dataesr/scientific-paragraphs-categorization huggingface dataset:
Data extraction from published studies Papers were screened by a single author (AS-S) in two phases (title and abstract and full-text screening) to exclude duplicates and articles that did not meet the eligibility criteria. Papers satisfying the inclusion criteria were examined and data on country, number and age of participants, site of screening, questionnaire used, prevalence value and 95% confidence interval (CI) and definition of asthma were recorded on a purpose-designed form. Doubts regarding the inclusion of papers and data extraction were clarified in agreement with a second investigator.
Epidemiologic studies datasets The Inquérito Nacional sobre Asma (INAsma) was conducted in 2010 under the auspices of the Portuguese Health Directorate and estimated a prevalence of current asthma (defined as affirmative to the question "Have you  Ever had asthma and at least one of 3 symptoms in the last 12 months: wheezing, waking with breathlessness or having an asthma attack  [9]  10. 3  [9. 5-11. 0] n. a. Ever had asthma and still have asthma  [15, 16]  n. a. n. a. Ever had asthma and was this confirmed by a physician and wheeze and/or taking medication(s) for asthma or wheezing in the past 12 months  [17]  n. a. n. a. At least one of the following: 1) Ever had asthma and was confirmed by a doctor and still have asthma or 2) significant bronchodilator response according to reversibility  [18]  n.
8% for the Portuguese population  [9]. The INAsma survey comprised two phases: estimation of the prevalence of current asthma and estimation of the proportion of asthmatic patients with controlled disease. The INAsma survey and current study were reported in accordance with the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement guidelines  [11]. The National Health and Nutrition Examination Survey (NHANES) 2005-2006 assessed the health and nutritional status of the United States population. And estimated a prevalence of asthma (defined as affirmative to the following questions: 1) Has a doctor or other health professional ever told you that you have asthma? 2) Do you still have asthma?) of 8. 8% for the U. S. population  [12]. Further details on databases can be found in Additional file 1.
Data analysis For each asthma definition recorded, we presented the corresponding prevalence value and 95% CI (if available) and computed the proportion of individuals with asthma using data from the INAsma survey and NHANES 2005-2006. The definitions found were reproduced as they appeared in each paper and were grouped into 3 general subgroups: (i) current asthma (if the definition included current asthma features such as symptoms, current use of medication or the "still have asthma" expression); (ii) diagnosed asthma (if the definition included an asthma diagnosis by a health care professional or ever use of medication for asthma, but not any current asthma feature) and (iii) lifetime asthma (if the definition included having ever had asthma, but not any current asthma feature or asthma diagnosis).
Statistical analyses were performed using IBM SPSS Statistics v21 (2012 SPSS Inc. , IBM Company, Chicago, US). The two phases of INAsma comprises data from 6 257 participants, both males and females from 0 to 98 years old. NHANES 2005-06 comprises data from 10 348 participants both males and females from 0 to 150 years old. Using the two phases of INAsma and NHANES 2005-2006 data, we estimated the proportion of individuals with 95% CI with asthma for each of the definitions in all applicable cases and when the necessary data were available. The elements used in each paper to define current asthma were plotted in a diagram (Figure  2 ). The size of the circles is proportional to the number of definitions in which the element is used. Different colors were attributed to definition elements depending if they were related to symptoms in the last 12 months, lung function tests or to physician diagnosis.
Results Of the 1738 papers retrieved from the Web of Knowledge database, 117 were included for analysis (Figure  1 ). The characteristics of the studies included are summarized in Additional file 2 and 3. They described prevalence studies conducted all over the world, on children (27. 4%), on adolescents (10. 3%), on children and adolescents (25. 6%), on adults (25. 6%), on elders (1. 7%) and on all population(8. 5%). Several questionnaires were applied, but the most common one was the ISAAC questionnaire, used in 45. 5% of studies. Lifetime asthma was defined in 34 papers, diagnosed asthma in 54 and current asthma in 61 (see Additional file 4). The 3 subgroups were defined in 8, 12 and 29 different ways, respectively (Tables  1  and 2 ). At least one of the following: 1) any symptom in the last 12 months: wheezing, shortness of breath, coughing bouts or dyspnea or 2) previous physician-based diagnosis of asthma or 3) use of medication for asthma  [68]  n.
