dataesr/scientific-paragraphs-categorization huggingface dataset:
B, Monoamine levels, measured by mass spectrometry, in the lumbar spinal cord confirmed that intrathecal 6-OHDA significantly reduced spinal noradrenaline levels. n values are given in brackets. **p Ͻ 0. 01 compared with other groups. C, Acute administration of duloxetine at high dose (30 mg/kg p. o. ) induced a rapid anti-allodynic effect (top graph), which was suppressed by a central noradrenergic lesion (middle graph), but not by a peripheral lesion (bottom graph). D, Chronic duloxetine treatment (200 g/ml) exerted a delayed anti-allodynic effect (top graph), which was suppressed by the peripheral noradrenergic lesion (bottom graph), but not by the central lesion (middle graph). Dul, Duloxetine; Sacch, saccharin. For all graphs, data are expressed as mean Ϯ SEM. "B" corresponds to the PWT before surgery; "acute n/chronic n" are given in brackets, chronic treatment start and duration are indicated by the dotted and the thick lines in D.
Figure6. Duloxetine plasma concentrations in mice and in human patients. In mice, right PWTs were assessed using von Frey filaments and neuropathic pain was induced by inserting a cuff around the main branch of the sciatic nerve at least 2 weeks before the start of duloxetine treatment. Dosage in the clinical setting was done on neuropathic pain patients chronically treated with duloxetine (Cymbalta, 60 mg daily in one intake) and reporting relief of at least 30% of their neuropathic pain since the initiation of the treatment. Duloxetine plasma concentrations were determined using mass spectrometry. A, Comparison of mean plasma concentration-time profile and behavioral data for acute duloxetine oral administration in mice (30 mg/kg, p. o. ; n ϭ 7 or 8 per each time point). The insert shows the correlation between duloxetine plasma levels and PWT for animals with detectable duloxetine plasma levels (time points from 1 to 24 h, n ϭ 38 total).
Figure11. Impact of chronic amitriptyline treatment on TNF-␣ levels in dorsal root ganglia. Neuropathic pain was induced by inserting a cuff around the main branch of the sciatic nerve. Chronic oral amitriptyline treatment started at least 2 weeks postsurgery and lasted 3 weeks. The presence and relief of mechanical allodynia were tested using von Frey filaments. L4, L5, and L6 dorsal root ganglia were collected for molecular analysis. Western blot analysis of dorsal root ganglia showed increased TNF-␣ levels in male mice with nerve injury and an anti-TNF-␣ action after chronic amitriptyline treatment (200 g/ml). The histogram represents the relative amount of TNF-␣ according to the density of the control group sham saccharin. Ami, Amitriptyline; Sacch, saccharin. Data are expressed as mean Ϯ SEM.
This work was supported by the  Centre National de la Recherche Scientifique  (CNRS, contract  UPR3212 ) and the  Universitede Strasbourg  ( UPR3212 ), the  Agence Nationale de la Recherche  (Euridol  ANR-17-EURE-0022 ), a  CNRS disability doctoral fellowship  (M. K. ), the  Fondation d'Entreprise Banque Populaire  (M. K. ),  Fedeeh-Hewlett-Packard  (M. K. ), the  Fondation pour la Recherche Medicale  (P. -E. L. ), the  Fondation Pierre Deniker  (P. -E. L. ), the  Institut UPSA de la Douleur  (M. B. ). We thank  Stephane Doridot  ( Chronobiotron, UMS3415 CNRS ) for animal care and genotyping. We thank  Prof. Brigitte Kieffer  ( McGill University, Canada ) and  Prof. Claire Gaveriaux-Ruff  (IGBMC, France.
INTRODUCTION Resistive switching (RS) behavior in organic devices has excited substantial attention because, in addition to the well-known aspects of high performance and low volatility that are inherent to RS memory, the use of organic components would yield easy-to-process, flexible devices. 1  RS has been reported for a wide variety of organic semiconductors including metal quinoline complexes. 2 4] [5] [6]  Interestingly the two phenomena are strongly correlated: MR can be turned "on" and "off" as well as smoothly tuned between a number of non-volatile states depending on the device resistive state obtaining a full electrical control of MR. 3, 4  This 'multifunctionality' is tantalizing for the development of new devices,  7, 8  with potential applications in multibit nonvolatile data storage and neuromorphic computing.
