dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Protection Participants will be allocated a unique 8-digit identification number made up of a center number, an individual patient number, and patient initials. This number is assigned to the patient and is used on assessment forms prior to transfer of data, so that they are anonymized at source. A list of names and corresponding identification numbers will be kept separately and securely on an encrypted university server. All guided support sessions will be audio-recorded if consent is given, and they will be stored on an encrypted university server.
Data Monitoring The DMEC will receive notice of SAEs and SARs for the sample as a whole and per treatment arm. If the incidence of SAEs of a similar type are greater than would be expected in this population, the DMEC will be able to retrieve data according to trial arm, to determine any evidence of excess in either arm. NSAEs will be included in the safety reporting of the completed trial. Suspected unexpected serious adverse reactions (SUSARs) will be reported separately.
Data Collection Qualitative semistructured interviews, digital audio-recorded, with fully informed consent, will be conducted with participants. An experienced qualitative researcher will conduct the interviews, using a semistructured approach  [54]. By ensuring the same topics are covered in each interview, we will collect comparable data about participants' experiences of the trial and treatment within each group. In addition, the semistructured approach will allow greater flexibility for participants to highlight their specific concerns, meanings, and priorities, even if not anticipated by the researchers. Topics will include before and after trial well-being, expectations of treatment, understanding of "baseline" and "recovery," the meaning of exercise, barriers and facilitators to treatment, and any outside influences on trial participation.
Data Management and Analysis The audio-recordings from the interviews will be professionally transcribed (a confidentiality agreement will be in place). The transcription will be reviewed against the audio by the researcher for errors and to remove any identifying information, and then returned to the participant to check accuracy and add any clarifying points at the end of the interview, with a deadline of one month to reply. Thematic analysis (ie, "identifying, analyzing, and reporting patterns within data") is a basic building block of all kinds of qualitative analysis  [55] , and will be the approach used for analysis in this study. Data will be inputted and coded in the qualitative data analysis software environment, NVivo. NVivo will aid the coding of themes, organization, and searching of all interviews in both patient outcome categories (improvement vs worsening).
Acknowledgments This paper presents independent research funded by the  National Institute for Health Research (NIHR)  under its  Research for Patient Benefit (RfPB) Programme (Grant Reference Number   PB-PG-0610-22060 ). The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR , or the Department of Health. This trial was sponsored by  Queen Mary, University of London. This study was supported by the  United Kingdom Clinical Research Collaboration -registered King's  Clinical Trials Unit at King's Health Partners , which is partly funded by the  NIHR Biomedical Research Centre for Mental Health at South London and Maudsley NHS Foundation Trust and King's College London  and the  NIHR Evaluation, Trials and Studies Coordinating Centre. The members of our  TSC  and  DMEC are Christopher Williams ,  Astrid Fletcher ,  Charlotte Feinman ,  Irwin Nazareth ,  Alastair Miller ,  Rona Moss-Morris , and a patient representative.
Analysis of genetic diversity and differentiation For nSSR loci, we computed observed (H o ) and expected (H e ; Nei 1987) heterozygosities and inbreeding coefficients (F IS ), and performed locus-by-locus exact tests of Hardy-Weinberg proportions (via the Markov-chain algorithm of  Guo and Thompson 1992 ; run for 10 7 steps after 10 6 dememorization steps), using ARLEQUIN ver. 3. 5. 1. 2 software  (Excoffier and Lischer 2010). Additionally, we calculated observed and effective  (Nielsen et al. 2003 ; eqn 16) numbers of cpSSR haplotypes and nSSR alleles. The population inbreeding model in INEST ver. 1. 0 software  (Chybicki and Burczyk 2009)  was used to jointly estimate null allele frequencies and inbreeding coefficients for nSSR loci, which provides information about the relative effect of null alleles versus actual inbreeding on observed deviations from Hardy-Weinberg proportions.
Genetic differentiation between samples was computed using both the AMOVA- based F ST -statistic implemented in ARLEQUIN software and Jost's bias-corrected differentiation index (D est ; Jost 2008) implemented in DEMETICS  (Gerlach et al. 2010 ) and SPADE  (Chao and Shen 2010). We tested whether differentiation estimates were significantly different from zero by randomly permuting genotypes among samples 10 000 times.
