dataesr/scientific-paragraphs-categorization huggingface dataset:
From these relationships, we propose an expansion of Lliboutry's approach, implemented within a hierarchical Bayesian framework, to reconstruct the ELA fluctuations of any glacier in the study area. Considering two climate variables and four morpho-topographics variables, we show that our approach is well suited to: (1) isolate an average temporal effect for each year and an interannual mean effect for each glacier; (2) model and explain their fluctuations from a reduced number of climatic and topographic explanatory variables whose respective weight and related uncertainties are quantified in a consistent manner; and (3) well approximate the full variability of our ELA data set for each year and for each glacier and identify specific spatio-temporal behaviors that do not match the assumption of complete separability of space and time effects.
Finally, both methods presented in this study, based on remote sensing and modeling, are powerful tools to provide annual series of ELA that will be useful in hydrological modeling of high mountain watersheds where there is a need to integrate the glacier component in such an approach and when no field data are available. Supplementary material related to this article is available online at http://www. the-cryosphere. net/7/ 1455/2013/tc-7-1455-2013-supplement. pdf.
Acknowledgements. This study was conducted in the context of the French glacier observatory GLACIOCLIM (http://www-lgge. ujf-grenoble. fr/ServiceObs/index. htm). We are grateful to  Imanol Goni  for his help with image processing. This work was possible thanks to the contributions of:  Météo-France ,  Electricité de France  and  Météo-Suisse  who provided the meteorological data, USGS-EDC and  GLIMS program  who granted free access to Landsat images and ASTER images, respectively. SPOT images were provided through the  CNES/SPOT-Image ISIS program , contracts #  0103-157 ,  0412-725 ,  2010-405  and  2010-435. We thank  G. Hilmar Gudmundsson  (handling editor),  M. Zemp  and an anonymous referee for their constructive comments used to improve the paper. Edited by:  G. H. Gudmundsson The publication of this article is financed by  CNRS-INSU.
To assess whether these conditionally immortalized neural stem cells retain the identity of their tissue of origin after prolonged in vitro propagation, we performed a genome-wide transcriptome analysis. This dataset was then analyzed in terms of the expression of homeodomain transcription factors known to play an instructive role in the identity of progenitor subtypes in the developing spinal cord  [12] , and the findings validated by immunostaining. The ventral spinal cord has four major interneuron progenitor subdomains (p0, p1, p2, and p3), and one motoneuron progenitor subdomain (pMN) specified by the cross-repressive activities of class I and II homeodomain transcription factors  [12]. The ventral p2 domain of the spinal cord, comprising Nkx6. 1 + /Irx3 + cells, gives rise to two main lineages of interneurons designated V2a and V2b, specified by differential Notch signaling  [13, 14].
Cell counting All cell counting was carried out in biologic triplicate, in which the experiment was replicated with cells plated several passages apart. Each biologic replicate consisted of three wells (technical replicates) for each condition. Each technical replicate consisted of three randomly placed nonoverlapping images taken per well with the 40× objective. Images were imported into ImageJ, and nuclei and target-positive cells were counted manually. The three-well images were averaged to generate one technical replicate. The three technical replicates (wells) were averaged to generate one biologic replicate (plate), which was then used for statistical analysis. All cell counting was carried out from images taken from the 1×70 inverted microscope (Olympus) and processed with the Axio Vision Digital Image Processing Software (Carl Zeiss Inc.
Microarray analysis RNA was extracted from cultured cells by using Trizol (Life Technologies), and DNase treated with Turbo DNase (Life Technologies). RNA was assessed for quality with an Agilent Bioanalyzer ensuring an RNA integrity number greater than 9. Genome-wide gene-expression profiling was performed with the Illumina HumanWG-6 v3. 0 expression beadchip array, at the Welcome Trust Centre for Human Genetics (Oxford, UK). Sample probe profiles were exported from GenomeStudio into lumi (bioconductor). Variance-stabilizing transformation was applied to datasets, quantile normalized, and log 2 transformed. All microarray data from this study are available through the Gene Expression Omnibus  [27] , with accession number GSE37282.
The FFP software controlled the acquisition of intensity data and provided functions for adjusting the signal values, the display and storage of the measured data, and calculations of ion concentrations. A CCD camera was used to visualize the cells. With fluorescence values corrected for background and dark current, [Ca 2+ ] i calculations were carried out from the ratio between 340-and 380-nm recordings. Fura-2 calibration was performed with the actual instrument by following the same procedure described previously  [28] , which yielded R min = 0. 16; R max = 3. 173; β = 2. 968; and K d = 224 at 37°C.
