dataesr/scientific-paragraphs-categorization huggingface dataset:
If patients are approached by their trusted health care provider, this can promote enrollment rates, and usual care conditions are best maintained  [26]. Dedicated study staff may be employed to overcome operational challenges for recruitment during a routine visit  [28]. Practitioner databases or electronic health records can also be used to select eligible patients with a chronic condition. These patients could be invited into the health center via a mail-out or informed about the study while they are in the waiting room for a regular visit. Direct contact in the waiting room has been shown to promote the participation rate but was considered less efficient than a mail-out  [30]. Accessing electronic patient information or contacting patients has been shown to be a challenge for recruitment through databases  [31].
The work leading to these results has received support from the  Innovative Medicines Initiative  joint undertaking under grant agreement no ( 115546 ), resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007/2013 ) and EFPIA companies' in kind contribution. The research leading to these results was conducted as part of the GetReal consortium and included literature review and interviews with stakeholders from academia, research institutions, contract research organizations, pharmaceutical industry, regulatory authorities, health care insurers, health technology assessment (HTA) agencies, general practitioners, and patient organizations. For further information please refer to http://.
omim. org/), encoding growth The schematic shows the membrane domain, with helical segments S0-S6 and the intracellular domain, divided into a membrane-proximal module (helices A-B) bound by CaM and a distal module (helices C-D), responsible for tetramerization. Filled circles with labels show the positions of the mutations, Arg116Leu and Pro369Leu. The double lines depict the plasma membrane. Below the schematic is a molecular graphics depiction of the Kv7. 1/CaM channel complex as based on the cryo EM Xenopus structure (PDB code: 5VMS)  11. The channel subunits are colored green, cyan, and teal. CaM is colored pink and shown with a surface representation on the right side. Gray spheres are Ca 2+ ions. Again, the straight lines denote the probable location of the plasma membrane. The residues that undergo mutation are drawn as CPK atoms and are labeled.
Results Pedigree and genetic analysis. We performed genome-wide linkage analysis in a large Finnish family  4  (Fig. 1a ) with this disease, and found statistically significant linkage, logarithm of odds (LOD) score > 3. 3, between the studied phenotype and a locus on 11p15. The highest LOD score, 3. 54, was reached with the marker rs7947959 (hg19 chr11:4784679). The most distal marker showing statistically significant linkage was located at 286 kb and the most proximal marker at 11. 36 Mb. No other chromosomal areas reached LOD scores >3. 0. We then performed whole-genome resequencing (Beijing Genomics Institute) in two affected family members. Novel variants in the linkage region shared between the two genomes, and not found in any databases (dbSNP, http://www. ncbi. nlm. nih. gov/SNP/; Exome Variant Server, http://evs.
gs. washington. edu/EVS/; 1000 Genomes, http://www. 1000genomes. org/; ExAC, http://exac. broadinstitute. org/), were annotated with SNPnexus (http:// www. snp-nexus. org/). The c. 347G>T p. (Arg116Leu) missense mutation in KCNQ1 (MIM: 607542), shared between the two genomes, was left in the linkage region after known variants, variants in the noncoding regions, and the synonymous changes were filtered out. PolyPhen-2 (http://genetics. bwh. harvard. edu/ pph2/) predicted this mutation to be deleterious with very high probability (score 0. 993). The presence or absence of the mutation was verified by Sanger-sequencing in all 16 family members, and in four additional affected family members participating in the study after completion of linkage analysis. The mutation segregated with GHD and/or short stature in the family and was absent from the SISu database (The Sequencing Initiative Suomi project, containing over 10,000 Finnish samples, http://www.
sisuproject. fi/). Sanger sequencing of the coding exons and exon-intron boundaries of the KCNQ1 gene in a second Finnish family (mother and twin boys) with GHD and GF revealed another novel missense mutation, c. 1106C>T p. (Pro369Leu), in all three members (Fig. 1a, b ). This mutation was also absent from the dbSNP, Exome Variant Server, 1000 Genomes, ExAC, and SISu databases and predicted to be probably damaging by PolyPhen-2 (score 0. 960). In the third family, a Swiss family originating from Argentina (Fig. 1a ), the index patient carried the same c. 1106C>T p. (Pro369Leu) mutation as de novo. Phenotypic features of KCNQ1 mutation carriers. Detailed clinical histories of mutation carriers are provided in Supplementary Note 1, and the summary of phenotypic features are given in Table  1.
