dataesr/scientific-paragraphs-categorization huggingface dataset:
A first task is to assess data at matching scales. These include historical data, e. g. , historical maps and climate data. To account for different scales, new approaches must be developed and tested for scale transition and bias correction. For example, results from global and regional climate models must be transitioned to the scales relevant for change assessment of land-water use and water flow, availability, and quality  (Jarsjö et al. 2012) , as well as to the scales relevant for biological and societal processes. Likewise, historical maps should be used to reconstruct historical landscapes over large areas. Because maps generally are biased toward productive and agricultural regions, it is necessary to find methods to interpolate between mapped areas using, for example, information on how land use was related to different soils at different times and in different regions  (Cousins 2009 , Pasanen-Mortensen 2014).
CONCLUSION Land use and water use, human population, and climate have changed through time in the NDB study area, and changes in biodiversity and ecosystem service delivery may be connected to these interacting drivers. Although projections of change are fundamentally difficult to develop, e. g. , because of shifts in human value systems and policies, projections are still valuable to communicate potential effects of change to decision makers and to enable development of anticipatory governance that takes projections and probable change scenarios into account  (Boyd et al. 2015). To better understand changes in biodiversity and ecosystem service delivery, we urge scientists to collaborate across disciplines to explore the importance of interacting drivers and feedback mechanisms, including synthesis and assessment of matching-scale data from multiple disciplines.
Acknowledgments: The authors engage in interdisciplinary research financed by the  Strategic Research Program EkoKlim at Stockholm University  through the  Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS). Georgia Destouni  and  Fernando Jaramillo  also acknowledge financial support from the  Swedish Research Council (Vetenskapsrådet , project number  2009-3221 ), and  Georgia Destouni  and  Ype van der Velde  from the  parallel Strategic Research Program BEAM  at  Stockholm University. We thank three anonymous reviewers for thoughtful comments, which improved the manuscript.
uNK cell chemokinesis The chemokinesis of uNK cells was assessed using Ibidi m-Slide Chemotaxis 3D chamber slides (Ibidi, 80 326, supplied by Thistle Scientific Ltd, Uddingston, UK). Isolated uNK cells were suspended in a collagen matrix (as above) and the response to E2 treatment was measured using time-lapse microscopy. Chamber slides were set up containing serum-free phenol red-free RPMI 1640 media in both reservoirs and uNK cells were treated +E2. Cells were imaged every 5 min for 4 h (n ¼ 30 per treatment) using an Axiovert 200 Inverted Fluorescent Microscope (Zeiss). Data were analysed using ImageJ (manual cell tracking plug-in) and chemotaxis and migration tool software (Ibidi).
Angiogenesis RT2 profiler PCR array RT2 profiler PCR arrays (SABiosciences, Frederick, MD, USA) were utilized to analyse a targeted panel of 84 genes related to angiogenesis. uNK cells were isolated from three patients and treated +E2 for 2 h. Total RNA was extracted from uNK cells using a Qiagen RNAeasy kit (Qiagen, UK) according to manufacturer's instructions and retrotranscribed using a RT2 single strand cDNA synthesis kit (SAB Biosciences, Frederick, MD, USA). PCR array data analysis was performed using the SABiosciences web portal according to the DD Ct method (http://www. SABiosciences. com/ pcrarrayanalysis. php). Expression of mRNAs encoding C-X-C chemokine receptor type 4 (CXCR4), Interleukin-15 receptor alpha (IL15RA), interferon gamma (IFNG) and CCL2 was assessed by quantitative PCR (qPCR) according to standard protocols.
Milliplex assay The concentration of CCL2 in uNK CM (n ¼ 5 per group) was quantified using a customized singleplex bead enzyme-linked immunosorbent assay (HCYTOMAG-60 K, Millipore, Schwalbach, Germany) according to manufacturer's instructions. Standards and samples were measured in duplicate using a Bio-Plex 200 HTF machine and analysed using BioPlex Manager Software (Version 5, BioRad).
