dataesr/scientific-paragraphs-categorization huggingface dataset:
2B ). An extensive bioinformatics analysis of miR-21 using GOmir software  [25]  indicated a list of 55 predicted target genes supplemented with gene descriptions and functional analyses (supplemental online Table  3 ). The transcription factor Sox2 was identified as a target gene containing predicted miR-21 binding sites. Our group recently showed that SS-AF-MSCs exhibited higher expression levels of Sox2 compared with RS-AF-MSCs  [4]. Similarly, in this study, we confirmed Sox2 expression at the RNA and protein level in both AF-MSC populations (Fig. 2Ci, 2Cii ). Based on these findings, we focused on the role of miR-21 and its binding partner Sox2 in the proliferation rate and differentiation properties of SS-AF-MSCs and RS-AF-MSCs (Fig. 2D ).
In this study, we defined the miRNA profiles of MSCs derived from fetal sources such as amniotic fluid and umbilical cord blood and directly compared them with the profile of BM-MSCs. Specifically, 67 miRNAs were differentially expressed in the three MSC sources. On the basis of the detailed analysis, no common miRNAs were detected as upregulated in all the populations, but miR-143, miR-487, miR-326, and miR-199* were downregulated in all the sample categories tested. These miRNAs have been shown to target genes that regulate various cellular processes that are critical for stem cells, such as proliferation, differentiation, and cell survival. For example, miR-143 has been found to target genes such as ERK5 (extracellular-signal-regulated kinase 5)  [41] , DNMT3A (DNA methyltransferase 3A)  [42] , FNDC3b (fibronectin type III domain containing 3B)  [43] , and Bcl-2 (B-cell lymphoma 2)  [44] , as indicated by the miRecords database  [26].
In addition to its oncogenic effect, miR-21 exhibits an antiangiogenic function by targeting the RhoB transcript in endothelial cells  [50]. In mouse embryonic stem cells, RE1 silencing transcription factor is responsible for the transcriptional inhibition of miR-21, resulting in the suppression of self-renewal and pluripotency as a result of the loss of expression of the critical regulators Oct4, Sox2, Nanog, and c-Myc  [16]. After extensive comparative bioinformatics analysis using GOmir software, Sox2 was identified as a predicted target of miR-21. In this study, we proved for the first time that Sox2 is negatively regulated by miR-21 at the post-transcriptional level by means of a specific target site within the 39UTR. miR-21 induction in SS-AF-MSCs led to a significant reduction in Sox2 expression at the RNA and protein levels, further supporting a direct interaction.
ACKNOWLEDGMENTS This research has been cofinanced by the  European Union (European Social Fund ) and Greek national funds through the  Operational Program "Education and Lifelong Learning " of the National Strategic Reference Framework Research Funding Program: Heracleitus II, Investing in Knowledge Society through the  European Social Fund. We are grateful to  Dr. Helen A. Papadaki  ( Medical School, University of Crete ) and  Dr. A. Stravoropoulos  ( Cord Blood Bank, BRFAA ) for providing BM-MSCs and umbilical cord blood samples, respectively. We also thank  Prof. L. Naldini ,  Dr. Panagiotis Politis , and  Dr. Elena Siapati  for providing plasmids and reagents, and for discussions, and we thank  Dr. Ariana Gavriil  for technical assistance with FACS.
AdaBoost One particularly successful implementation of the boosting algorithm is AdaBoost, introduced by Freund&Schapire  [8]. AdaBoost stands for adaptive boosting, referring to the fact that the learning procedure adjusts itself to the training data in order to classify it better. There are many variations on the same theme for the actual implementation, and it is the most common boosting algorithm. It typically leads to better results than without boosting, up to the Bayes limit as will be seen later. An actual implementation of the AdaBoost algorithm works as follows. After having built tree T k one should check which events in the training sample T k are misclassified by T k , hence defining the misclassification rate R(T k ). In order to ease the math, let us introduce some notations.
