of the position in the M2 crown of the highest δ 18 O value (x 0 ), using the period (X) of the cycle. Comparison of the archaeological data sets with the modern reference sets. For the Rousay reference, the rectangles indicate the whole variability (light grey) and 1 SD around the mean (1σ; dark grey). Data: see Tables  1  and 2   Sheep birth distribution in prehistoric herds appears between the KMZ (February) and ROU (late April/ early May) data sets, separated on average by only 1 month when a larger difference would be expected.
The season of birth The season of birth is determined by comparison with the modern M2 reference set (Figure  3 ). The slight disagreement between the KMZ and ROU data sets might suggest interbreed variability in the timing of tooth development, which must be confirmed using larger data sets. Caution is required when interpreting these ratios on a monthly scale. At Cheia, Bercy and Collet-Redon, sheep births started in late winter and occurred throughout the spring to early summer. The out-of-season birth at Cheia occurred in late autumn. The assemblage from Cheia, on which analysis was conducted both on the M2 and the M3  (Tornero et al. , 2013) , may be used to fix the period of birth in the M3 data set, until strong reference sets are obtained for the M3. On all sites from the central/southeastern Europe group, sheep births occurred from late winter to early summer, possibly with a slightly earlier onset at Alsónyék (Figure  4 ).
Acknowledgements The authors thank  Allison Sheridan  and the  National Museums Scotland  for permission to sample the Skara Brae sheep, and  Anett Osztás  and  István Zalai-Gaál  who led the excavations at  Alsónyék-Bátaszék. The authors also thank Soizic and  David Cuisnier  from the  Ferme insulaire de Kemenez  for providing access to their property and sheep. Over the last 12 years, this work has been supported by an  ECLIPSE II  project  2004-2005  (dir. A. T. and M. B. ) for the study of  Knap of Howar, Holm of Papa Westray, Bercy  and the Carmejane sheep; by funds from the  SRA Provence-Alpes-Côte d'Azur  to  E. B. for the study of Collet-Redon; by a  NERC  project ( NER/ B/S/2003/00223 , dir. I. Mainland) for the study of the Rousay sheep; the 'SIANHE'  ERC Starting Grant project  ( GA 202881 , dir.
Acquisition and Measurements Ground reaction forces and CoP data were obtained from the force platform. Surface EMG activity of TA and Sol was recorded using bipolar Ag-AgCl electrodes via wireless preamplifiers (Zero-wire, Aurion, Milan, Italy). Electrode sites and preparation was performed according to the SENIAM protocol  (Merletti and Hermens, 2000). EMG raw traces were bandpass filtered (10-500 Hz) with a second order Butterworth no-lag filter. Force platform and EMG data were digitized with an analog to digital converter at a sampling frequency of 1000 Hz and saved on a PC for off-line analysis. The mediolateral (ML) CoP instantaneous position curve was used to determine the onset of GI (t0), first foot off (FO1) and FC, in addition to the second foot off (FO2; Figure  2 ). The instant of t0 was determined as the instant when the ML CoP trace deviated 2 standard deviations from its baseline value.
, 2009a,b)  and elderly subjects  (Chong et al. , 2009)  have been already documented to have deficits in braking the downward acceleration of the CoM during single-stance. It is thought that such locomotive symptoms are due to supraspinal complications affecting the central command responsible for the proper generation of APA  (Rocchi et al. , 2012)  and step execution  (Demain et al. , 2014). In this study, a population of healthy young subjects was tested and the effects were obtained by mechanically constraining the ankle joint. Based on our data alone it is not possible to state whether constraining the stance leg further deteriorates the central command of patients which could worsen balance control and the active braking of gait. For instance,  Chen et al. (2015)  have shown that wearing an anterior (flexible) AFO improves lateral CoM displacement in stroke patients during upright stance.
Introduction The European Clinical Research Infrastructure Network, ECRIN, is a non-profit organisation which aims to facilitate multi-national clinical research across Europe. It does that by providing a wide variety of tools and services, as well as informational support, and by implementing programmes promoting greater consistency and quality in clinical trial management  [1]. Since 2013 ECRIN has been one of several 'ERIC's, i. e. a European Research Infrastructure Consortium, funded by direct contributions from the governments of the ECRIN member countries (which are not restricted to member states of the EU e the European Union). ECRIN first developed, however, as an infrastructure project within the EU's centrally funded research programmes, Framework Programmes (FP) 6 and 7. In 2009, within FP7, an ECRIN workgroup charged with developing support for data management within clinical trials units carried out a survey of information technology (IT) and data management practices in non-commercial trial units across Europe.
This confirmed the anecdotal evidence, of considerable heterogeneity in both systems and practices. Many different software products for data management were being used in clinical trials units, and these included many proprietary, locally developed solutions rather than professional products  [2]. Deficits in the quality of data management were also observed in some units, including missing SOPs (standard operating procedures) and limited validation and change management, of both underlying systems and individual trial databases. Most academic centres are constrained by limited human and financial resources and, historically, had sometimes found it difficult to provide levels of IT and data management which were fully and formally compliant with Good Clinical Practice (GCP), especially in the context of complex international trials.
