dataesr/scientific-paragraphs-categorization huggingface dataset:
Strains, growth conditions and fermentation medium The two parental strains used for the segregating population are S288c (MATα; SUC2, gal2) strain and 59A (MATa; ho), which is a haploid derivative of EC1118 (HO/ho), an industrial wine strain  [4]. Mutations between the two parental strains are accessible in the GenYeasTrait database previously described (http://genome. jouy. inra. fr/genyeastrait/ [4] ). We increased the population of 30 F1 segregants described in  [4]  with 14 randomly selected segregants. Thereby we disposed of two complete tetrads in the analyzed population, and the other segregants were selected from 24 different asci of two or three viable spores. The phenotyping was done on fermentation in 0,9l of synthetic SM425 medium (with 425 mg/l of assimilate nitrogen pH3. 3 and previously describe as SM300  [60] ) which mimics natural grape must.
Fermentation kinetics and phenotypic measures Each parental fermentation and phenotypic measures were realized in four replicates and segregants fermentation in two replicates. The fermentation kinetics were measured by the loss of weight due to CO 2 release. The weight was record every 20 min and the data were smoothed by polynomial method. The final population size was estimated at 45 g/l of CO 2 release and 66 gCO 2 /l using electronic particle counter (Beckman Coulter) and optic density at 600 nm (OD 600 , spectrophotometer SECOMAN-UVLine9400). Dry weights were measured at 88 gCO 2 /l by filtration using nitrocellulose filters (pore size 0. 45 μm; Millipore) and drying at 100°C (48 h). The metabolic compounds amounts in the medium was measured after the end of the fermentation by high-pressure liquid chromatography (HPLC) using ion-exclusion column (HPX-87H BIO-RAD) with 8 mM H 2 SO 4 mobile phase.
Quality and quantity of RNA were controlled at each step by spectrometry (NanoDrop 1000, Thermo Scientific). Agilent gene expression microarrays 8x15k was used for the micro array hybridization, with one-color method (Agilent Technologies, Santa Clara, CA, USA). Array design is based on ID 016322 completed with the 39 genes from the new region of EC1118  [13]  and available on GEO with GPL16012 as accession number. A quantity of 600ng of labeled cRNA were hybridized for 17 h in 65°C in a rotative hybridization oven (Corning) using the Expression Hybridization kit (Agilent Technologies, 5188-5242). Plates were washed with expression wash buffer kit (Agilent Technologies, 5188-5325 5188-5326). The array pictures were analyzed on a GenePix 4000B laser Scanner (Axon Instruments) and with the GenePix PRO7 software.
Linkage analysis The linkage analysis was performed using R/qtl package  [71]  on the three datasets corresponding to the three analyses (Analysis1, Analysis2 and Analysis3). The normal model with Haley-Knott regression method was used resulting in logarithm of odds (LOD) score for each marker and pseudo-marker every 2. 5 cM (7. 5 kbp) (interval mapping method). An interval estimate of the location of each QTL or eQTL was obtained as the 1-LOD support interval: the region in which the LOD score is within 1 unit of the peak LOD score. For phenotypic QTLs, individual LOD score threshold for a false discovery rate of 0. 05 was determined doing 1000 permutations. For eQTLs, the permutation was performed 20 times, and the average number of transcripts showing linkage at a specific LOD score threshold was used to calculate FDR.
Myxomycetes, or plasmodial slime-moulds, are one of the largest groups in phylum Amoebozoa. Nonetheless, only ,10% are in the database for the small subunit (SSU) ribosomal RNA gene, the most widely used gene for phylogenetics and barcoding. Most sequences belong to dark-spored Myxomycetes (order Fuscisporida); the 318 species of superorder Lucisporidia (bright-spored) are represented by only eleven genuine sequences. To compensate for this, we provide 66 new sequences, 37 SSU rRNA and 29 elongation factor 1-alpha (EF-1a), for 82% of the genera of Lucisporidia. Phylogenetic analyses of single-and two-gene alignments produce congruent topologies and reveal both morphological characters that have been overemphasised and those that have been overlooked in past classifications. Both classical orders, Liceida and Trichiida, and several families and genera are para/polyphyletic; some previously unrecognised clades emerge.