Circular correlation This example is derived from a dataset generated with the create_circ macro from $ROOTSYS/tmva/test/createData. C. The 1D and 2D representations of the two variables used are shown in Fig. 8. Several generic properties can be tested with this dataset: the impact of a longer boosting sequence (adding more and more trees), the meaningfulness of overtraining, the sensitivity to the splitting criterion and the difference between boosting many small trees or few big trees. Figure  9  compares the performance of a Fisher discriminant (for reference), a single decision tree and boosted decision trees with an increasing number of trees (from 5 to 400). All other TMVA parameters are kept to their default value. One can see how a Fisher discriminant is not appropriate for non-linear correlation problems.
The top left plot shows that they all have similar performance on this problem, even when zooming on the high efficiency side. One slight exception is the use of significance. It is also the only measure that doesn't respect some of the criteria suggested in Section 2. as a function of boosted decision tree output, which confirms that they all reach similar performance while the tree optimised with significance actually performs slightly worse than the others. A final illustration concerns the impact of the size of each tree in terms of number of leaves and size of each leaf, and their relation to the number of trees. In order to test this, the same create_circ macro was used, but to produce a much larger dataset such that statistics do not interfere with the test. All combinations of boosted decision trees with 20 or 400 trees, a minimum leaf size of 10 or 500 events and a maximum depth of 3 or 20 were tested, and results are shown on Fig.
Software Many implementations of decision trees exist on the market. Historical algorithms like CART  [ 1] , ID3  [20]  and its evolution C4. 5  [21]  are available in many different computing languages. The original MiniBoone  [9]  code is available at http://www-mhp. physics. lsa. umich. edu/∼roe/, so is the Stat-PatternRecognition  [2]  code at http://sourceforge. net/projects/statpatrec, and LHC experiments have various implementations in their software. I would recommend a different approach, which is to use an integrated solution able to handle decision trees but also other techniques and flavours, allowing to run several of them to find the best suited to a given problem. Weka is a data mining software written in Java, open source, with a very good published manual. It was not written for HEP but is very complete.
Consistently, InterProScan Search (http://www. ebi. ac. uk/Tools/pfa/iprscan/) identified three DNA binding domains (dbd), termed oligonucleotide/oligosaccharide binding (OB) folds, which were homologous to those of RPA1 (Figure  1B ). However, the orthologs of the Meiob group formed a family distinct from the Rpa1 group (Figure  1A , phylogenetic tree of MEIOB and RPA1 using MULTALIN)  [23]. These data suggest that Meiob may have evolved from an ancestral Rpa1 shortly before the appearance of multicellularity. RT-qPCR analysis of Meiob transcription pattern in adult and fetal tissues detected Meiob transcripts exclusively in fetal ovary, postnatal testis and liver (Figures  S2A  and S2B ). In the mouse ovary, Meiob expression started at 12. 5 dpc, reached a maximum at 15. 5 dpc and decreased to become undetectable in post natal life (Figure  1C ).
MEIOB is localized on meiotic chromosomes MEIOB immunostaining performed on chromosomal spreads from adult testes indicated that MEIOB form foci located on chromosome axes stained with SYCP3 in spermatocytes (Figure  2B ). These MEIOB foci were visible as early as leptotene indicated development stages. Black arrows indicate meiosis initiation. Mean6SEM, n = 3. (D) MEIOB expression pattern in whole fetal human ovaries and testes. MEIOB mRNA expression was measured using RT-qPCR. Gonads were harvested at the indicated developmental stages. Wpf, weeks post fertilization. b-actin mRNA was used as the endogenous reporter. Data are expressed as a percentage of the maximum mRNA expression. (E) Meiob mRNA expression profile in ovary germ and somatic cells. Ddx4, Fst and Meiob expressions were measured using RT-qPCR in purified germ cell fraction (Oct4-GFP+, black column) and somatic cell fraction (Oct4-GFP2, white column) from mouse Oct4-Gfp 13.