When cell movement was analysed using the chemotaxis and migration tool software (Ibidi) an increase in cellular activity was readily apparent following exposure to E2. Chemokinesis (non-directed cell movement) was measured: E2 treatment (10 28 M) was found to significantly increase both the velocity and the accumulated distance travelled by uNK cells (Fig. 1C  and D , P , 0. 01, n ¼ 30 per treatment) compared with control (DMSO). During decidualisation endometrial stromal cells secrete estrogens  (Gibson et al. , 2013)  as well as uNK chemoattractants such as CXCL12 and IL-15  (Kitaya et al. , 2005; Wu et al. , 2005). To investigate whether E2 could increase responsiveness of uNK cells to chemoattractants, the expression of the CXCL12 receptor (CXCR4) and the IL-15 receptor alpha (IL15Ra) was assessed in uNK cells by qPCR.
uNK cells were treated with control (dimethyl sulfoxide) or E2 for 2 h (n ¼ 3) and the differential expression of angiogenesis genes was determined. PCR array data analysis was performed using the SABiosciences web portal according to the DD Ct method (http://www. SABiosciences. com/pcrarrayanalysis. php) and gene expression expressed as fold change relative to vehicle control. The P-values are calculated based on a Student's t-test of the values for each gene in the control group and treatment groups. Angiogenesis genes that were differentially up-regulated in response to E2 are shown. Expression of vascular endothelial growth factor A (VEGFA) is also shown for comparison. MMP9, matrix metallopeptidase 9; PECAM1, platelet/endothelial cell adhesion molecule 1; MMP2, matrix metallopeptidase 2; CXCL10, chemokine (C-X-C motif) ligand 10; TIMP1, TIMP metallopeptidase inhibitor 1; CCL2, chemokine (C-C motif) ligand 2; IFNG, interferon gamma.
Acknowledgements We thank  Arantza Esnal ,  Ronan McLaughlin  and members of PTKS group for technical assistance. We thank  Catherine Murray  and  Sharon McPherson  for patient recruitment and collection of tissues and  Ronnie Grant  for preparation of figures. We are very grateful to  Dr Fiona Cousins  for proof reading and to  Prof. Steven Yellon  and  Prof. Jane Norman  for critical feedback on the manuscript.
The precursor ions were fragmented by collision-activated dissociation in the Q2 collision cell using rolling collision energy. The fragmented ions were captured and mass analyzed in the Q3 linear IT. Database searches (Swiss-Prot, NCBInr, or MSDB) and protein identification were performed using the MASCOT program (http://www. matrixscience. com/) with trypsin plus one missed cleavage and carboxyamidemethylation as a fixed modification and methionine oxidation as a variable modification, using a mass tolerance of 0. 5 Da for the precursor MWs and 0. 3 Da for the fragment MWs. The criteria used to accept a protein hit as a valid identification were two or more tryptic peptide matches with the protein sequence and at least one peptide with  < 0. 05.
Analysis of the Protein Glycosylation Sites. The sequence obtained from the MASCOT database was analyzed with Glycomod  [29] , which is available at http://www. expasy. ch/ tools/glycomod/. This program explores the mass values of ions obtained experimentally with MALDI-ToF and their relationships with sequences in the MASCOT database. The search parameters specified N-glycosylated and Oglycosylated proteins, with modifications of oxidized methylation and cysteine-treated iodoacetamide, using a mass tolerance of 0. 1 Da. 2. 14. Ae. aegypti RNA Purification. Groups of 25 female mosquitoes were homogenized and sonicated with RNAsefree water. The lysates were passed through a 0. 9 mm needle. RNA extraction was performed using a Nucleospin RNA II kit (Macherey-Nagel, Germany), and the RNA quality was evaluated using Agilent RNA Nano 6000 chips (Agilent 2100 Bioanalyzer).
Ae. aegypti CSAS and ST Gene Synthesis. BLink and BLAST searches for CSAS and ST genes were performed using the NCBI tBLAST algorithm based on the CSAS (gi|24667125) and ST (gi|24762715) sequences of D. melanogaster. Putative CSAS (XP 001663017) and ST (XP 001649590) genes were identified in the Ae. aegypti genome and confirmed by VectorBase (https://www. vectorbase. org/) as AAEL012868 and AAEL014772, respectively. cDNA synthesis was performed using 200 ng of RNA template (QPCR cDNA kit; Stratagene, USA) with random primers. Five microliters of cDNA was used in a 25 L PCR reaction, which was amplified with Taq DNA polymerase (Thermo Fisher Scientific) as follows: 95 ∘ C for 5 min; 38 cycles at 94 ∘ C for 1 min, 50 ∘ C for 1 min, 72 ∘ C for 1. 5 min, and 72 ∘ C for 10 min, holding at 4 ∘ C.
Statistical Analysis. Data were expressed as the mean and standard deviation and compared using a Mann-Whitney  test with Statistical Analysis Software version 8 (SAS Institute, USA). The significance level was set at  < 0. 05. To identify the D7 protein in MASCOT, and the score for an MS/MS match was based on the absolute probability () that the observed match between the experimental data and the database sequence was a random event. We used a probability-based MOWSE score; that is, the reported score was -10 log (), where  was the probability that the observed match was a random event, and the protein scores were significant at  < 0. 05.
