dataesr/scientific-paragraphs-categorization huggingface dataset:
The Messidor database, which contains hundreds of eye fundus images, has been publicly distributed since 2008. It was created by the Messidor project in order to evaluate automatic lesion segmentation and diabetic retinopathy grading methods. Designing, producing and maintaining such a database entails significant costs. By publicly sharing it, one hopes to bring a valuable resource to the public research community. However, the real interest and benefit of the research community is not easy to quantify. We analyse here the feedback on the Messidor database, after more than 6 years of diffusion. This analysis should apply to other similar research databases.
INTRODUCTION Public databases are precious tools for researchers. They bring the necessary data to develop and test new methods, and allow for quantitative comparisons between different approaches. The Messidor database is one of such databases. It was created within the Messidor project to evaluate different lesion segmentation methods for color eye fundus images, in the framework of diabetic retinopathy screening and diagnosis. It has been publicly distributed since 2008. Other databases are available for researchers on retinal image analysis. Concerning the segmentation of retinal blood vessels, the DRIVE  (Staal et al. (2004) ) and STARE  (Hoover et al. (2000) ) databases have become de facto standards. DRIONS-DB contains retinal images for optic nerve head segmentation benchmarking  (Carmona et al.
(2008) ). Concerning retinal lesions, like microaneurysms and exudates, several databases are available such as DIARETDB0, DIARETDB1  (Kauppi et al. (2007) ), HEI-MED  (Giancardo et al. (2012) ), ROC  (Niemeijer et al. (2010) ) and e-ophtha  (Decencière et al. (2013) ).
THE MESSIDOR DATABASE The Messidor download page 1  gives an appropriate description of the database, which we quote here: "The 1200 eye fundus color numerical images of the posterior pole for the MESSIDOR database were acquired by 3 ophthalmologic departments using a color video 3CCD camera on a Topcon TRC NW6 non-mydriatic retinograph with a 45 degree field of view. The images were captured using 8 bits per color plane at 1440*960, 2240*1488 or 2304*1536 pixels. 800 images were acquired with pupil dilation (one drop of Tropicamide at 0. 5%) and 400 without dilation. The 1200 images are packaged in 3 sets, one per ophthalmologic department. Each set is divided into 4 zipped sub sets containing each 100 images in TIFF format and an Excel file with medical diagnoses for each image. " Note that, as the description indicates, the database contains a medical diagnosis for each image, but no manual annotations on the images, such as lesions contours or position.
This is an important difference with respect to other databases, such as DIARETDB1 and e-ophtha. The download procedure asks the user to fillin the following fields: E-mail address; First Name; Last Name; Professional Interests; Country and University/Organization. An e-mail is then sent to a member of the Messidor team, who checks the validity of the request, and sends an appropriate link to the submitter. Some requests are not accepted, typically because the fields requested in the download procedure are clearly incorrectly filled. Precise statistics on refused requests are not kept, but we estimate that they represent less than 25% of the total number of requests. They are not taken into account in the statistics below. It should be noted that Messidor database users are asked to acknowledge the Messidor project partners in their related publications.
EXPERIENCE FEEDBACK ON MESSIDOR Most of the statistics on the Messidor database diffusion presented in this section are summarized in Fig. 1. People tend to underestimate support and maintenance costs associated to a publicly distributed database. For instance, given the increasing number of download requests for the Messidor database, processing these requests and related questions requires approximately one hour per week. On top of that, users ask general questions about the database -even if most answers are available in the website. Finally, hosting the database and web pages also takes resources. Another measure on the success of the database can be obtained through access statistics to the corresponding web page (see Table  2 ). Again, one can see a clear increase in web site access since 2008.
The number of visitors is approximately two times higher in 2013 than in 2011. This trend clearly appears in Fig. 1. The link between download requests or web access and the actual contribution to the research domain is not necessarily simple to apprehend. Indeed, people might download the database or consult the web site Image Anal Stereol ?? (Please use \volume):1-4 for reasons not related to public research. In order to clarify this point, we have looked into the number of citations of the Messidor database in scientific papers. The results are summarized in table 3. Interestingly, it can be seen that the Messidor database has been cited three times more often in 2013 than in 2011 -the same increase as for the number of download requests (see Fig. 1 ). Finally, if we pool the results for two of the most cited journals in the field of biomedical image processing, that is Medical Image Analysis and IEEE Transactions of Medical Imaging, we find that, since 2008, 47 papers deal with "diabetic retinopathy", and among these 10 papers cite the Messidor database.