Cross-sectional samples were prepared using standard 'lift-out' procedures on an FEI Nova Nanolab Focused Ion Beam instrument and were typically polished to ~100 nm thickness: further thinning tended to damage the delicate organic layer. Electron energy loss spectroscopy (EELS) data were acquired using the spectrum imaging  28  and dual EELS  29  methodologies within Gatan's Digital Micrograph software, which was also used for data processing. Full magnetoresistive characterization was carried out in a 4-point cross bar configuration by using a Keithley 236 SMU in the temperature range 100-300K with a maximum applied field of µH=0. 3 T. The LSMO electrode was biased while the Co was kept at ground. Impedance spectra were recorded with an Agilent E4980A LCR-Meter in the 20-100k Hz range in order to distinguish between capacitive and ohmic behavior.
71   In our case, the MR intensity depends on the specific resistive state in which the device is put: on the basis of results above, MR is absent when a more efficient insulating AlO x barrier is set and Gaq 3 cations are formed, while MR has its maximum value in case of defective AlO x barrier and after oxygen ions migration into the organic layer and the formation of complexes. We expect then that AlO x defective barrier  73  will generate a number of localized states able to promote the spin injection from the Cobalt electrode into the organic layer where the spin transport takes place in presence of Oxygen ions acting as dopant (or impurity), via the formation of complexes with Gaq 3. Our data are consistent with the formation of intragap impurity levels to describe spin and charge transport in our spin valves.
Funding Sources This work is funded through the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement GA No. 263104  and COST action 15128. We thank SOLEIL and ELETTRA synchrotrons for provision of synchrotron radiation facilities at beamline  GALAXIES  (proposal number 20130329) and beamline BEAR (proposal number 20135188) respectively.
All patients gave consent for participating to the collection and analysis of data of this study. All patients underwent a complete comprehensive Geriatric Assessment (CGA) by a geriatrician prior to initiation of radiotherapy. The past medical, personal and social history, current medication, body mass index, and home to study center distance were documented. The Cumulative Illness Rating Scale for Geriatrics (CIRS-G)  [23] , Activities of Daily Life (ADL)  [24]  and Instrumental Activities of Daily Life (IADL)  [25] , Mini Mental State Examination (MMSE)  [26] , mini Geriatric Depression Scale (GDS)  [27] , Mini Nutritional Assessment (MNA)  [28] , and the get up and go test (GUAGT)  [29]  were performed and the scores recorded. See Annexe 1 for details. Clinical target volumes and organ-at-risk volumes were determined according to the GETUG (Groupe d'Etude des Tumeurs Urogenital, french group for genito-urinary tumors study) recommendations for prostate  [30] , prostatic bed  [31]  and pelvic lymph node  [32]  contouring.
During a one-hour interview with a trained dietician/research nutritionist the food record was reviewed and checked frequently used household measures to ensure an adequate level of detail in describing foods and food preparation methods  [40]. Consumed foods were coded according to standardized coding procedures. The coding procedures comprised that all consumed foods and meals were coded into as much detail as possible. For example, a mixed dish had to be broken down into individual ingredients, including accompanying portions, and had to be coded as individual foods. Subsequently, each ingredient or food was translated into nutrients by use of local food composition tables (Nederlands voedingsstoffenbestand (NEVO) 2011 in The Netherlands  [41] , McCane and Widdowson's in The UK  [42] , Food Composition Database for Epidemiological Studies in Italy (Banca Dati di Composizione degli Alimenti per Studi Epidemiologici in Italia, BDA) and INRAN (Istituto nazionale di ricerca per gli alimenti e la nutrizione, now called Istituto nazionale di economia agraria, CREA) in Italy  [43, 44] , National Food and Nutrition Institute (NFNI) in Poland  [45]  and CIQUAL French food composition table in France  [46] ).
Other Variables Data on smoking status (current, former), educational level (years), employment (defined as: (1) Employed, (2) retired, or (3) other), physical activity (Physical activity scale for the elderly, PASE  [47] , and medical history (prevalence of hypertension [yes/no], heart disease [yes/no], diabetes mellitus type II [yes/no], hypercholesterolemia [yes/no], neurological diseases [yes/no], osteoporosis [yes/no]) were obtained by means of questionnaires. Frailty status (non-frail/pre-frail) was assessed using the frailty criteria from Fried et al. [48]. Height was measured with a stadiometer to the nearest 0. 1 cm. Weight was measured to the nearest 0. 1 kg with a calibrated scale while wearing light clothes. Body Mass Index (BMI) was calculated as weight/height 2. All measures were taken by trained research assistants.
Adherence to these SOPs was assessed and documented by means of a site visit by an independent researcher. Finally, the use of standardized and harmonized food based dietary guidelines across five study centers in Europe enabled us to provide a comparable dietary advice and to implement the same dietary principles at a large scale. Our study has limitations. Firstly, the use of country specific food composition databases and nutrient calculation systems could have resulted in differences in intake between the countries as a result of different food composition databases rather than a difference in actual intake. We have improved the comparability in calculations by agreeing on standardized procedures for the coding of the food records and by organizing training sessions in which all research dieticians of each research center participated.
