dataesr/scientific-paragraphs-categorization huggingface dataset:
The diazonium salt was produced by mixing 0. 1 mmole of aromatic amine (equivalent to  13. 7, 17. 3, 13. 6, 10. 8, 11. 8, or 18. 4 mg of 4-aminobenzoic acid, 4-aminobenzene sulfonic acid, N,N-dimethyl-1,4-phenylenediamine, 1,4-phenylenediamine, 4-aminobenzonitrile, or N-phenyl-4phenylenediamine, respectively) with 90 mL of 0. 5 M HCl in a glass bath for 30 min, before adding 10 mL of 10 mM NaNO 2 aqueous solution (this 10 mL solution contains 0. 1 mmol NaNO 2 ). The resulting mixture was stirred for 1 h. The electrografting was then carried out in potentiostatic conditions (-0. 8 V/SCE for 45 s), using a Biologic SP 150 potentiostat (Seyssinet-Pariset, France), computer-controlled with an EC-lab software. The blocking effect of the bare and modified ITO electrodes was investigated in a 1 mM Fe(CN) 3-/4-redox solution by cyclic voltammetry (CV) in the -0.
A first run was done with SM 0. 2 m 3 m -3 and L-VOD 0. 5 as the initial guess for the minimization. This allowed us to compute a mean L-VOD map per grid point. The final inversion was done using this mean L-VOD map as a first guess for L-VOD and a value of 0. 2 m 3 m -3 as a first guess for SM. The roughness and single-scattering parameters are assigned per International Geosphere-Biosphere Program (IGBP,  Loveland et al. , 2000)  land cover classes, based on  (Parrens et al. , 2017b, a)  and are averaged within a footprint according to the fraction of classes present in the footprint. The data used in this study are version 103 and are provided in the 25 km EASE-Grid 2. 0.
One must bear in mind that the time periods of the data compared with K/X/C-VOD are not the same, as the L-VOD period used in this study is 2011-2012 and more detailed comparisons of the sensitivity to AGB of VOD at different frequencies would be interesting. However, the non-linearity of the curve and the difference in sensitivity to high AGB from different frequencies is driven by the high AGB values in the dense equatorial forest, which is not supposed to vary strongly in a few years time at the SMOS spatial resolution. In addition, it is worth noting that the different shapes of the L-VOD and AGB relationships with respect to the K/X/C-VOD and AGB relationships are in agreement with what it is expected from the radiation transfer theory  (Wigneron et al. , 1995 (Wigneron et al. , , 2004;; Ferrazzoli and Guerriero, 1996)  and previous results on L-VOD and X/C-VOD comparison by  (Grant et al.
Comparison of the different AGB data sets Estimating the AGB from remote sensing measurements is complex and the errors of different retrieval methods are not easy to characterize. Interpreting why L-VOD compares better to a given AGB data set for a given IGBP class (Sect. 4. 3) is not easy. The Avitabile AGB data set shows that a sharp decrease from the equatorial region with distance is not seen in any other AGB map nor in the L-VOD maps. Avitabile AGB and L-VOD scatter plots are also significantly different to those computed with the original Baccini and Saatchi maps. For instance, the low AGB versus L-VOD slopes obtained for low shrublands, grasslands and croplands are much lower than those found with the original Saatchi and Baccini data sets. The scatter plot found with Avitabile for woody savannah resembles an overlay of the scatter plot obtained from Baccini and the scatter plot obtained from Saatchi.
Finally, the slope of the AGB versus L-VOD in evergreen broadleaf forest is ∼ 30% higher than those found with the other data sets. The singular behaviour of Avitabile AGB could arise from the fact that it is a pure data-driven method and that it is therefore very sensitive to the data used to train the method. In the  (Avitabile et al. , 2016)  training database, high AGB plots could be overrepresented. On the other hand, as mentioned in Sect. 4. 3, the distribution of Baccini AGB for woody savannah is significantly different to the other data sets, which have much higher values than those found for Bouvet-Mermoz and Saatchi AGB. Actually, with Baccini AGB, the value of the slope obtained for woody savannah is 80 % of that obtained for evergreen broadleaf forest, while this ratio is only 55 % for Bouvet-Mermoz and Saatchi AGB.