Acknowledgement We especially thank  Fernando del Caño  ( INIA-CIFOR, Madrid, Spain ) for his assistance during field collections, and  Carmen Garc ıa (INIA-CIFOR) ,  Ilaria Spanu  and  Federico Sebastiani  ( IBBR, CNR, Florence, Italy ) for support with laboratory work and genotyping. The help of Jes us Mart ınez-Fern andez (INIA-CIFOR) in producing Figs 1 and 2 is gratefully acknowledged. We also thank the editor and reviewers for helpful comments. This work was funded by the  Spanish Ministry of Economy and Competitiveness  (project  CGL2009-09428  and PhD grant  BES-2010-031797  to GMU). GGV wish to thank  COST Action FP1202  and the  Italian MIUR project 'Biodiversitalia' (RBAP10A2T4)  for the financial support.
Acknowledgements We are grateful to  Yoshiaki Tamura  ( GGP Datacenter, Japan ) for providing gravimetric data,  Severine Rosat  ( EOST, Strasbourg, France ) for technical discussions,  NIED  for providing F-net seismic data and  Shengji Wei  ( Earth Observatory of Singapore ) for providing an updated moment rate function. This work was supported by the  NSF  Grants  PHY 0855313 ,  PHY 1314529  and  PHY 1205512  to UF. J. -P. M. acknowledges the financial support of  Institut Universitaire de France. B. W. acknowledges sabbatical support from the  UniversiteParis Diderot  and  CNRS  through the  APC , where part of this work was carried out. J. P. A. acknowledges support by a grant from the  Gordon and Betty Moore Foundation  to Caltech. K. J. is supported by a PhD fellowship of programme  IDEX-USPC 'Double Culture'.
Data availability. The data from the superconducting gravimeter of Kamioka Observatory are publicly accessible from the Global Geodynamics Project web site (http://www. eas. slu. edu/GGP/tohoku2011. html) or upon request to Yoshiaki Tamura. The F-net seismic data are available on the web site: http://www. fnet. bosai. go. jp/waveform/?LANG=en. All codes were written for this study and a copy of the data are available on GitHub at the following URL: https://github. com/ kjuhel/prompt-gravity.
Multilocus-sequence-typing (MLST). MLST is based on the sequence determination of loci in the seven house-keeping genes abcZ, bglA, cat, dapE, dat, ldh and lhkA  26, 27. Each unique nucleotide sequence is assigned to an arbitrarily defined allele number, and the combination of allele numbers at the seven loci defines the allelic profile or ST. These data can be retrieved at the web-based MLST database from Institut Pasteur (http://bigsdb. web. pasteur. fr/listeria/). To perform MLST, genomic DNA of all isolates was obtained with the guanidium thiocyanate-phenolchloroform extraction method  66. Hundred-twenty-two isolates were subtyped by conventional MLST as previously described  27. Briefly, the seven housekeeping genes were amplified by PCR and the obtained PCR products were sequenced using universal sequencing primers  26.
Allelic profiles and corresponding STs were determined with the BioNumerics software (Version 7. 1, Applied Maths, Austin, TX). With the availability of next generation sequencing technologies we moved on to in silico MLST following library preparation and genomic DNA sequencing using either an Illumina HiSeq 2000 or 3000 platform (193 isolates) or an Illumina MiSeq platform (35 isolates) at the Institute of Genetics, Vetsuisse Faculty, University of Bern. The obtained data were assembled using Velvet 1. 2. 10: algorithms for de novo short read assembly using Bruijn graphs  67  and housekeeping gene allele numbers and the respective ST were determined with a specifically created workflow in Geneious R7 (http:// www. geneious. com). Finally, all MLST data were entered into the BioNumerics software in order to construct a minimum spanning tree (MST) and to identify CCs and evolutionary lineages.