Discussion The operational definitions of asthma varied extensively among studies and do not allow to compare estimates between studies, even if similar data collection methods were used. Additionally, when we applied the definitions of current asthma to two datasets from population studies, we found that the prevalence of asthma ranged from 5. 3% to 39. 5% in Portuguese population and from 1. 1% to 17. 2% in U. S. population depending on the definition used. Current asthma was most frequently defined by the combination of "ever diagnosed with asthma by a health professional" with "still having asthma". Wheeze was the most frequently used current symptom, especially in children. The Web of Knowledge was the only database used for the search. While this may be seen as a limitation of the present study, this approach allowed the inclusion of the most-cited papers on asthma prevalence conducted in both children and adults.
R&D projects implemented in FP6 and FP7 frameworks have successfully delivered a lot of new nanomedicines but few products onto the market. In this context, the Commission states that bridging the so called "Valley of Death" to upscale new KET technology based prototypes to commercial manufacturing, often constitutes a weak link in the successful use of KETs potential. This is meant to be the "European Industrial Renaissance" by covering the whole value chain from Lab-to-Market as the principal aim of H2020  [113].
Revised ALS Functional Rating Scale  [23]  data were obtained at MRI 1 , one year after MRI 1 in all 29 ALS patients and at MRI 2 for the 14 ALS patients. We calculated an arm ALSFRS-R subscore pertaining to functions such as handwriting, cutting food and handling utensils, and a leg ALSFRS-R subscore (walking, climbing stairs). The patients were also scored on manual muscle testing (MMT) using the Medical Research Council score  [24]. MMT was performed at baseline in all ALS patients and at one year in the subgroup of patients who had a second MRI. Seven muscles in each limb were assessed. Information about patients' demographics and clinical disability are given in Table  1. It includes disease duration, which was defined as the delay between the onset of weakness and MRI examination. Patients were scored on the day of each MRI.
MRI Acquisition Acquisitions were performed using a 3T MRI system (TIM Trio, Siemens Healthcare, Erlangen, Germany), with a body coil for signal excitation and a neck/spine coil for signal reception. Anatomical imaging was performed at the cervical and upper thoracic levels. DTI and magnetization transfer imaging were performed from vertebral C2 to T2 using the following protocol: Anatomical data. A sagittal T2-weighted three-dimensional (3D) turbo spin echo (TSE) image with slab selective excitation was acquired. Imaging parameters were: isotropic voxel size 0. 9 mm 3 ; FOV = 2806280 mm 2 ; 52 sagittal slices; TR = 1500 ms; TE = 120 ms; acceleration factor = 3; acquisition time , 6 min. Diffusion weighted-imaging. DTI data were acquired using a single shot EPI sequence with monopolar diffusionweighting scheme.
DATA Processing Cord atrophy. Cord cross-sectional area (CSA) was measured by an experienced operator on the segmentation technic (i. e. four years experience) on the T2-TSE images in the middle of vertebral levels from C2 to T6 using the semi-automatic method that has been shown to be accurate and with a low inter-observer variability  [28, 29, 30]. The plane perpendicular to the spinal cord was resampled to minimize partial volume between spinal cord and cerebrospinal fluid  [31]. To increase reproducibility and accuracy of the used segmentation method, images were segmented by an experienced operator on the technic. DTI. Data were corrected for motion slice-by-slice using FSL FLIRT  [32]  with three degrees of freedom (Tx, Ty, Rz). Diffusion metrics were estimated voxel-wise using FSL DTIFIT: fractional anisotropy (FA), radial diffusivity (l H ), axial diffusivity (l // ) and mean diffusivity (MD).
Acknowledgments We are grateful to all subjects and their relatives. We thank  Kevin Nigaud ,  Romain Valabre  `gue,  Alexandre Vignaud , Frederic Humbert,  Christelle Macia  and  Eric Bardinet  for helping with the acquisition and  Dr. Henrik Lundell  for providing us the code to measure the cord area. We thank the clinicians of the  Paris ALS Center ,  Drs Gae ¨lle Bruneteau ,  Lucette Lacomblez ,  Marie-Violaine Lebouteux ,  Timothee Lenglet  and Francois Salachas.