The sialylation process requires the biosynthesis of glycosylnucleotide cytidine 5  -monophosphate-N-acetylneuraminic acid (CMP-Neu5Ac) by CSAS and enzymes from the ST family, which transfer Sia to a glycoprotein or glycolipid acceptor substrate. Therefore, using the available genome database of D. melanogaster, we searched for the amino acid (aa) sequences of both enzymes, that is, CSAS (gi|24667125) and D. SialT6 ST (gi|24762715), and we performed BLAST and BLink analyses of the Ae. aegypti genome using the NCBI genome database. We detected hypothetical sequences for both proteins, that is, CSAS (XP 001663017; Ae. CSAS) and ST (XP 001649590; Ae. ST), in the Ae. aegypti genome, which were validated in the VectorBase database. The Ae. aegypti ST gene sequence was identified and associated with the ST6Gal, 2,6-sialyltransferase (ST6Gal) family, which is closely related to D.
The 45-kDa saliva protein that interacts with specific lectins for Sia as well as with DENV is similar to the peptide ion mass of the protein NCBI: gi|157113327 [Vectorbase: AAEL006417-RA], which is a putative molecule in the D7 family of Ae. aegypti. It had a 35% match in its primary sequence, with a score of 178 and an expected value of 6. 4 -13 ( < 0. 05). Based on the analysis of the sequence Proteins were identified by LC/ESI-MS/MS analysis after gel trypsin digestion. The table shows the protein name, the NCBI accession number, the theoretical (database) and observed (gel) MWs, the number of peptide sequences matched in the MASCOT database, the corresponding percentage sequence coverage, and the MASCOT score. The criteria used for accepting a protein as a valid identification were two or more tryptic peptide matches with the protein sequence and at least one peptide with  < 0.
Acknowledgments The authors thank  Dr. Jorge Guevara  of the  National Institute of Neurology  for his support with the lectin immunohistochemistry assays and  Gerardo Hurtado  and  Dr. Victoria Pando  of the  INSP  for the initial D7 MALDI-TOF assay. They also thank  Miguel Tapia Rodríguez  for his excellent support of the confocal assay. And they also thank  Dr. P. Stanley  from the  Albert Einstein College of Medicine at Yeshiva University, NY , for kindly providing LEC29. Lec32 cells. Jorge Cime  was a Ph. D. student in the  Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autómoma de México , and received a scholarship from  Consejo Nacional de Ciemcia Tecnologia (CONACYT) ,  México. This study was supported by  CONACYT  and  PAPIIT-UNAM  grants.
[48][13][14][15][16]e the biosynthetic capacity for endogenous Sia production. Our data are consistent with previous studies[12][13][14][15][16]of the expression of a functional D. melanogaster CSAS and the presence of -2,6-linked Sia moieties in D. melanogaster. Sia is distributed widely in nature at the nonreducing termini of glycoproteins, glycolipids, or secreted glycoconjugates, and it may be attached to different acceptors via -2,3, -2,6, or -2,8-linkages, which are determined by the specificity of different STs[48]. In this study, we demonstrated the presence of Ae. aegypti ST cDNAs in different Ae. aegypti tissues (Figures Using the available Ae. aegypti genome database, we identified two putative genes encoding enzymes (Ae. CSAS and Ae. ST) implicated in the Ae. aegypti sialylation pathway.
Sequencing was performed on an Ion Torrent PGM following the manufacturer's guidelines for bacterial amplicons and on a Roche 454 FLX titanium instrument for archaeal amplicons  (Dowd et al. , 2008). Approximately 2 × 10 6 raw bacterial reads were produced from bacterial primers. Reads were assigned to their respective rumen samples and trimmed of forward and reverse primers. Minimum sequence length was set at 150 bp. Operational taxonomic units (OTUs) were clustered at 97% of similarity and filtered using USEARCH  (Edgar, 2010). Following taxonomy assignment, non-bacterial sequences were removed. Alignment was done using PyNAST  (Caporaso et al. , 2010a)  and taxonomy with the RDP classifier  (Wang et al. , 2007)  against the Greengenes database (gg_otus-12_10-release). Raw archaeal reads were analyzed as bacterial reads but the minimum sequence length was set at 200 bp and following taxonomy assignment non-archaeal sequences were removed.