CONCLUSION The Messidor database has been publicly distributed since 2008. It is of interest mainly for researchers in a relatively specialized domain: retinal image processing, and more specifically computerassisted diagnosis of diabetic retinopathy. In spite of this, it has gathered a large amount of citations. We have also shown that the number of web site visitors, as well as the number of download requests, seem to be correctly correlated with the number of citations, which provides a simple and convenient method to monitor the success of a database. The experience gathered by our team on the management of the Messidor database allows us to propose some recommendations for the design of future databases: -Hosting and managing the database takes resources; this point should be taken into account during the database design, in order to reduce this cost as much as possible.
-The database is typically described on a web page. This description has to be clear and complete, in order to limit the number of requests for additional information (and therefore to reduce the management cost). -The database managers should ask potential users to acknowledge the database or, better, to cite a relevant paper on the database. This simplifies the evaluation of the success of the database. -Last but not least, we have shown that an automatic validation procedure seems to be enough to treat download requests. Moreover, we believe that this study confirms the important role that databases play in medical image processing. In the case of the Messidor database, this is true in spite of the fact that the images contained in the database are progressively getting outdated. Indeed, they were acquired before 2007, and modern fundus cameras offer increasing image resolutions and sensitivities.
ACKNOWLEDGEMENTS The  Messidor  project was funded by the  French Techno-Vision program. The Messidor database and site have been hosted by Crihan (http://www. crihan. fr)since 2008. Researchers from the  Center for Mathematical Morphology , including  Adnan Rashid  and  Estelle Parra-Denis , have offered support to the Messidor database users and processed their download requests for several years.
Introduction Temporality has recently received increased attention in Natural Language Processing (NLP) and Information Retrieval (IR). Initial works have been proposed in NLP and are exhaustively summarized in  (Mani et al. , 2005). More recently, the introduction of the TempEval task  (Verhagen et al. , 2009)  in the Semantic Evaluation workshop series has clearly established the importance of time to deal with different NLP tasks. The ultimate aim of research in this area is the automatic identification of temporal expressions (timexes), events and temporal relations within a text in the TimeML format  (Pustejovsky et al. , 2005). In IR, the time dimension has also received particular attention for the past few years. According to  (Metzger, 2007) , time is one of the key five aspects that determine a document credibility besides relevance, accuracy, objectivity and coverage.
Metric TWnL TWnP TWnH Fixed-marginal κ 0. 5073 0. 5199 0. 4197 Free-marginal κ 0. 5199 0. 5199 0. 4399 Table  6 : Inter-annotator agreement. These results evidence the difficulty of the task for humans as they do not agree on a great deal of decisions. This is particularly due to the fact that the temporal dimension of synsets is judged upon their glosses and not directly on their inherent concept. For example, "dinosaur" can be classified as temporal or atemporal as its gloss any of numerous extinct terrestrial reptiles of the Mesozoic era allows both interpretations. So, we performed a new experiment based on those examples where human annotator agreement was 100%. From this dataset, we performed an inter-annotator agreement process with four annotators (three human annotators plus the classifier).
Phase 1: an environmental scan determines and assesses the issues facing various SCI stakeholders, to inform research efforts and provide comparative baselines against which to measure success. At RHI, this is primarily undertaken through literature reviews and surveys. Phase 2: generation and synthesis of knowledge facilitates studies that develop new knowledge in response to key clinical needs and the gaps expressed by SCI stakeholders. This phase involves testing hypotheses and investigating the feasibility of interventions and treatments. In this phase, RHI primarily gathers and synthesizes the existing knowledge that is essential to enable translational research, such as population health data on SCI or current health care outcomes. Phase 3: knowledge validation verifies whether the new knowledge or practice has an evidence-based justification to • Consumer engagement, focusing on involving more individuals with SCI in research • Commercialization, focusing on facilitation of increased investment in the development of innovative precommercial therapeutics, therapeutics, medical devices, and diagnostics with application to SCI.
