dataesr/scientific-paragraphs-categorization huggingface dataset:
Data extraction Two out of three experts (YE, SB, EB) independently extracted the following information from each included systematic review: -citation details -objective of the systematic review or meta-analysis -type of databases sourced and searched and date range of database searching publication -information about the studies included in the review: publication date range, number and type of studies, country of origin, rating of the quality of the studies, details of participants and setting/context -types of exposure to working hour schedules and their duration -definition of the examined outcome and its assessment -adjustment factors -main results including the summary of the effect size estimate [risk ratio (RR), odds ratio (OR), hazard ratio (HR), or incident risk ratio] with the 95% confidence intervals (CIs) If there was a disagreement, a discussion with the third expert resolved it.
Quality assessment of the selected articles-Risk of bias To evaluate the quality of the reviews, we used AMSTAR 2 (a measurement tool for assessing systematic reviews, version 2)  (11). The sixteen items assessed were: 1: complete research question and criteria (PICO); 2: registered protocol; 3: justification of study design; 4: comprehensive literature search; 5: study selection in duplicate; 6: data extraction in duplicate; 7: justification of excluded studies; 8: description of included studies; 9: assessing the risk of bias (RoB); 10: reporting on the sources of funding for the studies included; 11: meta-analysis using appropriate statistical methods combining results; 12: meta-analysis assessing the impact of RoB; 13: interpretation/discussion of results must include risk of bias of studies; 14: discussion of heterogeneity; 15: investigation of publication bias in meta-analysis; 16: reporting conflict of interest.
Data of work schedules were reported from the workers through self-administered questionnaires or from other sources such as payment records, employment records, or a list of job titles and workplace characteristics. In the case of selfadministered questionnaires, several studies assessed exposure to shift work based on simple questions such as, "do you do shift work?" or "ever worked a night shift?" Other studies sought to distinguish permanent night shift from rotating night shift by asking the following question: "do you normally work (a) day, (b) evening, (c) night or (d) rotating shifts?" In 2011, the IARC Working Group (cancer research) published recommendations to improve exposure to shift work in a consensus report  (40). The authors notably pointed out the need to consider at least 3 h of work between midnight and 5 a.
We collected data on place of residence and telephone numbers from all trial participants and contacted them through mobile phone calls and automated short message service (SMS) to remind them to attend for dose 2 and followed up, with home visits if these were needed. At the clinic visits for dose 2, similar triage, eligibility and health checks were conducted. Participants who attended Open access more than 84 days post dose 1 were still able to receive the second dose until the study vaccination sites closed on 10 February 2021. After the study start, all individuals presenting to an ETC, peripheral health facility or an EVD testing centre were expected to have their rVSV-ZEBOV-GP and Ad26. ZEBOV, MVA-BN-Filo vaccination status confirmed as part of routine data collection, with this information being recorded in the routine Ministry of Health EVD surveillance database.
Parents are being asked to provide consent for children under 17 years and children aged 12-17 years to provide assent. Interviews are being conducted in the participant's/parent's/guardian's primary language. Exit interviews are extremely basic and rapid and simply record whether participants had any immediate concerns they wished to report. If in-person interviews are not possible, we conduct phone interviews with adult participants, with researchers reading out information from an information sheet. Interviews are conducted in French, Swahili or Nande and then translated to English. We remove personal identifiable information prior to uploading recordings to a SharePoint which is only accessible to the study team. We give each interview participant a unique ID number and delete their phone numbers from study databases immediately after use.
Open access Permissions to decrypt data are limited to key personnel. Working copies of databases such as those used by staff who are responsible for progress monitoring and statistical analysis are automatically stripped of personal identification data via a process applied by data managers at Epicentre. We anonymise interview transcripts and field notes and download them to password protected and encrypted computers for transcription and translation and analysis using NVIVO software. We keep all recordings, transcripts and field notes in locked filing cabinets at the study's Goma data centre, before moving them to the long-term archive at INRB Kinshasa. Details of database validation, data collection, coding, security, automatic, query, cleaning and safety data reconciliation can be found in the Data Management Plan.
gov ( ClinicalTrials. gov Identifier: NCT04152486). The population of North Kivu Province is considered vulnerable. Study participation is strictly voluntary. Participants can withdraw from the trial at any time. If provided, the reasons for withdrawal will be recorded. Both communities and, at the time of the outbreak, individuals were informed that study participation did not mean that other EVD infection protection and control measures should be discarded and that they should endeavour to attend for the second dose. Results of the study will be presented to relevant stakeholders in DRC, shared with WHO's technical expert groups and published in open access journals. Any research articles generating from this trial will be subject to the Publication Policy and the Authorship Guidelines agreed by all partners.
