dataesr/scientific-paragraphs-categorization huggingface dataset:
3 Hz of line broadening and were manually phased and baseline corrected using Topspin 1. 3 software (Bruker, GmBH, Germany) and referenced to α-glucose signal δ = 5. 225 ppm for serum samples and to the TSP resonance at δ = 0. 000 ppm for the urine and tissue samples. The correlation optimized warping algorithm, COW, and the icoshift algorithm implemented in Matlab (Matlab v. 8. 1, Mathworks Inc. ) were used to correct the peak positions (alignment). The spectra were normalized using the Probabilistic Quotient Normalization (PQN) method.
Preprocessing of variables prior to analysis A total of 27 serum, 19 urine and 26 tissue metabolites were assigned. The concentration of any NMR measured metabolite was obtained as a signal integral of the non-overlapping resonances (or a cluster of partly overlapping resonances). The metabolite resonances were identified according to assignments to chemical shifts published in the literature and online databases (Biological Magnetic Resonance Data Bank and Human Metabolome Database). All variables (originating from various fluids) were scaled to unit standard deviation, which guaranteed their equal importance (equal weight) in the model construction.
Fusion data Relative integral of resonances obtained from paired serum, urine, tissue and medical data samples were fused into one data matrix. For the purposes of model calculation, names of metabolites identified in the serum, urine and tissue have been replaced with X_S, X_U and X_T structure, respectively, where X was the name of metabolite. The clinical laboratory data (Table  1 ) were left without any markings. The prediction performance of the PLS-DA models was estimated based on receiver operating characteristic (ROC) curves and area under curve (AUC) values. For this purpose, a perfcurve function from the Matlab statistical tool-box (Matlab v. 8. 1, Mathworks, Inc. ) was adopted. Specificity and sensitivity were determined according to sample class prediction using the 7-fold cross-validated predicted values of the fitted Y-predcv (implemented in SIM-CA-P+ software) for observations in the model.
Results The median 1 H NMR spectrum of serum with 27 metabolites, the urine spectrum of 19 and tissue spectrum of 26 assigned metabolites in the balanced diet group is presented as an example in Figs 1, 2 and 3. The metabolite resonances signals were identified according to assignments published in the literature and in on-line databases (http://hmdb. ca). The statistical calculation almost coincides with the VIP plot for serum metabolites in each comparison (S1 Table ). The BDG vs. RG is accompanied by changes in the level of leucine, valine, isoleucine, alanine and acetone, RG vs. UDG in alanine and betaine perturbation, while leucine, valine, isoleucine, alanine acetone, pyruvate and betaine can be used to differentiate the BDG and UDG groups. Among these metabolites two, alanine and isoleucine, are important only in VIP selection.
Acknowledgments This publication is part of the project " WROVASC-Integrated Cardiovascular Centre " cofinanced by the  European Regional Development Fund  as part of Innovative Economy Operational Program 2007-2013 implemented in the Regional Specialist Hospital,  Research and Development Centre in Wrocław. Additionally this study was partly financed by an internal project of the  Wroclaw University of Science and Technology   S0401/0247/16  and  WCB KNOW 2014-2018.
Genetic diversity We tested for deviation from Hardy-Weinberg equilibrium and linkage disequilibrium between pairs of loci within each sample site with GENEPOP 4. 4  [41]  using default parameter values, and Bonferroni's correction was applied to multiple comparisons. In order to estimate the presence of null alleles we assessed whether heterozygote deficits may be due to null alleles with MICROCHECKER 2. 2. 3  [42]. Including closely related individuals can increase genetic structure; therefore, we also analysed pairwise relatedness to identify related individuals using the Queller and Goodnight estimator (QGM)  [43]  implemented in GENALEX version 6. 5  [44]  and then removed from the dataset one randomly-selected individual from each related pair, defined as pairs with QGM > 0. 7. For each site, the genetic variability of each locus, and the mean for all loci were described using the mean allele number (A), mean number of private alleles, observed heterozygosity (H O ), unbiased expected heterozygosity (uH E ) and inbreeding coefficients (F IS ) using FSTAT 2.