This study was supported by the  Association Francaise contre les Myopathies (AFM)  and the  Institut pour la Recherche sur la Moelle epiniere et l'Encephale (IRME). The research leading to these results has also received funding from the  program ''Investissements d'avenir' '  ANR-10-IAIHU-06. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Questions are asked about initial and current use, including number and duration of voice calls; use of hands-free kits, speaker phone, and/or Bluetooth headsets; laterality of use; proportion of time using phone in urban/suburban/rural areas; and other phone usage [i. e. , number of SMS and other messaging apps, and time spent using email, Internet, and voice over Internet Protocol (VoIP) (e. g. , Skype)]. Subjects are also asked about changes in phone use to further characterize their phone use history. All makes and models of phones used are identified with the assistance of a custom-made searchable database containing over 6,500 phones. Preliminary analyses show that <2% of the questions in the main questionnaire have 5% or greater "do not know" responses, indicating that the questionnaire is generally clear to the entire study population (results not shown).
In addition to the subject's questionnaire, parents (preferably the mother) are asked about maternal smoking history and other exposures before conception, during the pregnancy and the first trimester of life of the child, as well as about the pregnancy itself, the child's delivery, and her/his school history. Parental occupational histories are collected for both parents. Clinical data regarding the disease status, surgery, pathology, imaging needed for diagnosis verification, and tumor classification are collected from all available medical files.
VALIDATION STUDIES Validation of self-reported phone use is conducted by comparing responses of consenting subjects to network operator records. In addition, Mobi-Expo, a separate but complementary study of volunteers as well as a group of MOBI-Kids controls using softwaremodified smartphones (SMPS), collects self-reported phone use as well as use patterns, including laterality and data use recorded by the SMPS. Mobi-Expo will provide important information about mobile phone usage patterns in young people recorded by the SMSP as well as a means of validating self-reported mobile phone use.
EXPOSURE ASSESSMENT Collecting reliable valid data on complex and rapidly changing patterns of exposures, while minimizing recall bias and errors, has been a significant challenge in MOBI-Kids. Therefore, considerable effort was invested in developing the questionnaire's exposure sections based on an extensive ELF and RF-EMF measurement and modeling campaign  (26)  and experience gained from INTERPHONE and expert opinions. Much thought was given to minimizing recall biases (e. g. , use of prompts and a searchable mobile phone database to facilitate identification of phones used; first asking questions about present use and then previous use). In addition, we allow a maximum of 12 months between the case and control interviews (and also between the case's reference date and interview) to minimize differential recall bias between cases and controls.
MOBI-Kids includes numerous validation checks to ensure the accuracy of the questionnaire responses. The development of an electronic mobile phone database containing details on several thousand mobile phones has resulted in a substantial reduction in unknown phone models. Before launching the electronic mobile phone database in June 2011, 36% of phones were unknown (that is, subjects could not identify the make and/or model), whereas only 16% of phones since June 2011 are unknown, clearly demonstrating the benefit of a searchable mobile phone database to assist in identifying mobile phones, a factor that is an important in determining the exposure from the phones. Due to the rapid increase in the use of intermediate frequency (IF) technologies, some questions about sources of IF were added to the questionnaire in early 2013; the job histories will also be coded for IF-exposed jobs.
STUDY POWER As discussed above, despite our best efforts to reach the original expected sample size of approximately 2,000 cases, the revised projected number of case is just under 1,000. However, preliminary results on mobile phone use among controls indicate that 77 and 83% of males and females, respectively, were defined as ever using a mobile phone regularly (data not shown). In keeping with the INTERPHONE study, subjects who had used a mobile phone for <1 year were considered "never" regular users. Further, approximately 14% of all subjects in MOBI-Kids have used a mobile phone for 10 years or longer, the threshold for long-term use in INTER-PHONE. As this was a higher proportion than originally expected, our power calculations were revised based on the updated expected number of subjects and updated exposure indicators.