Myxomycetes are a firmly established clade within Amoebozoa, probably sisters to Dictyostelea, and more distantly related to Protostelida, Variosea, and perhaps Archamoebae, but relationships amongst these five taxa remain uncertain  [2] [3] [4] [5] [6] [7]. Myxomycetes are divided into two subclasses Exosporeae Rostaf. (i. e. Ceratiomyxa) and Myxogastria Fr. [8]. The primary phylogenetic bifurcation within Myxogastria is between the darkspored and bright-spored clades  [2, 9] , superorders Columellidia and Lucisporidia respectively  [8]. Lucisporidia has 34% of myxomycete species and is generally divided into two orders  (Liceida Jahn, 1928 and Trichiida Macbride, 1922) , 22 genera and 318 species (http://eumycetozoa. com/data/index. php, updated 20. 7. 12) (Table  1 ). Readers unfamiliar with these taxa can find excellent illustrated descriptions  [10, 11, 12]  or consult the online searchable database of the eumycetozoan project at the University of Arkansas (http://slimemold.
uark. edu/databaseframe. htm, last accessed: 19 Sep. 2012). We shall use only a few specialized terms, explained below. Differentiation of the plasmodium into a fruiting body (sporophore) forms three structures: peridium, capillitium, and spores. The peridium is the wall surrounding the fruiting body, and the capillitium is a system of threads interwoven throughout the spores (best seen in Fig. 1K ). The sporophore is named according to its shape: most common are individual sporocarps or sporangia, stalked (Fig. 1 A, J, K, O ) or sessile (Fig. 1 F, H, I, L, N ). In the former, the ensemble of the spore mass, peridium and capillitium (the two latter facultative) is called the sporotheca, to differentiate the ''fertile'' part from the stalk. Large sporophores (. 1 cm) are mostly a compound of multiple sporangia: if the sporangia are delimited, it is called a pseudoaethalium (Fig.
Accordingly, two (Dianemidae and Trichiidae) or three families (the same as above plus Arcyriidae) have been recognized. Similarly, the fourteen genera (Table  1 ) are difficult to delimit as many species possess features in common with two genera  [13]. Currently, phylogenetic clarification of the position of Myxomycetes in Amoebozoa and investigation of their ecological role in soil is hampered by the lack of a sequence reference database: in the 101 small subunit (18S or SSU) ribosomal RNA gene sequences reported  [15]  only 11 belong to the bright-spored Lucisporidia. To compensate for this bias, and to shed light on the taxonomic conundrum of Lucisporidia, we provide 66 small subunit rRNA (SSU) and elongation factor 1a (EF-1a) gene sequences for 81. 8% of the genera of Lucisporidia (Table  1 ).
Amplification parameters were adapted according to the elongation time (depending on the length of the expected product, 1-2 min) and annealing temperature of the primers (52-58uC). Amplicons were purified using SureClean (Bioline) or with the PCR DNA and Gel Band Purification kit (GE Healthcare Life Science), then sequenced at various facilities (Department of Genetics and Evolution, University of Geneva; Zoology Department, Oxford University; and Zoology Institute, University of Greifswald). Sequences are deposited in GenBank under accession numbers JX481280-JX481345 (Table  1 ).
Alignments GenBank nucleotide database was searched for all sequences belonging to the orders Trichiales (or Trichiida) and Liceales (or Liceida). Half the 22 SSU sequences thus retrieved appeared actually to be fungi or other contaminants (Table  S2 ), and one, Arcyria cinerea AF239231, although genuine, was too short to be included. Nineteen EF-1a sequences, on the other hand, although giving coherent BLAST matches, had to be excluded for insufficient length or quality, in particular because of the presence of indels disrupting the reading frame (Table  S2 ). The genuine, high quality Lucisporidia sequences were aligned by hand to our existing myxomycete alignments, using BioEdit version 7. 0. 9 [17]. The final SSU alignment comprised 48 Lucisporidia taxa, plus 3 sequences of Ceratiomyxa fruticulosa as outgroup, in total 51 sequences.