Population genetic structure and recent migration Genetic structure was explored using individual-based Bayesian clustering analyses with the program STRUC-TURE v. 2. 3. 4 (without spatial information)  [47] , TESS 2. 3. 1 (incorporating spatial information)  [48, 49]  and a discriminant analysis of principal components (DAPC)  [50]. First, to estimate the most likely number of genetic clusters (K) in STRUCTURE, no prior information about the location of populations was assumed and an admixture model with uncorrelated alleles was used with a burn-in phase of 1,000,000 iterations, followed by a run phase of 1,000,000 iterations. Posterior probability values for the number of clusters (K), ranging from 1 to 10, were calculated from ten independent runs to establish consistency. The most likely number of clusters was determined based on change of the posterior probability of the model, and its rate of change with respect to K using the ΔK statistic  [51].
Next, DAPC was used to identify genetic clusters by sequential clustering and model selection. This method provides a description of the genetic structuring using coefficients of alleles in linear combinations that give the largest between-group and smallest within-group variances in these loadings. In contrast to analyses in STRUCTURE and TESS, DAPC cluster detection within the genetic data does not consider any assumptions about HW proportions or linkage equilibrium  [52]. The most likely number of genetic clusters associated with the lowest Bayesian Information Criterion values was established using the R package adegenet 2. 0. 1. [52]. We explored values for the number of clusters between 1 and 30  [52]. In order to avoid overfitting of the discriminant functions due to retaining too many PCs, we performed DAPC retaining the optimal number of PCs based on the calculation of the α-score.
The level of genetic differentiation was estimated by F ST  [53] , and Jost's D EST  [54] , which corrects the F ST dependency for the amount of within-site variation, using FSTAT and GENALEX, respectively (significance was assessed by 28,000 permutations for F ST and 9999 permutations for D EST ). Differentiation between sample sites based on F ST was represented by a dendrogram using the program MEGA v. 6  [55]. Genetic differentiation was also described using the genetic uniqueness index (GUI), calculated as the average of the pairwise F ST values observed between a site and all other sites  [56]. To detect restriction in gene flow between sites in relation to forest cover, the GUI was correlated with the proportion of forest in the buffer zone (20 km width) around each site. Buffer zones and proportions of forests were calculated using ArcGIS 10.
Then, we performed the analysis under the OnePopFounderFlush demographic model for sites in which we (1) detected past contractions, and (2) suspected potential past founder events followed by expansions. Analyses under OnePopFounderFlush were thus run separately for the isolated site NE1 and for the pooled sites NE2 + NE3 and CE1 + CE2 due to the lack of genetic structure between them and to increase the signal strength with larger sample sizes. For both demographic models preliminary runs for every dataset were done using 200 points, 200 trees, and 10 iterations. Next, for the final runs, we used 400 points, 50,000 trees, and 10 iterations with narrow parameter ranges deduced from the preliminary runs.
Genetic diversity Twenty-two loci were genotyped for the 298 individuals sampled from the eight study sites. Thirteen individuals with high relatedness to others (QGM > 0. 7) and thirteen with missing genotypes were removed from this final dataset. Finally, 272 full multi-locus genotypes were obtained with 0. 30% missing data. After sequential Bonferroni correction (p < 0. 000027), deviation from HWE was detected in sites NE2 and CE2 for locus Ma1. Null alleles were found for one locus (Ma1) in four sites and for four loci in one or two sites only (Additional file 1: Table  S1 ). Due to the presence of null alleles in 4 sites, locus Ma1 was subsequently excluded from further analysis. Two out of 1848 pairwise locus exact tests of linkage disequilibrium were significant after Bonferroni correction.
Acknowledgements The authors are grateful for help in collecting samples for genetic analysis, especially we would like to thank to  M. Bartoszewicz ,  E. Bujko ,  J. Chilecki ,  P. Górski ,  E. Jędrzejewska ,  R. Kowlaczyk ,  D. Ławreszuk ,  T. Tumiel ,  I. Wierzbowska ,  A. Zbyryt  and stuff of  Mammal Research Institute PAS. Part of samples was collected as a result of activities carried out under the project  LIFE11 NAT/ PL/428. We are especially grateful for laboratory work to  H. Zalewska. We thank  C. O'Brien-Moran ,  J. Power  and  T. Diserens  for comments and English correction to earlier versions of the manuscript. We are grateful to two anonymous reviewers for constructive comments which highly improve the manuscript.
Funding Research was funded by  National Science Centre Poland , (grant number  UMO 2011/01/NZ/NZ8/04525 ) to AW. Part of this work was carried out by using the resources of the  INRA MIGALE  (http://migale. jouy. inra. fr), GENOTOUL  ( Toulouse Midi-Pyrénées ),  CBGP and Montpellier Bioinformatics Biodiversity (MBB) HPC  bioinformatic platforms. RL was supported by the  Agence Nationale de la Recherche  (project  GENOSPACE   ANR-16-CE02-0008 ).