Extract from HEK-293 cells expressing tagged-MEIOB produced the same results (Figure  S5B ). In order to evaluate the robustness of MEIOB binding to ssDNA, we performed ssDNA affinity chromatography. Protein extracts from HEK-293 cells expressing tagged-MEIOB were loaded on a column containing a ssDNA matrix, and proteins were eluted by progressively increasing salt concentration. Detection of eluted MEIOB by western blot revealed that MEIOB protein was eluted at a concentration of 0. 75M NaCl (Figures  3C  and S5C ), indicating high affinity for ssDNA all be it lower than that of the trimeric RPA (most RPA being eluted at 1M and above, data not shown).
Meiob mutant allele construction The Meiob null allele was established at the Mouse Clinical Institute/Institut Clinique de la Souris (MCI/ICS), Illkirch, France (http://www. ics-mci. fr/). The targeting vector was constructed as follows: a 4. 5 kb fragment encompassing exon 2 and part of the first intron, Red Fluorescent Protein (Rfp) cDNA, followed by a neomycin resistance cassette surrounded by two loxP sites and a 4 kb fragment encompassing exons 9 and 10 (Figure  S1A ). Meiob endogenous ATG in exon 2 was conserved to ensure Rfp cDNA expression and there was a stop codon at the end of Rfp cDNA (Figure  S1A ). The linearized construct was electroporated in BD10 C57BL/6J mouse ES cells. After selection, targeted clones were identified by PCR using external primers and further confirmed by Southern blot with a Neo probe, 59 external probe and 39 external probe (data not shown).
Western blotting Prior to gel migration, protein samples were supplemented with Laemmli buffer and resolved by 10% SDS-polyacrylamide gel electrophoresis (SDS-PAGE). Gels were electrophoretically transferred to polyvinylidene difluoride membranes (PVDF) (Amersham Biosciences, Buckinghamshire, England) before hybridization with appropriate primary antibodies and fluorescent dye coupled secondary antibodies (See Table  S1 ). Images were acquired using Typhoon 9400 scanner (Amersham Biosciences) and quantified with ImageJ software  [65].
Immunofluorescence on preparations of meiocyte chromosome spreads Spermatocyte and oocyte preparations were washed with 0. 4% Photoflo 200 (Sigma) and slides were air-dried 15 minutes before permeabilizing and blocking. They were then incubated overnight with the appropriate primary antibodies in blocking solution at room temperature, followed by 1 hour incubation with secondary antibodies at 37uC (see Table  S1  for antibodies). Slides were mounted with Vectashield DAPI medium (Vector Laboratories). Imaging was performed using a AX70 epifluorescent microscope (Olympus) equipped with a charge-coupled camera (Roper Scientific) and IPlab software (Scanalytics) or with a DM5500 B epifluorescent microscope (Leica Microsystems) equipped with a CoolSNAP HQ 2 camera (Photometrics) and Leica software (Metamorph).
Images were processed and specific structures were quantified with ImageJ software (Cell Counter and Foci Picker3D programs). For quantification of foci colocalization, foci overlapping for the most part were considered as colocalized without restriction to foci that share the same centroid (e. g. immediately adjacent foci were not considered as colocalized). ssDNA/dsDNA pull down assay HPLH purified biotinylated oligonucleotides were used for the DNA pull down assays: ss30-mer 2: 59-GAT CTC AGC GAT TCA CAC GCG TCC TAA CTC G-39-BiotinTEG, ss60-mer: 59-GAT CTG CAC GAC GCA CAC CGG ACG TAT CTG CTA TCG CTC ATG TCA ACC GCT CAA GCT GC-39-Biotin TEG (Eurogentec). Double strand hybridizations were performed in 50 mM NaCl, 25 mM Tris-HCl, pH 7. 5 buffer with complementary sequences at molecular equivalence by a denaturing step (3 minutes 94uC) and a slow progressive return to room temperature.
