dataesr/scientific-paragraphs-categorization huggingface dataset:
Microscope analysis In January, after 5 or 3 months from the water stress treatments, four replicates of 10 cm-segments for each combination (cultivar × treatment) were collected from the basal 5th internode of randomly selected canes. 6-µm microtome sections (stained with safranin/alcyan blue) were prepared and examined by light microscope (Fig. 1 ). Digital slides were acquired with an Aperio CS scanner (Leica, model AT2), at 5X magnification and anatomical parameters were analyzed by open-source ImageJ software. For bark parenchyma and phloem analysis, two orthogonal sections were selected from the total area. For starch visualization and vital tissue observation, fresh 10-µm microtome sections were stained with Lugol's Iodine or fluorescein diacetate solution and observed under stereomicroscope (Fig.
Statistical analysis The differences in Ψ stem , g S and A N between WW and WS at the end of the period of water stress were evaluated by t-test (p indicated), separately for the stress periods of July and September. One-way ANOVA was performed on NSC content and anatomical features data keeping separated the two grapevine cvv. When the differences were significant, SNK-test was applied to evaluate the differences between WW and WS (p<0. 05). The statistical analysis was performed with JMP 7. 0 (SAS Institute Inc. ), while point scatter graphs were prepared using SigmaPlot 13 (Systat Software GmbH, Erkrath, Germany).
Acknowledgments The authors would like to thank the  Interprofessional Fund for Agricultural Research and Council (FIRCA) ,  in Côte d'Ivoire , which financed this study: (Projet  N˚ 063/FIRCA/LBATPT/FILIERE COLA/2016 ). They would also like to thank  Professor KOUADIO James Halbin ,  Professor DIAKITE Aïssata ,  Dr. ADOU Marc  and  Dr AKELY Pierre Martial Thierry  for their technical support. They also thank  Mr. DIARRASSOUBA Aboudramane ,  President of FENAPROCO-CI  ( National Federation of Cola Professionals in Côte d'Ivoire ) for allowing to conduct this study in these premises.
All contractions were made at an initial length L0. Maximal isometric force was measured 60 s after each lengthening contraction and expressed as a percentage of the initial maximal force. Data was acquired with a sampling rate of 100 kHz (Powerlab 4/25, ADInstrument, Oxford, United Kingdom). After contractile measurements, the animals were killed by cervical dislocation and muscles were weighed and stored for further analyses.
To exclude PCR products amplified from genomic DNA, primers were designed, when possible, to span one exon-exon junction. The mean gene expression stability of 2 genes, Sdha (Succinate deshydrogenase complex, subunit A, flavoprotein), Hmbs (Hydroxymethylbilane synthase), was used as the reference transcript. Data were collected and analyzed using the LightCycler 480 software release 1. 5. 0 (Roche Diagnostics). Primers sequences used in this study are available on request.
This work was supported by funds from  CNRS (OA) ,  Sorbonne University  (OA),  University Paris Descartes (AF) , the  Association Francaise contre les Myopathies  (OA, contract number: 16605 ), the  LabEx REVIVE (OA ,  ANR-10-LABX-73 ). Y. H. supported by a fellowship from the  Association Francaise contre les Myopathies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
FLM variation relevance in nature To understand the natural implications of the polymorphism identified, we screened all A. thaliana strains with data available for SNP28958 (https://1001genomes. org). Surprisingly, out of the available 50 strains carrying this SNP, 45 come from the Iberian Peninsula (hereafter called IP strains), 2 from France, 2 from Italy and Can-0 from the Canary Islands (Supplemental Table1). To evaluate the impact of SNP28958 on FLM expression in natural IP strains, two groups of 9 IP strains carrying either the Can or the Col allele were selected for qRT-PCR experiments. A clear association between the allelic form and the accumulation level of FLM-β was observed (Fig. 5A ). Moreover, a shift in FLM-δ size was only detected in the Can-like IP accessions indicating that this SNP retain the same hypofunctional molecular function as the FLM-Can allele (Fig.
S9 ). We then wanted to know if the functional impact of SNP28958 on multiple traits could play a substantial role in IP strains adaptation to their habitat. We used a set of 186 IP strains for which we were able to gather geographic and phenotypic data from previous publications including the 46 strains carrying SNP28958  (Mendez-Vigo et al. , 2011; Vidigal et al. , 2016)  (Supplemental Table2). We used this dataset to fit a general linear model corrected by a kinship matrix to avoid bias due to population structure (Supplemental Table3). Consistent with our previous observations, we found significant associations between the FLM causal polymorphism and leaf production rate with or without vernalization (Fig. 5B ). Can-like IP strains do not cluster geographically and are scattered across Spain and Portugal (Fig.
