dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments In addition to the current authors, the  WDDP study group includes: Elodie Becquey (International Food Policy Research Institute ),  Inge D Brouwer  ( Wageningen University ),  Alicia Carriquiry  ( Iowa State University ),  Melissa C Daniels  ( University of North Carolina at Chapel Hill ),  Nadia Fanou-Fogny  ( Universit é d'Abomey Calavi ),  Elaine Ferguson  ( London School of Hygiene and Tropical Medicine ),  Maria L Joseph  ( General Dynamics Information Technology ),  Marie T Ruel  ( International Food Policy Research Institute), and Liv Elin Torheim (Akershus University College ). We thank participants at the " FAO Working Group Meeting on Deriving a Global Indicator for Defining Adequacy of Women's Diets " held in Rome, 19-20 March 2014:  Catherine Leclercq  (FAO),  Queenie Mak  (FAO),  Astrid Mathiassen  [ World Food Programme  (WFP)],  Giorgia Fiorella Nicol  ò (FAO),  Verena Nowak (FAO) ,  Marian Amaka Odenigbo [International Fund for Agricultural Development (IFAD) ],  Viviana Panetti  (FAO), and  Michele Rude  (FAO).
Outcomes Reduction in morbidity or mortality including surrogate outcomes or disease transmission. Study characteristics Design: systematic reviews, defined as a review with selection criteria, and searching of at least one database. and schistosomiasis both have the peculiarity of leading to severe chronic infections years after leaving endemic regions. Strongyloidiasis may be life-threatening in immunocompromised patients, and schistosomiasis may lead to chronic and fatal complications such as cancer. The rates have significantly increased in previously non-endemic regions. 6 Schistosoma haematobium can infest bodies of water in Southern Europe where intermediate competent host Bulinus (molluscum) is present. This could theoretically lead to foci of transmission in the EU. 35 Thus, there is a need to provide evidence-based guidance for voluntary testing and treatment that will reduce morbidity and mortality in high-risk migrant populations, in order to reduce transmission and out-of-pocket and health system costs, and to prevent the transmission of the infection.
Open Access Search strategy An experienced health information specialist with expertise in systematic review searching will develop electronic literature search strategies in consultation with infectious disease working groups (see online supplementary appendix 7 for an example of a draft search strategy for one disease for one database). We will search Medline, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Epistemonikos and Cochrane Central. The literature search will be restricted to studies published from 1 January 2010 to present. Our group published migrant health guidelines based on systematic reviews in 2011,  6  and we will use these as anchoring evidence-based guidelines to supplement with new systematic review evidence. The search strategy will use a combination of indexed terms and free text words.
Data extraction We will develop a standardised extraction sheet for each condition-specific subgroup. We anticipate some consistency across groups, especially with respect to how data are extracted, but there will be unique content aspects to each disease-specific data extraction as well. Prior to data extraction, reviewers will undergo a calibration exercise to ensure consistency. Teams of two reviewers will extract data in duplicate and independently. They will compare results and resolve disagreements by discussion or with help from a third reviewer. At a minimum we will extract (1) population, intervention, comparison and outcome elements of the research questions for interventional systematic reviews; (2) databases searched; (3) number of studies included in the systematic review; and (4) results (see online supplementary appendix 8 table  1 ).
Assessing the quality and certainty of the evidence The GRADE criteria will be applied to assess the quality and certainty of evidence for the included studies. The rating is based on an assessment of (1) risk of bias (study limitation); (2) inconsistency (heterogeneity) in the direction and/or size of the estimates of effect; (3) indirectness of the body of evidence to the populations, interventions, comparisons and/or outcomes; (4) imprecisions of results (few participant/events/observations and/or wide CIs); and (5) other considerations (effect size and publication bias). The quality of evidence may be downgraded if there are serious or very serious concerns related to any of the GRADE criteria (see online supplementary appendix 8 table  3 ). All key data will be entered in the GRADEpro software.