Fore-aft, lateral and vertical velocities as well as accelerations of the body were then calculated using the derivative of the COM position data and by smoothing the primary data by fitting gliding second-order polynomials to the time series, including four adjacent points on both sides at each position  [48]. Pitch was calculated in the side view as the angle between substrate and the connecting line between the pronotum marker and the thorax marker. To calculate yaw, the line connecting the pronotum marker and the abdominal marker in the top view was used. The connecting lines between pronotum and thorax marker and between thorax and abdomen marker were used to calculate the ventral angle between thorax and abdomen a b Fig. 3  Example gait patterns; bars depict ground contacts of the legs which are counted fore to aft with L indicating left and R indicating right side legs.
In the lateral direction, the boundaries are the 25% and the 75% quartiles of the lateral distributions for distinct anterior-posterior positions of the tarsi (see  [16] ). b Averaged contact areas on slippery substrate with median trajectories of the legs' tarsi with respect to the COM in side view. All body oscillations were referred to the stride periods of the second legs; lateral data was inverted for tripods consisting of L1, R2 and L3 such that all data was normalised onto the reverse set of legs (R1, L2, R3). Moreover, kinematic parameters of the legs such as stride frequency (f T ), duty factors (β), contact and swing rates (t C -1 and t S -1 ) and the positions of the legs in contact with the ground (see  [16] ) were also examined. Touch-down and take-off positions as well as the lateral distance of the feet to the COM were extracted for all legs while the second legs were used as proxy for the analyses of the distances travelled by the COM during contact with the ground (s C ) and during a whole stride (s T ).
5 ). On the slippery substrate, metachronal runs occurred at much lower running speeds which resulted in a wide overlap region (Additional file 1: Figure  S6 ). Therefore, no saturation of stride frequencies and swing rates could be observed. All analyses were performed using MatLab scripts (MATLAB 7. 10. 0; The MathWorks, Natick, MA, USA). All values are provided either as mean ± s. d. or as median and the 25% and 75% percentiles (Q25/Q75). Accordingly for pairwise comparisons, t-tests or nonparametric tests such as the Mann-Whitney U test were used. For multivariate analyses one way analyses of variance (ANOVA) with Tukey-Kramer post-hoc tests were applied. If not specified otherwise, all statistical tests refer to a 5% significance level. Oscillations of the COM accelerations were analysed in all three dimensions using Matlab's Fast Fourier Transformation (FFT) algorithm.
Data are representative of at least two independent experiments. (E) Effect of stable LSD1 knockdown on integrin β3 protein expression in H460 (KD21, KD22) and H1975 (KD2) cells in comparison to the corresponding shGFP control cell line. Immunoblot analysis of whole cell lysates was done. Data are representative of at least three independent experiments. (F) Effect of LSD1 knockdown on NF-κB transcriptional activity measured by luciferase assay. Data are expressed in relative luciferase units (RLU). The bar graph represents the mean ± SEM for n = 3. The P-value was estimated using Student's t-test in D and F. *P < 0. 05, **P < 0. 01, ***P < 0. 001. in combination with anti-proliferative cancer drugs might be a rational strategy for the treatment of lung adenocarcinoma to suppress tumor proliferation while preventing metastatic phenotype.
Furthermore, for LSD1 overexpression, cells were transfected with empty vector control or flag-LSD1 expression vector using a lentiviral system. Gene silencing and overexpression was confirmed by immunoblot analysis. The following plasmids were used: pSUPER. retro. shGFP. puro (a kind gift from Michael Hölzel, University Hospital Bonn, Germany) and pSUPER. retro. shLSD1. puro, pSIN-flag-LSD1-puro and pSIN-empty-puro. pSIN plasmids were modified from pSIN-EF2-Nanog-puro vector (Addgene plasmid #16578) and pCMX-flag-LSD1 (a kind gift from Roland Schüle, University Freiburg Medical Center, Germany)  11. The shRNA sequence for LSD1 (AAGGAAAGCTAGAAGAAAA) was cloned to pSUPER. retro. puro plasmid (OligoEngine, Seattle, USA) according to manufacturer's protocol. RNA sequencing and data analysis. Expression levels of LSD1 were determined by referring to the RNA-seq data of lung ACs (n = 40)  19, 20  , SQs (n = 9)  19  , pulmonary carcinoids (n = 69)  21  , and SCLC (n = 81)  3.
