dataesr/scientific-paragraphs-categorization huggingface dataset:
2-2 ng diluted DNA sample. For all of the mentioned targets the PCR cycling protocol consisted of initial denaturation at 95°C for 10 min, followed by 45 cycles of 95°C for 15 s, 60°C for 30 s and 72°C for 30 s. Melting curves were recorded to ensure qPCR specificity. Baseline and threshold calculations were performed with the StepOne v. 2. 2. 2 software. The inhibition was tested by serial DNA dilution from each site, and the dilutions without inhibition of qPCR reactions were used for quantification. All qPCR measurements were done in duplicate. Standards for qPCR were prepared by cloning fragments of target genes from Streptomyces europaeiscabiei DSM 41802 in pGEM-T Easy vector system (Promega). After PCR verification and isolation of cloned constructs by Pure Yield Plasmid Miniprep System (Promega), a linear standard was prepared by cleaving with SalI enzyme (New England Biolabs, UK) in a 200 µl reaction mixture containing 1× reaction buffer, 2 µg circular plasmid, and 20 U restriction endonuclease for 2 h in 37°C.
16S rRNA gene-based taxonomic microarray. A taxonomic microarray based on DNA probes targeting 16S rRNA genes representing 19 bacterial phyla at different taxonomic levels  (15)  was used to assess soil samples from potato fields. This microarray was validated previously  (15, 17). Twelve probes targeting the genus Streptomyces, as well as S. scabies and relatives (Supplementary Table  S1 ) were added to the previous probe set (1033 probes) in this study. The probe KO 08  (39)  for genus Streptomyces was obtained via the probeBase server  (40)  (http://probebase. csb. univie. ac. at). The other 11 probes (20-mers) were designed in this study using ARB sofware (41) (http://www. arb-home. de) and the parameters of the Probe Design function chosen by Sanguin et al. (31, 42). Probe specificity was tested with the Probe Match function in ARB against the reference Silva-104 and with the TestProbe online tool against Silva 126 database (43) (http://www.
Finally, a given feature probe was considered as truly hybridized when (i) hybridization signals were superior to the mean signal of the negative controls and (ii) at least 3 of 4 replicate spots displayed positive hybridization  (15). Resulting paired sequence reads were merged, filtered, aligned using reference alignment from the Silva database  (43) , and chimera checked using integrated Vsearch tool  (48)  according to the MiSeq standard operation procedure (Miseq SOP, February 2018)  (49)  in Mothur v. 1. 39. 5 software  (50). A taxonomical assignment of sequence libraries was performed in Mothur using the Silva Small Subunit rRNA Database, release 128 (51) adapted for use in Mothur (https://mothur. org/w/images/b/b4/Silva. nr_v128. tgz) as the reference database. Sequences of plastids, mitochondria, and those not classified in the domain Bacteria were discarded.
The sequence library was clustered into OTUs using the Uparse pipeline in Usearch v10. 0. 240 software  (13) , and the OTU table was further processed using tools implemented in the Mothur software. Distance matrices describing the differences in community composition between individual samples were calculated using the Yue-Clayton theta calculator  (52). Analysis of molecular variance (AMOVA)  (53)  was based on a matrix of Yue-Clayton theta distances.
Illumina Metastats analysis  (54)  was used to detect differentially represented OTUs. Statistical analyses. Analysis of variance (ANOVA) and Fisher LSD tests were used to test differences between soils and cultivars for soil chemical parameters and copy numbers of bacterial and actinobacterial 16S rRNA genes, and txtB genes in soil and periderm samples. Permutational multivariate analysis of variance (PERMANOVA) was used to compare distance matrices between microarray samples  (55). AMOVA was used to test differences between distance matrices (Yue-Clayton Theta) between Illumina samples. The distance matrices were plotted by Sammon's Multidimensional Scaling  (56). Accession number. MiSeq 16S rRNA gene amplicon sequences have been deposited in the NCBI Sequence Read Archive (www. ncbi. nlm.
The Homeostatic Feeding Response to a Dietary Fat Challenge Reveals Obesity Susceptibility For a 2-month old mice fed with standard diet, the typical daily energy intake is 0. 5 kcal per gram of body weight (The Jackson Laboratory, Mouse Phenome Database, http://phenome. jax. org/). It is well-known that high-energy foods cause transient overeating in most of the mice, which corresponds to an acute increase in energy intake during a few days  (14) (15) (16) , as illustrated in Figure  S1A. In this study, the period of overconsumption was variable between mice and normalization of energy intake occurred after 2 days or more (Figure  S1B ; in kcal/gram of body weight). Consequently, the individual cumulative energy intake during 1-week HFD, hereinafter referred to as the "feeding response, " ranged from 3.