Data availability statement The rights of study subjects and partners, the sharing of data between partners and the transfer of data to external third party will be governed by the Data Sharing Agreement. Deidentified participant-level data collected in this trial will be disseminated through a FAIR-compliant data repository, such as the LSHTM Data Compass (https:// datacompass. lshtm. ac. uk/), from 6 to 60 months after the publication of the main trial results. Other study documents (eg, full protocol, statistical codes, Statistical Analytical Plan, DSMB Charter) will be available on request to Deborah Watson-Jones (corresponding author, ORCID: 0000-0001-6247-1746), Tansy Edwards (study statistician, ORCID: 0000-0002-6110-014X) or Edward Choi (study coordinator, ORCID: 0000-0002-8148-120X).
WHO WILL BE ABLE TO SEE MY INFORMATION? We will keep your study information private. Staff working on the study, including people from overseas who are responsible for checking that this study is done properly, ethics committees, and regulatory authorities, may look at your medical records. All study staff are trained in confidentiality and will not remove any document with your name on it from the study files. All of these people understand that they must keep your identity private and they won't share your name or contact details with anyone. A description of this study will be available on the internet at http://www. ClinicalTrials. gov. No information that can identify you will be on this website. You can look at this website at any time. The study results will be shared, but no personal information will be included in the published study results.
QUI POURRA CONSULTER MES INFORMATIONS ? Nous préserverons la confidentialité de vos informations de l'étude. Le personnel de l'étude, y compris des personnes en dehors du pays chargées de vérifier le bon déroulement de cette étude, les membres des comités d'éthique et les autorités réglementaires, peuvent consulter vos dossiers médicaux. Tous les membres du personnel de l'étude sont formés à maintenir la confidentialité et ne supprimeront aucun document avec votre nom dans les dossiers de l'étude. Toutes ces personnes comprennent qu'elles doivent garder votre identité confidentielle et qu'elles ne partageront votre nom ou vos coordonnées avec personne. Une description de cette étude sera disponible sur le site internet http://www. ClinicalTrials. gov. Ce site internet ne contiendra aucune information permettant de vous identifier.
Allen Family Foundation. This work was also supported by the UK Department for International Development (DFID) and Wellcome [220506/Z/20/Z] and by the European Union's Horizon 2020 research and innovation programme under grant agreement No 857935. This publication reflects only the authors' view and the European Commission is not responsible for any use that may be made of the information it contains. CEPI, Wellcome and DFID advised on the study design and protocol. Competing interests DWJ, BG and LSHTM are partners on two research consortia (EBOVAC1, EBOVAC3) with Janssen Vaccines and Prevention B. V. funded by the European Commission.
Acknowledgments The authors would like to acknowledge the technical staff of  RENECOFOR -ONF (Caroline BRUYERE ,  Geoffroy PERALS ,  Mauryne VINCENT ,  Henri JUIF ,  Didier LAGREDE  and  Yves LE VALLEGANT)  for logistical support for the sampling of selected humus. We would like to acknowledge  Anne-Cécile GREGOIRE  ( IRSN/PSN-RES/SEREX/L2EC ) for advices and part of incubation material. This work was financed by the  Agence Nationale de la Recherche  with funds allocated in the ' Investissements d'Avenir' framework program  under reference  ANR11-RSNR-0002  and the  Région Nouvelle Aquitaine. We would like to acknowledge the translation company CAUPENNE & CO for English language assistance.
Future Work The main effort is the large-scale application and evaluation of the proposed methodology. Given the effects of the COVID-19 pandemic on airport traffic but also on the discussions on twitter and other social media, we will rely on historical data (i. e. data recorded prior to the pandemic) to do our processing. This eases the requirements on real-time processing and allows us to apply additional methods, like embeddings, which can improve the accuracy of sentiment classification. Unlike the "bag of words" representation used by our methods so far, where the context plays a small role; these newer methods are able to detect similarities and hence classify unseen words which are similar to other words seen in the training set. Recurrent Neural Network (RNN) offers a memory factor that helps to consider the previous and the following words to better predict the sentiment of current words and hence to efficiently predict the sentiment of the whole sentence, which improves the accuracy of the classifier.
A mobile application (AnimApp) has been developed to study the impact of the audio-visual environment on sound perception and on quality of life around airports. The method of experience sampling has been chosen, because it captures subjective experiences as they are experienced in-situ in real life. Perceptual data on the sound as well as the visual environment are collected in addition to acoustic data. The final study will take place during spring/summer 2021 at two locations: one at Ljubljana Airport in Slovenia, the other at London Heathrow Airport in the UK with the aim of convincing more than 60 participants in each site. This experiment will probably suffer from the reduction of air traffic due to the COVID-19 pandemic, but this application can be also used in the future to collect valuable perceptual data synchronised with acoustic ones with more participants.