Feedback from the audits in 2014 generated a further review process in 2015, but this time the version that emerged (v3. 0) exhibited a relatively small level of consolidation, with the 139 standards becoming 129. Much of the explanatory material was extended, however, because of: a) A variety of requests for further clarification and guidance, for instance about system validation, and b) The increased use by trials units of distributed IT infrastructures, e. g. the growing use of externally hosted database systems. It was necessary to clarify how the standards could be applied to both externally and locally hosted services. Version 3. 0 of the standards can be downloaded at  [6]. Further reviews are likely in the future but we hope changes in the standards themselves will be relatively minor, though the explanatory material may continue to expand.
Results The current small number of ECRIN certified trials units, after just two completed rounds of audits (n ¼ 10) and the still limited number of new multinational clinical trials supported by ECRIN (e. g. 16 in 2016), means evaluation of the impact of the program is confined to relatively local or short acting effects. Nevertheless, it is possible to already identify three areas where such effects are visible. The first is concerned with the development and dissemination of the ECRIN data centre standards, the second with the certification process itself, and the third is the effect on the units that have gone through certification.
The certification process itself can be a powerful incentive to get these issues resolved. Successful certification brings with it affiliate membership of ECRIN, through a framework contract with the certified data centre. This in turn makes it much easier to include the unit (as a linked third party) in Horizon 2020 bids and other funding initiatives where ECRIN is a partner. Until recently, this has been of relatively limited importance, partly because certification has been limited to only a few units and partly because the framework contracts only recently started to be implemented. As a consequence, certified units have only been involved in a few EU FP7/H2020-funded projects so far (6 ECRIN studies supported by 3 certified data centres), but the hope and expectation is that this will increase in the coming years due the framework contracts between ECRIN and the certified data centres.
Registration Another approach to identifying quality is by using selfassessment and self-reporting, against a pre-defined set of criteria. This is the method used in the UK, within its registration scheme for non-commercial clinical trial units. Registration brings both reputational and financial rewards, so the trials units are highly motivated to seek registered status. Within the registration process, an international review panel assesses compliance with the stipulated competencies every 5 years. In a recent report, improvements in IT systems and hosting of clinical trial databases were claimed as a result of the registration process  [14]. ECRIN considered a similar methodology for promoting quality but decided that the reliance on self-reporting, though a good initial step and certainly cheaper to implement than an audit based system, would give insufficient credibility to the certification decision.
Acknowledgements The authors wish to thank the external auditors who have or are currently supporting the  ECRIN Data Centre Certification Programme : Michael Wittenberg ,  Christian Ruckes ,  Jochen Dreß  (Germany),  Catherine Mai-Trang Pham (Sweden) ,  Michael Faherty (Ireland) ,  François Gueyffier ,  Catherine Cornu ,  Christine Kubiak ,  Gustavo Sabino  (France),  Ana Pais ,  Carlos Domingues  (Portugal),  Nancy de Bremaeker (Luxemburg) ,  Steve Canham (UK) ,  Teresa Bordoni  (Italy),  Marta Pavia ,  Sara Munoz  (Spain), Istv an Richter (Hungary),  Michael Scharfe  (Switzerland).
Background: MEDLINE is the most widely used medical bibliographic database in the world. Most of its citations are in English and this can be an obstacle for some researchers to access the information the database contains. We created a multilingual query builder to facilitate access to the PubMed subset using a language other than English. The aim of our study was to assess the impact of this multilingual query builder on the quality of PubMed queries for non-native English speaking physicians and medical researchers. Methods: A randomised controlled study was conducted among French speaking general practice residents. We designed a multi-lingual query builder to facilitate information retrieval, based on available MeSH translations and providing users with both an interface and a controlled vocabulary in their own language.
Background Evidence based medicine is increasingly encouraged in medical practice and decision-making, which requires evidence based on valid research. MEDLINE, created by the US National Library of Medicine (NLM), is the most widely used medical bibliographic database in the world. It is the largest component of PubMed, which is the largest free online database of biomedical journal citations and abstracts. PubMed currently contains 26,415,890 citations from 5,650 indexed journals from 81 countries and in 60 languages. Each PubMed record is indexed with the NLM's controlled vocabulary, the Medical Subject Headings (MeSH)  [1]. More than 82% of PubMed citations are in English and this can be an obstacle for some researchers to access the information the database contains  [2]. Nevertheless, some tools are, in fact, available to help non-native-English speakers to access PubMed references written in their native language: i.
e. BabelMeSH  [3, 4] , Patient, Intervention, Comparison, Outcome (PICO) Linguist  [4]  and LiSSa  [5]. Although some of these tools have demonstrated a high level of precision and coverage  [5] , they can only permit limited access to available evidence. Recent research has also confirmed the lack of skills to perform a literature search among physicians: they not only are unable to master the specific querying process of medical databases but also feel uneasy in performing research  [6]. The English used in the PubMed querying process might possibly explain some of the difficulties. Therefore, we were prompted to create a multilingual query builder to facilitate access to the PubMed subset using a language other than English (e. g. French, German, Spanish, or Norwegian), with an advanced multifunctional system.