Acknowledgements Grants:  AV0Z50390703 , GA AV:  IAA500390902 , The Charles Wolfson Charitable trust,  EU   6th Framework Programme. G. Dayanithi  and  O. Forostyak  were supported by the grant  P304/11/2373  from the  Grant Agency of the Czech Republic. The antibodies Isl1, Lhx3, and En1 were developed by  TM Jessell  and  S Brenner-Morton. Pax6 was developed by  A Kawakami , and  Nkx6. 1, developed by  OD Madsen , was obtained from the  Developmental Studies Hybridoma Bank developed  under the auspices of the  NICHD  and maintained by The  University of Iowa ,  Department of Biology, Iowa City , IA 52242,  USA. We thank  John Sindon  and  Erik Miljan  for facilitating the generation of the SPC cell lines at ReNeuron plc,  Dr. Michael Antoniou  for providing the UCOE lentiviral construct, and  Dr.
Cell sorting and flow cytometry Cells were analyzed by flow cytometry on the BD LSRII and sorted on the BD FACS ARIAII. Fluorophores were detected using the following lasers and filters: mCherryexcited 561 nm, acquired 610/20; mTagBFP-excited 405 nm, acquired 450/50; eGFP-excited 488 nm, acquired 525/50; iRFP-excited 640 nm, acquired 730/ 45. Data were analyzed using FlowJo software.
TLR assay The TLR assay was performed as previously described with slight modifications. Cells harboring the TLR or epigenetic TLR 2. 1 were plated at 1-2. 0 Â 10 5 cells/well in a 24-well dish 24 h before transfection or transduction. XtremeGene9 (Roche) was used at 2 ml/mg DNA to transfect cells with 0. 5 mg of both nuclease and green fluorescent protein (GFP) donor constructs. Transductions were performed at an MOI of 5-10 using polybrene. Cells were harvested 72 h after transfection/transduction and read on the flow cytometer. Data were obtained from BFP+(TLR 2. 1) or iRFP+/BFP+ populations (epigenetic TLR).
1 M ammonium acetate and 5% acetic acid. Plates were visualized by phosphorimaging, and data were analyzed using Multi Gauge V3. 0 software (FUJIFILM). The gray densities of [ 32 P] AMP spots were compared with the gray density of known [ 32 P]ATP concentrations. Rate constants were calculated based on the slope of the graphical presentation of [ 32 P]AMP formation plotted against time. Aminoacylation and deacylation assays with non-radioactive Nva and a-amino butyrate The tRNA Leu was labeled at the 3 0 -terminal nucleotide using the exchange reaction catalyzed by tRNA nucleotidyl transferase, which was purified from E. coli overproduction strain in our laboratory as described previously  (29). Reactions contained 50 mM Tris-HCl (pH 8. 0), 12 mM MgCl 2 , 0. 5 mM DTT, 5 mM tRNA Leu , 5 mM ATP, 20 nM [a-32 P]ATP (3000 Ci/mmol) and 3 mM tRNA nucleotidyl transferase.
Filter binding assays for determination of the K D for tRNA Leu   The radiolabeled E. coli [ 32 P] tRNA Leu (50 nM, 45 000 cpm) was incubated in 50 ml of binding buffer [50 mM HEPES-KOH (pH 6. 8), 30 mM KCl, 12 mM MgCl 2 ] in the presence of increasing concentrations of LeuRS proteins for 15 min at 4 C. Samples were filtered through nitrocellulose membrane (Milipore, 0. 22 mm) previously equilibrated in washing buffer [50 mM potassium phosphate (pH 5. 5), 50 mM MgCl 2 ]. Filters were then washed two times with 0. 3 ml of washing buffer. Filters were air dried before radioactive quantification. Data were analyzed using GraphPad PRISM software.
Experimental Crystal data (i) x; Ày þ 1 2 ; z À 1 2 ; (ii) Àx þ 1; Ày þ 1; Àz; (iii) Àx þ 1; y À 1 2 ; Àz þ 1 2. Data collection: APEX2  (Bruker, 2011) ; cell refinement: SAINT  (Bruker, 2011) ; data reduction: SAINT; program(s) used to solve structure: SIR2002  (Burla et al. , 2005) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: SHELXL97. Thanks are due to MESRS and ATRST (Ministere de l'Enseignement Superieur et de la Recherche Scientifique et la direction generale de la recherche -Algeria) for financial support. Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: HB7050).