Acknowledgments We thank  N. Frydmann ,  D. Moison ,  E. Lebourdais ,  A. Ridoux ,  A. Chicheportiche  and  X. Veaute  for their technical assistance,  B. Palancade  and  V. Doye  for critical reading,  A. Gouret  for her secretarial assistance and  Angloscribe (Calvisson, France ) for English language editing. We thank  Dr Ohbo ,  Dr Baudat  and  Dr Panthier  for the generous provision of Oct4-GFP, Spo11 and w/w mice. We thank the  Mouse Clinical Institute/ Institut Clinique de la Souris (MCI/ICS) (Illkirch, France)  and the staff within the  Department of Obstetrics and Gynecology of the Antoine Beclere Hospital (Clamart, France ). Flow cytometry and cell sorting were.
Nat. Bot. , sér. 7 9: 309 (1889) , ≡ Agaricus calyptriformis Berk. , Ann. Mag. Nat. Hist. , Ser. 1 1: 198 (1838) Genus Humidicutis (Singer)  Singer, Sydowia 12(1-6): 225 (1959)   [1958] , emended here by Lodge, type species Humidicutis marginata (Peck)  Singer (1959)   Redhead, Ammirati & Norvell (2012 ) [2011]  in  Redhead, Ammirati, Norvell, Vizzini and Contu, Mycotaxon 118: 456  backbone representatives as possible. We augmented the dataset used for the backbone with additional species and specimens that had at least an LSU sequence and performed a supermatrix analysis. In addition, we present paired ITS-LSU phylogenies that have greater species representation for four overlapping segments of the Hygrophoraceae. We have included more species and genera than previous analyses, though not all of the species or collections that we sequenced are presented.
Methods Species selection Lodge and Cantrell targeted several species per clade using previous unpublished preliminary analyses by Moncalvo, Vilgalys, Hughes and Matheny together with published molecular phylogenies by  Moncalvo et al. (2000 Moncalvo et al. ( , 2002)) ,  Matheny et al. (2006) ,  Lawrey et al. (2009)  and  Binder et al. (2010). Preference was for one basal and one distal taxon per clade and for types of genera and sections. In clades comprising difficult species complexes, we selected at least one named species known from a restricted geographic range (e. g. , Hygrocybe graminicolor, Humidicutis lewellianae). The sequences that were generated in this study together with those from GenBank and UNITE are given in Online Resource 1. We generated 306 sequences for this work: 90 ITS, 109 LSU, 65 SSU and 42 RPB2.
The rpb2 sequences we analyzed contain indels that caused reading frame shifts so they are not accessible in GenBank using the BLASTx protocol. The taxa for the backbone analysis were winnowed to two (rarely three) per clade based on whether all or most of the four gene regions could be sequenced, preferably from the same collection. When it was necessary to use multiple collections to obtain all the sequences, these were matched by the ITS region (> 97 % similar), except for some of Kovalenko's Russian collections that were matched by LSU sequences (> 99. 5 % similar in the LROR to LR7 section). Most of the names for Hygrocybe s. l. used in North America are those of species originally described from Europe/UK/Scandinavia. Many of the sequences in our initial iterations were from North American collections, but we found that they often did not match ITS sequences of European/ Scandinavian/UK collections by us, and later, published ITS sequences by  Brock et al.
(2010) ,  Matheny et al. (2006)  and the LSU analysis above; each data set was aligned separately to minimize loss of data from the ITS, and ML analysis was used. Outgroups were Hygroaster albellus for Group 1 (Hygrocybe s. s. ); Hygrophorus eburneus for Group 2 (Neohygrocybe, Porpolomopsis, Gliophorus, Gloioxanthomyces, Haasiella, Humidicutis, Chromosera and Chrysomphalina); Neohygrocybe ingrata for Group 3 (Hygrophorus ss, Neohygrocybe, Chromosera, Chrysomphalina, Arrhenia, Dictyonema, Lichenomphalia and Pseudoarmillariella); Macrotyphula fistulosa for Group 4 (Ampullocliticybe, Cantharocybe and Cuphophyllus). Sequences were initially aligned using the default settings in MAFFT version 6  (Katoh and Toh 2008)  and then manually aligned using SeAl version 2. 0a11  (Rambaut 2002). Ambiguously aligned positions and sequence ends were pruned from the datasets before running maximum likelihood (ML) analyses in GARLI v0.
