dataesr/scientific-paragraphs-categorization huggingface dataset:
TTGE migration was performed following a previously reported protocol  [21]  on 16 cm × 16 cm × 1 mm gels using the DCode universal mutation detection system (Bio-Rad Laboratories, Marne-la-Coquette, France). PCR products from pure bacterial cultures and about 50% of the bands produced by TTGE were sequenced. The remaining bands were assigned to an Operational Taxonomic Unit (OTU) by comparing their migration distance to that of sequenced bands.
Sequence analysis, taxonomy assignment and alignment The sequences were analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) software package version 1. 7. 0  [22]. Bacterial diversity of a sample analyzed using TTGE is generally reflected by the number of bands on the TTGE profile, each band usually representing an OTU. However, Manguin et al. [21]  reported bands with different migration distances belonging to the same OTU due to sequence heterogeneity among their 16S rRNA gene copies. This could lead to an overestimation of the bacterial diversity. Therefore to avoid this, sequences obtained from TTGE as well as those obtained from water sample cultures were clustered into OTUs, prior to analysis. Sequences were clustered into OTUs using the UCLUST pipeline  [23]  in QIIME at an identity threshold of 97% (i.
e. sequences that were 97% similar were binned into the same OTU). The most abundant sequence within each cluster was selected as a representative of the OTU. The resulting OTUs were assigned to taxa using the Ribosomal Database Project (RDP-II) classifier  [24]  trained on Greengenes reference database  [25]  via QIIME at a minimum confidence score of 80%. The OTUs were aligned against the Greengenes core reference alignment  [26]  using the Python Nearest Alignment Space Termination (PyNAST) alignment algorithm  [27]  in QIIME with a minimum identity of 75%. Relative abundance of bacterial taxa was computed and collated using the make_OTU_table. py and summarize_taxa. py scripts in QIIME. This was visualized on histograms and compared using two-sample t-tests in Excel. A phylogenetic tree, required to run diversity analyses down the QIIME pipeline, was constructed using the FastTree approximately maximum likelihood program  [28]  in QIIME.
The largest amount of variation is explained by the first principal coordinate (PCo1), the second largest by the second principal coordinates (PCo2) and so on. To test the strength and significance of the PCoA, the adonis function in PRIMER 6 (PRIMER-E Ltd. , Luton, UK) was used on unweighted UniFrac distance matrices via QIIME. The adonis function computes an effect size value R 2 , which shows the percentage of variation in distance matrices explained by the sample. Two-sample student's t-test to compare mean unweighted UniFrac distances between samples was calculated using the dissimilari-ty_mtx_stats. py script in QIIME with results presented in boxplots. Prior to diversity analyses samples were rarefied down to the mean number of sequences per samplefour for samples analyzed by TTGE, and two for water sample culturesin ten iterations to standardize number of sequence per sample.
Data summary TTGE profiles were obtained from 18 (72%) pooled mosquito samples, 22 (88%) mosquito positive and 16 (80%) mosquito negative water samples. The remaining samples showed no PCR amplification or faint PCR signals leading to undetectable TTGE profiles. A total of 236 sequences were generated from these TTGE bands; 61 from mosquito larvae, 117 and 58 from mosquito positive and negative water samples respectively. The sequences were binned into 134 unique OTUs, with 1-14 sequences per OTU, and average sequence length of 120 bp. Seven of the unique OTUs failed to align to any bacterial small subunit models (SSU) and were discarded. Sequences from 12 pools of mosquitos, 11 mosquito positive containers, and 10 mosquito negative containers were included in diversity analyses after rarefaction.
Acknowledgements This study was made possible with funding from the  Research Council of Norway (RCN). We thank our collaborating institutions: The Provincial health office,  Khon Kaen , District health office,  Manchakhiri , and the mosquito lab,  Department of Entomology, Kasetsart University, Bangkok  for help with facilitating the study. We are grateful to  Institut de Recherche pour le Développement (IRD) ,  University Montpellier 1 and Laboratoire d'Hygiène hospitalière, Centre Hospitalier Régional Universitaire de Montpellier, Montpellier, France  for letting us process our samples in their labs. We thank all the Village health volunteers from  Waileum village, Manchakhiri district ,  Khon Kaen  for their assistance during sample collection. We thank  Nanthasane Vannavong  for help with logistics during the field collections and  Sara Romano-Bertrand  for assistance and technical advice during the laboratory work.
