dataesr/scientific-paragraphs-categorization huggingface dataset:
A striking feature of the phase-2 dataset is its design-based difference with the total sample. As shown in table 2, 68% of the phase-2 controls were found to be exposed to asbestos compared to 52% of phase-1 controls. This biased selection, which is, moreover, different between cases and controls, might seem counterintuitive to epidemiologists. However, this is completely taken into account by the weighting performed in the dedicated statistical analyses for which packages in SAS  (14)  as well as in R (  17 ) are now available. These weights do not only correct for this lack of An efficient way to use expert time in case-control studies representativeness but also add some information from phase 1, ie, some power. This added information is more important if the stratification is finer, but for statistical reasons, the strata must not be too small.
46~52 (L. minor (46) and antimicrobial testing methods i. e. , MBC of P. vulgaris (48), MIC of P. aeruginosa (49), MBC of P. aeruginosa (50), MIC of S. marcesene (51), and MBC of S. marcesene (52) could not be predicted by Eq. (  4 ) (see No. 46, 48~52 in Figure  S2 ). In case of growth rate of P. subcupitata (48), the correlation by Eq. (  4 ) was dependent on dataset from two different research groups i. e. , Prof. Yun and coworkers  [40] [41] [42]  and Prof. Pretti and coworkers  43. Actually, the calculated values by Eq. (  4 ) for the former case were well correlated with observed ones with R 2 of 0. 857, while those of the latter case were scattered in the correlation (see Figure  S3 ). The exceptional cases from application domain of Eq. (  4 ) should be individually developed. Additionally, some outliers were observed and given in supporting information 2.
471 B c + 1. 650 V c + 2. 917 J + -0. 201 E a + 0. 418 V a + 0. 131 J --0. 709) can simply correlate with a few experimental dataset. For a validation test, some examples were made by correlating toxicity values from six algae species i. e. , B. paxillifer, G. amphibium, C. vulgaris, O. submarina, S. marinoi, and C. meneghiniana. Each species has 10 toxicity values measured by Latała et al  44, 45. In results, the calculated values of ILs by Eq. (  4 ) are well correlated with observed ones with R 2 of 0. 82~0. 98 (Fig. 1 ). Their sensitivity terms were estimated and are given in 53~58 of Table  1. For validating the predicted values of test set, the mean absolute error (MAE) criteria derived by Roy et al. 46. was used since R 2 -based metrics is sometimes strongly dependent on the distribution of data.
The MAE based estimation can be performed based on an internet freeware tool 'XternalValidationPlus' (available at http://dtcab. webs. com/software). Detailed information including theoretical background is given in the article by Roy et al. 46. The validation result using the MAE based criteria in the condition after removing 5% data with high deviations showed MAE (95% data) of 0. 2302 with an indication of a good predictability. Since each fitting between observed and calculated values of ILs has different slope (α x ) and constant (β x ), the model can be expressed as below: β = α. + ⁎ Log [1/EC50, 1/LC50, 1/MIC, 1/MBC or 1/IC50 ] in log unit of mM Calc value by Eq (4) x x In result, the calculated values by Eq. (  5 ) have an enhanced agreement with the measured toxicity values with R 2 of 0.
Experimental Database of ionic liquids and their abbreviations. For model development and some example study for the validation, half maximal effective concentration (EC50), half maximal lethal concentration (LC50), half maximal inhibitory concentration (IC50), minimum inhibitory concentration (MIC) and minimum biocidal concentration (MBC) values of ILs around 2200 data points towards 58 toxicity testing batteries (as given Table  1 ) were collected from literatures  6, [20] [21] [22] [39] [40] [41] [42] [43] [44] [45]. The selected ILs are comprised of several head groups (i. e. , piperidinium, sulfonium, melamine, morphodinium, guadinium, ammonium, phosphonium, imidazolium, pyridinium, quinolinium, and purinium with differently functionalized substitutes); which are around 200 types of cations, and 57 types of anions.
(  4 ). (3) Based on the system coefficients including z of Eq. (  4 ), an intrinsic value of ILs in specific toxicity testing system was calculated. (4) The calculated intrinsic values of ILs were correlated with experimentally measured toxicity values to determine the sensitivity-related terms (α x and β x ). Computational details. For calculation of LFER descriptors, several sub-parameters are needed. To obtain the subparameters, targeted IL structures are calculated by using density functional theory (DFT)  34  and conductor-like screening model (COSMO)  35  in the Turbomole program package (version 5. 10)  72. First, reasonably starting IL structure were optimized by calculation of (RI-)BP86/SV(P)  [73] [74] [75] [76]  in gas phase. The vibration frequencies of each structure were calculated by using AOFORCE  77, 78.
