dataesr/scientific-paragraphs-categorization huggingface dataset:
All PNTM isolates were subjected to rpoB, 16S rRNA, hsp65 and sodA gene in an attempt to unambiguously assign a species to each of them. Based on BLAST analysis using rpoB sequence data, following the previously adopted classification criteria  [11] , we could assign all but 2 isolates (90. 0% classification rate) to one of the following species/complex: M. kansasii, M. gordonae, M. fortuitum/M. fortuitum complex, M. Novocastrense, M. peregrinum, M. porcinum, M. flavescens, M. gadium, and M. chelonae (Table  2 ). In comparison, 16S rRNA sequencing allowed identifying 25 isolates to the species level (83. 3%) (Table  2 ). Among the 9 SGM isolates, seven (77. 7%) were identified as M. kansasii, making up 23. 3% (7/30) of all PNTMs characterized in this study. All M. kansasii isolates were confirmed by 16S rRNA, hsp65, and sodA typing, since they displayed 100% similarity to the type strain, and all proved to be of subtype 1 (data not shown).
Sensory abnormalities are also more difficult to elicit in infants and young children. Several tests can be indicated such as quantitative sensory testing and electroneuromyography but are rarely performed in routine medical practice except to rule out other underlying pathology. 3 Finally, assessment of pain-related disability is important, and quality of sleep, mood and role functioning should be standard assessments for chronic pain conditions including NP. In adults, drug therapy in NP is often unsatisfactory as the specific underlying mechanism is rarely well understood. 9 10 To date, in the absence of age-specific trials, guidelines for the management of NP in children have been developed exclusively based on data from clinical trials in adults. 11 The analgesics that are most frequently prescribed for NP in children include tricyclic antidepressants (amitryptiline and nortriptyline) and gabapentinoids (gabapentin and pregabalin).
MEthOds And AnAlysIs GAbA-1 trial design and setting This GABA-1 trial is designed as a randomised, doubleblind, double-dummy, active-controlled, parallel group, multicentre, non-inferiority trial. Tramadol was considered to be an adequate comparator to gabapentin in GABA-1 study because: (1) its efficacy in the management of NP has been shown in adults  14  and opioids' efficacy data from adults can be extrapolated to children,  22  and (2) it is currently the only weak opioid marketed for children throughout Europe. The choice to conduct a non-inferiority trial was based on the fact that gabapentin is not thought to be necessarily more effective than tramadol for the treatment of neuropathic or mixed chronic pain in children; however, gabapentin may be better tolerated than tramadol. Seven countries involving 14 academic paediatric hospital centres will be recruiting eligible children and adolescents (France: five centres; Italy: two centres; the Netherlands: two centres and Albania, Germany, Greece, Poland, UK with one centre each).
A minimum number of subjects per age group have been defined in agreement with regulators: (1) at least 12 subjects aged 3 months to less than 3 years; (2) at least 30 subjects aged 3 years to less than 8 years and (3) at least 40 subjects aged 8 years to less than 18 years. No maximum number of patients per age group is specified. data collection Electronic Data Capture system for GABA-1 trial is provided by Advice Pharma (Milan, Italy), a company specialised in the computer management of clinical trials and includes the eCRF and data management services. On completion of each visit, a study coordinator specifically designed and trained for the study will record all requested data on a standardised eCRF. The investigator will be responsible for the accuracy of the data entered in the eCRF.
Source documents and electronic health record data will be available to ensure the quality and integrity of all the data recorded in the eCRF. The eCRF will be available for review to designated sponsor representatives at each scheduled monitoring/audit visit. Also, processing and storage conditions of biological specimens for PK, pharmacogenomics and metabolomics analysis are specified in the 'Study manual' prepared specifically for the GABA-1 trial. All samples of PK, pharmacogenomics and metabolomics will be shipped together from each participating clinical site to the Department of Pharmacology of the School of Pharmacy UCL, London, UK, at the end of the trial.