Performance measurement and evaluation RHI has developed a performance measurement and evaluation strategy to assess the effectiveness of its praxis model. This strategy is based upon established practices,  [29] [30] [31]  and is in line with guidance from our Canadian federal government funders. 32 The theory of change elaborated within the RHI's praxis model has been summarized in the form of a logic model for the purposes of identifying results and associated performance measures. Due to the heterogeneity of activities in which the RHI engages, the logic model is designed to be simple and adaptive to all project contexts. The   At the output and immediate outcome levels, performance measurement data are collected primarily by project teams, and are aggregated in a central results database.
Following this initiative, all patients with SCI are screened by specially trained occupational therapists. The goal is to monitor closely the incidence and progression of pressure ulcers, evaluate the effectiveness of interventions such as pressure-relieving mattresses, and report on the long-term patient outcomes following discharge into the community. Furthering knowledge generation and synthesis activities for pressure ulcers, the RHI and the Ontario Neurotrauma Foundation have funded the development of the Canadian Best Practice Guidelines for prevention, assessment, and treatment of pressure ulcers in people with SCI. Additionally, the data collected in the RHSCIR study and the SCI community survey will be utilized to generate knowledge regarding pressure ulcers throughout the continuum of care.
Using data gathered through the RHSCIR, a simulation model of the continuum of care for patients with traumatic SCI was created as a novel approach to look at the access and timing of care for traumatic SCI in Canada. 43 This model will be used to evaluate the impact of new policy initiatives on patient and system outcomes, and allows RHI researchers to test different strategies for management and treatment of pressure ulcers to estimate outcomes based on certain interventions. 43 The results of these simulations will be valuable when analyzing the direct and indirect effects of pressure ulcer initiatives throughout the care continuum and optimizing future decisions for people with traumatic SCI.
Molecular Analysis of Postmortem Sample The coding regions for those genes linked to Long QT syndrome types 1, 2, 3, 5, 6, and 7 were amplified and subjected to capillary-based sequencing, as described earlier  [11] , but no mutations were identified. Amplification of the coding regions of the GPD1L gene (Refseq accession number NM 015141. 3; primer designs based on  [12] ) and subsequent sequencing revealed a heterozygous missense mutation in exon 4: c. 370A>G, p. Ile124Val (Figure  1 ).
In genetically predisposed children, fever is a potent trigger for malignant arrhythmias  [19, 20]. There is some conflicting evidence regarding the pathogenicity of the mutation reported here. I124V was initially discovered in a 5-week-old infant who died suddenly and had a negative postmortem examination  [7]. The mutation is in a highly conserved residue and, when coexpressed in vitro with SCN5A in heterologous HEK cells (Human Embryonic Kidney 293 cells), produces a significantly reduced sodium channel current  [7]. The NCBI database records the variant as likely pathogenic (http://www. ncbi. nlm. nih. gov/clinvar/? term=rs72552293; entry dated  August 18, 2011). In contrast, bioinformatic analysis of the mutation event at the protein level predicts a benign as well as a disease-causing effect, while three programmes predict no effect regarding splicing of the GPD1L gene transcript and one predicts an effect on SR protein binding sites (Table  1 ).
SR proteins can influence splice site selection  [21] [22] [23] [24] [25] , but transcript analysis of the GPD1L gene has not been undertaken in the case reported here. The c. 370A>G variant has been reported at frequencies of 0. 14% and 0. 11% in the 1000 Genomes project and GO-ESP databases, respectively. These data suggest that the variant is present in nominally unaffected individuals and so may be nonpathogenic. It is interesting that the father, who carries the same mutation, had a negative ajmaline challenge. Ajmaline is a cardiac sodium channel blocker, and 75-80% of Brugada syndrome mutation carriers can be unmasked by it  [26]. Variable clinical expression of these mutations is common, possibly due to the effect of common polymorphisms within the same gene or in SCN5A  [27]. However, the father does have a marginally prolonged QT interval.
