dataesr/scientific-paragraphs-categorization huggingface dataset:
Additional work is needed on the CRM technique to improve the measurement precision at a level closer to that observed for other instruments. -Biases in the measurements by the LP-LIF instruments are lower than their limit of detection with a few exceptions in some experiments for the Lille and Leeds instruments. The instrument zero in the PSU instrument varied by 1. 3 s -1 , but this value is amplified by the dilution factor of 5 that is not normally used in field measurements. The smaller number of data points for the CRM instruments makes conclusions about a day-to-day variability of a potential bias less accurate. However, the distribution of measurements during zero-air measurements becomes more compact for the LSCE and MPI CRM instruments if an offset is subtracted for each individual experiment (Table  5 ).
-Maximum absolute deviations of LP-LIF measurements from calculated reactivities or measured reactivities from the instrument taken as the reference (FZJS) are 12 % (mostly less than 5 %). Deviations are smaller than the accuracy of the calculation from measured OH reactant concentrations (Table  2 ). Results from this campaign demonstrate a high accuracy of LP-LIF instruments. -The accuracy of CRM instruments varies with chemical conditions. Whereas measurements agree on average with calculated reactivities within 5 % (higher deviations for LSCE CRM 31 %) if only CO, CH 4 or pentane are present, deviation from measurements with the FZJS LP-LIF instrument are up to a factor of 2 for mixtures containing terpenic compounds. Also, the scatter of data is larger in these cases. While the impact of OH recycling in the terpenic chemistry cannot be ruled out, losses of these compounds in inlet systems can explain the observed discrepancies.
Future work should focus on improving its performances in terms of precision and limit of detection. In addition, the accuracy of measurements would improve if corrections could be lowered. The results of this campaign demonstrate that all detection schemes that are currently applied to OH reactivity measurements give reasonable results for a range of chemical conditions which are relevant for ambient air measurements. These first comprehensive comparison campaigns were conducted to assess the current performance of the instruments. The results already led to the implementation of changes in some instruments to achieve better data quality. More work will be done in order to improve the instrument performance for issues that have been identified which currently limit the precision or accuracy of measurements.
Acknowledgements. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (SARLEP grant agreement no. 681529 ). This study was supported by the  European Commission 's  FP7 programme (PIMMS  grant agreement no. 287382 ). C. Schoemaecker ,  M. Blocquet ,  S. Coudert ,  S. Batut  and  C. Fittschen  acknowledge financial support by the  French ANR agency  under contract no. ANR-11-LabEx-0005-01   CaPPA (Chemical and Physical Properties of the Atmosphere)  and by the  CNRS  through  PRC  project no. 233277. S. Dusanter  and  N. Locoge  acknowledge support from the  French National Research Agency  ( ANR-11-LABX-0005-01 ) and the  European Funds for Regional Economic Development (FEDER)  through the  CaPPA (Chemical and Physical Properties of the Atmosphere)  project, and support from the  Région Hauts-de-France  and the  Ministère de l'Enseignement Supérieur et de la Recherche  ( CPER Climibio ) and the  European Fund for Regional Economic Development.
D. Cryer ,  C. Brumby ,  L. Whalley ,  D. Stone ,  P. Seakins  and  D. Heard  acknowledge financial support from the  Natural Environment Research Council (NERC)  under grant numbers  NE/J008990/1 ,  NE/H003193/1  and  NE/D004756/1 , and also the  NERC  funded  National Centre for Atmospheric Science. W. Brune ,  A. Thames , and  D. Miller  acknowledge support from the  US National Science Foundation  grant  AGS-1450838  and thank  Hartwig Harder 's group at the  Max Planck Institute for Chemistry in Mainz and Ronald Cohen's group  at the  University of California, Berkeley , for lending parts to complete the PSU instrument used in this comparison study. The authors thank  Mathias Bachner  (FZJ),  Georg Stange  (DWD) and  Markus Rudolf  (MPI) and  Ronald Cohen's group  ( University of California, Berkeley ) for technical support.