Ancestral characters are the stalked, individual fruiting bodies arising from a large plasmodium, while small plasmodia giving rise to a single fruiting body are derived. Figure  S2  EF-1a gene tree of Lucisporidia derived by Bayesian inference of 380 amino-acid positions of 38 taxa, with Ceratiomyxa fruticulosa as outgroup. Species names are followed by GenBank accession number, except for sequences obtained during this study (in bold), whose accession numbers and collection sites are in Table  S1 ; Groups are labelled and highlighted as in Fig. 1 , with labels in grey if appearing as polyphyletic, in black if monophyletic. Bayesian posterior probabilities (BPP)/ML bootstrap replicates (MLB) are shown for each branch; dashes indicate a conflicting topology in the ML tree; a dot on the line indicates maximum support in both analyses.
Supporting Information Figure  S3  Schematic secondary structure of the group I intron S1210 found in the SSU sequence of Licea marginata JX481296, according to  [22]. The putative 59 and 39 splice sites (SS) are indicated by an arrow. Flanking exon sequences are in lowercase and outlined. The substrate domains (P1 and P2), the catalytic domains (P3, P7, P8 and P9) and the scaffold domains (P4, P5 and P6) are labelled. When the sequence is not shown, the length of the helix is given. (PDF) Table  S1  List of new specimens used in this study, GenBank accession numbers and collection information. Herbaria: AMF-D = Anna Maria Fiore-Donno, DWM = David Mitchell, HS = Hacene Seraoui, MM = Marianne Meyer, MS = Martin Schnittler. (PDF) Table  S2  Publicly available sequences not included in this study.
Acknowledgments We are deeply grateful to  Michel Poulain  and  Alain Michaud  for the photos, to  David Mitchell  and  El-Hacene Seraoui  for providing specimens. A previous version of this manuscript benefited from suggestions of U. Eliasson and  M. Poulain. Some  of the analyses were run at the  Bioportal at the University of Oslo, Norway  (http://www. bioportal. uio. no, last accessed Oct. 2012).
This study was designed to identify predictive signatures of pathological complete response (pCR) in breast cancer treated by taxane-based regimen, using clinicopathological variables and transcriptomic data (Affymetrix Hgu133 Plus 2. 0 devices). The REMAGUS 02 trial (n ¼ 153,training set) and the publicly available M. D. Anderson data set (n ¼ 133, validation set) were used. A re-sampling method was applied. All predictive models were defined using logistic regression and their classification performances were tested through Area Under the Curve (AUC) estimation. A stable set of 42 probesets (31 genes) differentiate pCR or no pCR samples. Single-or 2-probesets signatures, mainly related to ER pathway, were equally predictive of pCR with AUC greater then 0. 80. Models including probesets associated with ESR1, MAPT, CA12 or PIGH presented good classification performances.
As expected, these pCR signatures are not unique, in that different methods of analysis used on the same data set, or different samples from the same population can lead to different signatures  [10e15]. In spite of these encouraging results, more precise classifications are required, because intrinsic tumour sensitivity may differ according to tumour phenotype. This study was designed in the context of a prospective randomized clinical trial, the REMAGUS-02 trial (R02)  [16]. The R02 trial was a phase II multicentre randomized trial designed to assess antitumor efficacy of sequential epirubicin/cyclophosphamide followed by docetaxel with randomized addition of celecoxib and trastuzumab, for HER2-negative and HER2-positive stage T2/T3 operable breast cancer patients, respectively. The present study concerned the subpopulation of 153 patients with good quality available material, as previously reported  [17]  and equivalent pCR levels.
Gene expression analysis All gene expression microarrays were profiled on Affymetrix chips according to the manufacturer's protocol (U133 Plus 2. 0 GeneChips, Affymetrix, Santa Clara, California) at the Institut Curie Translational Research Department (Paris). Details of our microarray hybridization methods have been previously reported  [17]. Transcriptomic data were available for 189 patients with homogeneous quality control characteristics. The present study concerns the subpopulation of 153 patients from arms A, B (both HER2-) and D (HER2þ, without neoadjuvant trastuzumab)  [17]. The M. D. Anderson data set (MDA), generated in a similar neoadjuvant breast carcinoma cohort of patients, was used as validation set. It included 133 patients with stage IeIII breast cancer. All patients received weekly paclitaxel and fluorouracil-doxorubicincyclophosphamide chemotherapy.
Data analysis The GCRMA procedure  [19]  was used to normalize microarray data. The two data sets (R02 and MDA microarray data sets) were normalized separately with 54,675 (U133 Plus 2. 0 chips) and 22,215 (U133A chips) probesets, respectively. Only probesets present in both data sets were then considered. Probesets expression levels of each set were standardized to ensure homogeneous expression levels within the two data sets.