Corrected QT interval (QTc) was calculated from the QT and inter-beat (RR) interval using Bazett's correction (QTc=QT/(RR) ½ ). Hormone measurements were done in the clinical laboratory. Linkage analysis. We performed genome-wide single-nucleotide polymorphism (SNP) genotyping of 16 family members (Illumina HumanOmniExpress microarray, Institute for Molecular Medicine Finland) of the large Finnish pedigree. Two-point parametric linkage analysis was performed using Pseudomarker 1. 0. 6 software with FASTLINK 4. 1P linkage package  51, 52. Individuals with GF and/ or GHD with short stature or low insulin-like growth factor 1 (IGF-1) levels were treated as affected; all others were considered healthy. A dominant model of inheritance with nearly complete penetrance was assumed: disease allele frequency was set to 0.
0001 with a penetrance rate of 0. 999 and a phenocopy rate of 0. 001. Allele frequencies were calculated from the genotypes of all individuals. Identification of shared variants. Shared variants within the linkage region in two affected members of the large Finnish family (Fig. 1a ) were examined by whole genome sequencing (Beijing Genomics Institute). Novel variants in the linkage region shared between the two genomes and not found in any databases (dbSNP, http://www. ncbi. nlm. nih. gov/SNP/; Exome Variant Server, http://evs. gs. washington. edu/EVS/; 1000 Genomes, http://www. 1000genomes. org/) were annotated with SNPnexus (http://www. snp-nexus. org/). Segregation analyses and sanger sequencing of KCNQ1. The Arg116Leu missense mutation in KCNQ1 was Sanger-sequenced in all 16 family members and in four additional affected family members participating in the study after completion of linkage analysis.
Data processing. SEQUEST search algorithm in Proteome Discoverer software (Thermo Fisher Scientific) was used for peak extraction and protein identification with the human reference proteome of UniProtKB/SwissProt database (www. uniprot. org). Allowed error tolerances were 15 ppm and 0. 8 Da for the precursor ARTICLE NATURE COMMUNICATIONS | DOI: 10. 1038/s41467-017-01429-z and fragment ions, respectively. Database searches were limited to fully tryptic peptides allowing one missed cleavage (in peptide mapping semi-tryptic with one missed cleavage allowed), and carbamidomethyl +57,021 Da (C) of cysteine residue was set as fixed, and oxidation of methionine +15,995 Da (M) as dynamic modifications. For peptide identification FDR was set to <0. 05. The high-confidence protein-protein interactions were identified using stringent filtering against control contaminant database.
The bait normalized relative protein abundances (% to the bait) were calculated from the spectral counts. Each average and SD was calculated from three biological replicates. The high confidence protein-protein interactions data were imported into Cytoscape 3. 4. 0 for the visualization combined with the known protein-protein interactions from PINA2 (Protein Interaction Network Analysis http://cbg. garvan. unsw. edu. au/ pina/)  57. Protein complexes were annotated against CORUM database  58  and Gene Ontology term of biological pathway enrichment analysis was using DAVID (https://david. ncifcrf. gov/) 59 (Supplementary Fig. 7 ). Molecular modeling of the KCNQ1 Pro369Leu mutant. Coordinates for the KCNQ1 proximal CT domain in complex with CaM (PDB code 4V0C), derived from the crystal structure  14  , were used to prepare the model.