The online version of this article contains a data supplement. There is an Inside Blood Commentary on this article in this issue. The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked "advertisement" in accordance with 18 USC section 1734. disease comorbidities. The patient was successfully treated on trial (NCT01347242) with GT using the same self-inactivating vector (LV-w1. 6 WASp), which had previously recruited only children. 14.
Clinical protocol The patient was managed under the care of the clinical immunology and adult bone marrow transplant services at the Royal Free London Hospital NHS Foundation Trust (London, England). The patient was treated on trial (#NCT01347242) sponsored by Genethon. 14 The trial protocol was approved by the UK Medicines and Healthcare Products Regulatory Agency, Gene Therapy Advisory Committee, and the National Research Ethics Service. Autologous hematopoietic stem cells were collected from mobilized peripheral blood (after cryopreservation of an unmodified back up stem cell harvest) and were genetically modified ex vivo during concurrent reduced-intensity conditioning of the patient, as described below. He received IV methylprednisolone 70 mg (1 mg/kg) on days 28 to 24 as prophylaxis against a potential granulocyte colony-stimulating factor-induced inflammatory reaction, lenograstim 1.
T-cell repertoire analysis by TCR spectratyping Clonality of T cells was monitored using TCR V b spectratyping. Briefly, the CDR3 region of the b chain was amplified from patient complementary DNA using a single constant region primer with 24 variable region primers. A fluorescent run-off reaction was then carried out, and the products were run on a 3500xL Genetic Analyzer. Subsequent spectratypes were analyzed using GeneMapper 5 software and flow analysis used the IOTest b Mark assay (Beckman Coulter).
The distance between the vector-host junction and the breakpoint of the DNA produced by shearing can be used to differentiate distinct cells with the same integration site. Since the DNA is randomly sheared via sonication prior to amplification, these DNA fragment lengths, or "sonic lengths,"  19  can be used to estimate the abundance of cell clones, avoiding the need to estimate based on read counts acquired after biased amplification and sequencing. Cells harboring the same integration site can be differentiated and directly counted by sonic length and then summed to estimate the abundance ("sonic abundance") of the integration site within the population. DNA sequence reads used in this study are available at the National Center for Biotechnology Information Sequence Read Archive under accession number BioProject ID PRJNA387194.
Acknowledgments The authors acknowledge support from the  Clinical Development Department of Genethon , notably that of  G. Honnet ,  M. L. Gourlay-Chu , and V. Delahais. E. C. M. and S. O. B. are supported by the  UCL Hospitals National Institute for Health Research Biomedical Research Centre. The study is sponsored by  Genethon , which is supported by funds from  AFM/Telethon  and the  French Muscular Dystrophy Association. A. J. T. is supported by the  Wellcome Trust  ( 104807/Z/14/Z ). The study was also supported by the  National Institute for Health Research Biomedical Research Centre at Great Ormond Street Hospital for Children NHS Foundation Trust and University College London. Authorship Contribution: E. C. M. , S. O. B. , T. F. , and A. J. T. analyzed the data and wrote the manuscript; E.
Following this, samples were centrifuged for 5 min at 350 g, the supernatant was removed and the samples resuspended in 200 μl PBS + 1% FCS for flow cytometric analysis. The nuclear stain DAPI was added to the samples as a live/dead marker. Analysis was carried out using the BD LSR Fortessa and data were collected using BD FACSDiva software and analyised using FlowJo software (Treestar, Ashland, OR, USA).
Microarray analysis Microarray data analysis was performed by Fios Genomics Ltd (Bioquarter, Edinburgh, UK). The analysis included quality control and exploratory analysis of the data sets followed by identification of differentially expressed genes (DEGs) and also functional analysis for enrichment of KEGG pathways amongst the DEGs. Raw data underwent quality control analysis using the arrayQualityMetrics package in Bioconductor  (Kauffmann and Huber, 2010)  to identify outliers. Two samples (both from PTL group) failed the quality control checks and were removed from further statistical analysis of the microarray data. The arrayQualityMetrics analysis scored the samples on the basis of four metrics (maplot, boxplot, heatmap and manual inspection). Boxplots of the raw, transformed and normalized data were also assessed manually, as were outlier and sample relation plots were generated for all stages of the processing.