The number of repeats in each locus was determined according to Sperry et al. 68. Missing products were registered as "zero alleles" to facilitate MLVA type and cluster analysis. MLVA data were uploaded into the BioNumerics software (Version 7. 1, Applied Maths, Austin, TX). A MST was created from a total of 219 L. monocytogenes isolates to identify complexes that were defined to contain strains differing in a maximum of two MLVA loci. The MST was produced using a maximum number of N-locus variants (N = 1) with a weight of 10000. MLVA complexes were identified in the MLST-based MST and vice versa (Fig. S1 ). Serotyping. For two isolates the molecular serovar group type was determined using the PCR based protocols as previously described  69. For 226 isolates with available NGS data, the serovar group type was determined in silico using a specifically created workflow in Geneious R7 (http://www.
monocytogenes. At indicated time points post infection, cells were washed with PBS and lysed with 0. 5% Triton-X100 (Sigma-Aldrich) to harvest CFUs. Serial dilutions were plated on brain heart infusion (BHI) agar and CFUs were quantified. For testing of invasiveness, CFUs were harvested at 2 h and for testing of intracellular replication, CFUs were harvested at 4 h, 8 h and 24 h post infection  46. Three independent experiments were performed in triplicates, and results were normalized to the inoculum. Virulence-associated gene analysis. The nucleotide and amino acid sequences of 45 previously described virulence factors known to be involved in the virulence of L. monocytogenes  39  were compared between 94 isolates with NGS data of sufficient quality (Table  S1 ). The 94 isolates belonged to 25 STs and included 35 rhombencephalitis isolates (cattle = 15; small ruminants = 20), seven clinical non-encephalitic isolates (cattle = 5; small ruminants = 2) and 52 environmental isolates (cattle = 33; small ruminants = 19).
The data was analyzed for mutations associated with attenuated virulence described in previous studies  23, 26, 41. Using the sequences of the 45 virulence genes from EGD-e (GenBank accession NC_003210), the corresponding genes were extracted from our 94 genome sequences after a MAFFT v7. 222 alignment  70. This was then followed by the construction of neighbour-joining trees based on the nucleotide level with synonymous and non-synonymous mutations and on the amino acid level with Geneious R7 (http://www. geneious. com). Additionally, neighbour-joining trees were built based on the concatenated nucleotide and amino acid sequences of all virulence factors following a MAFFT v7. 222 alignment. Recombination events in the 45 virulence genes were computed using the ClonalFrame v1. 1  47  and ML  71  as described in ref.
Acknowledgements We thank the farmers for giving us the opportunity to collect samples after obtaining previous information on the study aims. We acknowledge  Andreas Thomann  and  Amandine Ruffieux  of the  Institute of Veterinary Bacteriology, Bern  for useful discussions and help with the laboratory work, as well as the  Institute of Genetics, Bern  for producing NGS data. We thank  Martin Ganter  of the  University of Veterinary Medicine, Hannover  for collecting environmental samples of small ruminant farms in Germany. This work was financially supported by the  Swiss National Foundation SNF   Sinergia grant   CRSII3_147692. The funders had no role in the study design, data collection and interpretation or in the decision to submit this work for publication. Anna Oevermann's professorship for comparative neuropathology is funded by the  Ernst-Frauchiger foundation.
Data analysis The beginning of a trial was defined as the time point when the starter key was released, and the end of a trial was defined as the time point when the hand velocity dropped below 2 cm/s. The position data was recorded in earth-fixed coordinates. During post-processing, the positions of every trial were aligned, rotated, and scaled to a unity coordinate system such that the actual starting and target positions were located at 0/0 and 0/100, respectively. The data was then spatially normalized to obtain 100 equidistant data points per reaching trajectory, and averaged over all trials for each condition and participant. To assess the effect of TMS on the reaching behavior, we compared for each rotation condition the x-component of the trajectories (i. e. the direction perpendicular to the "ideal" straight reaching trajectory) with TMS stimulation to those without TMS stimulation across participants.
Acknowledgments This research was funded by a PhD scholarship from the  Max Planck Society  to A. R. , and by the  WCU (World Class University ) and  BK21 PLUS Program  through the  National Research Foundation of Korea  funded by the  Ministry of Education. We are thankful to  Betty Mohler  and  Joachim Tesch  for their assistance with the technical setup, and to  Sonja Cornelsen  for her help in data acquisition. The experiment was realized using Cogent Graphics developed by  John Romaya  at the LON at the  Wellcome Department of Imaging Neuroscience, University College London, UK.
The measured IASI columns tend to show less variability compared to surface measurements. Our study uses data from the morning overpass (09:30 local solar time when crossing the Equator) of IASI onboard the MetOp-A satellite from 2008 to 2012. Each day is gridded by computing the mean column concentration (and other properties) weighted by relative error of the native retrievals within each GEOS-Chem horizontal grid box at the nested resolution (0. 5 • × 0. 667 • ). The results of this gridding and averaging scheme are shown in Fig. 2  as the mean of all summers during the study period. We filter out retrievals with cloud cover greater than or equal to 25 % and skin temperature less than or equal to -10 • C as recommended by  Van Damme et al. (2014a). Post-gridded values are filtered by removing grid boxes with greater than 75 % relative error.