The biochemical pathways affected by ABA were identified using the Cytoscape program with the BiNGO plug-in software. The results indicated that these 839 genes were involved in several biological processes such as flavonoid metabolism, response to reactive oxygen species, response to light, and response to temperature stimulus. ABA affected ion and water transporters, particularly in the root. The protein amino acid phosphorylation process was significantly overrepresented in shoot tips and roots treated with ABA. ABA affected mRNA abundance of genes (CYP707As, UGTs, and PP2Cs) associated with ABA degradation, conjugation, and the ABA signaling pathway. ABA also significantly affected the expression of several transcription factors (e. g. AP2/ ERF, MYC/MYB, and bZIP/AREB). The greatest number of significantly differentially expressed genes was observed in the roots followed by cell cultures, leaves, berries, and shoot tips, respectively.
2 ; additional details of the gene annotations and expression values are provided in Additional file 3). Carotenoids are the precursors for ABA biosynthesis. ABA biosynthesis is often controlled by the rate-limiting step, nine-cis epoxycarotenoid dioxygenase (NCED)  [27, 28]. The transcript abundance of VviNCED3 [EnsemblPlants:VIT_19s0093g00550] significantly increased in cell cultures. Note this gene was originally named VvNCED1  [10] , but following the recommendations of the International Grape Genome Program Supernomenclature Committee  [29] , we have named this gene VviNCED3, because its closest ortholog in Arabidopsis is AtNCED3. The transcript abundances of genes encoding enzymes in the ABA biosynthesis pathway before the rate-limiting step did not change significantly in response to exogenous ABA in most organs; the exception was a significant increase in transcript abundance for β-carotene hydroxylase [EnsemblPlants:VIT_16s0050g01090] in cell culture (Additional file 3).
The transcript abundance of xanthoxin dehydrogenase and abscisic aldehyde oxidase genes, two steps involved in ABA biosynthesis after NCED, was not significantly affected by ABA. The ABA level in cells is highly regulated by degradation and conjugation processes. ABA is metabolized to 8′-hydroxyabscisic acid by ABA 8'-hydroxylase (ABAHase) and conjugated to ABA-GE by ABA UDP-glucosyltransferase (ABA-UGT). The relative expression of genes involved in ABA catabolism, CYP707As [EnsemblPlants:VIT_06s00 04g05050 and EnsemblPlants:VIT_03s0063g00380]  [30]  were significantly increased in roots and cell cultures in response to ABA. For ABA conjugation, the transcript abundance of the UGT genes was decreased in the shoot tips and roots but increased in cell cultures. The transcript abundance of an ABA transporter, VviABCG40 [Ensembl Plants:VIT_09s0002g05560], significantly decreased in roots.
Another transcript annotated as an ABC transporter G member 22-like [EnsemblPlants:VIT_18s0166g00080] may also be involved in ABA transport as it is a paralog of the Vitis ortholog [EnsemblPlants:VIT_08s0032g00790] of AtABCG22 (At5g06530); its transcript abundance increased significantly in berries in response to ABA. Heat maps displaying the gene expression profile of core components of the classic ABA signaling pathway (PYR/ PYL/RCAR, PP2C, SnRK2, and ABA-responsive element binding transcription factors) are shown in Fig. 3  and Additional file 3. The transcript abundance of the genes encoding PYL12/RCAR6 [EnsemblPlants:VIT_13s0067g01940] and PYL4/RCAR10 [EnsemblPlants:VIT_08s0058g00470] decreased significantly in roots and cell cultures in the presence of ABA. In contrast, the relative gene expression of PP2Cs increased significantly in roots [Ensembl Plants:VIT_16s0050g02570 and EnsemblPlants:VIT_06 s0004g06840] and cell cultures [EnsemblPlants:VIT_13s 0019g02200 and EnsemblPlants:VIT_06s0004g05460] in response to ABA.
There were no significant changes in gene expression of SnRK2s following exposure to ABA. A significant increase in transcript abundance of VviABF2 [EnsemblPlants:VIT_18s0001g10450] was found in roots. Both ABA-dependent and -independent transcription factors such as members of the AP2/ERF, NAC, bZIP/ ABRE, and MYC/MYB families were affected by ABA (Fig. 4 ; Additional file 3). Interestingly, the greatest change in transcript abundance of transcription factors was found in the AP2/ERF family, with 20 members changing in their transcript abundance with statistical significance. The highest increase in transcript abundance induced by ABA of the AP2/ERF TF superfamily was VviDREB2H [EnsemblPlants:VIT_13s0067g01960]; the response of Vvi-DREB2H was root specific. In the NAC TF family, there were 18 DEGs.
