dataesr/scientific-paragraphs-categorization huggingface dataset:
It should be noted that contrary to previous studies, our results are based on the analysis of individuals of known age who were monitored for 8 years. The life history variables are therefore known from raw data and no calculations were necessary to make assumptions on these variables. Several studies have focused on growth in pygmies, but the majority are founded on cross-sectional data from a few individuals of unknown age  25. In contrast, studies by Bailey  11  and Jans  12  are the only ones addressing growth in pygmies on the basis of longitudinal data with an exact, although short chronology (5 years). In both the cases, the authors were present at births and followed the growth of infants from birth to 5 years of age. Bailey measured height in the Efe and Lese, the neighbouring Bantu group in close contact with the pygmy group.
Acknowledgements We thank  X. Garde ;  B. Bordage  and all personnel of  IRD Yaounde  for logistical support, nuns at mission  Le Bosquet  for their kind hospitality,  L. Maget  and  S. Calvo  for recording our work,  R. Lacruz ,  H. Liversidge ,  S. Amselem ,  E. Heyer ,  B. Bogin ,  T. Cole ,  B. Holly Smith  for helpful comments. Special thanks to  P. Kalo ,  J. B. Etoa  and other Baka collaborators for their help, assistance and friendship. This work was supported by  L. Di Giorgio  ( Dental Film ), the  CNRS , the  IRD  and grants from the  Wenner-Gren Foundation (FRR) ,  National Geographic  (# 8863-10 ) ( FRR ) and  ANR program Blanc 'GrowinAP'.
EEG Recordings EEG recordings were performed at Fz, Cz, Pz, C3, C4, T3, T4, A1, and A2, according to the international 10-20 system, using active surface electrodes connected to a V-Amp amplifier (Brain Values are medians and 1st and 3rd quartiles. Products GmbH, Gilching, Germany). The electrooculogram was also recorded with Fp1 and Fp2 electrodes placed above both eyes. The EEG signal was sampled at 2 kHz and electrode impedance was maintained at 5 k at all sites during data acquisition. The EEG was recorded and stored on a laptop computer using Brain Vision Recorder software (Brain Products GmbH, Gilching, Germany) and subsequently analyzed with Brain Vision Analyser 2 software (Brain Products GmbH, Gilching, Germany). Laser-evoked Potentials (LEPs).
Statistics Statistical analyses were performed using SPSS 18. 0 (IBM, USA) and Prism version 5. 0 (Graphpad software Inc, CA, USA). The nature of the data distribution was assessed using the Shapiro-Wilk test. LEP-P2 latency, T E /T T , T I /T T , and PETCO 2 were not normally distributed, but LEP-P2 latency had a normal distribution after logarithmic transformation. Normal data sets were described in terms of their mean and standard deviation, whereas non-normal data sets were described in terms of their median and 95% confidence intervals (95%CI). The effects of CO 2 stimulation on the discrete variables describing breathing pattern (V T , T I , T E , T T , V T /T I , T I /T T ), respiratory discomfort, and scalp potentials were analyzed by Two-way analysis of variance (ANOVA) for repeated measures followed by Tukey post-hoc test (normal data) or Friedman's test followed by Dunn's post-hoc test (non-normal data).
FUNDING The study was supported by the program "Investissement d'Avenir ANR-10-AIHU 06" of the French Government, and a grant "Legs Poix" from the "Chancellerie de l'Université de Paris. " LD was supported by a scholarship from "Fonds de Dotation Recherche en Santé Respiratoire" and from "Fonds d'Etudes et de Recherche des Hôpitaux de Paris. " LL was supported by the Institut Universitaire de Cardiologie et Pneumologie de Quebec (IUCPQ) foundation, Quebec, Canada, a long-term research fellowship from the European Respiratory Society and a postdoctoral research fellowship from the Fonds de Recherche du Quebec en Santé.
Enrollment This study was approved by the University of California Irvine (UCI) Institutional Review Board (IRB) (HS 2011-8554) and registered with ClinicalTrials. gov (protocol ID NCT02020863). The study was conducted at UCI Medical Center in Orange, CA, USA, from September 2013 through February 2014. Patients eligible for the study were all those over 18 years of age scheduled for hepatobiliary, pancreatic or splenic surgery and expected to receive intravascular arterial blood pressure monitoring as part of their anesthetic care. Exclusion criteria were under 18 years of age, pregnant, body mass index >35 kg/m 2 , presence of moderate to severe valvular disease, cardiac arrhythmias, left ventricular ejection fraction <40% and right ventricular failure. Additionally, any patients who were found to have metastatic disease during a laparoscopic examination and who had their primary procedures aborted as a result were excluded.