17. 0) using exon coordinates from Ensembl database v73. Subsequent analyses were done using the edgeR package in R  38. Expression changes were calculated as log2 ratios and p-values were adjusted for multiple testing by controlling the Benjamini-Hochberg false discovery rate at 5%. Ingenuity (Qiagen Inc) pathway analysis was used to find enriched canonical pathways, and Upstream Regulators. Furthermore, the database for annotation, visualization and integrated discovery (DAVID) was used to identify the enriched gene ontology terms  39. Proliferation and transwell invasion assay. Cell proliferation assays were carried out as described previously  13. Briefly, cells were seeded at a density of 2,500 cells per well in 96 well microplates and cultured in standard medium. Viable cell numbers were determined 0, 1, 2, 3 and 6 days after cell plating using CellTiter 96 Aqueous Cell Proliferation assay kit (Promega, Madison, USA).
Next day, cells were treated with 2. 5 µg/ml human recombinant galectin-3 (PepproTech, Rocky Hill, USA) for 4 hours. Luciferase assays were then carried out using the Dual Luciferase Reporter assay kit (Promega) according to the manufacturer's protocol. Each assay consisted of three or four replicates and each experiment was repeated at least three times. Data were presented as relative luciferase units (RLU) normalized to the Renilla luciferase signal. Western blot analysis. Protein lysates were extracted from cells using the cell lysis buffer (Cell Signaling, Danvers, USA) and blotted as described in Lim et al. 13. The membranes were incubated overnight using the following antibodies and dilutions: α-LSD1 (Abcam) 1:1000; α-Galectin-3 (Leica, Wetzlar, Germany) 1:1000; β-actin (Sigma-Aldrich) 1:5000; α-Integrin β3 (Abcam) 1:1000; α-RalBP-1 and α-SFTPC (Santa-Cruz, Dallas, USA) 1:200; pMEK1/2 and pERK1/2 (Cell Signaling) 1:1000.
Quantitative real time-PCR. Complementary DNA was synthesized using the AB Reverse Transcription kit using random primers (Applied Biosystems, Foster City, USA) and quantitative PCR was carried out on real-time PCR machine (BioRad, Hercules, USA) with SYBR reagent (Promega). Expression values were normalized to the mean of HPRT1 and the 2 -ΔΔCT method was applied to calculate relative gene expression levels. A list of primers used for qRT-PCR validation is available in Supplementary Table  S4. Statistical analysis. Statistical analysis for immunohistochemistry was performed using SPSS 17. 0 program (SPSS) and Chi-Square tests were used to calculate the statistical significance for linear-by-linear association. Mann-Whitney U test or Student's T-tests were used to calculate statistical significance using GraphPad Prism software.
Accession numbers. The RNA-seq data of A549 cells has been deposited in Gene Expression Omnibus under accession code GSE86874. assay and plasmid cloning. M. K. , B. T. and M. R. S. carried out RNA-seq and data analysis. S. O. , J. G. and R. K. T. contributed to data interpretation and illustration. S. L. , R. B. and M. O. interpreted data, designed experiments and wrote the manuscript. All authors contributed to discussion and reviewed the manuscript.
The LSD1 expression in NSCLC-AC was classified using a low, medium and high immunostaining score as shown exemplarily for low and high LSD1 expression. Scale bars indicate 100 µm. (C-E) LSD1 expression analysed by immunohistochemistry on 182 lung adenocarcinomas was correlated with tumor grades (C), lymph node metastasis (D) and KRAS mutation (E). Chi-Square tests were used to calculate the statistical significance for linear-by-linear association. *P < 0. 05, ***P < 0. 001 and n. s. = not significant. Original data for C-E can be found in Supplementary TableS2. (F) LSD1 expression in non-small cell lung cancer cell lines with various mutational background determined by immunoblot.