Concentrations of short chain fatty acids (SCFAs) (acetate, propionate, n-butyrate, iso-butyrate, iso-valerate and n-valerate) were measured by gas chromatography (GC) as described by  Gagen et al. (2014). Iso-valerate (3-methyl butyrate) includes 2methylbutyrate, which co-elutes. The SCFAs were quantified by NMR as well and yielded similar results (data not shown). The NH 3 -N concentration was determined by a colorimetric method previously published  (Chaney and Marbach, 1962).
13 MHz and equipped with a 5 mm self-shielded z-gradient triple resonance probe and a SampleJet sample changer chilled to 6 • C. For each rumen sample a 1D NOESY spectrum was acquired at 298 K with the noesypr1d pulse sequence ((RD)-90 • -t1-90 • -τ m -90 • -acq. ) (Bruker Biospin pulse program library, Germany). The transmitter frequency was set to the frequency of the water signal, and water suppression was achieved by continuous wave irradiation during both the relaxation delay of 4. 0 s and the mixing time (τ m ) of 100 ms. After eight dummy scans, 128 transients were collected into 131,072 data points using a spectral width of 20. 0 ppm. All spectra were processed using TOPSPIN version 3. 2 (Bruker Biospin, Rheinstetten, Germany). The free induction delays (FIDs) were multiplied by an exponential function corresponding to a line broadening of 0.
3 Hz before Fourier transformation, manually phase and baseline correction. The resulting spectra were referenced to the DSS signal at δ = 0 ppm. The assignment of peaks to specific metabolites (Supplementary Table  S1 ) was based on identification in Chenomx NMR Suite 8. 2 (Chenomx Inc. , Edmonton, AB, Canada) as well as the online Rumen Metabolome Database  (Saleem et al. , 2013). To correct for slight differences in peak alignment, 1 H NMR spectra were aligned in MATLAB (The Mathwork, Natick, MA, United States) with the icoshift algorithm  (Savorani et al. , 2010) , and then automatically data-reduced to consecutive integral regions of 0. 001 ppm width ("buckets"), covering the range of δ = 10. 0 -0. 3 ppm. The chemical shift region at δ 5. 0 -4. 6 ppm was excluded to eliminate effects of imperfect water suppression.
For each spectrum, the resulting integral regions were normalized to the total spectrum intensity to correct for inter-sample differences in dilution. The bucketed 1D NOESY NMR spectra were initially subjected to untargeted multivariate statistical analysis in the SIMCA-P + 12. 0 software package (Umetrics AB, Sweden). This type of analysis reveals systematic changes in the intensity of any signal in the NMR spectra. These signals originate from aqueous metabolites of core metabolism that are present in at least micromolar concentrations. Identification data for specific metabolites are listed in Supplementary Table  S1. A Paretoscaled principal components analysis (PCA) was performed to investigate inherent sample differences among all sample classes, as well as between treatment and controls and between both treatments (Supplementary Figure  S2 ).
The pooled products were run in a 1% agarose gel and bands were visualized and excised under blue light trans-illumination. The amplicons were gel purified with QIAquick Gel extraction Kit (Qiagen, Hilden, Germany) prior to submission for Illumina Miseq sequencing (Macrogen Inc. , South Korea). Paired end short read sequence data generated on the Illumina Miseq was processed using the USEARCH package  (Edgar, 2010). De-multiplexed paired end sequences were first merged prior to sequence quality filtering, followed by denoising (error correction) and chimera checking and clustering of sequences to operational taxonomic units (OTUs) of 97% similarity. Taxonomic assignment of sequences was performed against the Greengenes database (  McDonald et al. , 2012). Additional analysis of OTUs was performed in the R packages vegan, Phyloseq, DESeq2 and the ggplot2 graphics package  (McMurdie and Holmes, 2013; Oksanen et al.
Statistical Analyses To study the pre-treatment effect and account for the time effect, data from 3-NOP, chloroform and their respective control periods were analyzed separately as a univariate repeatedmeasures analysis of variance using the GLM procedure of SPSS (IBM, version 21. 0). The effect of treatment was analyzed for CH 4 and H 2 production, dry matter intake (DMI), LW, DWG, ruminal fermentation metabolites and methanogen abundances. To study the effect of the compounds after 21 days of treatment, chloroform and 3-NOP groups were compared as a univariate model using the GLM procedure of SPSS, the treatment was considered the fixed effect with the animal as experimental unit and the pre-treatment data as covariate. The effect of the treatment was analyzed for CH 4 and H 2 , DMI, LW, DWG, ruminal fermentation metabolites and methanogens abundances.