Secondly, we had losses to follow-up-primarily pre-frail participants in the control group-likely because control participants did not receive food incentives and their frailty status could have limited their ability to comply with the intervention. Thirdly, to define adherence to the NU-AGE diet dietary intake data obtained by means of seven-day food records were used. Although this tool provides reliable data to evaluate short-term current dietary intake  [73] , in future, more objective biomarkers of dietary intake and status, such as 25-hydroxyvitamin D  [74]  could be incorporated in the NU-AGE index-not only to take into account the bioavailability, bioaccessibility and micronutrient status, but also to reduce misclassification bias as a result of recall bias in older adults  [75]. Finally, participants were apparently healthy and well-educated  [76, 77] , which limits the generalizability of our findings to the general elderly population.
Acknowledgments: This project was supported by the  European Union 's  Seventh Framework Programme  under grant agreement No. 266486  (' NU-AGE: New dietary strategies addressing the specific needs of the elderly population for healthy aging in Europe' ). Participants, staff and investigators of  NU-AGE  are gratefully acknowledged. FrieslandCampina,  Unilever, Lesieur, COOP Adriatica, MCO Health  are acknowledged for the provision of low-fat low-salt cheese, margarine, olive oil, whole grain pasta and vitamin D supplement, respectively.
HTS Data Analyses Demultiplexing was performed by GeT-Genotoul and Fasteris. The GeT-Genotoul platform used Bcl2fastq version 2. 20. 0. 422, allowing 0 barcode mismatches with a minimum trimmed read length of 35. Analyses of datasets were performed using Workbench 11. 0 software (CLC Genomics Workbench, Aarhus, Denmark). Prior to any downstream analyses, a FastQC was performed. For RNAseq datasets, only reads above 70 nucleotides (nts) were kept after trimming and quality check (Supplementary Tables  S3, S4 ). For smallRNAseq datasets, only reads between 20 and 27 nt were kept for downstream analyses after a quality check step (Supplementary Table  S4 ). The sanitary status of grapevine samples was assessed by mapping reads to a collection of curated reference sequences of viruses known to infect grapevine  (Martelli, 2017) , as previously described  (Hily et al.
, 2018c). Briefly, relaxed mapping stringency (0. 5/0. 7 or 0. 9/0. 9, corresponding to read length/similarity parameters for RNAseq and smallRNAseq data, respectively) was used to span maximum genome diversity within virus species. RPKM (Read Per Kilobase Million) was used as normalization method. RPKM values express abundance of viral RNAs, and are obtained by accounting for the number of reads mapping a virus genome, the genome size of the virus and the total number of reads from the sample. De novo assemblies (parameters of word size of 17 and minimum contig length of 200) were performed after removal of reads that mapped to the Vitis vinifera genome 1. Contigs were then tested against GenBank reference sequences using Blastn/Blastx 2. For the detection of possible intra-lane contamination, all reads that mapped to the GFLV consensus RNA1 and RNA2 sequences at relaxed stringency parameters (see above) were recovered from each sample.
Reads were then mapped to GFLV sequences obtained from the same sequencing lane (e. g. , IC-MaA8193, IC-P1a, IC-P2a, KX034936, and KX034888) or GFLV sequences that were not present in the same lane [F13-col, B844, CO1 (A17b), CO2 (A17d), and GHu] (Supplementary Tables  S1, S2 ). Mapping was performed at a very high stringency in order to confirm the origin of the reads [0. 95/0. 98 and 0. 99/0. 99 of (length/similarity) for RNAseq and smallRNAseq datasets, respectively]. A schematic representation of our protocol for detecting possible contamination is shown as Supplementary Data Sheet 1.
RT-qPCR for GFLV Diagnosis For viral detection and absolute quantification with RT real-time PCR, 20 ng of total RNA, quantified by Nanodrop TM , were reverse-transcribed into cDNA using Superscript III reverse transcriptase (Invitrogen, Carlsbad, CA, United States) with a mix of oligo (dT). Eva Green qPCR mix (Biorad TM ) was used with specific primers targeting GFLV-RNA1 and RNA2 for real time amplification (Biorad TM CFX96 real-time system). Degenerated primers (Supplementary Table  S5 ) were designed within the most conserved regions after alignment of 32 complete GFLV genomes (from NCBI and our own sequence database) for the amplification of about 120 bp fragments. PCR was performed in 96-well optical reaction plates for 30 s at 95 • C, 40 cycles of denaturation for 5 s at 95 • C, annealing and elongation for 5 s at 65 • C.