Most of these events were mild to moderate in intensity. As an antiepileptic drug, gabapentin has the potential for increased risk of suicidal thought or behaviour. A common side effect of gabapentin use in adults and children with epileptic conditions was urinary incontinence. Also, presence of hydroureter and hydronephrosis has been observed in rats taking high doses of gabapentin (unpublished data from the pre-GABA study, D6. 1 'DRF Study Report', submitted to the European Commission on 10 November 2014, available on request). For these reason, a renal ultrasound exam will be performed for the youngest subjects (<3 years of age) at the beginning and the end of the study period.
PK and PKPD analysis The population PK analysis of gabapentin and tramadol will provide the time course and variability of drug concentrations in plasma and a covariate analysis will also be performed to explore the influence of relevant demographic factors. Non-linear mixed effects modelling will be performed using NONMEM (Icon Development Solution, Maryland, USA) and covariate search will be based on a two-step approach including forward selection and backward elimination of each covariate. 61 The likelihood ratio test will be used for comparison of hierarchical models and goodness of fit will be assessed by graphical methods. All data manipulation, including graphical and statistical summaries will be implemented in R + software. 62 n the PKPD analysis, secondary PK parameters describing systemic exposure (AUC, Cmax and trough concentration (Cτ) will be derived and used subsequently to assess potential correlations with measures of efficacy.
Acknowledgements The authors would like to thank all collaborators, study investigators and patient representatives ( Mrs Patty Brouwer  and  Mrs Anouk Nijenhuis ) involved in the context of the  FP7 GAPP Project  ( GA n. 602962 ) and under the umbrella of  TEDDY -European Network of Excellence for Paediatric Clinical Research  for their contributions. Authors would like to acknowledge the input of the Ethics Board members,  Dr Helen Sammons ,  Dr Annagrazia Altavilla ,  Mrs Anouk Nijenhuis  and coordinator  Mrs Viviana Giannuzzi. The authors would like to thank the  CVBF, Bari, Italy , and the  PARTNERS platform, Paris, France , for preparing and coordinating regulatory submissions in the different European countries as well as exchanges among partners. The authors would like to thank  Mr Francois Luc  from the  Clinical Investigations Center of the Robert Debré Hospital, Paris, France , for assisting on preparation and layout of figures and tables.
, 2014). The 280 bp hypervariable V5-V6 region of the rrs gene was amplified in duplicates and sequenced with Miseq v. 3. sequencing platform (Illumina, San Diego, CA). Details on the protocol are available in the Supporting Information. Analysis of sequences was performed using mothur v. 1. 37. 6 following the Miseq Standard Operating Procedure described by the developers (http://www. mothur. org/wiki/ MiSeq_SOP)  (Schloss et al. , 2009). A total of 16,710,206 sequences were obtained after alignment of forward and reverse reads. Aligned sequences were selected within a size range of 250-350 bp with less than eight homopolymers and any ambiguous position. All sequences that did not align to the rrs Silva v. 123 database were filtered out. De novo chimera detection was performed using UCHIME implemented in mothur  (Edgar et al.
Metabolomic analysis of the leaf samples of host plant Plantago lanceolata After subtraction of the extremities, the remaining parts of each leaf sample were crushed with a sterile pestle in liquid nitrogen and the frozen powder was freeze-dried for 48 h. The extraction was processed using previously described protocol, and 1 H-NMR spectra of the metabolites were recorded (Supporting Information;  Kim et al. , 2010). NMR spectra were processed with MNOVA software v. 10. 0. 2 (Mestrelab research S. L. , Spain). Model compounds of Aucubin (Sigma-Aldrich, Germany), Catalpol (Sigma-Aldrich) and Verbascoside (Extrasynthese, France) were used for signal assignments of P. lanceolata defensive metabolites. Other primary or secondary metabolite shifts and J-coupling constants obtained from plant material using similar solvents were used as reference  (Kim et al.
Statistical analyses We analysed the data with hierarchical modelling of species communities (HMSC;  Ovaskainen et al. , 2017) , which approach belongs to the class of joint species distribution modelling  (Warton et al. , 2015). HMSC provide simultaneously species-and community-level inference on how species occurrences and/or abundances relate to environmental covariates, and how these relationships are structured with respect to species traits and phylogenetic relationships. HMSC additionally assesses the structure of co-occurrence patterns among the species that cannot be attributed to responses of the species to the measured covariates, either in spatially hierarchical or in spatially explicit context, depending on the nature of the study design  (Ovaskainen et al. , 2017). We performed two separate analyses, called hereafter caterpillar and plant models, which differed in whether the OTU data were derived from caterpillar or plant material.