A total of 193,367 raw reads were obtained from the pyrosequencing platform using the archaeal primers. The sequence data have been submitted to the EMBL databases under accession No PRJEB8814. Raw sequences were filtered and analyzed using the QIIME v. 1. 7. 0 pipeline with default options unless otherwise stated  (Caporaso et al. , 2010b). Chao1 for calculating richness and Shannon that accounts for abundance and evenness were calculated using QIIME. As an additional estimator of the representativeness of the sequences obtained, Good's coverage was calculated as G = 1-n/N, where n is the number of singleton OTUs and N is the total number of sequences in the sample. Rarefaction analysis was done using the lowest number of reads obtained from a sample for each of the target communities and a cut-off of 97%.
Statistical Analysis Rumen fermentation parameters, intake, digestibility, and body weight data were analyzed in repeated measures using the MIXED procedure of SAS v9 (SAS Institute Inc. , Cary, NC). The model included the fixed effect of inoculum, lambs age, group, and period × group interaction. The animal was considered as a random effect. Best fitting covariance structure was compound symmetry. Differences were tested with the LSMEANS statement for period, group and their interaction and with the LSMESTIMATE statement for testing the effect of inoculum at 20 and 26 weeks of age. Significance was declared at P < 0. 05 probability level and trends were discussed at P < 0. 10 probability level. The Kruskal-Wallis test as implemented in SAS was used to estimate the difference between samples in the number of sequenced reads.
All single-block models were computed with the SIMCA-P software (Umetrics, v. 13. 03, Sweden). Leave-one-out crossvalidation was used to evaluate optimal model size based on goodness of prediction Q 2. Model validity was verified using permutation tests. For each OPLS-DA model, the most discriminating variables were highlighted based on variable importance in the projection (VIP) and S-plots. The significance of individual variables between groups (G1 vs. G2) was further assessed using ANOVA test (R software, version 2. 11. 1). Consensus OPLS-DA was used to combine metabolomics and microbial data  (Boccard and Rutledge, 2013).
4 log 10 copies 16 s rRNA gene/mg DNA, P > 0. 05). The qPCR data also confirmed that decreases in Ruminococcaceae numbers could not be attributed  to the common fibrolytic genera, R. albus and R. flavefaciens that showed no changes or even a tendency to increase for the latter. Total bacteria, 16S rRNA gene copies per unit of DNA were logically higher in the absence/low density of protozoa (Supplementary Figure  S4 ). Beta-diversity analysis using the UniFrac metric  (Lozupone and Knight, 2005)  revealed that the presence of protozoa was the main discriminant parameter for the bacteria community, particularly influencing the relative abundance of taxa (Figure  2  weighted UniFrac) but also affecting the presence of rarer taxa (Supplementary Figure  S5  unweighted Unifrac plot). The effect of challenging events like adult microbiota inoculation and OTA contamination in the diet was assessed for each group by comparing the Bray-Curtis similarity for the same animal at different time points.
However, at 31 weeks, when lambs were all in the same lot, previous differences were effaced. The most discriminant metabolites were selected using a VIP threshold of 2. Figure  6  shows identified metabolites and their time course trajectory calculated using the relative concentrations and their changes in abundance compared to G1 at 14 weeks. Most abundant changes were related to metabolic pathways involving protein, amino acid and polyphenol metabolism. It should be noted that many discriminant metabolites could not be identified using wellcurated databases such as METLIN and HMDB. These are shown in Supplementary Figure  S8. To better comprehend the changes induced by treatments we also applied a novel data fusion approach for the joint analysis of metabolomic and microbial data, namely consensus OPLS-DA analysis  (Boccard and Rutledge, 2013).
Consensus OPLS-DA was performed on the most relevant information from the three  sources of data, i. e. , urinary metabolites, bacteria and archaea, for assessing the effect of protozoa through the comparison of all the fauna-free and faunated animals. The metabolite dataset was reduced to include the most significant metabolites (VIP > 2, n = 65). A stringent trimming was performed on the bacterial OTUs for obtaining a core set of phylotypes. Only OTUs that were in ≥50% of the rumen samples from a group at a given time and that represented ≥0. 1% of the total community counts were selected. From an initial 3946 OTUs, 517 fitted these criteria. For archaea, the dataset contained the 29 OTUs described in the text. A model with two latent variables (one predictive and one orthogonal component) was obtained using leave-one-animalout cross validation with satisfactory goodness of fit (R2Y = 0.
95) and goodness of prediction indices (Q2 = 0. 78). The faunation effect was clearly visible on the score plot of the two predictive latent variables (tp1 vs. tp2), separating the two groups (Figure  7A , fauna-free on the left and faunated on the right). The loadings allowed links between urinary metabolites and rumen microbial data (bacteria and archaea) to be drawn. This analysis confirmed the results obtained using different statistical tools for the individual datasets presented above but also provides a more integrative overview of potential links between rumen microbes and the host urinary metabolome (Figure  7B ). For instance, extreme loading values for metabolites associated to tryptophan pathway where not only linked to the presence of protozoa but also to bacterial OTUs distantly related to Clostridiales and Bacteroidales.