Validation of the DEP in the MDA population and construction of the test algorithms predictive of pCR Logistic models were then applied to this final set of stable DEP to predict pCR. This step was performed sequentially: from the final set of stable DEP, all logistic models with one probeset (1-P models) were applied to the full R02 data set (n ¼ 153). Scores corresponding to pCR prediction were calculated, and the performances of the models on R02 data were estimated by Area Under the receiving operating Curve (AUC). Using the same parameter estimates, all these 1-P models were then applied to the MDA data set. Their corresponding performances on this data set were also estimated by AUC. Only those 1-P models that showed performances greater than 80% on both the R02 and MDA data sets were considered to be valid 1-P models.
Results Patient characteristics of the training cohort (R02) and the validation cohort (MDA) are shown in Table  1. Microarray data are available on GEO with accession number GSE26639. A Supplemental Table  1  indicates the correspondence between microarray data and pathological response for each patient. From the common genomic set of 22,215 probesets analysed, IQR filtering (applied to each of the 100 pairs of training and test sets of R02 datas) provided a list of 9494 DEP that were present in at least 95% of the common IQR lists. From this list of probesets, SAM analysis (applied to each of the 100 training sets) resulted in the selection of 42 stable probesets (corresponding to 31 different genes) that were also found to be differentially expressed on the MDA data set between samples with and without pCR.
Discussion This study identified stable predictive biological markers of pCR. In order to generate a stable signature, REMAGUS 02 data was used as training set and the MDA data set was used as validation set. A re-sampling method was first applied to select stable probesets associated with pCR from the training set. The independent validation set was then used to list and describe all possible logistic models, showing good performances on both the training set and  This study highlights the ability of a single probeset or two probesets to predict pCR, with a performance that is even better than usual clinical factors. Not surprisingly, the '205225_at' probeset (continuous levels associated with gene ESR1) provided one of the best performances of the three 1-P models  [22]. In addition to ESR1, MAPT appeared to be strongly involved in pCR after taxane-based neoadjuvant chemotherapy, as two of the MAPT probesets showed very good performances.
To date, no other data demonstrated the involvement of PIGH in the pathophysiology of breast cancer. The present study, in which particular attention was paid to the stability of the molecular signatures, confirmed the major importance of estrogen receptor-related genes (ESR1, MAPT or CA12) in prediction of response to taxane-based neoadjuvant chemotherapy in breast cancer. Our findings agreed with those reported by Cheng et al. , indicating that estrogen receptor (ER) gene is the most important gene to predict chemotherapeutic response  [34]. In contrast, in the present study, tumour proliferation and differentiation genes were not shown to be predictive of pCR, although they have been reported to be important predictive markers of pathological response to therapy in breast carcinoma, particularly in ER-positive breast tumours  [35, 36].
Regardless its limited reliability as a maker differentiating nTreg from iTreg, Helios expression allows the identification of a subset of Treg cells presenting some peculiar characteristics. Murine Helios+ Treg express higher levels of CD103 and GITR at their surface and produce higher levels of TGF-β (94). Interestingly, murine and human data indicate that Helios+ Treg are relatively over-represented in tumors, pointing to this cell subset as a potential target for immune-modulating strategies  (94, 98, 99). Currently, no information is available concerning Helios expression on Treg cells during HIV infection. Further studies will eventually define whether HIV infection differentially affects Helios+ and Helios-Treg subsets homeostasis and whether these subsets play a distinct role on HIV pathophysiology.
We used a database that described the 3D root architecture of Pinus pinaster root systems (5, 12, or 19 years) from a stand in southwest France. We determined the relationship between CSD and V d for 10,000 root segments from intact root branches. Models were specified that did and did not account for root type. The relationships were then applied to the diameters of 11,000 broken root ends to estimate the volume of missing roots. CSD was nearly linearly related to the square root of V d , but the slope of the curve varied greatly as a function of root type. Sinkers and deep roots tapered rapidly, as they were limited by available soil depth. Distal shallow roots tapered gradually, as they were less limited spatially. We estimated that younger trees lost an average of 17% of root volume when excavated, while older trees lost 4%.