Acknowledgments The authors are most grateful for the family's support and permission to report this tragic case. The authors thank  Dr. Tim Koelmeyer  forensic pathologist (now retired) for his foresight in referring the case for investigation. A part of the genetic testing costs and  Dr. Skinner 's salary were funded by  Cure Kids. The sponsorship is in the form of a nondirective grant;  Cure Kids  had no role in study design or any other aspect of this report.
Larval DNA was used to determine the sex using Y-specific primers (F: 5 0 -CAAAACGACAGCAGTTCC-3 0 ; and R: 5 0 -TAAACCAAGTCCGTCGCT-3 0 ). n =16 L4 W- W+ % W+ n =6 n =14 n =16 n =16 16S rRNA profiling and sequencing. The 16S rRNA gene data set consisted of Illumina MiSeq sequences targeting the V4 variable region. Detailed protocols used for 16S amplification and sequencing were previously described  26. In brief, genomic DNA from testes and ovaries was subjected to 16S rRNA amplifications using primers incorporating the Illumina adapters and a sample barcode sequence, allowing directional sequencing covering the variable region V4 (15F: 5 0 -GTGCC AGCMGCCGCGGTAA-3 0 ; and 806R: 5 0 -GGACTACHVGGGTWTCTAAT-3 0 ). PCR mixtures contained 10 ml of diluted template (1:50), 10 ml of HotMasterMix with the HotMaster Taq DNA Polymerase (5 Prime) and 5 ml of primer mix (2 mM of each primer).
We obtained a set of 2,064 Wolbachia 16S sequences from which the 253-nt-long V4 region was extracted and clustered at 99% identity, generating 115 operational taxonomic units (OTUs) covering the diversity in the Wolbachia genus. The all-versus-all mapping of the 115 OTU sequence representatives provided a lower-bound estimate of the genus' total diversity (average identity of 95. 9%, s. d. 1. 75%, median 96. 0%). We then performed the mapping of the 24,800 reads of sample G23656 against the full SILVA database, retaining only those sequences with full-length percentage identity of at least 95% with a Wolbachia 16S. The resulting 5,412 reads have a best hit other than Wolbachia at o89% identity, confirming that a fifth of the sample (21. 82%) consists of V4 fragments from Wolbachia 16S rRNA.
The percentage identity was in the 95. 3-97. 6% interval, which is fully consistent with the observed diversity in the V4 hypervariable region of the Wolbachia 16S rRNA genes (average 95. 9%, s. d. 1. 75% as reported above). The V4 sequences from sample G23656 were then clustered into OTUs at 99% identity, discarding those OTUs with o50 sequences. The resulting OTUs were merged with the Wolbachia OTUs from the SILVA and NCBI repository (generated as described above) and aligned with MUSCLE version 3. 8. 31 (ref. 29). A V4 sequence representative of the Rickettsia 16S was added as outgroup. A phylogenetic tree was then built using RAxML version 7. 4. 2 (ref. 30) with the GTRGAMMA model, bootstrapping (1,000 replicates), best maximum likelihood tree inference, and displayed with GraPhlAn (https://bitbucket.
3 ). The matches were also confirmed by BLASTN with word size of length 7. Second step: candidate reads coming from the small and large ribosomal units (16S rRNA and 23S rRNA genes) were screened out by sequence mapping against the comprehensive ribosomal sequences in the SILVA database release 111. Third step: the remaining ribosomal-free candidate reads were mapped against the full RefSeq genomic database version 60 to identify any non-Wolbachia-specific hits using BLASTN with word size of length 7. Fourth step: on the basis of the mapping results of steps 1 and 3, the final set of reads from the Wolbachia strain identified in An. gambiae (wAnga) was compiled selecting all reads showing 480% identity over 495 nt to at least one Wolbachia strain, and no hits longer than 80 nt to other organisms.