All subjects had invasive arterial pressure monitoring via the radial artery planned as part of their anesthetic care. Standard practice in our institution for moderate-to high-risk hepatobiliary surgery patients is to use flow-based monitoring that includes a dynamic predictor of fluid responsiveness such as SVV to provide GDFT. For these cases (both study and control group), all patients were monitored with one of three dedicated EV-1000 monitors and a FloTrac sensor (Edwards Lifesciences, Irvine, CA, USA). Finally, all patients except those undergoing liver resection had a thoracic epidural placed by the pain service in the preoperative holding area prior to coming to the operating room. The epidural was not activated (save for a 3-ml test dose of 2% lidocaine with 1:200,000 epinephrine) until the surgical case was closing and emergence of the patient was beginning (and data collection completed).
Closed-loop setup The closed-loop software (Sironis, Newport Beach, CA, USA) was run on a Shuttle X50 Touchscreen PC (Shuttle Computer Group, City of Industry, CA, USA) running Windows 7 (Microsoft, Redmond, WA, USA). The system was connected via a USB to serial adapter to the serial output port of the EV-1000 for real-time capture of data. A Q Core Sapphire Multi-Therapy Infusion Pump (Q Core Medical, Netanya, Israel) was used by the closed loop to deliver fluid. The Sapphire pump is a single-channel volumetric pump capable of flow rates from 0. 1 to 999 ml per hour. The pump was controlled by the closed-loop system using software provided by Q Core via serial connection (Commands Server R. 00). Following placement of the arterial line (and central line if applicable), but before surgical start time, the closedloop system target was selected by the primary anesthesia team (scaled stroke volume (SV) increase of 7.
Case matching Following completion of enrollment, each study patient was matched to a non-closed-loop assisted patient treated during the same time period using a propensity score match to reduce bias  [24, 25]  (Figure  2 ). The propensity score was estimated using logistic regression to regress group assignment on the predictors in Table  1  (age, height, weight, sex, HR, Stroke Volume Index (SVI), MAP, SVV, specific scheduled procedure and surgeon). Each study patient was then matched 1:1 to a control case using calipers of 0. 04% width (20% of the standard deviation (SD) of the propensity score), creating a control manual GDFT (M) group to compare with the closed-loop assisted (CL) group. The complete propensity match process, data table and matched pairs are detailed in Additional file 1.
Outcomes, data collection and analysis The following data were collected continuously at 60-second intervals for each patient by the anesthesia information management system (AIMS) (Surgical Information Systems, Alpharetta, GA, USA): HR, MAP, SV, CO and SVV. Fluid volume, urine output, blood loss, and vasopressors were also recorded in the AIMS by the primary anesthesia team. In study cases, the closed-loop controller also recorded continuous hemodynamic data (HR, MAP, SV and SVV) provided by the EV-1000 at 2-second intervals, as well as fluid administration and provider interactions (set point changes, bolus stops and test boluses administered). Baseline values for HR and MAP were taken from the preoperative anesthesia assessment. Baseline values for SVI and Cardiac Index (CI) were taken as the mean of the first minute's recording 5 minutes after placement of the arterial line and calibration of the hemodynamic monitoring system.
Mean case value for each hemodynamic variable was calculated as the average value of all recordings between baseline and the beginning of surgical closure. Final case value for each hemodynamic variable was defined as the average value of each parameter over the last minute before the beginning of emergence (lowering of anesthetic agent in preparation for extubation). Finally, a Postoperative Morbidity Survey (POMS) score  [26]  was calculated for each patient, and length of Surgical Intensive Care Unit (SICU) and hospital stay were recorded. The primary outcome between groups was GDFT compliance, defined as percentage of case time where SVV was ≤12%. Upon study initiation in September 2012, the original endpoint was cardiac SV. When our case series in Europe was finished, informal comparison with other standard case data showed strong effects on SVV for the CL group, but weaker effects on SV itself  [21].
Provider interactions with the system were also recorded (start and stop of boluses and changes in controller set point). Based on local data, case time with SVV ≤12 in the case types to be studied was approximately 80 ± 10% over the previous 1-year period, or about 20 ± 10% noncompliance time. Cutting non-compliance time in half (or more) was considered a clinically relevant endpoint that, assuming a power of 0. 8 and a significance level of 0. 05, would require 21 patients in each group to be adequately powered. Continuous scalar data were tested for normality using a Kolmogorov-Smirnov test. If normal, the data are reported as mean ± SD and comparisons were made using Student's t-test. Non-normally distributed scalar data and ordinal data (namely, POMS scores) are reported as median (25th, 75th percentile), and comparisons were made with the Mann-Whitney U test.