Northern Blot for GFLV vsiRNAs Detection Total RNA was extracted from 0. 1 g of frozen tissue using 1 ml Concert Plant RNA Reagent following manufacturer's protocol (Invitrogen, Carlsbad, CA, United States). Low molecular weight RNA was analyzed using 30 µg of total RNA separated by denaturing polyacrylamide gel electrophoresis and transferred to Hybond N + membranes (GE Healthcare, Piscataway, NJ, United States). Following chemically crosslinking with EDC treatment  (Pall et al. , 2007) , hybridization was done with a mix of five radiolabeled DNA probes amplified from five genetically distinct GFLV isolates (F13-col, B844, CO1 (A17b), GHu, and IC-MaA8193) in siRNA hot spot regions of RNA1 and RNA2 according to our smallRNAseq datasets (Supplementary Table  S5 ). DNA probes labeled with α-32P dCTP (Hartmann, Braunschweig, Germany) were obtained using the Prime-a-Gene R labeling system (Promega, Madison, WI, United States).
GFLV Detection The same strategy was used to analyze the RNAseq and smallRNAseq datasets with most detection parameters being relaxed for the recovery of maximal GFLV diversity. Although GFLV is genetically diverse  (Vigne et al. , 2004; Mekuria et al. , 2009; Zhou et al. , 2017) , we mapped all reads to RNA1 and RNA2 consensus sequences obtained from seven GFLV genomes  (Hily et al. , 2018c). As a result, many reads mapping to GFLV consensus sequences were recovered in all samples tested (Table  1 ) with 75 reads mapping to the GFLV-RNA1 consensus sequence for sample ENTAV-E39 and 183,385 reads mapping to the GFLV-RNA2 consensus sequence for sample IC-P1a for the RNAseq dataset; and with 254 (sample IC-MaA8191) to 797,497 (sample IC-MaA8193) reads for the smallRNAseq dataset (Table  1 ). The eight samples used for this study had either a 'high read count' (HRC) with elevated reads (67,540 to 797,497) mapping to the GFLV consensus sequences (highlighted in gray in Table  1 ) or a 'low read count' (LRC) with less than 1,000 reads recovered from either HTS techniques.
GFLV; it was not observed when mapping other virus genomes. For example, 0 to 6 reads mapped to the genome of grapevine virus A (GVA; whose genome size is close to that of GFLV RNA1) and 671 to 11,620 reads mapped to the genome of grapevine rupestris stem pitting-associated virus (GRSPaV), an ubiquitous virus of grapevine (Table  1 ). Sequencing depth was proportional to the number of reads mapped to each GFLV sequence. By focusing on the genome coverage percentage, close to the full-length GFLV genome sequence was covered by reads from the RNAseq dataset for HRC samples (97. 6 to 100% genome coverage) and, to a lesser extent, for LRC samples (58. 7-98. 1% genome coverage). The difference in genome coverage between the two categories of samples was also observed with the smallRNAseq dataset but with a lower coverage (48.
A Protocol to Identify GFLV Intra-Lane Contamination To discriminate between low GFLV titer or intra-lane contamination due to the sample multiplexing step, we designed a framework of basic bioinformatics steps for RNAseq datasets (Figure  2 ). First, all reads mapping to GFLV consensus sequences at low stringency (parameters 0. 5/0. 7, length/identity, see section "Materials and Methods") were recovered. Reads were then mapped to a series of beforehand de novo assembled GFLV genomes that were either present within the same sequencing lane (e. g. , IC-P1a, IC-P2a and IMA-GMO) (Supplementary Tables  S1, S2 ) or to GFLV sequences that were not present on the same sequencing lane (e. g. , F13-col and B844) (Supplementary Table  S2 ). For this step, very stringent parameters (0. 95/0. 98) were used (Table  2 ).
Even at extremely high stringency and using only a small portion of GFLV sequences recovered from the same sequencing lane, 69-80% reads for GFLV RNA1 and 60-96% reads for GFLV RNA2 corresponded to these sequences (Table  2 ). In contrast, from the same pool of reads, only 0-2% of them mapped to sequences from GFLV variants not present in the sequencing lane, with the exception of 24% for GFLV RNA1 of sample IC-MaA819. This dichotomy in percentages indicates a probable source of contamination from samples being multiplexed within a particular sequencing lane. This was true also for smallRNAseq datasets although a higher percentage of reads mapping to GFLV variant sequences from either the same (88-99%) or different (26-52%) sequencing lanes was obtained. The higher percentage obtained with smallRNAseq compared to RNAseq datasets could be explained by the fact that smaller sequences (20-27 nt vs.