Also, we would like to thank Susanne Brilmayer and Silke Thomas for their contribution to the planning and implementation of MOBI-Kids in Germany. India: we are deeply thankful to Dr. Rakesh Jalali for helping us with the recruitment of study participants. Italy: we thank all cooperating professionals who made this study possible, Antonio Argentino, Anna Maria Badiali, Emma Borghetti, Valentina Cacciarini, Laura Davico, Laura Fiorini, Francesco Marinelli, Sara Piro, and Caterina Salce. Japan: we appreciate generous support and advice offered by the Japanese Epidemiological Committee. Spain: we would like to thank Carmen Caban for setting up, and Alex Albert for establishing and maintaining the complex electronic database. We are grateful to Patricia de Llobet for her valuable contribution in testing the electronic database as well as creating and updating the mobile phone.
ACKNOWLEDGMENTS We would like to thank all of the research assistants and interviewers in the study centers for their enormous efforts to ensure database. We would also like to acknowledge  Laura Argenté  as project manager. Finally, we appreciate the enormous time and effort of the regional coordinators ( Irene Gavidia ,  Eva Ferreras ,  Marta Cervantes ,  Angeles Sierra , and  Angela Zumel ). The Netherlands: we would like to acknowledge the following contributions: the  Dutch Society for Pediatric Oncology (SKION) , represented by  A. Antoinette Y. N. Schouten-van Meeteren , for the support and collaboration in setting up the case-control study in The Netherlands, the  Dutch Childhood Cancer Parent Organization (VOKK) , represented by  M. C. Naafs-Wilstra , for the collaboration in patient recruitment.
Funding: the research leading to these results has received funding from by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement number  22687  3 -the  MOBI-Kids  project. International coordination of the  project is partly supported by a grant from the  Spanish Ministry of Science and Innovation (MICCIN). Australia: Australian participation in MOBI-Kids is supported by the  Australian National Health and Medical Research Council  with a five-year research grant (grant number: 546130 and Chief Investigators are:  Malcolm R. Sim ,  Bruce Armstrong ,  Elizabeth Milne , and  Geza Benke ). Austria: Austrian participation in MOBI-Kids is partly supported by a grant from the  Ministry of Science. Canada: Canadian participation in MOBI-Kids is supported by a universityindustry partnership grant from the  Canadian Institutes of Health Research (CIHR) , with the  Canadian Wireless Telecommunications Association (CWTA)  serving as the industrial partner.
CWTA provides technical information on wireless telecommunications in Canada and facilitates access to billing records from  Canadian  network operators, but has no involvement in the design, conduct, analysis, or interpretation of the  MOBI-KIDS study. Health Canada has also provided financial support to facilitate coordination between Canadian and international MOBI-Kids investigators. Daniel Krewski  is the  Natural Sciences and Engineering Research Council of Canada Chair in Risk Science at the University of Ottawa. France: this project received funds from the  French National Agency for Sanitary Safety of Food, Environment and Labour (ANSES , contract  FSRF 2008-3 ),  French National Cancer Institute (INCa) ,  Pfizer Foundation  and League against cancer. Germany: the  German branch of MOBI-Kids  is supported by the  Federal Office for Radiation Protection.
Greece: Greek participation is partially supported by  ELKE (Special Account for Research Grants of the National and Kapodistrian University of Athens)  and  GGET (General Secretariat for Research and Technology ). India:  MOBI-Kids India  is supported by the  Board of Research in Nuclear Sciences (BRNS). Italy: Ministry of Health RF-2009-1546284. Japan: Japanese participation in MOBI-Kids is supported by the  Ministry of Internal Affairs and Communications. Korea: MOBI-Kids Korea  is financially supported by the  Ministry of Science, ICT and Future Planning (MSIP) ,  Korea  in the  ICT R&D Program. New Zealand: MOBI-Kids New Zealand  is supported by grants from  Cure Kids New Zealand  and the  New Zealand Health Research Council. Spain: Spanish participation is partially supported by the  Spanish Health Research Fund  ( FIS PI10/02981 ), the  Andalusian Consejeria de Salud  ( PI-0317/2010 ) and  Conselleria de Sanitat, Generalitat Valenciana  under grant number  025/2010.
20 mosquito vector species have been identified (MalAvi Database). Vectors, however, play a key role in dynamics and epidemiology of the disease. Vector populations fluctuate temporally and spatially, and these processes generate variability in the host biting rate, which ultimately bear on the parasite prevalence and population dynamics of the infection  [14]. In addition, vectors play a key role in structuring host-parasite relationships by, for example, restricting the access of certain parasites to a particular subset of hosts (but see  [2, 8] ) or by limiting or blocking the transmission of parasites to which the vector is less susceptible  [15, 16]. Identifying the parasites transmitted by a particular vector and the factors that render this vector susceptible to the parasite are therefore key steps to understanding the epidemiology of the disease.