This work was supported by  Medical Research Council  (grant number  MR/ L002657/1 ) and Tommy's, the baby charity. Jane Norman  has had research grants from the charity Tommy's and from the  National Institute for Health Research  on PTB during the lifetime of this project. Jane Norman also sits on a data monitoring committee for GSK for a study on PTB prevention and her institution receives financial recompense for this. The other authors do not have any conflicts of interest to declare.
More precisely, our study focused on bacterial taxonomic richness (in terms of number of Operational Taxonomic Units or OTUs at 95% of sequence similarity, corresponding roughly to the genus level)  [1, 6]. Bacterial richness was determined in all 2,173 soils samples of the RMQS by using a pyrosequencing of bacterial 16S rRNA genes directly amplified from soil DNA. Geostatistics was applied to these data to provide the first comprehensive map of soil bacterial richness variation along the environmental gradients encountered in France. The ecological processes structuring the variation of bacterial richness were identified and ranked by variance partitioning analysis. Finally, a statistical predictive model was developed according to the environmental filters identified. This model represents an operational tool highly complementary with the predictive model of soil molecular microbial biomass developed previously  [16, 17]  to establish a comprehensive diagnosis of the soil microbiological status (in terms of abundance and diversity of soil microorganisms).
The soil samples were gently air-dried, sieved to 2mm and then stored at -40˚C before analysis  [23]. Physico-chemical parameters were measured for each composite soil, e. g. particle-size distribution, pH water, organic C, N, C/N ratio, soluble P contents, calcareous, cation exchange capacity (CEC) or exchangeable cations (Ca, Mg). Physical and chemical analyses are available for 2,131 soils and were performed by the Soil Analysis Laboratory of INRA (Arras, France, http://www. lille. inra. fr/las). Available climatic data for the RMQS were annual rain, evapotranspiration and temperature. These data were obtained for each node of a 12 x 12 km grid defined by Meteo-France, obtained by interpolating observational data using the SAFRAN model  [26]. Obtained measures for the period 1992-2004 were then averaged, to integrate all transitory effects into one value corresponding to a global effect of climate on soil microbial communities.
Finally, the RMQS site-specific data were linked to the climatic data by finding for each RMQS site on the grid of 16 x 16 km the closest node within the 12 x 12 km climatic grid. Land use was recorded according to the coarse level of the CORINE Land Cover classification (http://land. copernicus. eu/pan-european/corine-land-cover), which consists of a rough descriptive classification into five classes: forests, croplands, grasslands, others and perennial crops (corresponding to vineyards and orchards). All these data were available in the DONE-SOL database  [21].
100μl of TE buffer (Roche) was used for the elution. Pyrosequencing was then carried out on a GS FLX Titanium (Roche 454 Sequencing System) by Genoscope (Evry, France). Bioinformatics sequence analysis. Bioinformatic analyses were done using the GnS-PIPE developed by the GenoSol platform (INRA, Dijon, France)  [28]. Chosen parameters for each step can be found in S1 Table and the details of all steps have been already described previously  [27]. Regarding the filtering step, it was then carried out to check all single-singletons (reads detected only once and not clustered) were checked in order to eliminate PCR chimeras and large sequencing errors produced by the PCR and the pyrosequencing, based on the quality of their taxonomic assignments. More precisely, each single-singleton was compared with a dedicated reference database from the Silva curated database using similarity approaches (USEARCH), with sequences longer than 500 nucleotides, and kept only if their identity was higher than the defined threshold (S1 Table ).