FLM allele evolution Pairwise genetic distances calculation among 1,135 sequenced strains of A. thaliana recently showed that 26 of them, referred to as "relicts", were highly divergent and are supposed to be old lineages coming from ice-age refugia (Alonso-Blanco et al. , 2016). Among them, 21 strains form a group coming from the Iberian Peninsula including 13 carrying the SNP28958 (Supplemental Table2). This allele is thus enriched in the relict group (13/21 = 62%) compared to the overall Iberian Peninsula (46/187 = 24. 5%; z-test : p=0. 00083). A complex pattern of genomic introgressions has recently been highlighted between relict and non-relicts IP strains showing that their genome is rather a mosaic of the 2 groups  (Lee et al. , 2017). We used this published dataset to estimate the probability of being relict around FLM in the IP strains according to the causal polymorphism.
The fact that our absolute values for these traits fell outside of the range previously observed across species and among A. thaliana accessions can be explained by the growth conditions in which plants from the flm dataset were measured (Fig. 4B ). Indeed, plants were grown in the greenhouse under relatively low light intensity (ca. 65 µmol m -2 s -1 PPFD), compared to the natural accessions grown in the PHENOPSIS phenotyping platform under moderate light intensity (ca. 195 µmol m -2 s -1 PPFD;  Granier et al. , 2006) , or the interspecific data collected in common garden experiments, presumably under much higher (and fluctuating) light intensity. Still, the relationship between these traits was conserved in the flm-3/Col-0 comparison (p>0. 05, r²=0. 17). The loss of function of FLM triggered coordinated changes in functional traits that incrementally re-oriented wholeplant physiology toward the resource-acquisitive side of the LES.
, 2018). We did not observe associations with precipitation variables but our dataset differs from  Frachon et al. (2018)  as it covers the whole Iberian Peninsula potentially encompassing a much wider range of genetic and environmental variation. Evaluation of the fitness consequences driven by natural allelic variation in the gene MITOGEN ACTIVATED PROTEIN KINASE 12, which modulates WUE, showed that low WUE genotype were advantageous in the face of competitors  (Campitelli et al. , 2016). It would be interesting to investigate the adaptive value of FLM by combining water availability, temperature fluctuation and competition using our Arabidopsis transgenic lines differing only at SNP28958. Reciprocal transplant experiment could also be considered to quantify the adaptive effect of the FLM alleles  (Ågren and Schemske, 2012).
Genetic material A segregating population (named 19RV) derived from the cross between Can-0 (accession 163AV) and Col-0 (accession 186AV) is described and available from the Versailles Arabidopsis stock centre (http://publiclines. versailles. inra. fr/; Simon et al. , 2008) ). Description and associated data are gathered at http://publiclines. versailles. inra. fr/page/19. 358 RILs were phenotyped in similar experimental design as previously described  (Marchadier et al. , 2019). HIF was fixed as previously described  (Marchadier et al. , 2019; Loudet et al. , 2005)  from the following RIL (19RV337) with a residual segregating region covering position 27,401,322bp to 29,693,733bp on chromosome 1 (See Supplemental Table4). For fine-mapping, 3,360 progenies of heterozygous 19HV337 were genotypically screened to find recombinant individuals within the candidate region (rHIF).
Statistical analyses Significance of the difference in the box plots were calculated with the Mann-Whitney-Wilcoxon test for the pairwise comparisons ('. ' p<0. 1; '*' p<0. 05; '**' p<0. 01; '***' p<0. 001) or with the Tukey HSD test for multiple comparisons, the different letters representing groups at p < 0. 01. Linear regressions between A mass and LMA were examined with standard major axis (SMA), using the package smatr in R  (Warton et al. , 2012). Physiological traits (i. e. LMA and A mass ) across Arabidopsis accessions were obtained from  Sartori et al. , 2018 , while physiological traits from interspecific data were obtained from the GLOPNET database  (Wright et al. , 2004). All analyses were performed in R 3. 2. 3 (Team RC 2014). A-Expression of FLM-β was measured by qRT-PCR using the primer pair F3+R3b in 9 Arabidopsis strains from the Iberian Peninsula carrying either the Can (green) or the Col (red) genotype at the position 28,958,437bp.