Acknowledgments  D. Graviou ,  D. Alvarez  ( UMR1213   Herbivores ), and  H. Jakobsen  ( Tech. U. Denmark ) for their technical assistance. M. Fabre  and  S. Alcouffe  ( UERT ) for animal care. This project was partly funded by the  French National Research Agency  through the program  FACCE-JPI RumenStability  and national funds appropriated to INRA.
Physical Measurements IR spectra (4000-400 cm ´1) were recorded with a Nexus Nicolet (Madison, WI, USA) FT-IR spectrophotometer in KBr pellets. DSC measurements were carried out on a Mettler Toledo (Leicester, UK) DSC 821e over the range 120-500 K with a scan rate of 5 K/min in N 2. The magnetic susceptibility measurements were carried out in the temperature range 2-300 K (2-400 K for 2) with an applied magnetic field of 0. 1 T on polycrystalline samples of Compounds 1-3 (with masses of 37. 64, 19. 27 and 31. 75 mg, respectively) with a Quantum Design (San Diego, CA, USA) MPMS-XL-5 SQUID susceptometer. The isothermal magnetizations were performed on the same samples at 2 K with magnetic fields up to 5 T. The susceptibility data were corrected for the sample holders previously measured using the same conditions and for the diamagnetic contributions of the salt as deduced by using Pascal's constant tables (χ dia = ´422.
Crystallographic Data Collection and Refinement Suitable single crystals of Compounds 1-3 were mounted on glass fibers using a viscous hydrocarbon oil to coat the crystals and then transferred directly to the cold nitrogen stream for data collection. X-ray data were collected at 170 K (for 1) and 120 K (for 2) on a Supernova diffractometer equipped with a graphite-monochromated Enhance (Mo) X-ray Source (λ = 0. 71073 Å). For Compound 3, X-ray crystal data were collected at 170 K on an Oxford-Diffraction Xcalibur CCD diffractometer equipped with a graphite-monochromated (Mo) X-ray source (λ = 0. 71073 Å). The program CrysAlisPro, Oxford Diffraction Ltd. , was used for unit cell determinations and data reduction  [55]. Empirical absorption correction was performed using spherical harmonics, implemented in the SCALE3 ABSPACK scaling algorithm.
Acknowledgments: We thank the  Spanish MINECO  (Projects  CTQ2011-26507 ,  CTQ2014-52758-P  and  MAT2014-56143-R ) and the  Generalitat Valenciana  ( Projects PrometeoII/2014/076 ,  GVACOMP2015-246  and  ISIC)  for financial support. Author Contributions: S. B. participated in the preparations, characterizations and X-ray structural analysis. S. T. participated in the X-ray structure analysis. C. J. G. G. performed the magnetic and DSC measurements, analyzed the data and wrote the manuscript.
Statistical Analysis The nominal and categorical parameters were expressed as absolute numbers and percentages. Means and standard deviations (± SD) were calculated for the normally distributed variables, and medians and interquartile ranges (IQR) for the skewed variables. The non-parametric test for paired data (Wilcoxon test) was used to analyze the variations in RBV bioavailability and the eGFR before, during, and after triple therapy. A P value < 0. 05 was considered statistically significant. All the analyses were performed using Statistical Package for the Social Sciences (SPSS), version 19. 0 for Windows (SPSS Inc. , Chicago, Illinois, USA).
Results The data on 37 consecutive HCV patients initiating triple therapy were analyzed. The patients' characteristics are presented in Table  1. Male gender was predominant (73. 0%), and mean age was 56. 5 years (± 10. 7). Thirty-one per cent of the patients were cirrhotics, among whom 18% had some sign of decompensation. Three (8. 1%) patients had renal insufficiency defined as 30 < eGFR < 60 mL/ minutes/1. 73 m² (CKD stage 3). One patient with severe renal insufficiency (eGFR < 30 mL/minutes /1. 73 m²) was excluded from the analysis. A significant increase in RBV bioavailability was observed between T-1 and W4 (N = 37, 0. 06 ± 0. 09 mg/L/dose/ kg; P < 0. 001). This increase was even stronger between T-1 and W8 (N = 20, 0. 18 ± 0. 21 mg/L/dose/kg; P < 0. 001). A significant increase was also observed between W4 and W8 (N = 20, 0.