In order to decrease xylem cell wall autofluorescence, the samples were photobleached for two minutes before each acquisition. Sample spectra were acquired over an exposure time of 6 x 10 s using 512 scans (the same amount of scans were also performed for the background). were removed from the datasets so that saturation effects and errors due to holes in the tissue sections could be avoided. Spectra comparison between tissue or genotype was performed on baseline-corrected and area-normalized spectra. For Raman microspectroscopy, the spectra of different xylem cell types were smoothed by the Savitsky-Golay algorithm (3 rd order polynomial and nine-point filter). The spectra were then baseline-corrected by subtracting a linear baseline between 350-3500 cm -1 and area-normalized. Both SR-FTIR and Raman spectra were preprocessed using Unscrambler software (The Unscrambler, CAMO Process AS, Oslo, Norway).
Univariate analysis of the SR-FTIR and Raman spectra Peak area measurements were performed on baseline-corrected and area-normalized FTIR spectra in OMNIC 9. 2. 41 and TQ Analyst EZ 9. 2. 34 software (Thermo Scientific). In both cases, the baseline between the peak start and end marker was computer generated. The following peaks were measured for the SR-FTIR spectra: 930-1180 cm -1 for cellulose; 1695-1770 cm -1 for hemicellulose; and 1475-1520 cm -1 for lignin. Average spectra and boxplot representations were generated in R software (R Development Core Team, 2016) using the HyperSpec and Ggplot2 packages, respectively  (Wickham, 2009; Beleites, 2012). Peak areas and their ratios were first checked for normality (Shapiro-Wilk test) and homoscedasticity (Levene test). Since neither of these criteria were fulfilled, an approximate (Monte Carlo) Fisher-Pitman permutation test was performed (non-parametric one-way ANOVA).
S2G ). More precisely, while the xylem cell walls of both genotypes showed similar cellulose/hemicellulose ratios (Fig. 3A ), the phloem cell walls of the double mutant demonstrated a disequilibrium in the cellulose/hemicellulose ratio (Fig. 3B ). Therefore, we show that in addition to affecting xylem cell wall composition, mutations in both SWEET11 and SWEET12 genes also impact phloem cell wall composition. Even if additional independent experiment should be performed in the future to strengthen this conclusion, we could suggest that these mutations seem to only affect the hemicellulose composition of phloem cell walls. In order to further identify wavenumbers that could be specifically associated with the sweet11-1sweet12-1 double mutant, we applied a CART analysis procedure. For this purpose, our original dataset was split into calibration (80% of the total dataset) and validation (20% of the total dataset) datasets, after which the CovSel algorithm was applied to the calibration dataset to identify the 10 wavenumbers with maximum covariance  (Roger et al.
, 2011). The CART tree resulting from the analysis shows that, out of the 10 selected wavenumbers, only five IR wavenumbers -at 891, 1086, 1369, 1562 and 1712 cm -1can be used to distinguish between the wild-type and sweet11-1sweet12-1 phloem spectra (Fig. 3C ). To evaluate the performance of this analysis, the CART model obtained from the calibration dataset was used as an input and applied on the validation dataset. Table  2  summarizes the results of both genotypes for the model calibration (after ten-fold cross-validation) and validation datasets. When applied on the calibration dataset, the CART model correctly classified 88. 6% of the wild-type spectra (specificity) and 92. 5% (sensitivity) of the double mutant spectra. When applied on the validation dataset, the model correctly classified 82.
7% (specificity) and 79. 5% (sensitivity) of the WT and DM spectra, respectively. Moreover, the predictive positive value (PPV) of the validation model was calculated to be 75%, which means that most of the identified WT spectra are not false positives (Table  2 ). On the other hand, the model's negative predictive value (NPV) was determined to be 86. 1%, which means that a majority of the identified sweet11-sweet12-1 DM spectra are not false positives (Table  2 ). Overall, the CART model was able to accurately predict 80. 8% of the spectra present in the validation dataset (Table  2 ). In this way, the CART model produced using the calibration dataset can discriminate both genotypes based only on the analysis of five major FTIR wavenumbers. Among these marker wavenumbers, the 891 cm -1 wavenumber can be linked to the cellulose fingerprint region  (Kačuráková et al.