Proteins identification, quantification and statistical analysis Database searches were done using an internal MASCOT server (version 2. 1, matrix Science; http://www. matrixscience. com/) using the UniProtKB/Swiss-Prot protein knowledgebase (http://www. uniprot. org/) with the search parameters as described in File S1. For protein quantification, raw data were processed by an in-house label-free software, DIFFTAL (DIFferential Fourrier Transform AnaLysis)  [37]  as described in the in File S1. For statistical analysis, DIFFTAL data normalizations (loess normalization at sample level and median central tendency at match set level), protein ratio (Effect size) and statistic p-value (ANOVA) calculations were performed using DanteR 0. 0. 1 software (http://omics. pnl. gov/software/).
Protein annotation For subcellular localization and molecular function annotations all the proteins identified in this study were analyzed using the Ingenuity Pathway Analysis Knowledge Base (http://ingenuity. com/), Human Protein Reference Database [38], UniProtKB/ Swiss-Prot protein knowledgebase, the Gene Ontology consortium (http://geneontology. org/) and the PANTHER classification system (http://www. pantherdb. org/) [39]. Transmembrane topology and signal peptide prediction of every identified protein was obtained from the UniProtKB/Swiss-Prot protein knowledgebase.
Human gene and protein expression analyses Gene expression data were downloaded from the BioGPS website (http://biogps. gnf. org/) [49]  and from the Gene Expression Atlas database (http://www. ebi. ac. uk/gxa/). Gene expression data from ArrayExpress Archive (http://www. ebi. ac. uk/ arrayexpress/) are as follow. E-MTAB-62: Human gene expression atlas of 5372 samples representing 369 different cell and tissue types, disease states and cell lines; E-MTAB-37: Transcription profiling of human multiple cancer cell lines (950 samples); E-GEOD-4290: Transcription profiling of human glioma tumor samples; E-MEXP-567: Transcription profiling of human grade II astrocytic tumor (astrocytomas) vs grade IV tumors (glioblastoma); E-GEOD-8692: Transcription profiling of brain tumor cells to assess endogenous mRNA fluctuations detects a microRNA signal in an in vivo expression set of mRNAs.
Microarray and quantitative real-time PCR Total RNA was isolated from cell cultures using the RNeasy Mini kit (Qiagen) according to the manufacturer's protocol. Microarrays were performed with GeneChip HT Human Genome U133 Array Plate Set (Affymetrix) using an Affymetrix GeneChip Array Station. Plate arrays were scanned using an Affymetrix GeneChip HT Scanner. Gene expression data analysis was done with the Rosetta Resolver gene expression analysis software (version 5. 0, Rosetta Biosoftware). Reverse transcription polymerase chain reaction (RT-PCR) was conducted using the High Capacity cDNA Reverse Transcription kit (Invitrogen) according the manufacturer's protocol. Real-time PCR was performed using cDNA with TaqMan Universal PCR Master mix (Invitrogen) on an ABI Prism 7900HT Sequence Detection System (Applied Biosystems).
U373 MG cells treated with AC 4 ManNAz exhibited a dose-dependent increase in fluorescence that indicated the accumulation of biotin moieties on the cell surface, whereas untreated cells showed only a background level of fluorescence after exposure to the biotin-phosphine probe. The background fluorescence was identical to that observed with U373 MG cells that were not exposed to any reagents and thus represents autofluorescence of cells and not nonspecific uptake of the biotin-phosphine probe or streptavidin-Alexa Fluor 488 (data not shown). As quantified by flow cytometry, the AC 4 ManNAz-treated U373 MG cells displayed about 280-450 fold greater Alexa488-specific fluorescence intensity than the control vehicle-treated cells. Reliable fluorescence labeling was achieved at a 10 mM concentration of azidosugar, however optimal results were obtained at a concentration ranging from 25 to 50 mM.