In addition, two health economists will systematically search the National Health System Economic Evaluation Database, the Cost-Effectiveness Analysis Tuft's registry and Google Scholar databases for economic studies. A sample PRISMA flow chart is provided for economic studies for TB (see online supplementary appendix 9). The health economists will screen the results for systematic reviews and primary studies of resource use, costs or cost-effectiveness of screening and treating each of the priority infectious diseases, then independently screen the fulltext articles and assess the systematic reviews for quality using AMSTAR. Studies will not be excluded on the basis of AMSTAR scores. Data will be independently extracted from primary studies of resource use, costs or cost-effectiveness aligned with the disease group's aims, including GRADE EtD considerations around size of resource requirements, certainty of evidence of resources and cost-effectiveness favouring the intervention or comparator.
A one-page narrative summary of the economic evidence will be written, and evidence about the resource use and costs will be incorporated into the GRADE evidence profiles and summary of findings tables where appropriate. 43 s economic evidence has not previously been reported in guidelines, we will systematically search all databases from inception to June 2016. Two reviewers will independently select, appraise and extract data. In case of disagreement, we will use discussion or a third reviewer. Evidence will be summarised for each of the infectious disease conditions. We will use AMSTAR for quality assessment of systematic reviews and GRADE to appraise certainty of evidence in the primary economic evaluations. Phase 3: update systematic reviews of effectiveness We will search for, select, appraise and synthesise new prospective trials and systematic reviews to update the existing systematic reviews.
Where important gaps exist, we will address them with focused de novo systematic reviews. dIssEMInAtIon We will publish the separate systematic review in an open-access journal for public health stakeholders and/ or the GRADEpro database of evidence profiles ( dbep. gradepro. org). We will make the results available to panels of experts to use the evidence to develop international guidelines for migration. ECDC will publish a technical report, and we plan to submit a final guideline summary paper to a European clinical journal, for example, the British Medical Journal. We will use the ECDC, the International Conference on Ethnicity, Race and Migrant Health, our Campbell and Cochrane Collaboration Equity Methods website, and other social media to push out results. dIscussIon During the past 50 years, many national and some international disease detection and control, immunisation, and communicable disease prevention strategies have been successful.
Our reviews will facilitate evidence-based management of migrants with the studied infectious diseases, and will likely identify key areas for future research, and provide a framework for conducting overviews of systematic reviews on causation. We will use the evidence from these reviews to inform GRADE EtD criteria. 38-42 46 These EtD summaries will also include data on migrants' preferences, stakeholder acceptability and feasibility and health equity. These summaries will support an ECDC scientific panel in developing guidance statements on infectious diseases for newly arriving migrants to the EU/EEA. Details on GRADE EtD methods process will be published separately.
Methods An online feedback and evaluation survey was available to all GPs who completed the audit. The evaluation survey was open at the same time as the Clinical e-Audit from February 2012 until October 2013. The survey comprised multi-choice and openended questions, which used Likert scales to assess the usefulness of the audit to the clinical management of patients with URTIs; the extent to which the audit met the learning objectives; and asked participants to report on any self-reported changes made to clinical practice; as well as open-ended questions about any barriers to best practice and planned actions to overcome these barriers. Descriptive analysis of the multi-choice responses was conducted using SPSS, ver. 22 (SPSS Inc. , Chicago, IL, USA). Open-ended responses were analysed using a thematic approach and then grouped based on emerging themes and subthemes.
Since 2007, the American Medical Informatics Association emphasized the value of secondary use of medical data: "Secondary use of health data can enhance healthcare experiences for individuals, expand knowledge about disease and appropriate treatments, strengthen understanding about the effectiveness and efficiency of our healthcare systems, support public health and security goals, and aid businesses in meeting the needs of their customers"  [4]. In the oncology field, it is necessary to identify and describe incident cancer cases within a population in order to facilitate research and public health monitoring. For instance, cancer registries exhaustively record incident cases of cancer in a given territory (which correspond to all new cancer cases occurring over a geographical territory).