A transcript encoding a gene ortholog to VviNAC1 [EnsemblPlants:VIT_19s0027g00230] underwent a significant increase in transcript abundance in leaves. There was a slight change in the transcript abundances of seven members of the bZIP/ABRE TF family. One DEG of the bZIP/ABRE TF family was VviGBF3 [EnsemblPlants: VIT_02s0025g01020], which was found to be significantly upregulated in roots and cell cultures. There is no ortholog of this gene in Arabidopsis; its function is unknown. For the MYC/MYB TF family, there were 10 DEGs. Most notably, VviMYB121 [EnsemblPlants:VIT_14s0083g01060] exhibited the greatest significant increase in transcript abundance in roots. The transcript abundance of genes in several metabolic pathways was affected by exogenous ABA, especially the flavonoid biosynthesis pathway (Fig.
5 ; Additional file 3). Genes encoding stilbene synthase [STS: EnsemblPlants:VIT_16s0100g00860 and EnsemblPlants: VIT_10s0042g00870] significantly increased in transcript abundance in response to ABA treatment in berries but decreased in roots and cell cultures. On the contrary, genes encoding flavonol synthase [EnsemblPlants:VIT_18s 0001g03510] and flavonoid 3'-hydroxylase [Ensembl Plants:VIT_17s0000g07200] were significantly increased in roots in response to ABA. Interestingly, the transcript abundance of many genes encoding anthocyanidin 3-Oglucosyltransferase was significantly increased in response to ABA only in roots, while other organs showed decreasing or unchanging mRNA abundance.
Differential gene expression responses of organs to ABA ABA affected many biological processes of the gene ontology (GO) functional categories; statistically significant DEGs were involved in protein folding and response to heat, hydrogen peroxide, high light, and temperature (Additional file 4). However, each plant organ had a different response to ABA. The individual responses of each organ are summarized below: Berry There were 33 genes that showed organ-specific gene expression responses to ABA in berries (Fig. 1b ; Additional file 2). The gene with the highest increase in transcript abundance to ABA was VviABCG22-like [EnsemblPlants: VIT_18s0166g00080]; while the largest decrease in transcript abundance was a gene encoding a glycine-rich cell wall structural protein [EnsemblPlants:VIT_05s0077g00900].
The gene encoding the transcription factor (TF) with the highest transcript abundance in response to ABA in berries was abscisic acid insensitive 3-like [VviABI3: Ensembl Plants:VIT_07s0005g05400]. ABI3 is a B3-domain transcription factor that is a part of the core ABA signaling network  [31]. The transcript abundance of a peroxidase gene [EnsemblPlants:VIT_04s0008g07040] involved in porphyrin and chlorophyll metabolism significantly decreased in response to ABA. Another transporter that significantly increased in transcript abundance was a vacuolar amino acid transporter [EnsemblPlants:VIT_19s0027g01870]. The transcript abundance of a GABAT (γ-aminobutyric acidtransaminase) gene [EnsemblPlants:VIT_03s0017g01720] increased significantly, while the expression of AST [aspartate aminotransferase: EnsemblPlants:VIT_05s0020g03410] decreased significantly, which may cause an increase of succinate followed by an increase of malate.
Shoot tip Genes with significantly decreased transcript abundance in the ABA treatment of shoot tips that had significantly overrepresented GO categories were involved in protein amino acid phosphorylation (Additional file 4) such as a clavata1-like receptor kinase [VviCLV1; Ensembl Plants:VIT_04S0008G00350], receptor kinase homolog LRK14-like [EnsemblPlants:VIT_16s0098g00080], and malectin/receptor-like protein kinase-like [Ensembl Plants:VIT_16s0039g01260]. There were 17 genes that showed organ-specific gene expression responses to ABA (Fig. 1b ; Additional file 2). The most highly expressed genes in response to ABA were the genes in the AP2/ERF TF family. A VviDDF2 [EnsemblPlants:VIT_02s0025g04460] was the gene with the greatest increase in transcript abundance and was significantly changed in the shoot tips.
A VviORA47 [EnsemblPlants:VIT_11s0016g00670] was also significantly increased in response to ABA. The genes with the largest decrease in transcript abundance in response to ABA were involved in secondary cell wall biosynthesis [fasciclin-like arabinogalactan protein genes: VviFLA11; EnsemblPlants:VIT_08s0040g01990]. Another gene that is also involved in cell wall biosynthesis [xyloglucosyltransferase: EnsemblPlants:VIT_14s0060g01670] was significantly decreased in transcript abundance in response to ABA. This result indicates that there may be an inhibition of shoot growth in response to ABA.