3A  and 3B ). Consistent with flow cytometric findings, staining was completely dependent on the concomitant presence of AC 4 ManNAz and Staudinger phosphine probe confirming that the labeling via azide-containing glycoconjugates is highly specific and that azide-bearing cell surface glycans were readily imaged by using streptavidin-Alexa Fluor 488 (Fig. 3C  and 3D ). Furthermore, there was no evidence of cell surface labeling for the control groups, confirming that background fluorescence staining is negligible (Fig. 3E ). Taken together, the results suggest that AC 4 ManNAz was selectively, efficiently, and homogenously incorporated into cell surface sialoglycoconjugates. The strategy consists of six steps: 1) metabolic labeling of cells with Ac 4 ManNAz; 2) chemoselective conjugation of azidelabeled glycans with a biotin-linked phosphine; 3) cell lysis; 4) affinity enrichment of the biotin-tagged proteins on streptavidin-conjugated beads; 5) on-beads trypsin digestion of affinity-captured sialoglycoproteins; 6) identification using high-performance liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis using a nano-LC-LTQ Orbitrap mass spectrometer platform and sequence database searching, and quantitative differential analysis of the sialoglycoproteins using label-free analysis with DIFFTAL (DIFferential Fourier-Transform AnaLysis) software algorithm.
com/), the Human Protein Reference Database (http://www. hprd. org/) [38], the Gene Ontology Consortium (http://geneontology. org/) and the UniProtKB/Swiss-Prot Protein Knowledgebase (http://www. uniprot. org/). The number of N-linked glycoproteins identified is certainly underestimated since the use of the UniProtKB/Swiss-Prot database to determine if a protein is glycosylated is notoriously under-representative. Table  S1  lists the information of the 843 proteins identified from GBM tumor cells, fetal and adult astrocytes and NPCs. Of these proteins, 485 (58%) and 121 (14%) could be assigned to the ''plasma membrane'' and ''extracellular space'' GO categories, respectively, whereas 237 (28%) proteins were annotated as ''intracellular'' including 205 (24%) ''integral to membrane'' (GO:0016021) or ''membrane-bounded organelle'' proteins, 12 (,1%) ''lysosomal'' proteins (GO:0005764) and 16 (,2%) proteins assigned to ''organelle lumens'' or ''cytoplasm'' (Fig.
5A  and Table  S1 ). Interestingly, 601 (87%) of the 690 membrane-associated proteins and 16 (,13%) of the 121 proteins annotated ''extracellular space'' contain at least one predicted TM segment and therefore are likely to be constituents of the plasma membrane. Finally, 708 (84%) of the 843 sialoglycoproteins identified in our study were predicted by the SignalP 3. 0 software (http://cbs. dtu. ck/services/) to contain a signal peptide. These data suggest there was a significant enrichment of the pool of cell surface sialoglycosylated proteins using this method. In order to classify the plasma membrane and extracellular sialoglycoproteins identified into biological context, the molecular functions of the 606 cell surface proteins were assigned using the PANTHER classification system (http://www.
Fig. 6  shows a scatter plot of the median protein intensity in the tumor group versus the Effect size for the 65 cell surface proteins up-regulated in the tumor group. Levels of the 65 detected proteins spanned approximately five orders of magnitude. Of these, 52 (80%) were detected in all four tumor samples indicative for common expression of these proteins among GBM tumors, while 8 (12%) and 5 (8%), were found in at least three or two, among the four tumor samples, respectively. The 52 tumor-associated proteins extend over almost the entire distribution of the proteome expression. Clusterin (CLU) was the protein with the highest expression value, while protocadherin 18 (PCDH18) was the least abundant protein. Several functional Analysis Knowledge Base (http://ingenuity. com/), the Human Protein Reference Database (http://www.
hprd. org/), and the Gene Ontology (GO) Consortium (http://geneontology. org/). (B) Molecular functions were assigned using the PANTHER classification system (http://www. pantherdb. org/). Of the 843 unique sialoglycoproteins confidently identified from the ten samples, 485 (58%) and 121 (14%) proteins could be assigned to the ''plasma membrane'' (GO:0005886) and ''extracellular space'' (GO:0005615) GO categories, respectively. A total of 819 molecular function hits were allotted to the 606 cell surface proteins. Of these, approximately one-third (34%) correspond to receptor activity including all major classes of cell surface receptor proteins. doi:10. 1371/journal. pone. 0110316. g005 protein classes were found in the PANTHER database for the 52 proteins up-regulated in the four tumor samples (Tables  3  and S3 ).