This high slope for woody savannah is responsible for the lower non-linearity of the global AGB and L-VOD relationship using the Baccini data set. Woody savannah in the IGBP classification is defined as herbaceous vegetation and a forest canopy cover between 30 % and 60 %. AGB could be overestimated in this heterogeneous land cover class in the Baccini data set due to the fact that no microwave data but only MODIS is used for the spatial extrapolation (Sect. 2). Figure  S4  shows scatter plots of the four AGB data sets as a function of the  (Simard et al. , 2011)  tree height estimation. The relationship is almost linear for  (Baccini et al. , 2012 ) AGB, which is not the expected behaviour from allometric relations  (Chave et al. , 2014). Radar observations in low vegetation regions such as shrublands and grasslands are thought to be very sensitive to biomass variations, in spite of a significant sensitivity to soil moisture.
The high correlation of the two AGB maps involving radar data, either as the main source of information  (Bouvet-Mermoz)  or together with optical and elevation data  (Saatchi) , with SMOS L-VOD in grasslands would confirm this fact, as the high correlation in shrublands for Bouvet-Mermoz. The low slopes found for shrublands and grasslands when being compared to Baccini AGB also support this interpretation. Interestingly, the Bouvet-Mermoz AGB data set, which has been obtained from L-band SAR data and is the only one developed with a particular focus on savannahs, shows a linear relationship between L-VOD and AGB with a very low dispersion.
Conclusions Three different SMOS-based L-VOD data sets were evaluated and compared to precipitation, tree height, NDVI, EVI and AGB data. Lower dispersion and smoother relationships were obtained by using SMOS-IC L-VOD compared to the iL2 and L3 L-VOD data sets. Consistently, the rank correlation values obtained with SMOS-IC were significantly higher by 5 %-15 % than those obtained with level 2 and level 3 L-VOD data sets. The relationships between AGB estimates and L-VOD were strong (R = 0. 85-0. 94) but differed among the products. For low vegetation classes (grasslands to woody savannah), the best performance was achieved with the Bouvet-Mermoz, Baccini and Saatchi biomass data sets. The biomass data by Baccini and Saatchi showed the best agreement with L-VOD for dense forest (R = 0. 70-0.
79). Avitabile's AGB data showed low correlation values with L-VOD for low vegetation classes and a similar performance to Bouvet-Mermoz for dense forest (R = 0. 64-0. 67). The AGB and L-VOD relationships can be fitted over the entire range of both variables with a single law using a sigmoid logistic function. However, an analysis per land cover class showed that within the same land cover class, the L-VOD and AGB relationship is close to linear. Therefore, the global non-linear relationship, found when all the different land cover are considered together, arises from different slopes in the L-VOD/AGB relationship obtained for different land cover classes considered separately. For low vegetation classes, the annual mean of L-VOD spans a range from 0 to 0. 7 and could be related to the mean annual precipitation.
The relationship between AGB versus L-VOD was compared to the ones between AGB versus NDVI and AGB versus K/X/C-VOD from  (Liu et al. , 2015). As expected, NDVI saturates strongly and it becomes weakly sensitive to AGB changes from ∼ 70 to ∼ 300 Mg h -1. With respect to K/X/C-VOD, the AGB also increases slowly as VOD increases for most (∼ 70 %) of the K/X/C-VOD dynamic range but it saturates for VOD > 0. 8. In contrast, AGB values show a steady increment as L-VOD increases over the whole L-VOD dynamic range. The equations computed in this study can be used to estimate AGB from SMOS-IC L-VOD. Of course, these equations depend on the data set used as reference to fit the AGB and L-VOD relationship. Three of them (those determined with  (Baccini et al. , 2012) ,  (Saatchi et al. , 2011)  and  Bouvet-Mermoz)  gave very similar values when the 5th and 95th percentiles of the distributions were taken into account.
Acknowledgements. Nemesio J. Rodríguez-Fernández ,  Jean-Pierre Wigneron ,  Thomas Kaminski ,  Arnaud Mialon  and  Yann H. Kerr  acknowledge partial support from the  ESA  contract no. 4000117645/16/NL/SW Support To Science Element SMOS+Vegetation and by  CNES  ( Centre National d'Etudes Spatiales ) TOSCA programme. They also acknowledge interesting discussions with other colleagues involved in the  SMOS+Vegetation  project ( Marko Scholze ,  Matthias Drusch ,  Michael Vossbeck ,  Wolfgang Knorr ,  Cristina Vittucci  and  Paolo Ferrazzoli ). SMOS level 3 data were obtained from the  "Centre Aval de Traitement des Données SMOS" (CATDS) , operated for  CNES (France)  by  IFREMER (Brest, France). Martin Brandt  is supported by an  AXA  post-doctoral fellowship. Edited by: Peter van Bodegom Reviewed by: three anonymous referees.