ACKNOWLEDGEMENTS We thank  Yann Serrand  for the supervision of the Phenoscope, and  Lilian Dahuron  for expert care of our plants. We also would like to thank  Markus Schmid  ( MPI Developmental Biology, Tübingen, Germany ;  Umea Plant Science Center, Umea, Sweden)  who provided seeds of the flm-3 mutant;  Carlos Alonso-Blanco  for sharing his experience in sequence diversity analyses;  Cheng-Ruei Lee  ( National Taiwan University ) for sharing his analysis concerning relict accessions.
FUNDING This work was supported by funding from the  European Commission   Framework Programme 7 ,  ERC Starting Grant  ' DECODE'/ERC-2009-StG-243359  to O. L. The IJPB benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). FV was supported by  INRA ,  CNRS  and the  Agreenskills fellowship programme  (grant agreement n°  3215 , FV), which has received funding from the  EU's Seventh Framework Programme  under the agreement  N° FP7-609398.
10 µl of PCR product was purified and prepared for pyrosequencing according to a previously described protocol  30, 31. DNA methylation analysis was performed using PyroMark Gold SQA Q96 Kit (Qiagen) on a PyroMark Q96 MD (Qiagen) and analyzed with PyroMark CpG software (Qiagen). DNA methylation data of the 100 blood samples from French individuals are available in Supplementary Dataset. xlsx. statistical analysis and graphical representation. All statistical analysis and graphical representations were performed using R (https://www. r-project. org/) and MS Excel (Microsoft). The correlation between chronological age and DNA methylation predicted age was assessed using the Pearson r correlation coefficient. The coefficient of determination R² was calculated as the square of the r coefficient.
Complexity of the EEG can be assessed by entropy measures (e. g. , spectral entropy, K complexity, or permutation entropy). Functional connectivity between distant electrodes can be assessed using the spectral dimension (e. g. , the debiased weighed phase lag index [wdPLI]) or information theoretical approaches (e. g. , the weighed symbolic mutual information [wSMI])  [12, 13]. Similar methodologies assessing cortical functional connectivity have been developed using fMRI  [14]. Candidate EEG features can then be used to train on an existing dataset of patients with known level of consciousness using a multivariate classifier to evaluate the EEG of a new patient  [15]. Among these techniques, markers derived from resting state EEG seem to be the most promising in the ICU as imaging tests cannot be easily repeated and transport-related risks need to be considered for these sick patients.
Indeed, physiologic biomarkers that correlate with consciousness have been developed on models trained on large datasets using clinical labels. Consequently, the confidence of how accurately a given patient is classified using these approaches mainly relies on the quality of the labels used in the training set. These labels are derived from clinical examinations which we know are imprecise. The relevance of neural correlates of conscious processing such as the P3b are still debated. In addition, evidence of conscious processing of simple stimuli does not imply the existence of conscious processing of more complex mental contents. Following commands revealed by fMRI or fEEG appears as the strongest evidence since it usually relies on statistics performed at the patient level (using for instance machine learning and permutation test).
Acknowledgements This work was supported in part, by the  DANA Foundation ,  James S. McDonnell Foundation , the  NIH  under award number  R01LM011826 , and  NSF  under award number  1347119. Additional support included postdoctoral grants from  "Amicale des Anciens Internes des Hôpitaux de Paris & Syndicat des Chefs de Cliniques et Assistants des Hôpitaux de Paris" (AAIHP -SCCAHP) , " Assistance Publique -Hôpitaux de Paris" (AP-HP) , and the  Philippe Foundation.
0%) received AZA 5-2-2; and 78 (39. 0%) received AZA 7 consecutively days regimen. ORRs appeared higher with the AZA 5-2-2 dosing schedule (67. 9%) compared with AZA 7 (51. 3%) and AZA 5 (39. 4%), (overall, P = 0. 0094), but there was no impact on survival. Otherwise, 58. 5% of patients included in the study presented IPSS Low-or Int-1-risk MDS. 16 ecently, an update of data registry of the GESMD between 2000 and 2013 included 251 patients with higher risk MDS patients (defined by an IPSS risk score of 1. 5 or more) treated with AZA. Dosing schedule was available in 179 patients (71%). Seventy-six patients received a standard 7day regimen (42. 5%), and the remaining 103 (57. 5%) were treated with less intensive dosing. There were no differences in survival between these two dosing schedules (P = 0.