Finally, the number of high-quality reads for each sample was normalized (i. e. 10,000 high-quality reads for each sample) by random selection to allow efficient comparison of the data sets and avoid biased community comparisons (see S1 File). A total of 1,798 soil samples were finally kept for subsequent analyses. A post-processing filtering was then applied to this global dataset to account for potentially artefactual data. First all the homogenized high-quality reads from all samples (encompassing a total of 17,980,000 reads) were merged and aligned. Then, as the analysis of microbial community richness relies on the construction of similarity clusters (called OTUs), we chose here to use OTUs to examine the distribution of 16S rRNA gene sequences in our datasets. However, there is no single best definition of 'species', 'genus' when this approach is used, because of controversy about thresholds of similarity allowing clear differentiation of taxonomic units  [29].
Moreover, a recent study regarding the diversity of bacterial genomes demonstrated that when the standard threshold of 97% is used, some species can fall to different OTUs due to intragenomic or intraspecific differences  [30]. So, we decided to apply the 95% threshold of sequence similarity, usually considered as the 'genus' level. This clustering was realized with a PERL program that groups rare reads to abundant ones, and does not count differences in homopolymer lengths. A post-processing step was then applied to remove all singleton OTUs that occurred only once in the overall dataset, and comprised only a singleton (reads detected only once after the dereplication step and not clustered)  [27]. This post-processing step reduced the number of total OTUs from 205,590 to 92,571 (loss of 50%), but the number of reads only from 17,980,000 to 17,866,981 (loss of less than 1%).
For each sample, the number of deleted reads with this step was 62 ± 60 on average (minimum: 10, maximum: 1,093). Finally, contingency tables of OTUs were obtained with the samples in lines and OTUs in columns, indicating the number of reads in each OTU for all samples. The retained high-quality reads were then used to determine OTU richness and rarefaction curves (see S2 File)  [31]. All raw data sets are publicly available in the EBI database system (in the Short Read Archive) under project accession PRJEB21351.
We tried to fit several models and retained the one that minimized the objective function  [34]. The validity of the best fitted geostatistical model was then assessed in terms of the standardized squared prediction errors (SSPE) using the results of a leave one out cross validation. If the fitted model was a valid representation of the spatial variation of the microbial property, then these errors would have a χ 2 distribution with a mean of 1 and median of 0. 455  [35]. The mean and median values of the SSPE were also calculated for 1,000 simulations of the fitted model to determine the 95% confidence limits. The 'gstat' package of R software (version 3. 2. 2) was used for geostatistical analysis and kriging  [36]. Variance partitioning. The relative contributions of soil physicochemical parameters, land use (forests: 492 sites, croplands: 740 sites, grasslands: 464 sites, perennial crops, corresponding to vineyards and orchards: 36 sites, and others: 36 sites), climatic conditions, geomorphology and space in shaping the patterns of soil bacterial richness and evenness were estimated by variance partitioning.
For the first step, two tools were used to assess colinearity between the explanatory variables, namely correlation coefficients and variance inflation factors (VIF). Only those with a correlation coefficient ranging from -0. 7 to 0. 7 and with a VIF 4 were considered in the modeling steps. The VIF values were calculated using the vif function in the car R package  [42]. This selection step allowed the exclusion of highly collinear variables and defined a reduced explanatory dataset more comprehensive and of easier use for the following steps. Since the number of explanatory variables was large (less than 50), the best explanatory variables were selected by applying the exhaustive search method described by  Miller, 2002 [43]. This approach involved using the regsubsets function in the leaps package in R  [40].
The selection criteria were the Bayesian Information Criterion (BIC) and the adjusted coefficient of determination (R 2 adj ) by minimizing the first and maximizing the second. For the second step, the bacterial richness dataset was randomly divided into a modeling dataset (90% of the data, 1,618 soil samples) and a cross-validation dataset (about 10% of the data, 180 soil samples), selected by applying the KennardStone algorithm. The kenStone function of the "prospectr" package was used to determine the distribution of the modeling and cross-validation datasets. Different polynomial linear models were then compared, with different numbers and types of explanatory variables as well as different degrees. Model selection was therefore based on maximizing R 2 adj , while minimizing BIC and by cross-validating the model on the cross-validation dataset.