Data availability. SMOS level 3 and IC products are available from CATDS at ftp://ext-catds-cpdc:catds2010@ftp. ifremer. fr/Land_products/GRIDDED/ (CATDS, 2018a) and ftp://ext-catds-cecsm:catds2010@ftp. ifremer. fr/Land_products/ L3_SMOS_IC_Vegetation_Optical_Depth/ (CATDS, 2018b), respectively. SMOS Level 2 products are available from ESA (2018) at https://smos-diss. eo. esa. int. The Supplement related to this article is available online at https://doi. org/10. 5194/bg-15-4627-2018-supplement.
Integration Site Analysis The 5ʹ long terminal repeat (LTR) of the HTLV-1 provirus was amplified using primers targeting the end of the 5ʹ LTR (RU5) and the beginning of the gag open reading frame, adapted to HTLV-1c, 5LTRFW1c: 5ʹ-CTCGCTTCTTTCCCTCACG-3ʹ, 5LTRRV1c: 5ʹ-GCGCTACGAGGGAAGATTTG-3ʹ. Amplified LTR products were Sanger sequenced (accession numbers: MG783271-MG783311) and the sequences were used to design primers for ligation-mediated polymerase chain reaction (LM-PCR) and sequencing: these primers were specific for HTLV-1c sequences (Supplementary Table  1 ) based on previously published primers  [10]. Apart from the use of these new primers, LM-PCR was performed as described previously  [8]. Libraries were mixed and sequenced on 2 HiSeq 2500, rapid flow cells (2 lanes, 50 paired-end, Illumina) with samples control common to the 2 flow cells to guarantee similar sequencing efficiency.
The reads were aligned to the human genome reference (UCSC hg18, excluding haplotype and "random" sequences) and the HTLV-1 reference. The only difference was the use of HTLV-1c reference for the alignment of the reads (accession number: KF242506). The quantification of number of copies of each unique integration site was deduced from the respective number of shear sites and corrected using a calibration curve, as previously described  [8]. See Supplementary Tables  2  and 3  for details of sequencing results.
Statistical Analyses Statistical analysis was carried out using R version 3. 4. 1 (http:// www. R-project. org/). As previously described, the oligoclonality index (an application of the Gini index) is a measure of the frequency dispersion of a cell population, and ranges from zero to 1  [8]. An oligoclonality index = 0 reflects a polyclonal cell population in which all the clones have the same frequency, and an oligoclonality index = 1 corresponds to a monoclonal population in which a single infected clone carries all the proviral load. The oligoclonality index was calculated using the ineq package (https://cran. rproject. org/web/packages/ineq/index. html) on samples with >5 unique integration sites, and adjusted for type I error  [4]  to limit underestimation of the oligoclonality index due to a small number of detected clones.
Acknowledgments. We are grateful to  Laurence Game  and  Marian Dore  of the  Genomics Laboratory, Medical Research Council Clinical Sciences Centre, Hammersmith Hospital, London, United Kingdom , for their assistance with sequencing. Financial support. This work was supported by the  Australian Centre for HIV and Hepatitis Virology Research , which is funded by the  Department of Health and Ageing, Australian Commonwealth Government ; and by the  Wellcome Trust  ( Charles Bangham Senior Investigator Award number   WT100291MA ). Potential conflicts of interest. All authors: No reported conflicts of interest. All authors have submitted the  ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
1) and the proportion of woods was treated as a separate habitat class (cf. Rollin et al. , 2013). The proportions of habitat classes and the distance to SNE (m; Table  A. 2) were calculated in ArcGIS  (ESRI, 2013). The Shannon landscape diversity index (SHDI) was computed based on raster data in FRAGSTATS 4. 2  (McGarigal et al. , 2012)  and CHLOE  (Boussard and Baudry, 2014) , the latter software was also used for validation of the index. Further, the mean slope per landscape circle (Table  A. 2), representing a terrain factor, was calculated using a digital elevation model with a resolution of 10 m in ArcGIS.