Acknowledgements We thank  S. Ilboudo ,  H. Guel ,  D. D. Soma ,  S. Sawadogo  and  E. Bilgo  for help with sample collection and dissection,  V. De Sanctis  and  R. Bertorelli  ( NGS Facility at the Centre for Integrative Biology and LaBSSAH, University of Trento ) for WGS sequencing and  D. E. Neafsey  and  L. Waldron  for helpful discussions. We thank the Broad sequencing platform for 16S sequencing. This work was funded by a  European Research Council   FP7 ERC Starting Grant  project ' Anorep ' (grant ID:  260897 ), a  Biotechnology and Biological Sciences Research Council  grant (grant  BB/I002898/1 ) and a  Wellcome Trust  grant (grant ID:  093553 ) to F. C. , by the  European Union   FP7 grant  ( PCIG13-618833 ) and startup funding at the  Centre for Integrative Biology (University of Trento ) to N.
S. , by the  EC   FP7 Networks of Excellence EVIMalaR  ( 242095 ) to E. A. L. and F. C. , and by an  ANR-11-BSV7-009-01 GIME  to E. A. L. F. C. was supported by a  NIH  grant (grant ID:  NIH   1R01AI104956-01A1 ). J. P. was supported by fellowships from  FRM  and the  Fondation Des Treilles.
Scope: The Phenol-Explorer web database (http://www. phenol-explorer. eu) was recently updated with new data on polyphenol retention due to food processing. Here, we analyze these data to investigate the effect of different variables on polyphenol content and make recommendations aimed at refining estimation of intake in epidemiological studies. Methods and results: Data on the effects of processing upon 161 polyphenols compiled for the Phenol-Explorer database were analyzed to investigate the effects of polyphenol structure, food, and process upon polyphenol loss. These were expressed as retention factors (RFs), fold changes in polyphenol content due to processing. Domestic cooking of common plant foods caused considerable losses (median RF = 0. 45-0. 70), although variability was high. Food storage caused fewer losses, regardless of food or polyphenol (median RF = 0.
Introduction Polyphenols are a large and complex family of phytochemicals whose consumption from plant foods may offer protection against cardiovascular diseases, type II diabetes, cancers, and other health benefits  [1]. Polyphenols are subdivided into numerous classes and subclasses and range from simple phenolic acids to high molecular weight compounds with up to 15 phenolic groups. The classes of greatest dietary importance are flavonoids, characterized by their three-ring skeleton, and phenolic acids, usually consumed as hydroxycinnamic and hydroxybenzoic derivatives. Phenol-Explorer (http://www. phenol-explorer. eu), a web database on polyphenols, contains detailed polyphenol compositions of over 450 foods  [2]. These data are particularly valuable for the study of associations between polyphenol intake and health and, e.
g. have been used to calculate the polyphenol intakes of cohort study subjects from dietary questionnaires  [3, 4]. Phenol-Explorer has recently been enhanced with data on the effects of food processing on the polyphenol contents of foods  [5]. Food processing often causes losses in polyphenol content, usually brought about by oxidation, enzymatic action, removal of skin or seeds, and leaching into oil or water that is then discarded  [6]. Simpler derivatives may increase in concentration upon the breakdown of others. For some widely consumed processed foods such as coffee, orange juice, and wine, experimentally determined polyphenol contents are already available. For other foods often consumed after processing, contents must be estimated based on that of the raw plant food postharvest. In these cases, the effect of processing can be accounted for by multiplying raw food polyphenol content by a retention factor (RF), a predetermined fold change in polyphenol content due to processing.
RF < 1 indicates a reduced polyphenol content in the processed food whereas RF = 1 and RF > 1 indicate full retention or an increase, respectively  [7]. To construct the food processing update, as many RFs as possible were calculated from published data, each specific to a combination of food, process, and polyphenol. This is necessary because even small variations in structure cause large changes in chemical activity, and each food matrix represents a unique physicochemical medium  [8]. Phenol-Explorer RFs were standardized by adjusting for loss of weight during processing and similar data aggregated and averaged. All data, and their sources, can be retrieved through the food processing section of the Phenol-Explorer web interface. These new data have many applications but, in particular, will allow epidemiologists to improve estimation of individual intake in cohort studies where previously only content data from raw foods was used  [9]  or RFs were used but based on one food only  [3].