Activity scaling The base scenario anthropogenic ammonia emissions are constant for all years of study. This is not realistic due to year-to-year changes in agricultural activity and the meteorological dependence of emissions (Sect. 6. 2). We define agricultural activity as livestock population and fertilizer application. Using data from the US Department of Agriculture National Agricultural Statistics Service (USDA NASS) (www. nass. usda. gov), we compute annual scale factors for agricultural activity based on the changes in these sources (for a description of the methods, see Sect. A1 in Appendix A). As shown in Fig. 11 , this scaling results in large increases in livestock ammonia emissions compared to the base scenario in Iowa (13 % by 2012), although this is relatively constant during the study period (only a 2.
A1 Activity scaling Scaling of agricultural activity refers to the influence changing livestock population and fertilizer application has on ammonia emissions. For livestock population, we use data from the USDA NASS for cattle, goats, chickens, hogs, and sheep. The portion of beef cattle vs. dairy cattle is determined by the ratio of beef cows to dairy cows. The census population of each species per county is gridded to the nested simulation grid box resolution for  2002, 2007, and 2012  to obtain the animal density in each grid box. We weight each species density by its relative emission factor (emission per head) to calculate the emissions value per grid box  (Pinder et al. , 2004; Faulkner and Shaw, 2008; Velthof et al. , 2012; Paulot et al. , 2014). Linear interpolation of effective emission is applied between census dates, and the emission for each year is scaled against the base year of 2005, as that corresponds to the NEI-2005 used in the base scenario, to achieve an annual scale factor for livestock population.
These scale factors are applied to the livestock portion of the anthropogenic ammonia emissions. We use data for county-wide fertilizer expense (gridded to nested resolution grid boxes) and the national fertilizer price index from the USDA NASS to develop annual scaling factors for fertilizer application. Fertilizer expense census data are available for  2002, 2007, and 2012. Each of these years is matched with the fertilizer price index (price per mass) for that year to calculate the total fertilizer mass purchased in each grid box. We assume all fertilizer purchased is applied to a field or that a similar fraction of fertilizer purchases is left unused in each year. Fertilizer mass for each of these years is interpolated linearly and then scaled in comparison to 2005 values as with livestock population above.
Acknowledgements. This work was supported by  NOAA  ( NA12OAR4310064 ). We thank  D. Ridley  for assistance with surface site weighted sampling and gridding county-level data,  F. Paulot  for guidance with emissions scaling,  S. McKeen  for NEI-2005 emissions sectors,  A. van Donkelaar  for NEI-2005 emissions gridding,  J. Mao  for implementation of FINN in  GEOS-Chem , and the  GEOS-Chem  support staff and community for model documentation and issue resolution. IASI is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). C. Clerbaux  is grateful to  CNES  for financial support. L. Clarisse  is a research associate with the  Belgian F. R. S. -FNRS. Edited by:  R. Harley Reviewed  by: two anonymous referees.
Data availability The GEOS-Chem model data used in this study are archived at MIT and are available on request from the authors (schiferl@mit. edu). The IASI ammonia observations are available at www. pole-ether. fr/etherTypo/ index. php?id=1700&L=1 and the product used for this study (Van Damme et al. , 2014a)  is available on request at ULB. The AMoN ammonia observations are available at nadp. sws. uiuc. edu/dataLib/AMoN/csv/all-ave. csv (NADP, 2014). The airborne ammonia observations are available for individual campaigns: (1) ICARTT, www. esrl. noaa. gov/csd/groups/csd7/measurements/ 2004NEAQSITCT/P3/DataDownload/ (NOAA ESRL, 2016); (2) TexAQS, www. esrl. noaa. gov/csd/groups/csd7/ measurements/2006TexAQS/P3/DataDownload/ (NOAA ESRL, 2015b); (3) CalNex, www. esrl. noaa. gov/csd/ groups/csd7/measurements/2010calnex/P3/DataDownload/ (NOAA ESRL, 2011); (4) DISCOVER-AQ, www-air.