Array and qPCR data are detailed in Tables  S4  and S6 , respectively. c The gene expression up-or down-regulation in glioblastoma are obtained from a large scale meta-analysis of glioblastoma public microarray data from independent studies downloaded from the Gene Expression Atlas database (http://www. hprd. org/). The number of studies the gene is over-or -under expressed in glioblastoma tissues or cell lines is indicated in parentheses. Data are detailed in Table  S5. d Protein expression profiles in malignant glioma based on immunohistochemistry for a large number of human tissues, cancers and cell lines are obtained from The Human Protein Atlas portal (http://www. proteinatlas. org/). For each indicated protein, overall malignant glioma tissue staining statistics (percentage of tumors positive for the protein studied) are reported in Table  S7.
ND: not determined. doi:10. 1371/journal. pone. 0110316. t003 Plate Set (Table  S4 ). Using the fold change of the microarray gene expression ratio between tumor samples and adult astrocytes, we could evaluate gene expression for 43 out of the 52 proteins upregulated in the tumor group and listed in Tables  3  and S3. Of the 43 genes analyzed, 25 (,58%) were up-regulated (median fold change. 2) in tumors, whereas only one gene (OLR1) was more highly expressed (median fold change. 2) in adult astrocytes. We compared our mRNA microarray results for these genes to those reported in a large scale meta-analysis of glioblastoma public microarray data downloaded from the Gene Expression Atlas database and found them, in general, to be in good agreement (Tables  3  and S5 ). For a subset of 23 genes, the proteomic and microarray results were further validated by quantitative TaqMan real-time PCR assays for gene expression in GBM patient tumors and adult astrocytes.
Tissue distribution of selected glioblastoma-associated sialoglycoproteins Ideally, an optimal cell surface target for immunotoxins or ligand-directed toxins should be overexpressed or uniquely expressed on human tumor cells at a significantly high level with limited or preferably no target expression on vital normal tissue. To study human tissue distribution of the 52 selected cell surface proteins overexpressed in human GBM tumors compared to nontumorous brain cells, we queried different human gene and protein expression web-accessible databases. BioGPS gene portal (http://biogps. gnf. org/) [49]  provides information on gene expression patterns from a diverse set of tissues and cell types. The Human Protein Atlas (HPA) portal (http://www. proteinatlas. org/)  [50]  is a database describing protein expression profiles based on immunohistochemistry for a large number of human tissues, cancers and cell lines.
In total, 801 proteins were annotated as glycoproteins using the UniProtKB Knowledgebase including N-glycoproteins. Among the 42 non-glycosylated proteins, 11 were predicted to contain a signal peptide (SignalP) and sites of N-linked glycosylation (NetNGlyc) (data not shown). These results showed a high degree of specificity for the detection of cell surface proteins, with less than 2% of identified proteins resulting from co-isolation of intracellular and non-glycosylated proteins. To the best of our knowledge, this represent the most extensive data set of cell surface N-glycoproteins confidently identified so far in GBM tissues. The proteins identified in human GBM tissues and non-tumorous brain cells were quantified using a label-free analysis with DIFFTAL software algorithm. DIFFTAL is an automated tool for label-free quantitation analysis using an efficient peptide alignment approach, spectral data validation and extracted ion current (XIC) as quantitative index  [37].
XIC of the peptide is the intensity of corresponding ions in MS survey scans. Out of the 606 cell surface proteins identified and quantified with high confidence, we found 35 proteins significantly up-regulated (Effect size. 3) in the tumor group compared to the astrocyte group and NPCs in all four primary cultures derived from GBM patients indicating a common expression of these proteins among HGGs. For 25 proteins, quantitative proteomic results were validated by gene expression analysis using microarrays and/or qRT-PCR. Furthermore, our results proved to be in good agreement with those reported in a large scale meta-analysis of glioblastoma public microarray data downloaded from the Gene Expression Atlas database. Among the sialoglycoproteins overexpressed in GBM cells identified in this study, we found well-known proteins reported to be involved in various aspects of glioma biology (e.