A recent alternative tool, such as Matrix Assisted Laser Desorption Ionisation -Time of Flight -Mass Spectrometry (MALDI-TOF-MS), is used for both fungal and bacterial colony identifications  [9, 10]. The MALDI-TOF MS-based identification technique analyses the protein content from treated or intact microorganisms in the form of a spectrum that is considered to be a protein fingerprint that is specific to a microorganism. An unknown bacterial or fungal colony is identified by comparing its spectrum with the spectra in the reference database  [9]. MALDI-TOF MS-based identification is simple, fast, accurate and allows for the rapid screening of large numbers of colonies isolated in culture  [10]. Culturomics methods, which consist of the application of highthroughput culture conditions followed by MALDI-TOF MS and/or 16S rRNA identification, allow the known diversity of the gut microbiota to be expanded  [10].
Culture-independent methods Molecular tools have emerged since 1990 for digestive tract mycobiota and microbiota investigations. PCR-based methods used primers that targeted 16S RNA in microbiota studies and primers that targeted 18S, 5. 8S, 26S, and 28S RNA and the ITS (Internal Transcribed Spacer) regions in mycobiome studies. The variability of the ITS region enables the classification of fungi into the genera and species levels. Then, computational algorithms are used to align the operational taxonomic unit (OTUs) with those from the fungal database. Other molecular techniques, such as cloning libraries as rDNA-based denaturing gradient gel electrophoresis (DGGE), restriction fragment length polymorphism (RFLP) analysis, in situ hybridization techniques, and Sanger sequencing, have been used on stool samples or digestive mucosa biopsies to investigate the fungal and bacterial diversity  [1, 14, 15].
Participants The data set used in this study came from three different cohorts. The first cohort examined 182 participants from the Dominantly Inherited Alzheimer's Network Trial Unit (DIAN-TU)  [17]  (https://clinicaltrials. gov/ct2/show/NCT0 1760005) with baseline amyloid PET imaging data using both PiB and florbetapir. A total of 194 initial participants were recruited, but 12 were either missing imaging data or the processing did not pass quality control. In brief, enrollment criteria were being at risk for an autosomal dominant AD (ADAD) mutation, a Clinical Dementia Rating (CDR) score  [18]  of 0, 0. 5, or 1, and an estimated years to symptom onset (EYO) of 215 to 110. Among the 182 DIAN-TU participants included in this study, 50 did not carry ADAD mutations (29 of these 50 participants were younger than 45), and 132 were mutation carriers (see Table  1  for more information).
In addition, longitudinal PiB (N 5 54) and florbetapir (N 5 26) data for participants from Knight ADRC with positive baseline amyloid scans were included for further comparison of these two tracers in measuring rate of amyloid accumulation in a late-onset AD spectrum. Including amyloid-positive participants only allows more accurate assessment of the rate of amyloid accumulation, as PET measurement in people with minimal amyloid burden is mainly influenced by nonspecific binding and other factors unrelated to amyloid. A summary of cohort characteristics is presented in Table  1. For the longitudinal cohort, primary analysis was based on two time points rather than the full longitudinal data set because very few participants had more than two florbetapir scans. For all participants across the cohorts, dementia status was assessed using the CDR and CDR sum-of-boxes (CDRSB) score.
Imaging For the DIAN-TU participants, all florbetapir PET scans include data between 50 and 70 minutes after injection and all PiB PET scans include data between 40 and 70 minutes after injection. The interval between florbetapir and PiB scans was less than a month (range 0-28 days). For the sporadic AD PiB-florbetapir crossover study, PiB PET includes data between 30 and 60 minutes after injection and florbetapir PET includes data between 50 and 70 minutes after injection. The PiB-florbetapir scan interval was also less than a month (range 1-18 days). For the longitudinal cohorts, same protocols were used for PiB and florbetapir as the sporadic AD crossover study. In addition to PET, 3D sagittal T1-weighted images of the head were also acquired for each participant.
Image analysis PiB and florbetapir images were analyzed using our standard processing pipeline (PUP; https://github. com/ysu001/ PUP)  [23, 24]. Briefly, FreeSurfer (v5. 3; Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA; https://surfer. nmr. mgh. harvard. edu/fswiki) was used to process T1-weighted magnetic resonance imaging data and facilitate regional analysis of PET data. PUP processing includes scanner resolution harmonization filter  [25] , interframe motion correction, PET-to-magnetic resonance registration, regional intensity extraction, RSF-based PVC  [23, 26] , and SUVr analysis using the cerebellar cortex as the reference region. Although the brainstem/pons is commonly used as the reference region for the investigation of ADAD  [2, 5] , we used the cerebellar cortex to be consistent among cohorts, and also it has been shown recently that using the cerebellar cortex generated similar results as brainstem referencing in DIAN  [27].