Leaf There were 33 genes that showed leaf organ-specific gene expression in response to ABA (Fig. 1b ; Additional file 2). The greatest increase of transcript abundance of TFs was VviNAC1 [EnsemblPlants:VIT_19s0027g00230]. The gene with the largest decrease in transcript abundance in response to ABA was a gene encoding ent-copalyl diphosphate synthase [EnsemblPlants:VIT_07s0151g01070], which is involved in gibberellic acid (GA) biosynthesis. The transcript abundance of ent-copalyl diphosphate synthase (At4G02780) in Arabidopsis also decreased in response to ABA  [32]. Most genes with significantly increased transcript abundance in leaves were involved in protein folding. There were also genes encoding (E,E)-alpha-farnesene synthases [EnsemblPlants:VIT_00s0361g00060 and EnsemblPlants:VIT_00s0392g00030] that significantly increased in transcript abundance in response to ABA in leaves but significantly decreased in shoot tips.
(E,E)-alphafarnesene synthases are involved in terpenoid biosynthesis. Some genes with statistically significant decreased transcript abundance, such as epidermal patterning factor-like protein 3 [EnsemblPlants:VIT_09s0002g01700] and TAP-ETUM 1 [EnsemblPlants:VIT_01s0137g00030] have roles in leaf morphogenesis and bract formation. Furthermore, there were genes with significantly decreased transcript abundance related to transport such as an aquaporin GAMMA-TIP3 [EnsemblPlants:VIT_06s0061g00730] and an ortholog of ABC transporter member 5, AtMRP5 [EnsemblPlants:VIT_07s0005g04460], which is an inositol hexakisphosphate transporter involved in ABA signaling and regulation of guard cells  [33].
Root Roots have the largest number of genes responding to ABA and there were a number of GO categories that were significantly overrepresented (Additional file 4). The main Biological Process GO categories for genes with a significant increase in transcript abundance in root in response to ABA was response to stimuli such as light, heat, and reactive oxygen species. The molecular function of (+)-abscisic acid 8′-hydroxylase activity was significantly overrepresented for genes with a significant increase in response to ABA. For genes with a significant decrease in transcript abundance to ABA, many of them were involved in protein amino acid phosphorylation. The transcript abundance of VviMYB121 [EnsemblPlants:VIT_14s0083g01060] and Vvi-DREB2H [EnsemblPlants:VIT_13s0067g01960] was significantly upregulated only in the root (Additional file 2).
These two transcription factors have been found to respond to abiotic stress. qPCR results also confirmed that the transcript abundance of VviMYB121 and VviDREB2H increased in response to ABA in roots (Additional file 5). The genes with the largest decrease in transcript abundance in response to ABA in the root were two pectinesterases [EnsemblPlants:VIT_06s0009g02560 and EnsemblPlants:-VIT_06s0009g02570] (Additional file 2), pectin-degrading enzymes that are involved in cell wall metabolism. The transcript abundance of a number of heat shock protein genes increased in the roots. The transcript abundance of genes encoding glucosyltransferase enzymes [Ensembl Plants:VIT_05s0062g00310, EnsemblPlants:VIT_05s0062g 00300, and EnsemblPlants:VIT_05s0062g00340] was significantly downregulated by ABA in the root.
Interestingly, there were auxin-, anthocyanin-, and cytokinin-glucosyltransferases DEGs whose transcript abundance was decreased by ABA only in roots. Several genes involved in transport were significantly increased in roots such as VviABCG33 [EnsemblPlants:VIT_14s0060g00470] an ortholog of AtPDR5, the aquaporin GAMMA-TIP3 [Ensembl Plants:VIT_06s0061g00730], and a sulfate transporter.
Cell culture The GO categories that were significantly overrepresented in cell cultures of up-regulated genes in response to ABA were involved in hormone stimulus (Additional file 4). In addition, there were a large number of genes with increased transcript abundance that were associated with xyloglucosyltransferase activity such as EnsemblPlants:VIT_11S00 52G01190, EnsemblPlants:VIT_11S0052G01330, Ensembl Plants:VIT_11S0052G01220, and EnsemblPlants:VIT_11S0 052G01300. The TF that had the highest increase in transcript abundance in response to ABA in cell cultures was an ortholog [EnsemblPlants:VIT_18s0001g05850; Fig. 4 ) of AtERF022, which is involved in somatic embryogenesis and ethylene signaling  [34]. The gene with the largest decrease in transcript abundance in response to ABA was the ABA receptor PYL12/RCAR6 [EnsemblPlants: VIT_13s0067g01940] (Additional file 5).