Finally, using social media as a means to survey people's opinions on various subjects, including quality of life and issues of noise around airports, seems to be promising and produces credible results. The process gives us insights based on existing online discussions and based on complex learning pipelines; it discovers, classifies and localises the opinions of the users. The complexity of the current processing architectures is significant but the results produced so far are promising for the future. Being able to combine those data with additional offline data and data from multiple sources (e. g. other opinion sites, land use, etc. ) could improve the quality of the insights provided into people's responses to aircraft noise, and, thus, and allow to further refine the process of aviation noise management in airport regions.
47. Wilson T, Wiebe J, Hoffmann P (2005) Recognising contextual polarity in phrase-level sentiment analysis. In: Proceedings of human language technology conference and conference on empirical methods in natural language processing, pp 347-354 48. Yadollahi A, Shahraki AG, Zaiane OR (2017) Current state of text sentiment analysis from opinion to emotion mining. ACM Comput Surv (CSUR) 50(2):1-33 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
1D ). The main diagonal of this matrix contains the similarity between two separate acquisitions of the same subject (self-similarity or I-self); the off-diagonal elements contain the similarity between each subject with the test or retest acquisition with respect to the other subjects (I-others). Furthermore, the difference between I-self and I-others, also known as differential identifiability (I-diff), provides a robust score of the overall fingerprinting assessment of a dataset. Finally, we estimated the success rate as the percentage of times in which an in-diagonal element coefficient was higher than the out-diagonal elements coefficients belonging to the row and column of the in-diagonal element taken into consideration.
In contrast, a 2006 study  12  found that, in India, the ratio between neonatal and postnatal mortality was about one-third higher in the Indian National Family Health Surveys (ratio between 0•8 and 0•6 in the surveys of 1992-93 and 1998-99) than in historical data of England and Wales (ratio between 0•6 and 0•4 at the same levels of infant mortality). However, this deviation was not considered an indication of a different age pattern of mortality, but instead attributed to errors in the data. In this paper, we show that detecting age patterns of under-5 mortality that are specific to low-income and middle-income countries (LMICs) is only possible when a fine degree of age granularity is available in the data sources and retained in the modelling approach. We used a model developed in 2022 that is based on high-quality historical and contemporary vital registration data from 25 high-income countries sourced from the Under-5 Mortality Database.
We used the date of birth and the age at death from all records collected between 1986 and 2020 to estimate death rates by detailed age with a standard event-exposure method. 13 here were no missing data in DHS datasets and no exclusion criteria. To validate the divergent age patterns found in DHSs, we searched for all publicly available alternative prospective data providing the same detailed age breakdowns over similar periods of time in a given country. The largest alternative source of data that we identified is the Health and Demographic Surveillance Systems (HDSSs), which is a population-based health and vital event registration system that is publicly available on the INDEPTH data repository. We also relied on bilateral agreements to access some additional non-public data, including case studies and sample-registration systems (appendix p 16).
Results for each individual survey are in the appendix (p 21). Surveys for which ratios are more than 1 (37 [13%] of 277 surveys; appendix p 6) display an early age pattern See Online for appendix For more on the INDEPTH data repository see https://www. indepth-ishare. org of under-5 mortality, which is suggestive of a higher rate of mortality before 28 days of age in the DHS data than the average age pattern of the vital registration model at a given level of under-5 mortality. Surveys with ratios of less than 1 (59 [21%] of 277 surveys) captured a late age pattern, which suggests a higher rate of mortality after 28 days in the DHS data than the average age pattern of the vital registration model (appendix p 2). Nevertheless, the ratios for the early and late categories remain within the range of predictions of the model.
Acknowledgments Research reported in this article was fully supported by the  Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health  (award  R01HD090082 ). Early versions of this paper were presented in 2021 at the Population Association of America Annual Meeting as well as at the  International Population Conference. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the  United Nations. The authors would like to thank participants of these seminars, as well as  Jere Behrman ,  Robert Black , and  Ken Hill  for their feedback.
Assessment of OM Safety was evaluated by chart review, and adverse events (AEs) were graded according to the NCI-CTCAE v5. 0. An examination of the oral mucosa was performed at each CareMin650 session to assess local tolerance and detect any new OM lesions. For each case of OM, time of onset, grade according to the NCI-CTCAE v5. 0, and Oral Assessment Guide (OAG) scales for mucositis, analgesic dose, and time to resolution were reported. At each session, data were collected on pain, using a visual analog scale (VAS) graduated from 0 (no pain) to 10 (intolerable pain), analgesic consumption, and in case of OM, consequences on food intake. Patient and user satisfaction were retrospectively analyzed through questionnaires completed at the end of the treatment.