We used a database of 3D root system architecture that was compiled from a stand of P. pinaster trees over time (trees were 5, 12, or 19 years when uprooted). The database included 49 trees, 11,000 roots, and 60,000 root segments. Relationships between CSD and descendant volume were derived for all intact roots in each of 10 architectural types. These relationships were then applied to the broken tips of all root axes to estimate missing volumes.
ROOT SYSTEM DATASETS The root systems used in this study came from the control plot in a fertilization × irrigation experiment; the design is described thoroughly in  Trichet et al. (2008). The experiment took place on a 5. 6 ha stand of P. pinaster in Pierroton, France, 20 km south-west of Bordeaux. In that region, mean annual rainfall is 850 mm and mean annual temperature is 13 • C. The water table generally fluctuates close to the soil surface during rainy winters, but sinks to 1. 5 m depth in late summer. The experimental plot was 60 m asl and was underlain by a moderately humid, sandy spodosol, with a discontinuous deep hard pan at approximately 70 cm depth. The plot was surrounded by 0. 5-1. 2 m deep ditches. In spring 1993, a field was prepared by first removing stumps that remained from a clear-cut, and then plowing the soil to 0.
3 m depth. One-year-old P. pinaster seedlings were subsequently planted at 2 m × 4 m spacing. Seedlings were of local provenance and were in 200 cm 3 turf plugs before planting. Major storms damaged the stand in December 1999 and February 2009. The first storm toppled 20% of trees in the control plot; these were later straightened and secured with cables for 2 years. Trees were harvested for root architectural analysis when trees were 5, 12, and 19 years old (Table  1 ). The respective datasets will be referred to as L5, L12, and L19. In the L5 dataset, trees were selected from across the diameter at breast height (DBH) range represented in the stand. Trees were uprooted by pulling the stem upward with a logging crane after loosening the soil with hand tools. Further details of measurement and uprooting are given in  Danjon et al.
(1999a,b). In the L12 dataset, the sample consisted of seven of the largest DBH trees, as well as five trees that spanned the range of DBH values in the stand  (Danjon et al. , 2007). In L19, only the largest, most dominant trees were sampled  (Augusto et al. , 2013). In L12 and L19, trees were uprooted after removing soil from shallow roots with an air-lance, loosening the soil between the shallow roots with the bucket of a mechanical shovel, and then pulling the stem vertically. Details of uprooting and measurement for the L12 and L19 datasets are given in  Danjon and Reubens (2008). Digitizing in 3D was performed using a Fastrack positional measurement system with a Long Ranger magnetic source (Polhemus, Colchester, VT, USA). Each root was divided into approximately 20 cm long segments so as to record changes in direction or diameter, as well as branching points.
We used fixed limits for L5 and a percentage of maximal depth for L12 and L19, according to  Danjon et al. (2005). In L5, the limit for the ZRT was fixed to a radial distance of 2. 5 times the DBH. In L12 and L19, the ZRT extended from the root base to the last segment for which the taper from root origin was larger than 2% per cm for L12 and 1. 25% per cm for L19  (Danjon et al. , 2005). Using AMAPmod  (Pradal et al. , 2008) , we computed several characteristics for each of the 60,000 distal cross sections in the database or the roots originating from them: (1) Cross-sectional diameter measured over bark; where cross sections were elliptical and two diameters were measured, CSD was computed as the quadratic mean. (2) Total root volume originating from the section, V d0. (3) Root volume originating from the section, but including only segments with diameters larger than 1 cm, V d1.
STATISTICAL ANALYSIS We assessed the relationship between CSD and the two measures of V d. First, we assessed structural roots defined as sections larger than 1 cm, segments in the root stump, and segments bearing branches without break points >1 cm in diameter: V d1 ij = λ i + γ i CSD ij + ε ij (LR 1 ) where V d1 ij is the volume of section j in root type i, λ i is the effect of root type i, γ i is a root type-dependent slope, and ε ij is a residual term. A simplified LR 1 model was also generated, in which four root types were specified rather than ten. In some studies, log-log transformations have been used to obtain a linear relationship between CSD or CSA and DW d (Le  Goff and Ottorini, 2001; Nielsen and Hansen, 2006). For our database, this transformation did not yield Gaussian-distributed residuals, whereas the square root transformation of V d1 ij did.