Computing details Data collection: APEX2  (Bruker, 2011) ; cell refinement: SAINT  (Bruker, 2011) ; data reduction: SAINT  (Bruker, 2011) ; program(s) used to solve structure: SIR2002  (Burla et al. , 2005) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: SHELXL97  (Sheldrick, 2008).
METHODS Initial models of BM2/detergent micelles were generated from the NMR structure of the BM2 transmembrane domain  (18)  (PDB ID 2KIX) via protein/detergent self-assembly simulations  (22). In the case of the BM2-DHPC/DPC complexes, 100 ns atomistic (AT) MD simulations with the protein positionally restrained starting from 200 detergent molecules in initial random positions and orientations in the simulation box were used to generate protein-detergent complexes containing ~70 DHPC and ~130 DPC detergent molecules. The remaining detergent molecules formed detergent-only clusters, i. e. , separate from the main PDC, and thus were discarded before a 100 ns ATMD simulation with the protein free to move. An ~200 mM concentration of DHPC was chosen to match the experimental conditions used in the NMR structure determination.
ATMD simulations were performed using GROMACS (31) (www. gromacs. org) and the OPLS-AA force field  (32). The topology and parameters (GAFF) of the detergents DHPC, DDM, and DPC were generated in AMBER format with ANTECHAMBER  (33, 34)  and converted to GROMACS format using the amb2gmx. pl script  (35). The detergents were optimized with Gaussian 03  (36)  at the high-frequency level with the 6-31G basis set. Restrained electrostatic potential charges  (37)  were assigned as the atomic partial charges for the detergent by optimizing the charge fitting to quantum-mechanics electrostatic potential maps. A time step of 2 fs was used, and bond lengths were constrained using the LINCS algorithm  (38). The BM2-DHPC SMD simulations were also performed using the GROMOS G43A force field, and qualitatively similar behavior was observed (Fig.
The pulling rate used was 1 nm/ns and the force was applied using the pull code implemented in GROMACS. The simulation was continued until the PDC and any directly and indirectly interacting water molecules (a molecule is added to the PDC when the distance of any of its atoms from any other atom in the PDC is <3. 6 A ˚) were separate from bulk water (typically 15-20 ns). Pressure coupling was switched off and the integrator chosen was stochastic dynamics with an inverse friction constant of 2 ps. In ESI-MS, the sprayed droplets travel through a collision cell, which is typically at pressures of 10 À6 -10 À7 mbar, so collisions and friction due to the presence of gas particles is expected  (40). The use of stochastic dynamics applies a friction term to each atom, mimicking this effect. It also acts as a thermostat, keeping the overall temperature of the system constant.
Ethics and sample collection All aspects of the study were approved by the local ethics committee, 'Comite d'ethique de l9IFR 48, Service de Medecine Legale' (Facultede Medecine, Marseille, France), under the accession number, 10-002 (January 2010) untitled metagenomic study of the gut microbiota. Written consent was obtained for all the patients. To avoid the possible bias associated with gender, fecal samples were collected from eight female subjects. Three were obese (BMI kg m 22 : 35, 46. 8 and 51. 3, respectively; age: 42, 21 and 65 years old, respectively) that were admitted to the Timone Hospital of Marseille to determine if they were eligible for bariatric surgery. Three were anorexic women (BMI kg m 22 : 9. 8, 10 and 13. 7, respectively; age: 19, 23 and 49 years old, respectively) that were admitted to the nutrition department because they suffered from active restrictive anorexia nervosa complicated by severe undernutrition.
Selection of the genes encoding CAZymes One hundred and seventy-four genomes (finished or highquality drafts) of distal gut bacteria were selected from the HMP-IMG database (http://img. jgi. doe. gov/cgi-bin/imgm_hmp/main. cgi). This resulted in an overall phylum distribution similar to that found in the human gut microbiota  [24]. Each genome was searched for its potential CAZyme genes using the family assignment procedure used for the daily updates of the CAZy database (www. cazy. org) [20]. A total of 6,855 GHs and 175 PLs were retrieved by this procedure. The list of selected GH and PL genes, their CAZy family classification and the corresponding bacterial reference genome, along with their corresponding phyla, are reported in Table  S1. The DNA sequences of the selected genes were used for the probe design.
Biological controls We used biological positive controls, which included 123 probes targeting the bacterial 16S rRNA genes of 41 fecal bacteria (three probes for each gene) and the genes encoding CAZymes belonging to glycosyltransferase family 28, which are involved in peptidoglycan synthesis (three different probes for 172 genes encoding GT28 enzymes). We also used negative controls with 36 probes targeting 12 human lysozyme genes, 59 synthetic probes (for which BLASTn against nr database did not provide any hits) and four genes encoding GH6 cellulases.