Estimation of avian malaria parasite prevalence Haemosporidian detection rates have been shown by qPCR to be strongly dependent on host parasite load: low parasite loads can be easily mistaken for lack of infection leading to an underestimation of prevalence  [17, 18]. To correct for false negatives and thereby obtain a more accurate estimate of prevalence, we applied a site-occupancy modeling framework to the dataset  [19, 58]. Site occupancy models are based on the repeated sampling of each individual "site" (in our case a "site" corresponds to an individual mosquito) in order to obtain an estimate of the probability of detection or, in other words, an estimate of the sensitivity of the test (i. e. false negatives, see  [17]  for more conceptual and methodological information). For this purpose, we repeated the nested.
4% were positive only once. After this intense sampling effort, 12 individuals originally assigned to the "control pool" (n = 128 after reassignment) were re-assigned to the "haemosporidian pool" (n = 110 after reassignment). False positive diagnoses were assumed not to occur (the nested PCRs systematically used a negative control). This method gave us what we call 'naïve prevalence' hereafter. We fitted models using the software PRESENCE version 3. 1  [59]  and we used the Akaike Information Criterium (AIC; see  [60] ), to select the best fit model ("1 group constant probability"). The number of sampling occasions was set to 12 (the maximum number of PCRs carried out on a single individual) since the program controls for missing data. The individual detection probabilities given by the model were used to correct the prevalence of haemosporidians per location and sampling month: called 'estimated prevalence' hereafter.
Phylogenetic analysis of avian Plasmodium parasites Molecular identification and phylogenetic analysis of avian malaria parasites was carried out using 96 samples randomly picked from the "haemosporidian pool" (96 is the number of wells in a standard PCR plate). Fragments (at least 448-bp-long) of the cytb gene were sequenced in forward sense, using the internally nested primer HAEMF from products of the initial PCR  [57]. Sequences were edited and aligned using the program ClustalW included in the software MEGA version 5. 1 beta  [61]  with additional manual editing. Mitochondrial DNA lineages were blasted against known avian malaria lineage sequences available in the MalAvi database  [5]  and in GenBank. The chromatograms were also checked for double nucleotide peaks to infer possible cases of mixed infections of two different parasite lineages.
The sequences were assigned to an already described lineage only if they were identical to a reference sequence present in the MalAvi database. For the lineages differing by one nucleotide from the known lineage the term "-like" was appended to the name (e. g. "SGS1-like") for the phylogenetic analyses but, for simplicity, they were considered to be the same lineage for all other analyses. New sequences have been deposited in GenBank (see Table  1 ) and whenever the information was available, lineages were then assigned to a given morphospecies using the MalAvi database (see Table  1 ). We compared 448-bp-long fragments of the sequences obtained from this study with five published sequences that have been reliably identified to Plasmodium morphospecies level and/or that are as close as possible to the new sequences obtained in this study: CXQUI01, GRW04, MANSON01, PADOM05 and SYAT03 [GenBank: AB308051, AF254975, AB308052, EU708328 and AY831752, respectively].
We rooted our tree with four mammalian malaria parasites sequences  [78]. The program jModelTest version 0. 1. 1  [79]  indicated that the most likely model of sequence evolution was TIM2 + G. We used Maximum-Likelihood analysis implemented in PhyML (v. 3. 0) [80]  to reconstruct a phylogeny using these parameters and the software FigTree v1. 3. 1 was used to draw the phylogenetic tree. Node supports in the resulting phylogeny were tested using 5000 bootstrap replications.
Filarial nematodes were detected using a PCR assay amplifying a fragment of the cytochrome c oxidase 1 gene (CO1) using the generalist primers COlintF and COlintR as described in  [82]. DNA from a rodent filarial nematodes, Litomosoides sigmodontis, was used as positive control in each PCR assay. DNA sequencing of the obtained products was performed in reverse sense using COlintR and sequences have been deposited in GenBank.