Acknowledgements Ms. Lea Puhakka  is thanked for skillful technical assistance. Dr. Päivi Miettinen  is thanked for commenting on the manuscript. This work was supported by the  Academy of Finland  ( 138124  to J. T. , 251413 to T. R. , 294173 to M. V. ),  Foundation for Pediatric Research  ( 7495  to T. R. ),  Sigrid Juselius Foundation  ( 2613  to T. R. ),  Emil Aaltonen Foundation  ( 2170  to T. R. ),  Novo Nordisk Foundation  ( 4761  to T. R. ),  Helsinki University Central Hospital  research funds (2010307),  Jalmari and Rauha Ahokas Foundation  (to J. T. ),  Paulo Foundation  (to J. T. ),  Danish Council for Independent Research  ( DFF-1331-00313B  to T. J. ),  Agence Nationale de la Recherche ,  ANR ,  France  ( ANR-14-CE12-0015-01  RoSes and GnRH to P. G. :  ANR 12 BSV1 0032 Peri-Pulse  to both P.
I. Data Our data on drug sales is drawn from the MIDAS dataset produced by IMS Health, a market research firm. This data is sourced from audits of retail pharmacies, hospitals, and other sales channels, and includes sales to both private and public purchasers. These data record quarterly unit sales and revenues at the "package" level -e. g. sales of bottles of 30 10mg tablets of Lipitor -from 2000-2013. Our drug quality measure is from Haute Autorité de Santé (HAS), France's public health authority. This data is available for drugs currently sold in France or whose sale in France stopped within the past three years. 1 The HAS data include two quality measures: SMR (service médical rendu/actual benefit), an absolute rating of drug importance that determines whether reimbursement in France is justified; and ASMR (amélioration du service médical rendu/improvement in actual benefit), a relative rating of therapeutic value compared to existing treatments.
We link the HAS ASMR ratings to non-generic drugs recorded by the IMS MIDAS data as sold in France by merging on product name. Specifically, we first attempt a straight match of local French IMS product names with HAS product names. For any unmatched IMS product names we attempted a manual match to HAS product names. We then assign the ASMR rating for a given French product name to all observations in the IMS data with the same international product name and/or molecule name. Of the around 2,000 non-generic products that the IMS data records as being sold in France between 2000-2013, 60% are matched to the HAS data. Our comparison countries are Australia, Canada, Switzerland, and the UK. Of note is that our HAS drug quality measures are only available for drugs commercialized in France, which complicates the UK comparison in the following sense.
Acknowledgments We are grateful to  Jane Choi  for excellent research assistance, and to  Amitabh Chandra ,  Dan Fetter ,  Amy Finkelstein , and  Jon Skinner  for helpful comments. Research reported in this publication was supported by the  National Institute on Aging  and the  NIH Common Fund , Office of the NIH Director, through grant  U01-AG046708  to the NBER; the content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH or NBER. Financial support from  NSF  Grant  1151497  and the  Intellectual Property and Markets for Technology Chair at MINES ParisTech  is also gratefully acknowledged. Kyle thanks  Pfizer  for access to the IMS data used.
We decided to increase the sample size to 3400 to take into account the presence of incomplete questionnaires. 26 27  Data collection A research assistant located in Geneva contacted the randomly selected GPs practicing in Switzerland (in France, this task was carried out by the local GPs' representative structure: Union Régionale des Professionnels de Santé Alsace and Pays de la Loire) and informed them about the aim of the study and the practical procedures to be followed to complete the anonymous questionnaire. The questionnaire (see online supplementary file 1) included sociodemographic questions (age, gender, number of working days per week and number of working years in the practice), and questions about the frequency of use of 12 preventive practices (blood pressure, weight and height measurements, screening for dyslipidaemia, hazardous drinking (and advice to decrease drinking for hazardous drinkers), tobacco smoking (and advice to stop smoking for smokers), colorectal and prostate cancer; and influenza immunisation for patients >65 years and Open Access at-risk patients <65 years), using a 5-point Likert scale ranging from 'never performed' to 'always performed'.
statistical analyses We computed the proportion of GPs delivering each of the five preventive practices, defined as the proportion of GPs scoring 4 or 5/5 on the Likert scale, with estimated 95% CIs. Proportions were also split by country, gender and age group. Multivariate analysis adjusting for country, gender and class of age were also performed. Differences of proportion were tested with χ 2 tests. For quantitative values (eg, age, frequency and number of glasses) t-test were used. Statistical significance was set at a two-sided p value of ≤0. 05. All the analyses were carried out with TIBCO Spotfire S+ 8. 1 for Windows (TIBCO Software, Palo Alto, California, USA) or R V. 3. 2. 2 (R Foundation for Statistical Computing, Vienna, Austria). results Among the 3400 GPs who were selected at random (1000 in Switzerland and 2400 in France), 764 (response rate: 47% in postal group and 11% in online group) responded to the questionnaire (428 in Switzerland and 336 in France).