This task remains time consuming if it is performed manually. As early as 1998, a technical report was drawn up by the International Agency for Research on Cancer describing the methods used by different registries for establishing automated procedures to identify new cases using available data  [7]. Methods have been proposed for automatically identifying and registering cancers using structured data indexed with standard terminologies  [8] [9] [10] [11] [12]. However, many different medical specialties are contributing to record information in EHRs. As a result, within EHRs, data describing diseases are recorded according to multiple heterogeneous terminologies even for a single disease occurring in a single patient. For instance, in France, reimbursement data use the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10)  [13]  to describe diseases, whereas pathology data use either ADI-CAP (a French pathology terminology) or the 3rd edition of the International Classification of Diseases for Oncology (ICD-O-3)  [14]  and data from multidisciplinary meetings in oncology use ICD-O-3.
NCI Metathesaurus [21] The NCI Metathesaurus (NCIm) is a biomedical terminology database "that covers most terminologies used by NCI for clinical care, translational and basic research, and public information and administrative activities"  [21] , including ICD-10 and ICD-O-3. It has been built and is maintained by the NCI. Its structure and a significant part of its concepts is based on the UMLS Metathesaurus  [22]. Inside the NCIm, elements coming from different terminologies but representing the same biomedical notion are grouped into the same Concept Unique Identifier (CUI).
Evaluation of the model The National Cancer Institute provides, within the Surveillance, Epidemiology, and End Results (SEER) Program, a set of tools for ICD conversions  [29]. We used the 2014-05-08 conversion file of ICD-O-3 to ICD-9-CM, to ICD-10 (Causes of Death) and to ICD-10-CM (available at http://seer. cancer. gov/tools/conversion/) as a gold standard for evaluating how ICD-O-3 and ICD-10 could be related. Based on this file, we have rebuilt ICD-O-3 topography-morphology combinations mapped to ICD-10 codes. A 2-step evaluation was then performed: • For each combination, we queried the proposed model in order to evaluate how many branches of the diagnosis lattice were instantiated by both the ICD-10 code and the topography-morphology combination. • We tried to build mappings based on the proposed model with a simple algorithm (ICD-10 codes and topography-morphology combinations with the minimum hierarchical edge-based distance were considered as mapped) and compared it with the gold standard.
Comparison with the SEER conversion file Based on the SEER conversion file, excluding metastatic and uncertain behaviors from ICD-10 and ICD-O-3 morphologies, we were able to build 103950 mappings between an ICD-10 code and an ICD-O-3 topographymorphology combination. Due to the absence of some correspondences within the NCIm between the NCIt and ICD10 and between the NCIt and ICD-O-3, some ICD-10 and ICD-O-3 codes do not instantiate any class in the "derivative" model. As a result, 59% of these mappings could not be evaluated (because the ICD-10 code, the ICD-O-3 topography or the ICD-O-3 morphology was missing). Table  3  presents the results of the evaluation over the 42260 mappings combining codes which instantiate classes within the resulting model. The model relates 100% of the mappings through at least a diagnosis.
As a result, the obtained diagnoses subsumption lattice is a valid formal representation of diagnoses hierarchy in respect to the definitions of classes contributing to their description In order to instantiate the model, we have used the NCIm to identify links between the NCIt and the terminologies (namely, ICD-10 and the ICD-O-3 topography axis). For ICD-10, we had to add a restriction based on the semantics available within the ICD-10 classification in order to ensure that primary tumors were described according to a primary site. The resulting model could not Table  3  Comparison with the SEER conversion program according to the tumor type (hematopoietic and solid tumors) and the number of branches of the diagnosis lattice that are identified for an ICD-10 code / ICD-O-3 combination * Related in the model means that there is at least a common diagnosis inside the model that is instantiated by both the ICD-10 code and the ICD-O-3 combination ** Mappings rebuilt from the model corresponds to the mappings that we were able to rebuild automatically from the model a More than 1 branch means that there is more than one branch of the diagnosis lattice that was instantiated by both the ICD-10 code and the ICD-O-3 combination b Non unique mappings means that the topography-morphology combination was also mapped to another ICD-10 code (inconsistent with the SEER file) be instantiated completely by ICD-10 and ICD-O-3 codes for different reasons: • The NCIt completeness for describing diagnoses.