4A and 4B ). There were no significant differences in the ratio of C-H bonds to C-O bonds between cell types (Fig. 4C ). Therefore, the observed differences between cell walls between VV, VF and FF can mainly be attributed to a lower intensity of the aromatic ring stretching vibration (1598 cm -1 ) in the cell walls between xylem fibers (Fig. S3A )  (Özparpucu et al. , 2017). To further identify Raman shifts associated with different xylem cell types, a CARTbased classification method was applied on the 1000-1800 cm -1 Raman shift range, which includes the predominant constituents of the xylem cell wall (Prats  Mateu et al. , 2016; Özparpucu et al. , 2017). The CART model was built on the calibration dataset (80% of the total dataset), without a variable selection step, and the resulting classification tree shows that only four Raman shifts are sufficient to distinguish the three different cell wall types (Fig.
4D ). The overall accuracy of the model produced from the calibration dataset was 85. 9%, with good prediction values (PPV) of 100%, 77. 7% and 83. 3% for the VV, VF and FF groups, respectively (Table  3 ). This model was then applied to the validation dataset (20% of the total dataset), and showed an accuracy value of 80%, which is close to that of the calibration dataset (Table  3 ). Even though the predictive sensitivity for spectra between adjacent xylem vessels (VV) was low, with only 50% of spectra correctly classified as VV spectra (Table  3 ), the PPVs for VF and FF spectra (87. 5 and 100%, respectively) were good (Table  3 ). Therefore, our CART model can be used to distinguish VF and FF spectra, but the results should be interpreted with caution in the case of VV spectra. Nevertheless, our data   , 2015)  while the Raman shift around 1256 cm -1 has been linked to hemicelluloses  (Gierlinger et al.
Disruption of SWEET11 and SWEET12 expression differentially affects the cell wall composition of different xylem cell types To further understand how modifications in facilitated sugar transport influence xylem secondary cell wall formation, we acquired Raman spectra for different xylem cell types from the sweet11-1sweet12-1 double mutant (Fig. 1 ). As previously described, CART-based classifications were built to compare the different xylem cell types from both genotypes. For each cell type (VV, VF or FF), the original dataset was split into a calibration dataset and a validation dataset, after which the CovSel algorithm was applied on the calibration dataset to select the 10 Raman shifts showing maximum covariance. The CART models were then built on the calibration datasets and later applied on the validation datasets (Table  4, 5  and 6 ).
The resulting CART tree classifications are displayed in Fig. 5. Regarding the cell walls between adjacent xylem vessels, two Raman shifts, namely, 1001 and 1093 cm -1 , were sufficient to differentiate wild-type spectra from the double mutant spectra (Fig. 5A ). Interestingly, these   and Schwanninger, 2007; Özparpucu et al. , 2017). Model performance was estimated for both calibration and validation datasets (Table  4 ), with the results demonstrating that the model can accurately discriminate spectra from both genotypes since the overall accuracy, sensitivity, specificity, PPV and NPV calculated for the validation dataset were between 76% and 90 % (Table  4 ). Additionally, data obtained from the CART model produced using Raman spectra acquired for the walls between xylem vessels and fibers (VF spectra) show that seven Raman shifts -at  1093, 1134, 1296, 1372, 1606, 1618  and 1743 cm -1can be used to discriminate the genotypes (Fig.
5B ). The 1093 cm -1 shift appears three times and the 1296 and 1743 cm -1 shifts appear twice in the CART tree, suggesting that these three shifts are most important to discriminating the two genotypes (Fig. 5B ). Based on the literature, the 1093 and 1372 cm -1 shifts are related to cellulose  (Chylinska et al. , 2014; Özparpucu et al. , 2017) , while the 1743 cm -1 is assigned to the υ(C=O) ester in pectins or hemicelluloses compounds  (Chylinska et al. , 2014). The 1606 and 1618 cm -1 Raman shifts have been reported to describe lignin bands (Prats  Mateu et al. , 2016). The performance of the model produced from VF spectra (for both calibration and validation datasets) was similar to the previous model. Finally, the CART tree built using the spectra acquired for the walls between adjacent xylem fibers (FF spectra) shows that four Raman shifts can be used to discriminate between the wild-type and double mutant spectra.