951  (Zwickl 2006 ) using a general time reversible model of nucleotide substitution with a gamma distributed rate heterogeneity and a proportion of invariant sites (GTR+G γ+I). ML searches were repeated three times for each dataset. GARLI was used to generate 100 ML nonparametric bootstrap replicates (MLBP) with the generation threshold halved to 5,000 as suggested by the program; the replicates were used to calculate a majority rule consensus tree in PAUP* 4. 0b10  (Swofford 2002)  to assess clade support. The third set, henceforth referred to as the 4-gene backbone analysis, consisted of four loci including the nuclear ribosomal gene regions (5. 8S, 18S, and 25S) and the RNA polymerase II (rpb2) region between conserved domains 5 and 7. Positions deemed ambiguous in alignment were pruned from the nexus file before conversion to Phylip format using SeaView 4.
Alternatively, it could be argued that Hygrocybe s. l. and Cuphophyllus spp. are more tolerant of the harsher climatic conditions of grassland habitats (large diurnal/seasonal fluctuations in temperature and humidity) from which even soil organisms are only partially insulated. This latter factor may explain why these species are often latefruiting in European grasslands, a feature also found in Hygrophorus spp. Young (2005)  suggested that shady forests and dense thickets in Australia may provide a humid microclimate close to the ground. Despite stable isotope ratios that suggest that most Hygrophoraceae are biotrophic, a search of GenBank using BLAST searches of ITS sequences from two species per clade found mainly Hygrophorus s. s. sequences from root tips (Online Resource 2). A sequence of an unknown species was obtained from an unidentified bryophyte (GenBank AM999704,  Kauserud et al.
2008 ) and similar ITS sequences were obtained from live Deschampsia grass roots (Poaceae) in the boreal zone (GenBank FJ517589-FJ517592,  Tejesvi et al. 2010, Online Resource 2). These root and moss associated sequences cluster near Chromosera in our ITS analysis (Online Resource 3), but support is low for placement in tribe Chromosereae (20 % MLBS in our analysis, Online Resource 3; 33 % MLBS in the analysis by  Ercole, pers. com. , 16 Nov. 2012). The ecology of the moss-grass root clade is more consistent with tribe Lichenomphaleae, and it might eventually be placed there once more gene regions have been sequenced and analyzed. BLAST Searches of GenBank (November 2012) using ITS sequences of two species per clade revealed many Cuphophyllus and Hygrocybe sequences from soil or litter but not roots, which suggests they are neither mycorrhizal nor endophytic, though  Persoh (2013)  and  Tello et al.
(2013)  has since presented evidence of Hygrocybe and Cuphophyllus as endophytes. A study of fungi in the rhizosphere of Picea glauca in Canada by  Lamarche, Seguin and Hamelin (unpublished, study described in Lamarche and Hamelin 2007 , fungal sequences deposited in Genbank 2008), showed 5 clones of Hygrocybe cf. splendidissima (EU690689 and others), 26 clones of H. aff. punicea (GenBank EU690689 and others), 33 clones of H. chlorophana (EU690793 and others), >23 clones in the H. ceracea-H. insipida clade (EU690866 and others), and 39 clones of H. reidii (EU690490 and others). Little is known regarding transfer of plant compounds to rhizosphere fungi, though the fungal-specific Mrt gene in Metarrhizium robertsii was shown to function in transport of sucrose and raffinose-related oligosaccharides from root exudates  (Fang and St.