In both models, the response variable was the vector of rarified sequence counts of the microbial OTUs. We employed a hurdle approach, in which we first used a probit model for OTU presence-absence, and then a lognormal model for OTU abundances conditional on presence. We restricted the analyses to OTUs that were present in at least five samples (562 and 610 OTUs for caterpillars and plants respectively). We further excluded samples for which plant OTUs or metabolites were missing. The analysed data set consisted of 142 caterpillars collected from 55 host plants (Fig. 2 ). In the caterpillar model, our aim was to examine how the OTU composition depended on the properties of the focal caterpillar, and on the OTU and metabolite compositions of its host plant. We included as fixed effects (i) the sex of the individual (0 for female and 1 for male), (ii) the infection status of the individual (0 for non-infected and 1 for infected by the parasitoid wasp), (iii) the abundance of the focal OTU in the host plant where the individual was residing, (iv) the plant OTU community composition and (v) the plant metabolite composition.
e. host plant level), and a non-structured random effect corresponding to the level of the individual caterpillars. In the plant model, we included as the sole fixed effect the plant metabolite composition, and single spatial random effect of the plant. We fitted both the caterpillar and the plant models using the HMSC-Matlab implementation of  Ovaskainen et al. (2017)  with default prior distributions. To examine how much of the variation in OTU occurrences can be attributed to the fixed effects and to associations among the OTUs, we evaluated the predictive power of the model in three different ways. All of these accounted for the fixed effects, but differed on how the random effects were treated. Prediction P1 is aimed at measuring the predictive power based solely on fixed effects, and thus we integrated the random effects over their prior distributions rather than using sampling unit-specific fitted values.
We measured predictive powers by Tjur's R 2  (Tjur, 2009)  for the probit models and standard R 2 for the log-normal models. Given that HMSC framework has not previously been used in microorganism studies and thus may be unfamiliar to microbial scientific community, we ran a series of complementary analyses with more traditional methods to support our HMSC-based results. Specifically, both for caterpillar and plant OTU communities we exploited the Dirichlet-mixture approach, proposed by  Holmes et al. (2012)  to test how many of distinct clusters does the data segregate to. The details are given in Supporting Information. Finally, we analysed whether the overwintering survival of caterpillar nests (siblings of the caterpillars assessed above) was dependent on metabolite and OTU composition of the host plant they were residing on.
ACKNOWLEDGEMENTS Funding for this project was provided by grants from the  European Research Council  ( Independent Starting Grant  No. 637412  ' META-STRESS ' to M. S. ) and the  Academy of Finland  (Decision Nos. 273098  and  265641  to M. S. and  1273253 ,  250444  and  284601  to O. O. ) and the  Research Council of Norway  ( CoE Grant   223257 ), as well as by the  Biocenter Finland  related to the NMR core facility at the  Institute on Biotechnology. We acknowledge  Juha-Matti Pitkänen  for help with DNA extraction,  Sami Ojanen  for the coordination of sampling, and field assistants for sample collection. We would also like to thank  Aapo Kahilainen  for his help with genotyping analysis.
The corresponding datasets are described in  Bessardon et al. (2019). According to Kalthoff et al. (2018), LLSCs form most nights at the three supersites instrumented during the DAC-CIWA field campaign. Their bases are roughly around 300 m above ground level (a. g. l. ) when they form and typically rise up to 800 m a. g. l. at noon on the following day. Defining LLSCs using criteria based on a median cloud base fraction of 100 % at Savé and Kumasi, and a net longwave radiation threshold of -10 W m -2 for Ile-Ife, Kalthoff et al. (2018)   noted some differences in the onset times of the LLSCs. These authors found that the onset times varied on average among the three sites: 21:00 UTC at Ile-Ife, 00:00 UTC at Kumasi, and 03:00 UTC at Savè. Beyond this general description, large variability of the LLSC characteristics is observed from one night to the next.