These shifts occur at 1134, 1332, 1597 and 1691 cm - 1 (Fig. 5C ), and are combined differently to distinguish both genotypes. The 1332 and 1597 cm -1 shifts are used twice in the classification trees, suggesting that both of these shifts are important to differentiating the WT xylem fiber cell wall from the DM xylem fiber cell wall (Fig. 5C ). Interestingly, both bands are related to lignin compounds (1332 cm -1 : aliphatic O-H bending; 1597 cm -1 : aromatic ring stretching)  (Özparpucu et al. , 2017). The different descriptors for the models built from the calibration and validation datasets range from 67 to 89%, suggesting that the models can be used to distinguish the xylem fiber cell wall compositions of the two studied genotypes (Table  6 ). To summarize, it seems that Raman shifts that discriminate cell walls between xylem vessels of the two genotypes are related to polysaccharides while the cell walls between xylem vessel/fibers and between adjacent fibers of the two genotypes can be discriminated A c c e p t e d M a n u s c r i p t 18 using both lignin and polysaccharides shifts.
Nevertheless, with both techniques we observed small but significant differences between both genotypes at the tissue and cell-level that support the use of SR-FTIR and Raman spectroscopy to study fine variations of the cell wall composition that could have been missed by analyzing a greater acquisition zone with conventional FTIR as described in our previous experiments (Le  Hir et al. , 2015). In addition, the research approach presented here offers the possibility of studying changes in cell wall polysaccharide composition at the cellular level and could be applied to investigations of how nutrients transport affects cell wall formation in the vascular tissue of both herbaceous and ligneous species. with the model calibration dataset (80% of the total dataset) using a ten-fold cross-validation method, and the validation dataset (20% of the total dataset) using a CART-based algorithm.
Classification results for using Raman shifts to predict which genotype cell walls between a xylem vessel and fiber represent, with the model calibration dataset (80% of total dataset) using a ten-fold cross-validation method, and the validation dataset (20% of total dataset) using a CART-based algorithm. NPV: negative predictive value, PPV: positive predictive value. Downloaded from https://academic. oup. com/jxb/advance-article-abstract/doi/10. 1093/jxb/ery396/5165365 by guest on 09 November 2018 Comment citer ce document : Dinant, S. , Wolff, N. , De Marco, F. , Vilaine, F. , Gissot, L. , Aubry, E. , Sandt, C. , Bellini, C. , Le Hir, R. (Auteur de correspondance) (2019). Synchrotron FTIR and Raman spectroscopy provide unique spectral fingerprints for Arabidopsis floral stem vascular tissues.
Classification results for using Raman shifts to predict which genotype cell walls between xylem fibers represent, with the model calibration dataset (80% of total dataset) using a ten-fold cross-validation method, and the validation dataset (20% of total dataset) using a CART-based algorithm. NPV: negative predictive value, PPV: positive predictive value. Comment citer ce document : Dinant, S. , Wolff, N. , De Marco, F. , Vilaine, F. , Gissot, L. , Aubry, E. , Sandt, C. , Bellini, C. , Le Hir, R. (Auteur de correspondance) (2019). Synchrotron FTIR and Raman spectroscopy provide unique spectral fingerprints for Arabidopsis floral stem vascular tissues. Journal of Experimental Botany, 70 (3), 871-884. , DOI : 10. 1093/jxb/ery396.
Acknowledgements We thank  Bruno Letarnec  for his assistance in the greenhouse. The research was supported by the SOLEIL, the  French national synchrotron facility  (project n° 20150210). This work benefitted from a  French State grant  ( LabEx Saclay Plant Sciences-SPS , ref. ANR-10-LABX-0040-SPS ), managed by the  French National Research Agency  under an " Investments for the Future" program  (ref. ANR-11-IDEX-0003-02 ). The authors declare no competing financial interests. The ChemFlow interface was supported by the  Agropolis Foundation  under the reference  ID-1401-005  through the  "Investissements d'Avenir" program  ( Labex Agro :  ANR-10-LABX-0001-01 ).
Method To identify studies reporting the effects of tDCS on depression and cognition in MDD, two authors (E. H. and D. B. ) searched the Pub-med database following PRISMA recommendations  [14]. The identification of articles was based on the following keywords: "depression", "transcranial direct current stimulation". Inclusion criteria for this review were: (a) publication in English; (b) inclusion of depressed patients treated with tDCS; (c) meta-analysis, randomized controlled studies (RCTs) and open-label trials; (d) assessment of depressive symptoms and/or cognition. Working independently and in duplicate, the two reviewers examined all titles and abstracts, obtained full texts of potentially relevant papers, and read the papers to determine whether they met the inclusion criteria. Of the 381 initial references, 67 papers were retained.