Objectives: The objective of the RIETECAT study was to compare the long-term effectiveness and safety of enoxaparin versus dalteparin or tinzaparin for the secondary prevention of VTE in adults with active cancer. Methods: We used the data from the multicenter, multinational RIETE registry to compare the rates of VTE recurrences, major bleeding, or death over 6 months in patients with active cancer and acute VTE using full doses of enoxaparin versus dalteparin or tinzaparin, and a multivariable Cox proportional hazard model was used to analyze the primary end point. Results: From January 2009 to June 2018, 4451 patients with active cancer received full doses of the study drugs: enoxaparin, 3526 patients; and dalteparin or tinzaparin, 925 (754 + 171) patients. There was limited difference in VTE recurrences (2.
RIETE (clinicaltrials. gov identifier: NCT02832245 ) is an ongoing prospective, multicenter, multinational registry of consecutive patients with objectively confirmed VTE. 13  To date, RIETE is the world's largest database of patients with VTE. The RIETECAT study is an analysis of patients with active cancer from RIETE presenting with VTE and receiving initial and long-term therapy with full doses of enoxaparin versus dalteparin or tinzaparin. The information on LMWH regimen (once daily or twice daily) was introduced in RIETE on January 1, 2009. Thus, the study included patients from January 2009 to June 2018. Full doses were defined as (i) enoxaparin: 1 mg/ kg ± 20% twice daily or 1. 5 mg/kg ± 20% once daily for initial and long-term therapy; (ii) dalteparin: 200 IU/kg ± 20% once daily during the first month, and then 150 IU/kg ± 20% once daily; and (iii) tinzaparin: 175 IU/kg ± 20% once daily for initial and long-term therapy.
Data Collection During the locomotor examination, as part of the clinical routine, horses were systematically equipped with the EQUISYM ® (Arioneo, LIM France, Nouvelle-Aquitaine, France) system consisting of seven wireless IMUs placed on the head, the withers, the pelvis, and the four cannon bones (Figure  1 ). They recorded tri-axial angular velocity within a range of 2000 • /s and tri-axial acceleration within a range of 16 g, at a frequency of 200 Hz during approximately two trot-ups, corresponding to a mean of 14. 7 ± 7. 8 trot strides on a straight line. Data were recorded on the sensors and downloaded wirelessly. During the locomotor examination, as part of the clinical routine, horses were systematically equipped with the EQUISYM® (Arioneo, LIM France, Nouvelle-Aquitaine, France) system consisting of seven wireless IMUs placed on the head, the withers, the pelvis, and the four cannon bones (Figure  1 ).
Data Processing First, stance phase periods, e. g. foot-on and foot-off times, were determined based on the analysis of the gyroscopic signals recorded on the four cannon bones owing to the method developed by Hatrisse et al. [25]. One stride was defined as the time between two consecutive foot-on of the left forelimb. Then vertical displacements of the head, withers and pelvis were segmented into strides. The acceleration signal measured along the dorso-ventral axis of the horse was integrated twice and high-pass filtered using a fourth-order Butterworth filter with a cutoff frequency set to 1 Hz to obtain displacement curves  [4, 26]. Based on the vertical displacement of the head (_H), withers (_W) and pelvis (_P) occurring along a stride, four variables were calculated for each sensor location.
Data Processing First, stance phase periods, e. g. , foot-on and foot-off times, were determined based on the analysis of the gyroscopic signals recorded on the four cannon bones owing to the method developed by Hatrisse et al. [25]. One stride was defined as the time between two consecutive foot-on of the left forelimb. Then vertical displacements of the head, withers and pelvis were segmented into strides. The acceleration signal measured along the dorso-ventral axis of the horse was integrated twice and high-pass filtered using a fourth-order Butterworth filter with a cut-off frequency set to 1 Hz to obtain displacement curves  [4, 26]. Based on the vertical displacement of the head (_H), withers (_W) and pelvis (_P) occurring along a stride, four variables were calculated for each sensor location.
Data Analysis Mean and standard deviation (SD) were calculated from data collected in each group. Normality was assessed using graphical methods  [27]. Open software RStudio (RStudio Inc. , Boston, MA, USA, version 4. 1. 3) was used, including the packages ROCR, pROC and boot. The four AIs calculated from head, withers, and pelvis were analyzed. Receiver operating characteristic (ROC) curves were plotted to discriminate each lame limb group (RF, LF, RH, LH) from the control group (sound horses). Area under curve (AUC) of the ROC curves was calculated. Then, thresholds with highest specificity and sensitivity using the top-left method were calculated. The top-left method involves choosing the threshold related to the curve point closest to the upper-left corner of the graph. 95% confidence interval (95% CI, which values are expressed into [;] in the text) was obtained from the repartition of the best specificities and sensitivities calculated for 400 samples, using the bootstrap method based on resampling to estimate the confidence interval  [28].