Predictive model of soil bacterial richness Based on the RMQS dataset of bacterial richness and environmental parameters we have developed a predictive statistical model to provide a reference value of bacterial richness for a given pedoclimatic condition. The linear models with the smallest BIC (-700) and the largest R 2 adj (0. 34) highlighted eight environmental parameters as significant explanatory variables of the bacterial richness, which were ranked as follows: pH > Clay content > C:N ratio > X (longitude) > elevation > C org content > Y (latitude) > Silt content  (Fig 4). This observation confirmed and refined the hierarchy of the environmental filters obtained with variance partitioning  (Fig 3). At this step, climate data are not retained despite their significant role, since they are rarely available and expensive to obtain, limiting the use of this model to compare predicted and measured values in a context of soil biological diagnosis for and by soil users  [17].
Each spatial descriptor is presented with its estimated model coefficients and its marginal effect assessed by a permutation test (P<0. 05). Missing values indicate that the variable was not retained in the model. Spatial components were summarized according to the spatial scale considered: coarse, medium or fine. To validate the model, we evidenced the normality distribution of model residuals, which was confirmed by the Shapiro-Wilk test of normality (P = 0. 149), as well as a good homogeneity of the residuals tested by the Breush-Pagan test of homogeneity (P = 0. 5469). In addition, plotting the measured richness against the predicted richness using the cross-validation dataset revealed an important scatter of the points around the y = x line (S3 Fig), which validated the high predictive ability of the developed polynomial model.
Based on the referential dataset, the predictive model developed in this study complements the one developed for molecular microbial biomass  [17] , as both present innovative and operational mathematical tools for assessing a comprehensive soil microbiological status in the French pedoclimatic context. Comparison of predicted and measured values provides a robust diagnosis of soil microbial abundance and diversity and their evolution under environmental pressures such as agricultural practices, industrial pollutions or more global changes. Altogether, mapping and a predictive model of bacterial richness involving over 1,700 geo-located samples covering the French territory could help policy makers to produce conservation policies based on soil biodiversity. Based on this primary analysis of bacterial richness other aspects of soil bacterial beta-diversity such as evenness, community structure, taxa-area relationships and variations in the core bacterial taxa across France need to be investigated to have a comprehensive overview of the biogeography of microorganisms.
Acknowledgments This study was granted by  ADEME (French Environment and Energy Management Agency)  and by " France Genomique" through involvement of the technical facilities of Genoscope  (project number  ANR-10-INBS-09-08 ). In addition, due to the involvement of technical facilities at the  GenoSol platform of the infrastructure ANAEE France , it also received a grant from the  French  state through the  National Agency for Research under the program  " Investments for the Future"  (reference  ANR-11-INBS-0001 ). RMQS soil sampling and physico-chemical analyses were supported by a  French Scientific Group of Interest  on soils: the " GIS Sol ", involving the  French Ministry of Ecology, Sustainable Development and Energy (MEEM) , the  French Ministry of Agriculture (MAP) , the  French Institute for Environment (IFEN) , the  Environment and Energy Management Agency (ADEME) , the  French Institute for Research and Development (IRD)  and the  National Institute for Agronomic Research (INRA).
This study was granted by  ADEME (French Environment and Energy Management Agency)  and by " France Genomique" through involvement of the technical facilities of Genoscope  (project number  ANR-10-INBS-09-08 ). In addition, due to the involvement of technical facilities at the  GenoSol platform of the infrastructure ANAEE France , it also received a grant from the  French  state through the  National Agency for Research under the program  " Investments for the Future"  (reference  ANR-11-INBS-0001 ). RMQS soil sampling and physico-chemical analyses were supported by a  French Scientific Group of Interest  on soils: the " GIS Sol ", involving the  French Ministry of Ecology, Sustainable Development and Energy (MEEM) , the  French Ministry of Agriculture (MAP) , the  French Institute for Environment (IFEN) , the  Environment and Energy Management Agency (ADEME) , the  French Institute for Research and Development (IRD)  and the  National Institute for Agronomic Research (INRA).