Data analyses Honey bees (Apis mellifera) were excluded from analysis because their occurrence and abundance was biased on nearby hives (cf. Kennedy et al. , 2013) , present close to some of the investigated vineyards. Because of the low number of observations in the category "very high" forage availability (n = 5) we decided to merge this category with the level "high", representing > 50% flower coverage in further analysis. This was also done to avoid deterioration of model quality due to influential observations in the level "very high". All statistical analyses were computed in R 3. 3. 2 (R Core Development Team, 2017; RStudio Team, 2015). To check for spatial autocorrelation we conducted Moran's Test with the R package "ape"  (Paradis et al. , 2004)  on the response variables species richness and abundance across the respective distances (m) between each vineyard, calculated with the Geographic Distance Matrix Generator V1.
, 2015). To account for the temporal nonindependent observations within the same vineyard and to analyse both study years together the months (April to August) of each year (N = 10) were chosen as random factors. For each response variable a model set of 40 GLMMs was formulated by combining non-collinear field and landscape parameters. Model selection was carried out by using the second order Akaike Information Criterion corrected for small sample size (AICc;  Motulsky and Christopoulos, 2003). The cut-off to decide whether a model is more likely to be correct than the next best model was set at Δ AICc < 2 (R package "AICcmodavg";  Mazerolle, 2016). This resulted in a set of three equally correct models for each response variable (Table. 2) and thus a high degree of model selection uncertainty  (Burnham and Anderson, 2002).
Therefore, model averaging was carried out using the so called Zero Method  (Burnham and Anderson, 2002) , as the focus was to determine which variables would have the strongest effect on wild bee diversity and abundance  (Grueber et al. , 2011; Nakagawa and Freckleton, 2011). The predictor variables in the model sets were found to be on different scales (i. e. percentage scale of landscape variables, metric scale of distance measurements, levels of categorical variables) and therefore standardized using Gelman's approach (R Package "arm"  Gelman et al. , 2016). Model averaging was done using the R Package "MuMIn"  (Barton, 2016). To analyse functional traits, community-weighted means (CWM) were calculated with the "functcomp" function in the R package "FD"  (Laliberté et al. , 2015). A Detrended Correspondence Analysis (DCA) was constructed with the R package "vegan"  (Oksanen et al.
(2016)  to combine methods that provide data with spatio-temporal resolution or high coverage. The approach in this study fulfils these requirements because forage availability was estimated on each sampling date (temporal resolution) and for each of the two neighbouring inter-rows, that comprised one transect, separately (spatial resolution). On a wider spatial scale, entomophilous crops were mapped and treated as independent predictor in data analysis. The results of the strong effect of forage availability are reliable because the RF revealed it to be the most important variable for the abundance of different traits. Further, this strong effect was documented in agroecosystems  (Williams et al. , 2015)  as well as in other ecosystems like woodland remnants  (Williams and Winfree, 2013) , different types of fallows  (Kuussaari et al.
Acknowledgements This research is part of the  BiodivERsA  project  VineDivers  (www. vinedivers. eu) and was funded through the  BiodivERsA/FACCE JPI  (2013-2014 joint call) for research proposals, with the national funders:  Austrian Science Fund  (grant number  I 2044-B25FWF ),  French National Research Agency (ANR) ,  Spanish Ministry of Economy and Competitiveness (MINECO) ,  Romanian Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI)  and  Federal Ministry of Education and Research (BMBF/Germany). The open access funding was provided by the  Austrian Science Fund :  I 2044-B25. We are grateful to  Johann Grassl  and to the winegrowers in the  Neusiedlersee-Hügelland and Carnuntum region  for allowing us to study their vineyards. Thanks to  Andreas W.
In the latter study, higher than normal ACDase enzyme activity was detectable in peripheral blood cells, in the bone marrow, the spleen and liver for more than a year  [181]. Additionally, the animals had decreased ceramide levels  [181]. At the time that this manuscript was written, a gene therapy trial was initiated for the treatment of SMA type I (clinicaltrials. gov ID NCT02122952). This trial involves the use of adeno-associated virus serotype 9 (AAV9), a non-integrating virus that encodes the SMA1 cDNA, infused through a peripheral vein. While the trial is still ongoing, preliminary data demonstrate a reduced need for pulmonary support, and patients could feed themselves, indicating a potential improvement in swallowing function  [182, 183]. Although these results are for a different type of SMA, it is possible that a similar gene therapy approach may also be promising for patients with the SMA-PME phenotypes.