A total of 2 mL of Agilent Labeling Master Mix was added then to 8 mL of DNA fragments. For each reaction, the Labeling Master Mix contained 0. 75, 0. 25, and 1 mL of nucleasefree water, ULS-Cy3 and 106 labeling solution, respectively. The eight reaction mixtures were incubated at 85uC for 30 minutes and then transferred immediately on ice for 3 minutes. The removal of non-reacted ULS-Cyanine 3 was performed using Agilent KREApure columns (the Genomic DNA Purification Module). The efficiency of the DNA labeling was measured using a NanoDrop ND-1000 spectrophotometer to determine the absorbance at A 260 nm for the labeled DNA and at A 550 nm for Cy3 dye. The degree of labeling was thus calculated as indicated by the manufacturing protocol as follow: Degree of Labeling ~340 x pmol per mL dye ng per mL genomic DNA x 100 x 100:.
Microarray scanning and data processing After hybridization and cleaning, the arrays were immediately scanned using an Agilent Microarray Scanner with Agilent Scanner Control software v7. 0 and a resolution of 3 mm. The scanned images were analyzed by quantifying the pixel density (intensity) of each spot using the Agilent Feature Extraction (AFE) software v9. 5. The mean signal was determined for each spot of the eight hybridized arrays. The local median background signals were subtracted from the mean spot intensity using the ''back-groundCorrect'' function and ''half'' method implemented in the 'limma' package available from the Bioconductor website (http:// www. bioconductor. org/). The signal values of all spots were then normalized between the arrays using the ''limma'' function ''normalizeBetweenArrays'' and the ''quantile'' method  [40].
GH6-specific polymerase chain reaction We used the NCBI primer design tool (Primer-Blast) to design specific primers to amplify the gene encoding the GH6 enzyme of Brachybacterium faecium (NCBI accession number ACU85160) from our fecal DNA samples. Forward (59-GCTTCTCGCTCAACGTCTCGAACT-39) and reverse (59-AGCAGGAGCTCCGCGTCCTC-39) primers were used to amplify a 220-bp amplicon of the gene. As a positive control, genomic DNA was extracted from the standard strain, Brachybacterium faecium, which contains the GH6 enzyme gene used in microarray design. Each PCR contained 2. 5 ml of 106PCR Buffer (Qiagen), 2. 5 ml of dNTPs (final concentration of 200 mM for each dNTP), 1. 2 ml of MgCl 2 (1. 5 mM), 1 ml of Taq DNA polymerase (Invitrogene), 0. 5 ml of each primer and 0. 5 ml of DNA (with a concentration of 20 ng/ml).
Microarray validation We have established a dataset of 7,030 genes encoding GHs and PLs for the design of custom microarray platform. Probe design was positive for 6,564 gene targets representing 93% of the initially selected CAZyme genes from 174 reference genomes (Table  S1  and Table  S2 ). The description of the microarray platform and the corresponding data have been deposited on the online database GEO (http://www. ncbi. nlm. nih. gov/geo/) and are publically available under the accession number GPL17807. The near identical gene sequences (over 95% identity and over 95% coverage) in our dataset were removed to ensure the design of unique probes and to avoid cross-hybridization events. Thus, for all targets, high-quality probes that do not cross-hybridize to other sequences were selected to construct the microarray.
We used the R function ''CV. rep. probes'' of the package ''Agi4644PreProcess'' to identify the sets of replicated probes and calculate the corresponding %CV for every probe set. The median value of %CVs within each array was considered as the CV of this array. A lower CV median indicates a better array reproducibility of signal intensity and hybridization uniformity. The median %CV of all the arrays tested was less than 2%, indicating good reproducibility of the array (Figure  S2 ). The mean value of the melting temperature (Tm) of the designed probes was 83. 6uC, with a standard deviation of 1. 64uC, and the mean value of the GC content was 43. 75% (see Table  S2  for details of all the probes).
For a survey of the substrates of the GH families, see reference  [24]. A GH6-encoding gene detected in the human gut Enzymes of the GH6 family target and break down cellulose, the major component of the plant cell wall. However, a systematic absence of these GH6 enzymes has been observed in published metagenomic studies of the human or herbivore animal gut. Therefore, we spotted four genes encoding enzymes belonging to the GH6 family as negative controls on the microarray. To our surprise, one of these genes was detected in all eight members of the study cohort. For each sample, the detection sensitivity varied from seven to nine positive probes over 9 spotted probes. The detected GH6-encoding gene corresponded to a cellobiohydrolase gene with GenBank accession number ACU85160. The presence of this enzyme was verified and positively confirmed by PCR using specific gene primers (Figure  4 ).