larc. nasa. gov/cgi-bin/ArcView/discover-aq. ca-2013 (NASA LaRC, 2015a); (5) SENEX, www. esrl. noaa. gov/csd/ groups/csd7/measurements/2013senex/P3/DataDownload/ (NOAA ESRL, 2015a); (6) FRAPPE, www-air. larc. nasa. gov/cgi-bin/ArcView/discover-aq. co-2014?C130=1 (NASA LaRC, 2015b); (7) SONGNEX, available on request. The USDA NASS data are available at quickstats. nass. usda. gov (USDA, 2015). The IM-PROVE speciated PM observations are available at views. cira. colostate. edu/fed/DataWizard/Default. aspx (FED, 2015). www. atmos-chem-phys. net/16/12305/2016/ Atmos. Chem. Phys. , 16, 12305-12328, 2016.
A concordance of 75% indicates that 75% of the samples could be correctly assigned to one of the two binary categories (H0˚or the higher HARS severity group) using the predictive model. In the case of complete separation of data sets (concordance equals 100%), missing p-values using logistic regression analysis were replaced by p-values (Chi-Square) generated by frequency distribution comparisons. All calculations were performed using SAS (release 9. 2, Cary NC, USA).
Bioinformatic approach: Analysis of miRNA-mRNA interactions We investigated whether there were potential regulatory interactions between the 33 HARS specific non-coding miRNAs identified in this study and the 29 coding genes (mRNAs) with HARS associated regulation, which we defined in our previous study  [15]. For this purpose we have downloaded a dataset encompassing 22. 9 million predicted miRNA-mRNA interactions from the TargetScan 7. 0 database (http://www. targetscan. org/; [18] ) and filtered it based on our lists of HARS related mRNA (n = 29) and miRNAs (n = 33). From the remaining miRNA-mRNA interactions, we selected those that were predicted based on the human genome sequence as qPCR and microarray analysis in our study do also refer to the human genome. Results of this screen, and corresponding annotation data such as RNA expression levels and cumulative weighted context++ scores, which indicate how effectively a given miRNA may target mRNAs  [19] , were visualized in Cytoscape  [20].
Results: We describe existing biomarkers in NF and report consensus recommendations for SOP and a minimal clinical dataset to accompany samples derived from patients with NF1, NF2, and SWN in decentralized biobanks. Conclusions: These recommendations are intended to provide clinicians and researchers with a common set of guidelines to collect and store biospecimens and for establishment of biobanks for NF1, NF2, and SWN. Neurology ® 2016;87 (Suppl 1):S40-S48 GLOSSARY 5-S-CD 5 5-S-cysteinyldopa; ADM 5 adrenomedullin; MIA 5 melanoma inhibitory activity; MPNST 5 malignant peripheral nerve sheath tumors; NF1 5 neurofibromatosis 1; NF2 5 neurofibromatosis 2; PNF 5 plexiform neurofibromas; REiNS 5 Response Evaluation in Neurofibromatosis and Schwannomatosis; sAXL 5 soluble growth factor receptor Axl; SOP 5 standard operating procedure; SWN 5 schwannomatosis.
The REiNS group is composed of several working groups. 2 The biomarker working group, which includes neurologists, oncologists, geneticists, pathologists, dermatologists, pediatricians, and basic scientists, has the goals to (1) define biomarker needs in NF1, NF2, and SWN; (2)  summarize existing data on biomarkers in NF1, NF2, and SWN; (3) outline initial recommendations for sample collection and biomarker development; and (4) harmonize sample collection and processing protocols where possible to allow for data comparison between studies by publishing standard operating procedures (SOPs). This article summarizes the progress toward these goals. The biomarker group has concentrated on biomarkers in blood, urine, and tissue. Imaging biomarkers are discussed separately by the REiNS imaging working group.
3 THODS The biomarker group first performed a literature search, and reviewed and summarized existing data on biomarkers in NF1, NF2, and SWN. The group then met during a series of meetings in collaboration with the Children's Tumor Foundation (1) to establish SOPs for sample collection of NF tissue specimens that facilitate data comparison between studies and (2) to develop a minimal clinical dataset that would accompany each sample. The working group anticipates that these recommendations will be updated periodically as new information on biomarkers becomes available. Detailed assay protocols will be available on the REiNS Web site (www. reinscollaboration. org).