A natural history study is currently underway on clinicaltrials. gov (ID NCT03233841), which aims to gain greater insight into the natural history of ACDase deficiency through retrospective and prospective patient data. It also aims to establish clinical information, biomarkers and other functional data to access the efficacy of future therapies, such as rhACDase ERT. The establishment of a complete natural history will greatly improve and potentially fill in gaps in the current definition of ACDase deficiency. Finally, due to the wide spectrum of clinical presentations, the precise number of patients is likely to be underrepresented. An improved understanding of the disease and increasingly effective knowledge translation will allow more patients to be identified, efficiently diagnosed, and effectively managed.
(J-L) Pink-1 promoter activity (J, n = 9) and mRNA levels (K, n = 12) in control MEF cells (PS 1/1 ) or MEF cells devoid of both PS1 and PS2 (PS 2/2 ). In (L), Pink-1 promoter activity was measured (n = 7) in PS 2/2 cells transiently transfected with either PS1 or PS2 complementary DNA as described in the experimental procedures. Representative gels of PS1 and PS2 transfection efficiency and actin as gel charge control are provided in (L) (upper panel). In all histograms, data are expressed as percentage of control (CT) taken as 100 and represent mean 6 SEM (*p ,. 05; **p ,. 01; ***p ,. 001; ns, nonsignificant). Pink-1 Regulation by Presenilins diminution, while PS2 depletion remains biologically inert. This suggests a dominant role of PS1 over PS2 in the control of Pink-1. Indeed, invalidation of both PS1 and PS2 lowers both Pink-1 promoter activation (Figure  1J ) and mRNA levels (Figure  1K ), a phenotype similar to the one observed with PS1 depletion only (Figure  1H, I ).
ACKNOWLEDGMENTS AND DISCLOSURES This work was supported by the  Conseil départemental des Alpes Maritimes  and the  Foundation Claude Pompidou. This work was developed and supported through the  LABEX (Laboratory of Excellence, Investments for the Future) DISTALZ (Development of Innovative Strategies for a Transdisciplinary Approach to Alzheimer's Disease)  and the  Hospital University Federation OncoAge. TG is funded by the  Ligue Contre le Cancer. We sincerely thank  A. Y. Abramov ,  O. Corti ,  T. Dawson ,  B. de Strooper ,  F. LaFerla , and  U. Muller  for providing valuable cellular and animal models, and we thank  G. Thinakaran  and  Elan Pharmaceuticals  for providing the anti-PS antibody and g-secretase inhibitor  D6 , respectively. The authors report no biomedical financial interests or potential conflicts of interest.
Patients Between 2006 and 2013, patients from French, Belgian and Swiss centers with documented (by cytogenetics and or molecular biology), newly diagnosed APL who were aged 70 years or less were eligible for inclusion in the APL 2006 trial, after giring informed consent. The trial was approved by local ethical committees (ClinicalTrials. gov Identifier: NCT00378365). Eligibility criteria in this trial were a morphological diagnosis of APL based on French-American-British criteria and no contraindication to intensive chemotherapy. No minimal performance status was required and patients with therapy-related APL could be enrolled. Induction treatment consisted of ATRA 45 mg/m 2 /day until complete remission with idarubicin 12 mg/m 2 /day for 3 days and AraC 200 mg/m 2 /day for 7 days starting on day 3.
In terms of size, a typical machine will weigh around 250 kg and fit in a 1 m × 1 m footprint (Fig. 5 ). As we have seen, one of the major improvements with WBCT is the built-in reliability, ensuring that the output volume corresponds in terms of dimensions and density to the real investigated object. This reliability is checked on a regular basis through quality assurance testing (QA). The QA procedures are designed to check for image quality, accuracy of distance measurements, consistency and radiation output. They consist of scanning specific QA phantoms provided by the manufacturer. Image data is then captured and compared with the known values.
Learning curve The amount of data provided by WBCT is far greater than with weight-bearing 2D XR or non-weight-bearing conventional CT, so we are in fact at the beginning of the learning curve in the clinical setting. In an unpublished document presented at the Open WBCT ISG 16 meeting at the 2017 IFFAS congress, one of the authors commented on the time required for a human observer to perform a large number of flat foot measurements (necessary to an ongoing research) equivalent to known 2D XR methods using WBCT datasets. This time was 20 minutes per WBCT scan, compared with the 5 minutes necessary to perform similar measurements on 2D XR. Despite this being in the research (as opposed to clinical) setting, it highlights the fact that the challenge mainly stems from having to find the correct plane in which to perform the measurement.