As the global index of amyloid burden, a mean cortical SUVr was calculated based on a selected set of cortical regions defined by FreeSurfer, that is, frontal, parietal, temporal, and precuneus  [24]. To facilitate between tracer comparison, PiB and florbetapir SUVrs were both converted to the Centiloid scale. The Centiloid conversion for PiB data was reported previously  [28]. For DIAN-TU data, the conversion equations for 40-to 70-minute time window were used; and for the crossover and longitudinal data set, the equations for 30-to 60-minute time window were used (see Supplementary Material for details). To establish the Centiloid conversion equations for florbetapir PET processed using PUP, we followed the recommended level-2 calibration procedure  [13]. The PiB PET data from the calibration data set were analyzed using the Centiloid pipeline to obtain PiB Centiloid SUVr according to Klunk et al.
To test whether the two tracers are equivalent in inter-individual variability and correlation to other variables in the context of multivariate model, a likelihood ratio test (test statistics approximately follow c 2 distribution) was performed. A major advantage of using the multivariate-model-based test is that other covariates can be added into the model straightforwardly and be adjusted for. One of the 132 mutation carrier DIAN-TU participants was missing EYO data and excluded from this correlation analysis. These analyses were done using Proc Glimmix, SAS 9. 4 (SAS Institute Inc. , Cary, NC). Correlation analysis was also performed for the cross-sectional sporadic AD spectrum data to compare the two tracers.
Acknowledgments This research was supported by  National Institute of Health  grants:  P50AG005681 ,  P01AG003991 ,  P01AG026276 ,  U01AG042791 ,  R01AG046179 ,  P30NS048056 R01AG055444 , and  R01AG031581. Image acquisition and analysis received additional support by  UL1TR000448 ,  P30NS098577 , and  R01EB009352. Research was also supported by  BrightFocus Foundation  grants  A2017272S  and  A2017330S ,  Alzheimer's Association Research  grant  AARG-17-532945 ,  Arizona Alzheimer's Research Consortium , the  Charles and Joanne Knight Alzheimer Disease Research Center Support Fund , the  David and Betty Farrell Medical Research Fund , the  Daniel J. Brennan Alzheimer Research Fund , the  Fred Simmons and Olga Mohan Alzheimer Research Support Fund , the  Barnes-Jewish Hospital Foundation , the  Alzheimer's Association, Eli Lilly and Company, Hoffman La-Roche ,  Avid Radiopharmaceuticals, GHR Foundation , and an anonymous organization.
1 ). A temperature of 8 °C was chosen as the standard temperature of household refrigerators, a cooling rate of 30 °C min -1 as the average cooling rate throughout yoghurt production and a heating rate of 2 °C min -1 as a compromise to be close to that of the mouth but also to be able to correctly measure melting behaviour. Cooling down to 8 °C was followed by a quick quenching at -10 °C to obtain a good baseline for the data treatment. The calorimetric parameters retrieved were those of melting. STARe Excellence software (Mettler-Toledo, Greifensee, Swiss) was used to obtain the peak (Tpeak, °C) and final (Tendset, °C) melting temperatures as well the total variation in enthalpy (∆H, J g -1 ). Based on the work of  Lopez, Briard-Bion, Camier, & Gassi (2006) , the order of magnitude of solid fat content (SFC) formed by the emulsified AMF, olein and stearin fractions at 8 °C was calculated using the following melting enthalpy ratio: (%) = Δ Δ (1) where ∆Hpartial (J g -1 ) is the variation in the melting enthalpy of creams measured after cooling to 8 °C and ∆Htotal (J g -1 ) is the one measured after cooling bulk fats down to -55 °C.
Statistical analysis Statistical analysis was performed using XLSTAT 2015. 1 software (Addinsoft, Paris, France). Analysis of variance (ANOVA) was performed to evaluate differences between average values using Tuckey's test. Significance levels of p ≤ 0. 05 or p ≤ 0. 1 were used. Principal component analysis (PCA) was used to analyse all the texture properties measured in instrumental conditions that mimicked oral processing.