psittacinus or Hygrocybe aff. citrinopallida DJL05TN10, the two species examined in detail. Reverse reads proceeded to near the LR3, where 31-37 nucleotides were missing, followed by a forward read beginning in or near the LROR. Group I introns have frequently been reported from mitochondrial genomes of ciliates, green algae, plants, fungi and slime molds, and are transmitted both vertically and horizontally  (De Wachter et al. 1992; Gargas et al. 1995; Hibbett 1996; Wang et al. 2009). Group I fungal introns of about 400 bp have previously been found in nuc-rDNA SSU sequences of several basidiomycetes including Artomyces pyxidatus, Auriscalpium vulgare and Lentinellus and Panellus stipticus  (Lickey et al. 2003; Hibbett and Donoghue 1995). BLAST searches in the NCBI database using the intron sequence revealed additional basidiomycetes with similar introns, including Descolea maculata (Cortinariaceae) AFTOL-1521, DQ440633), Piloderma fallax (Atheliaceae, GU187644), Galerina atkinsoniana (Strophariaceae, AFTOL-1760, DQ440634), Tubaria serrulata (Strophariaceae, AFTOL-1528, DQ462517), Porotheleum fimbriatum (MeripilaceaeAFTOL-1725, DQ444854) and Oudemansiella radicata (Physalacriaceae, AY654884).
Phylogenetic support Support for a monophyletic Porpolomopsis is strong in our ITS-LSU, ITS and 4-gene backbone analyses (100 % MLBS, 100 % MLBS, and 97 % MLBS and 100 % BPP), but weaker in our Supermatrix analysis (65 % ML BS). The ITS analysis by  Vizzini and Ercole (2012) [2011]  shows a single representative of Porpolomopis (as Humidicutis calyptriformis) on a separate, long branch emanating from the backbone that also gave rise to the Gliophorus clade. Species included Type: Porpolomopsis calyptriformis. Species included based on molecular data are Porpolomopsis lewelliniae (Kalchbr. ) Lodge, Padamsee and  Cantrell, comb. nov. (below) , and three unnamed species from the USA, UK and Russia. Hygrocybe pura (Peck) Murrill) is included based on morphology. Comments Porpolomopsis was segregated from Hygrocybe by  Bresinsky (2008)  based on the color and absence of DOPA pigments.
Phylogenetic support Subf. Lichenomphaloideae appears as a moderately to well-supported monophyletic clade in our fourgene backbone analyses (81 % MLBS, 1. 0 Bayesian PP), a monophyletic clade in our ITS-LSU analysis, a monophyletic clade with low support in our Supermatrix analysis (38 % ML BS), but as a paraphyletic grade lacking BS support in our LSU analysis. Previous LSU analyses show Lichenomphaloideae as a moderately supported monophyletic clade  (Lutzoni 1997, 68  % and 53 % MP BS for unpruned and pruned data sets) or as three clades emerging from a backbone  (Moncalvo et al. 2002). Using ITS together with LSU data improved support for a monophyletic Lichenomphaloideae in  Lutzoni (1997; MPBS 83  % in equally weighted and 70 % in unequally weighted data sets) and  Redhead et al. (2002; 79 % MP BS) , but not in  Lawrey et al.
Phylogenetic support Tribe Arrhenieae appears as a strongly supported monophyletic clade in our four-gene backbone (97 % MLBS; 1. 0 BPP), Supermatrix (99 % MLBS) and ITS-LSU (97 % MLBS) analyses, and moderately supported in our LSU analysis (67 % MLBS). Similarly, Lawrey et al. (2009)  show strong support for a monophyletic Arrhenieae using a combined ITS-LSU data set (96 % MPBS and 100 % MLBS). Only our ITS analysis shows tribe Arrhenieae as a paraphyletic grade. Genera included Arrhenia, Acantholichen, Cora, Corella, Cyphellostereum, Dictyonema and Eonema. Comments The monophyly of the new tribe Arrhenieae, established by  Lawrey et al. (2009) , is confirmed here. It includes the non-lichenized genera Arrhenia s. l. (paraphyletic) and Eonema and the genera lichenized with cyanobacteria -Acantholichen,  Cora, Corella, Cyphellostereum, and Dictyonema (Dal-Forno et al.
Agaricus ovinus Bull. , Herbier de la France 13: 592 and plate 580 (1793)] Lectotype here designated as fig. M in Bulliard, Herbier de la France 13: plate 580 (1793)]; Epitype here designated GEDC0877, coll. Griffith, Ffriddoedd Garndolbenmaen, Wales, UK, 19 Oct 2006, K(M)187568, GenBank sequences KF291228, KF291229, KF291230.