, 2019). Assuming a mean wind of 6 m s -1 , this would mean that the MI should reach Savè between 19:00 and 21:00 UTC. Adler et al. (2019)  and  Babić et al. (2019a)  studied the processes involved in the formation and evolution of LLSCs. They both highlighted the important role played by the MI and NLLJ within the monsoon flow diurnal cycle. The present study aims to describe the day-to-day variability and mean characteristics of the monsoon flow, the NLLJ, the MI, and the LLSC at Savè, Benin (Fig. 1 ), during the 20 June-30 July 2016 period, based on the DACCIWA dataset. The focus on the Savè supersite for this study is motivated by the fact that only this site was instrumented in such way that continuous profiling of the wind up to several kilometers and continuous determination of the cloud summit were accessible.
Experimental data During the DACCIWA field campaign, several remote sensing and in situ instruments were jointly deployed at Savè by the Karlsruhe Institute of Technology (KIT) and the Toulouse Paul Sabatier University (UPS). These instruments are all described in detail in  Bessardon et al. (2019). The collected dataset has been presented in four published datasets:  Handwerker et al. (2016) ,  Kohler et al. (2016) ,  Wieser et al. (2016)  (for the KIT instrumentation), and  Derrien et al. (2016)  (for the UPS instrumentation). Furthermore,  Kalthoff et al. (2018)  give a comprehensive overview of the observations made at the three ground-based supersites of the DAC-CIWA projet, including Savè. Below, we describe the instruments used in our statistical analysis, according to the object of study.
2 K below 550 m, 0. 5 K in the 550-1000 m layer, and 2 K in the 1000-2000 m layer. This finding is consistent with the accuracies noted by  Crewell and Löhnert (2007)  (< 1 K below 1000 m). Only microwave radiometer measurements below 550 m are used in this study. The available dataset of this instrument covers the 30 June to 30 July time period.
Monsoon flow characteristics Applying the previous criteria to the UHF wind profiler dataset, the temporal evolutions of the monsoon flow depth and strength from 15:00 UTC on day D-1 to 15:00 UTC on day D are calculated for day D from 20 June to 30 July 2016. The results are presented in Fig. 2. Except for 13, 16 and 17 July, dates for which the UHF wind profiler data are missing, the short periods shown in white in Fig. 2 , are for a wind direction not falling within the 135-270 • sector. As discussed in the previous section, the strong variability of the monsoon flow depth observed on some days, like 27-28 June, is associated with a large wind-sheared layer and a low wind. However, the intra-seasonal variability of the monsoon depth (from a few hundred meters to 4 km) and strength (from almost zero wind to 10 m s -1 ) can often be linked to synoptic conditions  (Couvreux et al.
LLSC detection West African LLSCs have not been extensively studied in the past, generally due to a lack of observations. However, the systematic visual observations by local meteorologists at every meteorological station made over a time period of years constitute a unique dataset  (Schrage and Fink, 2012). Besides this historical approach, several instruments have been used in the context of international projects to estimate LLSC characteristics from a vertical point of view: radiosoundings, cloud radar  (Bouniol et al. , 2012) , and ceilometer  (Schrage and Fink, 2012). Satellite data have also been used to analyze the horizontal variability LLSCs. Van Der Linden et al. (2015)  and  Bouniol et al. (2012) , for example, made a climatology of the occurrence of the LLSCs in this region. Here, we use a ceilometer to estimate the CBH (considering only CBHs below 1500 m a.
Discussion and conclusions An analysis of a 41 d DACCIWA dataset during the WAM in 2016 in Benin enabled a quantified documentation of lowlevel stratiform clouds and dynamical structures in the low troposphere. A key need for observations to compare with numerical weather and climate models motivated the field campaign in the DACCIWA project. The months of June and July in West Africa are characterized by deep convection, and the conditions at Savè supersite are frequently disturbed by MCSs. Among the 41 d period, 16 d have been excluded from the quantified statistics. How-   ever, many other MCSs may have impacted the flow between the coast and Savè without being detected at Savè. These MCSs are an important part of the monsoon in West Africa and participate in the variability of the low-level cloud and dynamical structures discussed below.