As our method relies on NCIt diagnoses, the resulting classes which were built depend on their existence within the NCIt (e. g. , C00. 1 External Lower Lip malignant neoplasm is not available within the NCIt). • The NCIm provides a way to identify common concepts using the CUI but it can be incomplete or wrong (e. g. , 20 ICD-O-3 topographies were mapped erroneously to non-anatomic concepts). However, when the codes were found, we were able to identify a common diagnosis for all cases described in the SEER conversion program and, using a simple algorithm, 42% of the SEER mappings (corresponding to codes instantiating the model) could be rebuilt from the model. Our aim is not to enable conversion between codes but to provide a machine usable and semantically integrated view over them. From this perspective, the model is consistent because it provides links between ICD-10 diagnoses and ICD-O-3 topography-morphology combinations when they exist within the SEER conversion file.
Moreover, the model describes many more possible relationships between diagnoses than the SEER conversion program does. For instance, in the latter, there is no relationship between Adenocarcinoma, NOS -Colon, NOS (C18. 9 -M8140/3) and Malignant neoplasm of rectosigmoid junction (C19. 9) whereas our model identifies successfully that they are both instances of Malignant, primary site -Large Intestine Reflexive part.
As a result, the corresponding ICD-10 codes were defined as instances of multiple diagnosis classes and 36% of SEER mappings evaluated were retrieved as being related to more than one branch in the diagnosis lattice. For instance, the ICD-10 code C18. 0 Malignant neoplasm: Caecum was mapped to the NCIt concept C9329 Malignant Cecum Neoplasm, which is related to multiple anatomic sites: Gastrointestinal System, Cecum, Colon, Intestine and Colorectal Region. As there is no relationship between Cecum, Colorectal Region and Colon within the NCIt (except that they are part of the large intestine), C18. 0 instantiates the following classes: • Malignant, primary site -Cecum Reflexive part • Malignant, primary site -Colon Reflexive part • Malignant, primary site -Colorectal Region Reflexive part Two issues can be identified: (i) Malignant Cecum Neoplasm should not have Colon as an associated anatomic site within the NCIt because Cecum is neither a part, nor a subclass of Colon, (ii) Cecum and Colon should be related to Colorectal Region.
We will apply the same approach to other resources (e. g. , SNOMED-CT and FMA) so as to enrich the "derivative" model. This future work will allow a better coverage and we will take advantage of existing links between concepts within these resources (i. e. , anatomical descriptions from the FMA) and between resources (i. e. , mappings available within the NCIm). In addition, based on this "derivative" model, algorithms for disease identification can be built. This resource can manage heterogeneity by providing an integrated view of diagnoses recorded in EHRs. As a result, algorithms based on classes of the "derivative" model can use transparently available data coded with disease classifications and focus on building consistent rules for disease identification.
Degrees from the Indian Institutes of Technology (IITs) are among the most selective in India and often constitute a passport to access top positions in the Indian corporate world. Drawing upon a rare and original dataset about one of the IITs, Henry and Ferry objectify the processes through which IITs continuously differentiate and exclude students from Scheduled Castes (SCs) and Scheduled Tribes (STs). They notably show that the categories produced by the institution actually have deep social effects and that they contribute to rigidifying class and caste boundaries on the campus. Students from modest origins are filtered out at multiple levels: after they have cracked the JEE but before they enter the IIT, during their schooling at the IIT and at the moment when they enter the labor market.
The authors argue that a better understanding of the structure of social relationships between students on the campus might be the key to further our understanding of the link between academic (under-)achievement and social dispositions. Jivanta Schottli and Markus Pöhlmann further contribute to the renewal of the sociological analysis of the various paths to elite positions by developing a systematic analysis of the profile of the managers of top companies in India. Drawing upon a database of 111 CEOs, they revisit a study conducted in 1963-1964 by Sagar C. Jain and they argue that the evolution of Indian career patterns is similar to what is observed in many other countries. They also stress the crucial role played by education in accessing top business positions. By focusing uniquely on managers, Schottli and Pöhlmann's article offers an interesting complement to Naudet et al.