Enrollment and case matching Forty patients were enrolled in the study over the study period, out of a total of one hundred one patients who met the eligibility criteria. Four were excluded when surgery was aborted because of discovery of metastatic disease on initial laparoscopic examination, and one patient was excluded because the primary team chose not to place an arterial line after enrollment. The remaining 35 patients were matched 1:1 against the control patients using the propensity score. Of the 61 control patients, 6 had metastatic disease or did not have CO monitoring during their procedures, leaving 55 completed controls that were performed during the same time period. A total of 25 matched pairs were found within the predefined caliper distance. Demographic data for the two groups, as well as baseline hemodynamic values (HR, SVI and MAP) and specific procedure distribution, are shown in Table  1.
Controller actions The closed-loop system gave a total of 214 discrete 100-ml fluid boluses across the 25 study cases, with an average of 8. 5 ± 6. 5 boluses per case. The minimum given in a case was one bolus, and the maximum was twenty-six. Of the 214 fluid boluses given by the controller, 137 resulted in a scaled cardiac SV increase of at least 15% over 500 ml, and 124 resulted in a scaled SV increase of at least 10% over 250 ml. No comparison data are available for the control group, as individual fluid boluses were not recorded by the providers in the AIMS. The controller's predictive specificity improved with increased experience with the patients. The first to third fluid boluses given to patients had a mean scaled SVI increase of 10. 2 ± 43%, the fourth to seventh 12 ± 36% and the eight and later 18 ± 35%.
Acknowledgments We are grateful to  Luis Garcia  ( Neuromuscular disease Biotherapy laboratory, St Quentin en Yvelines University, France ) and  Bruno Cadot  ( Center of Research on Myology, UMRS 974 INSERM ) for their help in microscopy image acquisitions. We thank  Miss Mathilde-Alexia Pioline  for her expertise in image processing. This work was supported by  SMA-Europe ,  Association Française contre les Myopathies , the  University Pierre et Marie Curie (UPMC) , the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Centre National de la Recherche Scientifique (CNRS) , and the  Association Institut de Myologie  (A. I. M).
Background: This article provides an overview of the first BIOASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BIOASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. Results: The 2013 BIOASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PUBMED documents with MESH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MESH headings to curators.
Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BIOASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. Conclusions: A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MESH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PUBMED Central; produce "exact" and paragraph-sized "ideal" answers (summaries).
g. , including specialised drug databases and ontologies) often need to be combined. Furthermore, they mostly retrieve possibly relevant texts or structured information, which the users then have to study, filter, and combine by themselves to obtain the answers they seek. By contrast, QA systems aim to directly produce answers  [4]. Semantic indexing, i. e. , annotating documents with concepts from established semantic taxonomies or, more generally, ontologies, provides a means to combine multiple sources and facilitates matching questions to answers. In recent years, many methods have been developed that utilize existing ontology structures and concepts to index documents and perform semantic search  [5]. Current semantic indexing, however, in the biomedical domain is largely performed manually, and needs to be automated to cope with the vast amount of new information that becomes available daily.
At the same time, current semantic indexing and QA methods require more research to reach a level of effectiveness and efficiency acceptable by biomedical experts. BIOASQ sets up ambitious, yet feasible and clearly defined challenge tasks, intended to lead to integrated, efficient, and effective semantic indexing and QA methods for the biomedical domain. In addition, BIOASQ helps in the direction of establishing an evaluation framework for biomedical semantic indexing and QA systems. It does so by developing realistic, high-quality benchmark datasets and adopting (or refining) existing evaluation measures for its challenge tasks. Figure  1  provides a general overview of biomedical semantic indexing and QA in BIOASQ. Other recent approaches also follow a similar approach  [4, 6]. Starting with a variety of data sources (lower right corner of the figure), semantic indexing and integration brings the data into a form that can be used to respond effectively to domain-specific questions.
g. , in "What is known about the metabolism of drug Z?"). More precisely, the BIOASQ challenge evaluates the ability of systems to perform: (1) large-scale classification of biomedical documents onto ontology concepts, in order to automate semantic indexing, (2) classification of biomedical questions on the same concepts, (3) integration of relevant document snippets, database records, and information (possibly inferred) from knowledge bases, and, (4) delivery of the retrieved information in a concise and user-understandable form. To realize the challenge, BIOASQ organized two tasks, namely Task 1a (covering point number 1 from the aforementioned list) and Task 1b (covering the rest of the points from the aforementioned list). In Task 1a, named "Large-scale online biomedical semantic indexing", participants were asked to classify new abstracts written in English, as they became available online, before MED-LINE curators annotated (in effect, classified) them manually; at any point in time there was usually a backlog of approximately 10,000 non-annotated abstracts.