Acknowledgements. The  DACCIWA  project has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603502. The authors thank also  Laboratoire d'Aérologie ,  Université de Toulouse ,  CNRS ,  UPS, France , and  KIT (Karlsruhe Institute of Technology)  and  UPS  ( Université Toulouse ) for helping to install the equipment, as well as the people from INRAB in Savè for allowing the equipment on their ground. We thank the  Aeris data infrastructure  for providing the DACCIWA Operation Center during the campaign and the access to the data used in this study. Financial support. The research leading to these results has received funding from the  European Union Seventh Framework Programme  (FP7/2007-2013) under grand agreement no. 603502.
Data availability. The DACCIWA embargo period is over; the data of the Savè supersite are available on the baobab database (https://doi. org/10. 6096/dacciwa. 1618, Derrien et al. , 2016; https://doi. org/10. 6096/dacciwa. 1686, Handwerker et al. , 2016; https://doi. org/10. 6096/dacciwa. 1690, Kohler et al. , 2016; https://doi. org/10. 6096/dacciwa. 1659, Wieser et al. , 2016) for scientists interested in boundary-layer studies in southern West Africa (http://baobab. sedoo. fr/DACCIWA, OMP, 2019).
Measured Groundwater Temperatures. There have been several groundwater measurement campaigns in the metropolitan area of Paris. Data from 1990 to 2015 were measured by local authorities in wells of the Paris metropolitan area and are available in the French national database on groundwater resources  [51]. The well coordinates and measuring depths are accessible at InfoTerre  [52]. Even though a rich dataset of 3018 GWT measurements exists, the records are heterogeneous, often incomplete over time and information on the specific sampling methods is sparse. The majority of GWT data were measured on site while pumping. Therefore, the given depths represent the mean depth of the screened well interval. During preprocessing, we excluded outliers exceeding a 6 K offset to the median for a single well location for further analysis.
Moreover, measured values with an offset above 2. 5 times the interquartile range (IQR) were expelled if the IQR was above 1 K. This was necessary to exclude outliers most probably caused by poor handling during sampling and typos. In addition, four outliers that are directly influenced by a waste disposal site north of Paris were eliminated. A total of 74 outliers (2. 3% of the available data) were removed from the data pool. The remaining data covers 377 sampling wells in the outer and inner suburbs of Paris. The derived spatial coverage is nonuniform and there are no data points in the city center (Figure  1 ). Diffre et al. [53]  provide GWT data that include measurements in the city center (Figure  1(b) ) from February 1977. As the corresponding coordinates of the observation wells were not given in that report, they had to be extracted from the online database InfoTerre  [52].
Satellite-Based Estimation of Groundwater Temperatures. Recently, satellite data was suggested to investigate the thermal ground conditions in cities and SUHIs. Zhan et al. [54]  estimated ground temperatures in Beijing, China, from MODIS land surface temperatures. The results were compared with recorded temperatures (measuring depths of 0. 05 m, 0. 40 m, and 3. 20 m). A time delay between the maximum temperatures of the atmosphere and the ground was detected depending on the measuring depth. Hafner and Kidder  [55]  used Advanced Very High-Resolution Radiometer (AVHRR) satellite data to determine the albedo of the land surface as well as soil thermal and moisture properties around Atlanta, USA. These parameters were utilized for a numerical simulation of Atlanta's atmospheric temperature dynamics.
For a global GWT dataset, they could reduce the root mean squared error (RMSE) of prediction by 0. 5 K to 1. 4 K and increase the squared Spearman correlation coefficient by 0. 5 to 0. 95 compared to a prediction relying solely on LST. Due to the fact that we have a strong spatial variety of our measured GWT in Paris (Figure  1 ), in the following method, we combine the individual approaches of Benz et al. [38]  and Benz et al. [59]  to estimate GWT in the region of Paris, both for rural and urban areas at the same time (Figure  2 ). This combined approach has been suggested by Benz  [60]  for German cities, and it has the specific advantage of covering natural and urban processes within one estimate. Estimated groundwater temperatures (eGWT) are calculated in three steps: eGWT rur = LST + 3 5 ± 0 2 • 10 4 K m 2 • s kg • ET + 6 6 ± 0 3 K • SD, 1 eGWT urb = max LST, LST • 1 -BD + BT • BD, 2 3 Geofluids eGWT c = max eGWT rur , eGWT rur • 1 -BD + BT • BD 3 The first step consists of estimating GWT in rural areas.