Ideally, an optimal cell surface target for immunotoxins or ligand-directed toxins should be overexpressed or uniquely expressed on human tumor cells at a significantly high level with little or no expression in normal vital tissues. To evaluate human tissue distribution of the 52 selected cell surface sialoglycoproteins overexpressed in human GBM tumors compared to non-tumorous brain cells, two complementary methods were employed. We examined protein expression patterns in normal human tissues, cancer and cell lines using high quality immunohistochemistry data available at the HPA web portal. We also examined the expression status of the corresponding transcripts in normal human tissues and cells using microarray datasets available at the BioGPS Gene Portal web site. Our comprehensive gene and protein expression profiles analyses revealed that the majority of glioblastoma-specific proteins displayed moderate to strong expression in several vital normal tissues.
Figure1. Profiling cell surface sialoglycoproteins via the bio-orthogonal chemical reporter strategy combined with quantitative shotgun proteomics. The strategy consists of six steps: 1) metabolic labeling of cells with Ac 4 ManNAz; 2) chemoselective conjugation of azidelabeled glycans with a biotin-linked phosphine; 3) cell lysis; 4) affinity enrichment of the biotin-tagged proteins on streptavidin-conjugated beads; 5) on-beads trypsin digestion of affinity-captured sialoglycoproteins; 6) identification using high-performance liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis using a nano-LC-LTQ Orbitrap mass spectrometer platform and sequence database searching, and quantitative differential analysis of the sialoglycoproteins using label-free analysis with DIFFTAL (DIFferential Fourier-Transform AnaLysis) software algorithm.
Figure5. Classification of identified sialoglycoproteins by GO annotation. (A) Cellular components were assigned using Ingenuity Pathway Analysis Knowledge Base (http://ingenuity. com/), the Human Protein Reference Database (http://www. hprd. org/), and the Gene Ontology (GO) Consortium (http://geneontology. org/). (B) Molecular functions were assigned using the PANTHER classification system (http://www. pantherdb. org/). Of the 843 unique sialoglycoproteins confidently identified from the ten samples, 485 (58%) and 121 (14%) proteins could be assigned to the ''plasma membrane'' (GO:0005886) and ''extracellular space'' (GO:0005615) GO categories, respectively. A total of 819 molecular function hits were allotted to the 606 cell surface proteins. Of these, approximately one-third (34%) correspond to receptor activity including all major classes of cell surface receptor proteins.
ENVISAT Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS, 120 m) data are used to understand and quantify impacts on Metop (AAdvanced Scatterometer (ASCAT, 25 km) soil moisture retrieval during the snow free period. Sites of interest are chosen according to ASAR WS availability, high or low agreement between output from the land surface model ORCHIDEE and ASCAT derived SSM. Backscatter variations are analyzed with respect to the ASCAT footprint area. It can be shown that the low model agreement is related to water fraction in most cases. No difference could be detected between periods with floating ice (in snow off situation) and ice free periods at the chosen sites. The mean footprint backscatter is however impacted by partial short term surface roughness change. The water fraction correlates with backscatter deviations (relative to a smooth water surface reference image) within the ASCAT footprint areas (R = 0.
Soil moisture is highly variable in space and time, controlled by precipitation, redistribution and evapotranspiration. Attempts to address this spatio-temporal variability have led to the development of remote sensing techniques employing surface soil moisture (SSM) retrieval algorithms (e. g. ,  [8] [9] [10] [11] ). The availability and applicability of satellite data in the Arctic is in addition crucial because of the remote nature of this region. Both passive and active microwave sensors are used to produce SSM data, including global coarse resolution (25-50 km) datasets  [12]. Past studies have often focused on developing methods using active microwave sensors. Scatterometer data have become central to SSM retrieval studies. The ERS scatterometer derived SSM datasets have been used in a number of studies (e.
Permafrost models are either developed as stand-alone algorithms or as modules that are incorporated into Land Surface Models (LSMs) and General Circulation Model (GCMs)  [34]. One such LSM is ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms). This model is designed to represent terrestrial fluxes (carbon, water and energy budget). As such, it is a physically-based model that simulates the soil hydrological and thermal dynamics, incorporating a soil freezing scheme necessary for appropriately modeling high-latitude processes  [35]. This present study comes in support of a comparison study between modeling results and satellite-derived surface soil moisture  [36] , by putting effort in the evaluation of the limitations associated with the radar backscatter behavior. In  [36]  ORCHIDEE output is compared to an ASCAT SSM product, derived using the algorithm put forward by  [17] , aggregated on weekly time steps and adjusted to polar regions  [27].