The answers of the systems were compared against model answers in English constructed by biomedical experts, using evaluation measures from QA and summarization. A running example of a participating system that answers a natural language question, progressing through the BIOASQ competition tasks, in order to illustrate how the various steps of the competition are combined to allow systems to address natural language QA is provided in Additional file 1. The benchmark datasets that contain the development and , semantic indexing and integration brings the data into a form that can be used to respond effectively to domain specific questions. A semantic QA system associates ontology concepts with each question and uses the semantic index of the data to retrieve the relevant pieces of information.
g. , recognising named entities, particular relations between named entities, or biological events mentioned in scientific articles). The 2009 and 2011 BioNLP workshops featured shared tasks in these areas  [17]. Similar biomedical information extraction and data mining challenges have been (and continue to be) organised in the context of BioCreative, with a recent additional emphasis on helping curators of biomedical document collections (e. g. , prioritising articles to be manually curated)  [18]. Overall, although there have been several competitions relevant to BIOASQ, which required searching large-scale document collections or structured datasets, question answering or text summarization, and hierarchical classification or semantic indexing, very few of them were directly concerned with biomedical data.
Furthermore, none of them required the participants to answer biomedical natural language questions by searching in both structured data (e. g. , databases, ontologies, and the Linked Open Data cloud) and unstructured data (e. g. , biomedical articles), and none of them pushed at the same time towards matching questions to answers at the conceptual level, i. e. , by using concepts from domain ontologies to annotate both questions and answers. Hence, BIOASQ has a broader scope, incorporating hierarchical classification, text and passage retrieval, retrieving RDF triples, QA for exact answers, multi-document summarization, and natural language generation.
Description of Task 1a Task 1a, titled "Large scale online biomedical semantic indexing", deals with large scale classification of biomedical documents into ontology concepts. The purpose is to investigate the performance of the state of the art methods and the new methodologies proposed by participants compared to the manual annotation that is widely used in public databases. As new articles are uploaded on a daily basis in MED-LINE and are annotated manually with concepts from the MESH hierarchy, MEDLINE offers the means to assess the performance in a real large scale setting. Task 1a takes advantage of two observations of the PUBMED workflow: a large number of uploaded articles (approximately 4,000 on a daily basis), and from them a sufficient fraction are annotated in a relatively short time suitable for the BIOASQ challenge (approximately 10% within two weeks, and more than 50% within 12 weeks).
Creation of benchmark datasets for Task 1a In Task 1a the data that are available to the participants consist of biomedical articles published in MED-LINE. Specifically, for each article in the training data, BIOASQ provides its title and abstract as it appears in MEDLINE and the MESH labels assigned to it. In the testing phase of the challenge the data contain only the title, the abstract, the journal and the year of the corresponding article without any further information. The articles are provided in their raw format (plain text) as well as in a pre-processed one (bag of words with weights). The subsection ' An extract from the training data of Task1a' presents an example of two articles extracted from the BIOASQ benchmark training data. The format used for the distribution of the data is the JavaScript Object Notation  [23]  (JSON).
There were only two requirements regarding the collection of the articles: (1) that the articles had a title, an abstract and MESH labels assigned, and, (2) that their indexing with MEDLINE was made before the beginning of the Task 1a, i. e. , in our case this date was set to March 1st, 2013. Table  1  presents the statistics of the training data set for Task 1a. For the creation of the test data three batches were designed, each containing 6 test datasets. Each week a test dataset was distributed to the participants. Thus, each batch was 6 weeks long. The following requirements were used for the creation of the test datasets: (1) that the articles should have a title, an abstract, but no MESH headings Size in  GB 22  assigned to them yet, (2) that the articles should have MESH headings assigned in few weeks time after their release, (3) the articles that have been released in a previous dataset should not be released in any other future test set.
To fulfil all three requirements, a web service was set up to automatically fetch from MEDLINE the articles of this kind. Requirements (1) and (  3 ) are easy to satisfy with a PUBMED query. For requirement (2) we analyzed the average time it takes to assign MESH labels to articles, per journal, for all of the MEDLINE indexed journals. From this analysis, only the journals for which the average assignment of MESH labels took up to 90 days were kept. In total, 1,993 such journals were used. This list of selected journals can be found in Additional file 2. Hence, requirement (  2 ) was now reduced to the selection of articles that are published in one of the journals in the list. An example of a PUBMED query that is used by the BIOASQ web service to create test datasets can be found in Additional file 3.
In collaboration with NLM we used a set of additional filters that excluded articles that are editorials, comments, reviews, letters, and news from the resulting article list. With the satisfaction of all three requirements, the test datasets would comprise unique articles for which the MESH terms were not known at the time of the release, but would become known on average up to 90 days after their release date, thus, allowing the BIOASQ consortium to evaluate Task 1a on time before the end of the challenge. In Table  2  we present the number of articles of each test dataset in each batch of the evaluation procedure. The numbers in parentheses are those articles of the corresponding test dataset that were annotated with MESH labels by the NLM curators by the time that the Task 1a results were frozen (September 2013).