56 Second, the recruitment of GPs was restricted to four French-speaking regions (two in Western Switzerland and two in France); this relatively restricted sample may not be representative of all GPs practicing either in Europe or in Frenchspeaking continental Europe. Third, although nearly half of those invited to participate by post returned the questionnaire, response rates in those who were invited to respond online were very low (11%). Therefore, the hypothesis of a different profile between responders and non-responders cannot be ruled out. However, we assumed a total participation rate of 25% in the calculation of our sample size, and our study sample size was in fact higher than the estimated minimal required sample size for our study. In addition, our sample appears to be representative in terms of age and gender of all GPs practicing in Switzerland and France (data from Pays de la Loire).
Acknowledgements We would like to warmly thank all the doctors who participated in this study,  Antoine Abhamon , our research assistant and the  Unions Régionales des Professionnels de Santé (URPS) Alsace and Pays de la Loire , who were responsible for the organisational aspects of the project, and  Drs Jean-Baptiste Caillard  and  Thomas Herault  for their support and assistance throughout the study.
Statistical analysis Dichotomous variables are given in percent and continuous variables as median and interquartiles (IQ). We tested for correlation between the results of the different methods used to assess the hydration status by calculating Pearson correlation coefficient. Logistic regression models were used to study factors associated with the tolerance of the dialysis sessions. We first performed univariable logistic regressions on all the variables to determine which ones to include in our final model. All variables with a P-value less than 0. 2 were included in the multivariable logistic regression models. All tests were performed at an α-risk value of 0. 05. Statistical analysis was performed with SAS 9. 2.
Therefore, all mental disorders present in the chapter V of the International Classification of Diseases tenth revision (ICD-10) were included in this study  (WHO, 2010) , except nicotine addiction, intellectual disability and dementia, but including topics related to self-harm and suicide, as it has been recommended for estimating the true burden of mental disorders  (Vigo et al. , 2016). We considered research among all age groups including children and adolescents. We excluded grants aimed at academic training, clinical interventions and other health assistance if not associated with research. Whenever possible, we used a bottom-up method to collect data on funding for mental health research as it is deemed more informative for policy making  (Tarricone, 2006). We also collected the total amount of funding dedicated to health research, defined as all research related to any disease or medical specialty including biomedical and clinical research, for each considered funding body in order to determine the share of health research budget allocated to mental health.
Spain In Spain, we used a bottom-up method and identified all public and non-profit funding agencies for health research. Public funding in Spain comes either from the State or from the regional governments of the seventeen autonomous communities, and both types of funding were included. We collected the list of grants for research infrastructures, projects, personal fellowships and awards funded by each agency in the areas of health and mental health during 2011. For some public funders, only the research area (but not the project titles) associated with personal fellowships was available. In those cases, only fellowships from areas of biomedicine, clinical medicine, epidemiology and psychology were included in the study for the computation of the total amount of health research funding. Whenever possible, the amount devoted to mental health research for those fellowships was estimated by extrapolating the percentage of mental health versus health research funding obtained for other research grants of the same agency for which the title of the project was provided.
It is also made less costly by having access to good quality national health registers and databases. Similar considerations would apply for other kinds of research that require appropriate infrastructure to set up and manage, for instance large-scale genetic studies, imaging studies, and large RCTs. However, the final set of high-level priorities for mental health research identified in the ROAMER roadmap in most cases recommended research advances that would require setting up infrastructures  (Wykes et al. , 2015a). This includes the ROAMER recommendations of preventative and lifespan research; investigating causal factors in mental disorders, including -omics and biomarkers  (Schumann et al. , 2014) ; developing and maintaining international interdisciplinary research networks; developing and implementing research using scientific technological advances, including large clinical trials  (Emmelkamp et al.