This result is consistent with the high expression of a PP2C [Ensembl Plants:VIT_06s0004g05460], which is part of a negative feedback loop in the ABA signaling pathway. Ligninforming anionic peroxidase genes were also decreased in transcript abundance by ABA [EnsemblPlants: VIT_01s0010g01920, EnsemblPlants:VIT_01s0010g020 00 and EnsemblPlants:VIT_01s0010g02020]. Downregulation of an anionic peroxidase alters both lignin content and composition  [35]. The gene with the highest increase in transcript abundance was a U-box domaincontaining protein 19 [EnsemblPlants:VIT_17s0000g08080] that may have a U-box type E3 ubiquitin ligase function (Additional file 5).
Analysis of microarray data All NimbleGen custom oligonucleotide array images were first examined visually in their raw data format for gross spatial variation due to fibers or bubbles. All raw array data were processed and normalized first by Robust Multi-Array Average (RMA)  [80]  using the R package affy  [81]. Specifically, expression values were computed by applying the RMA model of probe-specific correction of perfect match probes. The processed probe values were then normalized via quantile normalization, and a median polish was applied to compute one expression measure from all probe values. The three expression measurements for each biological state were inspected individually for each element on the array as in  [82]. An additional quality control step was performed at this point  [83] [84] [85].
Of the 88,650 sets of replicates, 14 % exhibited a coefficient of variation greater than 50 %. Any set of replicates that displayed a coefficient of variation of greater than 0. 5, and that included one or more replicated measures lying more than 1 standard deviation away from the mean was scrutinized. The maximum standard deviation for three replicates in this dataset was 1. 15; 33 % of all measurements observed were greater than 1 standard deviation away from the mean of their set of replicates. Of the 886,500 expression values measured in this experiment, 11,671 (1. 31 %) were excluded as single outliers. Upon this correction, there remained 171 sets of triplicates that still had coefficients of variation that were still greater than 0. 75, which were completely excluded from further analysis.
Metabolic pathways affected by ABA were determined using a gene ontology (GO) file created using the EnsemblPlants BioMart  [87]  for Vitis vinifera and BiNGO analysis  [88]  (v. 2. 44, http://apps. cytoscape. org/apps/bingo) with Cytoscape  [89]  (v. 2. 8. 3, http://www. cytoscape. org/). Overrepresented categories were determined using a hypergeometric test with a significance threshold at 0. 05 after a Benjamini and Hochberg false discovery rate correction. qPCR Array verification was performed by qPCR for 10 differentially expressed genes (Additional file 6). A coefficient of determination (r 2 ) between microarray and qPCR was 0. 69, P < 0. 0001. Total RNA extraction was the same as that used for microarray analysis described above. One μg of RNA was reverse transcribed to cDNA using the iScript reverse transcriptase supermix (Bio-Rad) according to manufacturer's instructions.
GAPDH [EnsemblPlants:VIT_17s0000g10 430], EIF4F [EnsemblPlants:VIT_04s0008g06770], and EF1alpha [EnsemblPlants:VIT_06s0004g03220] were tested for suitable and reliable reference genes for this experiment. A GAPDH gene was chosen in the assays as an internal control for normalizing the variations in all data due to a low coefficient of variation of mRNA through microarray analysis. The relative expression ratio value was calculated according to the Pfaffl equation  [90].
Acknowledgements This research was supported by the  USDA National Institute of Food and Agriculture, Hatch  Grant (Project  NEV00345 ); part of the multistate project ( NE1020 ):  Multi-state Evaluation of Winegrape Cultivars and Clones. The cell culture work was supported by a  scholarship award  to RG from the  Grape Research Coordination Network , part of the  National Science Foundation  grant no. DBI 0741876. The microarray data analyses were supported by a grant from the  National Institute of General Medical Sciences  ( P20GM103440 ).
The primary cause for differentiation is therefore known and relies upon change in the information carried by the extracellular environment. The differentiation process in those cells has been previously analyzed at the population level  [33] [34] [35]. We first selected a pool of 110 relevant genes on the basis of RNA-Seq analysis performed on populations of T2EC in self-renewal state or induced to differentiate for 48 h. Multivariate statistical analysis of the data allowed us to select 92 genes for further analysis. We then performed high-throughput reverse transcription followed by reverse transcription quantitative PCR (RT-qPCR) of the 92 selected genes on single-cells collected at six time-points of differentiation. Several dimensionality reduction algorithms were used to visualize trends in the datasets.