The PCR products from the eight samples were then sequenced and analyzed for sequence similarity using Blast and the non-redundant GenBank database. The best Blast hit for all the PCR products as well as the positive control was a gene from Brachybacterium faecium DSM 4810, which shared at least 99% sequence identity with the target region of the cellobiohydrolase in the samples (Table  1 ). The second (and final) Blast hit partially matched a gene encoding a putative endo-1,4-b-glucanase of Streptomyces avermitilis MA-4680, which also belongs to the GH6 family.
This C-terminal region encodes a carboxylase domain, which is the only known functional domain of the accD protein  (Zhang et al. , 2003). One of the primer pairs designed to part of this conserved region amplified a transcribed sequence using poly(A) primed complementary DNA (cDNA) from T. caeruleum (Fig. 1 ). The sequence of this 128-bp product does not correspond to any region of T. caeruleum plastome and shows 83% to 84% nucleotide identity to a region of the carboxylase domain of Asteraceae pt-accD genes. These sequence data suggest that a nupt encoding part of accD gene is actively transcribed and polyadenylated in T. caeruleum. The entire sequence of this putative n-accD transcript was obtained by RACE-PCR. The putative n-accD gene encodes a protein of 331 amino acids, compared with the approximately 500 amino acids encoded by the accD gene in the plastomes of Asterids (Fig.
, 2008) , and sequences in the 39 untranslated region of some plastid genes may fortuitously encode polyadenylation sites  (Stegemann and Bock, 2006; Lloyd and Timmis, 2011). To determine the number of nuclear regulatory elements that accD acquired to become functional in the nucleus, we attempted to identify the origin of the different regions of the gene. Apart from the chloroplastic target peptide that may derive from KAS I, the nuclear regulatory sequences acquired by the n-accD gene show no sequence similarity to any other sequences available in the public databases and therefore the promoter and the polyadenylation signal most presumably derive from preexisting nuclear sequences rather than plastidic sequences. How has the plastidic accD gene been functionally replaced in a few angiosperms? From the complete plastome sequences currently available in public databases, at least six instances of loss of pt-accD can be inferred during angiosperm evolution.
DNA Blot Analysis For DNA slot blotting, 350 ng of total cellular DNA per slot was fixed to Amersham Hybond-N+ membrane (GE Healthcare) using a SRC 072/0 Minifold II apparatus (Scleicher and Schuell). Membranes were hybridized with [ 32 P] dATP-labeled probe (accD or psbA). The probes were generated by PCR using primers accD963F2 and accD1593R (accD) or cp957F and cp1469R (psbA) from H. annuus gDNA as template. Detection and quantification was performed using a Typhoon Trio Imaging system and ImageQuant TL software (GE Healthcare).
Transit Peptide Prediction Transit peptide predictions were made using BaCelLo  (Pierleoni et al. , 2006) , MultiLoc  (Höglund et al. , 2006) , Predotar  (Small et al. , 2004) , Protein Prowler  (Hawkins and Bodén, 2006) , and TargetP  (Emanuelsson et al. , 2000)  software programs.
DNA sequence data All sequences were examined by eye and edited using FinchTV v1. 4. 0 (Geospiza Inc. , Seattle, WA). Consensus sequences were created in Bioedit v7. 0. 9. 0  (Hall 1999). Table  1. Details of multiplex combinations, whether the multiplexing was done pre-or post-PCR, allele size ranges, and null allele frequencies within the Jersey population, number of Jersey individuals successfully genotyped (n), Hardy-Weinberg equilibrium test P-value (P HWE ) (those with a * are significantly out of HWE at 0. 01 level,indicates that the test was not performed) for each population and number of alleles per locus (A), mean observed (H o ), and expected (H e ) heterozygosity in both the Jersey population and the published data  (Todd 2000; Hale et al. 2001  Additional red squirrel control region sequences were downloaded from Genbank  (Benson et al.
Microsatellite analysis Genotypes were scored using Genemapper software v3. 7 (Applied Biosystems, Foster City, CA). Observed and expected heterozygosities were calculated per locus in GenAlEx6  (Peakall and Smouse 2006) , null alleles were checked for using Cervus  (Marshall et al. 1998) , significant deviations from Hardy-Weinberg equilibrium (HWE) were tested for using GenePop v4. 0. 10  (Raymond and Rousset 1995; Rousset 2008) , and sex linkage was checked for by eye.