DespiteFinally, it is regrettable that we could not compare our results with figures of other areas of health research such as cancer or cardiovascular that could be assumed more costly that mental health research. Indeed, all health research fields are not equal in costs and infrastructures requirements to set up, maintain and deliver. For instance, epidemiological research is costly to set up, but not so costly to continue or maintain. It is also made less costly by having access to good quality national health registers and databases. Similar considerations would apply for other kinds of research that require appropriate infrastructure to set up and manage, for instance large-scale genetic studies, imaging studies, and large RCTs. However, the final set of high-level priorities for mental health research identified in the ROAMER roadmap in most cases recommended research advances that would require setting up infrastructures  (Wykes et al.
1-Introduction Lower-limb gait asymmetry (GA) is characteristic of a number of chronic neurological and musculoskeletal diseases  [1] , including cerebral palsy  [2] , Parkinson's disease  [3]  and knee and hip osteoarthritis  [4] [5]. Onset of GA can also follow acute events such as stroke  [6]  and amputation  [7]. Thus, quantification of GA has become central to the clinical monitoring and treatment of many chronic and acute neurological and musculoskeletal diseases  [8] [9]. Gait asymmetry is typically evaluated using kinematic data and spatio-temporal gait parameters (step/stride length and duration). Based on these global variables, indicators have been introduced to quantify asymmetry, including the Symmetry Ratio  [6]  and Symmetry Index  [6]. These indicators provide global asymmetry information for a studied gait cycle, but cannot be used to analyse how asymmetry occurs at specific instances within the gait cycle.
To reduce noise, a representative gait cycle made of a sequence of averaged depth images (obtained with hundreds of gait cycles) was used to compute ILong. However, no benefit of ILong over CRP was investigated. Moreover, the authors validated this approach with a Kinect placed in front of a treadmill in order to capture and use numerous gait cycles. This was achieved using a specialised treadmill with a remotely placed console so as not to obscure the camera view. ILong computed from an anteriorly placed Kinect would not be applicable to standard treadmills available within a clinical setting, all of which have a front fitting console for safety reasons. Therefore, this study aimed to evaluate 1) the validity and sensitivity of the CRP computed with Kinect, 2) validity and sensitivity of ILong for measuring gait asymmetry given the noisy data provided by Kinect, 3) the clinical applicability of ILong by placing the Kinect posteriorly rather than anteriorly to the treadmill, to reduce occlusion caused by front-fitted treadmill consoles, and 4) the number of strides needed to reliably calculate ILong.
The main hypotheses were: -H1: CRP computed with Kinect data is not reliable, supporting the idea of developing a noise-resistant continuous asymmetry index based on Kinect data, -H2: ILong computed with Kinect and Vicon data is valid and sensitive enough to detect asymmetrical gaits compared to natural ones, -H3: placing the Kinect behind the subject does not affect the validity of the ILong, -H4: the number of gait cycles averaged to compute ILong does not strongly affect the results, supporting the idea that a low number of cycles may be possible for some patients.
2. 3. 2-Processing of Vicon data ILong and CRP were both computed using Vicon data. Joint centres were computed using the ISB recommendations, and hip joint centre position was computed  [20]. The resulting joint positions were filtered with a zero-phase Butterworth filter with a 7Hz cut-off frequency  [15]  similar to the Kinect data. Strides were detected using the same method as applied to Kinect data. Vicon and Kinect data were synchronized using longitudinal distance between legs  [15]. CRP at shank and thigh were computed using Vicon data  [12]. ILong has been designed for Kinect data, but could also be used with Vicon data  [16]. Details about the computation of ILong using Vicon data are given in the supplementary materials. Segments between joint centres were used to artificially generate a depth image and stick figures were used in the remaining process to compute ILong.