≈ 146 nt, Supplementary Table  S4 ) allowed for more reads to be mapped onto a genome by the former compared to the latter technology. Our 'intra-lane contamination' protocol was applied to all LRC samples. Consequently, the intra-lane GFLV contaminant  Reads that first mapped to GFLV-consensus sequences are indicated (Table  1 ). They were recovered and then mapped to de novo assembled sequences (RNA1 and RNA2) obtained from positive samples present on the same sequencing lane (same lane variants) or to sequences absent from that lane (other lane variants, Supplementary Tables  S1, S2 ). Number of reads and percentage (in parenthesis) that mapped are indicated. Mapping parameters were set very stringently, 0. 95/0. 98 and 0. 99/0. 99 (length/identity) for RNAseq and smallRNAseq datasets, respectively.
reads were removed from the datasets and all LRC samples were ultimately defined as negative (Figure  2  and Table  1 ). Even if a contamination might be observed for other viruses (see GRSPaV in sample ENTAV-E39, Table  1 ), this protocol could not be used in our datasets for viruses other than GFLV since their accumulation was not sufficient enough to trigger the implementation of the 'intra-lane contamination' method. For example, the number of reads mapping to GRSPaV sequences corresponded to only 1. 1-4% of those mapping GFLV sequences in samples IC-MaA8193, IC-P1a and IC-P2a (Table  1 ).
Nonetheless, some viruses with a non-poly-A-tailed genome were detected via RNAseq probably because the viral titer was high or the viral sequence might contain a stretch of adenine that was selected by the poly-(A) selection process (see GLRaV2 and GLRaV3 in samples Va4 and IC-MaA8191, respectively, Table  3 ). What was unexpected was that grapevine Red Globe virus (GRGV), a virus with a poly-A-tailed genome from the family Tymoviridae, was not detected by RNAseq in sample Va4 but identified using smallRNAseq (Table  3  and Supplementary Figure  S3 ). From this study, it seems that smallRNAseq libraries allow for the detection of more viral species than the RNAseq technique. However, complete viral genome sequences were rarely obtained via de novo assembly when using the smallRNAseq datasets because many genome regions were not covered by reads.
In contrast, it was very straightforward to obtain complete (to near complete) viral genomes using RNAseq datasets. Furthermore, we were able to assemble and identify different variants of a virus species (GRSPaV and GRVFV) within the same sample from either libraries (Table  3 ), however, de novo assembly was much easier from RNAseq compared to smallRNAseq datasets. No new viral species were detected in any of the samples selected for this study using the RNAseq or smallRNAseq datasets.
GFLV Sample name Technique RNA1 RNA2 GRSPaV GYSVd1 HSVd GFkV GRVFV GRGV GLRaV2 GLRaV3 IC-MaA8191 smallRNAseq - - √ √ √ - - - - 1 RNAseq - - 2 √ √ √ √ - - √ - - RNAseq 1 1 4 1 1 - - √ - - Va1 smallRNAseq - - √ √ √ 1 - - - - RNAseq - - 2 2 1 1 - - - - Va4 smallRNAseq - - √ √ √ 1 - 1 * 1 - RNAseq - - 3 1 1 1 - - * √ - Names of viruses detected ( √ ) and number of genomes assembled (# and highlighted in gray). * Sample Va4 showed 281,643 reads for grapevine Red Globe virus (GRGV) in the smallRNAseq datasets and 118 reads (23. 6% genome covered) in RNAseq datasets. See Supplementary Figure  S3  for more information. * * Sample IC-MaA8193 showed 60,875 reads for grapevine leafroll-associated virus 3 (GLRaV3) in the smallRNAseq datasets and 10 reads (4. 71% genome covered) in the RNAseq datasets.
Additional Information on the Virome in Two Selected Vineyard Plots in the Champagne Region of France From the RNAseq datasets, information on the presence of viroids and other poly-A-tailed virus genomes was obtained (Supplementary Table  S7 ). GRSPaV, HSVd and GYSVd1 were readily detected and assembled from all samples. A majority of samples (70%) was also infected with grapevine fleck virus (GFkV). Grapevine rupestris vein feathering virus (GRVFV) and grapevine Syrah virus 1 (GSyV-1) were also easily detected. Sample Pa10 was infected with grapevine Pinot gris virus (GPGV). Additionally, the presence of grapevine leafroll-associated virus 2 (GLRaV-2) with a non-poly-A-tailed genome was unquestionably detected in samples Pa4, Pa8, Py14, and Py17, although the full-length viral genome was not obtained.