Stimuli and materials Experiments were conducted in a darkened room at the Laboratory of Visualization and Perception in the University of Minho. The stimuli consisted of binaural white-noise (frequency rate = 44. 1 KHz; intensity magnitude from 77 to 89 dB) processed in free-field and generated in Matlab1 using the MIT HRTF (head related transfer function) database  [26]. The anechoic sound was divided in several portions (from a minimum of 10 samples per second to a maximum of 17, depending on the stimulus' travelled distance) along the trajectory and each small sample was convolved with the corresponding HRIR (head related impulse response)-the time domain representation of the HRTF-for each position, thus originating a dynamic sound. This convolution of the HRIR with white-noise maintains the ITD and ILD temporally updated in order to correspond to a new spatial location along the trajectory  [27].
Stimuli were generated at 0˚elevation, allowing the observer to perceive the sound at their ear level (defined by the sound projection's configuration) and disposed in the sagittal plane 1m from participants' right shoulder. Stimuli were processed by a Realtec Intel 8280 IBA sound card and presented through a set of Etymotic ER-4P MicroPro in-ear headphones. Forty-nine looming sounds with the duration of 1 second were generated in Matlab1. As in previous studies on visual TTP  [7] , seven simulated initial distances-0. 78, 0. 85, 0. 93, 1, 1. 09, 1. 18, 1. 29 m-were combined with seven simulated constant velocities-0. 76, 0. 83, 0. 91, 1, 1. 1, 1. 2, 1. 32 m/s-resulting in 49 total theoretical TTP (TTPt): 24 that passed the subject before 1 s (passage by the participant occurred), one that stopped at the ear level precisely at 1 s, and 24 that would pass after 1 s (passage by participant did not occur).
Psychoacoustic measures Since our stimuli consisted of a broadband sound source presented in the sagittal plane, we considered that the psychoacoustic cues most relevant for analysis would be the binaural cues of ILD and ITD. In order to extract ILD, stimuli from all conditions were recorded using Bru ¨el & Kjӕr head and torso simulator (HATS) type 4128-C and Pulse Analyzer type 3560-C. These measures were divided into left and right channels using Matlab1 2013b and served as input for the analysis carried out for the extraction of the psychoacoustic variables. To calibrate each channel a stimuli corresponding to the POS for each occlusion condition was used (starting at 1 m from the observer and travelling at 1 m/s). The psychoacoustic measure extracted for each channel, was the Instantaneous Loudness in sones (according to the model of Time-Varying Sounds from Moore, Glasberg & Baer  [29] ), using Psysound3 Toolbox.
Analysis Cumulative Gaussian curves were fitted to the pooled data in order to generate distributions of the proportion of trials in which passage of the auditory stimuli was later than 1 s. We estimated two parameters, the point of subjective simultaneity (PSS) and the standard deviation (SD). PSS was defined as the point at which the TTPt value reached the 50% of "later" responses. A PSS larger than 1s (curves shifted to the right) means that the stimulus was perceived as passing earlier than what was simulated, i. e. an anticipation of passage. SD is inversely related to the slope of the function and provides information about the uncertainty underlying the participants' performance. We estimated both parameters from individual and pooled data for all participants. Parametric bootstrap  [32]  was run to obtain 95% Confidence Intervals of the two parameters of the cumulative Gaussian functions for each occlusion condition.
Acknowledgments The authors would like to thank  Professor Joan Lopez-Moliner  for the insightful contribution throughout the study. This work has been supported by  Fundacão Bial  (Grant  143/13 ),  Fundacão para a Ciência e Tecnologia  ( SFRH/ BPD/ 63393/2009; PTDC/EEA-ELC/112137/2009 ). This work was funded by  COMPETE :  POCI-01-0145-FEDER-007043  and  FCT-Fundacão para a Ciência e Tecnologia  within the Project Scope:  UID/CEC/00319/2013.