Acknowledgements This work was supported by  Center for Molecular Medicine Cologne (CMMC)  to RB and MO and by the  German Cancer Aid  as part of the  Interdisciplinary Oncology Centers of Excellence program  to the  Center for Integrated Oncology Köln Bonn. In addition, the project was supported by the  Federal German Ministry of Science and Education (BMBF) as part of the e-Med program  (grant no. 01ZX1303A  and  01ZX1603A  to RB and RKT) and the  German federal state North Rhine Westphalia (NRW)  and by the  European Union  ( European Regional Development Fund: Investing in Your Future)  as part of the  PerMed. NRW initiative  (grant  005-1111-0025  to RKT and RB) as well as the  EFRE initiative  (grant  LS-1-1-030  to RKT, RB, MS and MO), by the  German Consortium for Translational Cancer Research (DKTK) Joint Funding program (RKT)  and a grant from the  Volkswagenstiftung (Lichtenberg program ) to MS.
These hydrological conditions represent the average hydro-meteorological context of the region studied, according to flow and rainfall (around 55% of flows and rainfall cumulative distribution functions, Meteo France Database 2 ). Water and sediment samples were collected the same day from upstream to downstream. Water samples were collected at each station of the continuum using auto-samplers (1 L collected every hour for 24 h). In order to monitor the waterbodies, the starting time of the auto-sampling was chosen taking the flow rate of the rivers into account. Superficial sediments (0-1 cm depth) were sampled, using a sterile sub-corer (50-mL sterile plastic tube; Corning), on the temporary deposit areas of particles close to (±1 m) the four sampling sites. In June 2010, no sediment deposit areas were observed near the Selles sampling site.
(2004). The O-type of intestinal pathogenic and B2 strains was determined by an allele-specific PCR, as previously described  (Clermont et al. , 2007). Multilocus sequence typing (MLST) was a Dataset (data set from the French Water Agency, Seine Normandy); b Transit times correspond to the time to go to the upstream site from the downstream site; c Dilution rates correspond to the ratio discharge between downstream and upstream sites; d transit time from Selles site to Risle site; e transit time from Tourville site to Risle site; f annual mean; g cumulative sum of human densities from upstream to the sampling site; h upstream of the sampling site, the Risle River drains a watershed with 155,000 inhabitants and flows through an urbanized zone with 11,342 inhabitants; i data from ONCSF 2010; j head of cattle on pasture closest to the sampling site; k on the run-off watershed; l located in the Pont Audemer urban area; SPM, suspended particulate matter; DOC, dissolved organic carbon; NA, not available.
ACKNOWLEDGMENTS This work was supported by the scientific program  FLASH -Seine Aval  (http://www. seine-aval. fr/),the  CNRS   EC2CO  and the research federation (FR CNRS SCALE). KO held a research grant ( FR SCALE)  from the  Haute-Normandie Regional Council (France). This work was partially supported by a grant from the  Fondation pour la Recherche Médicale  (équipe FRM  2016 ,  DEQ20161136698 ). The authors wish to thank:  Florian Van Dooren  and  Michel Simon  for their technical assistances,  Agence de l'Eau Seine Normandie (AESN)  for hydrological data, French agricultural Chamber for land use data, farmers that let us access and equipped the pasture site.
Statistical analysis Statistical analysis involved determination of mean, median, standard deviation, minimum, maximum, skewness, coefficient of variation (CV %) and ANOVA. In order to meet the principles of the analysis of variance (additivity, homogeneity of variance and normality of distribution), the data were subjected to logarithmic transformation prior to the analysis. The differences between means were detected by Tukey's test at significance level of 0. 05. In order to identify the important parameters that affect the zinc content in soil, Pearson's correlation matrix and principal component analysis (PCA) were used. All statistical analyses were run with Statistica 11 software.
Materials and methods Distribution of NSAP classes in sequenced microorganisms Archetypal proteins for each NSAP class listed by  Gandhi and Chandra (2012)  were used as starting points to generate sets of reference proteins for each NSAP class. JackHMMER, part of the HMMER ver. 3. 1b1 software suite, was used to generate a collection of homologous protein sequences for each group of archetype protein sequences from the UniprotKB database, as well as generating a profile hidden Markov model (pHMM) for each enzyme  (Durbin et al. 1998; Eddy 2011). Cut-off Expect (E) values were adjusted for each protein family, however values were typically 1 × 10 -80 ≥ E ≥ 1 × 10 -100. The BLOSUM45 substitution scoring matrix was used, together with gap opening and gap extension penalties of 0. 02 and 0. 4 respectively.
NSAP protein localization To determine the subcellular localization of NSAP proteins we employed PSORTb ver. 3. 0. 2 (Yu et al. , 2010). PSORTb employs support vector machines for each of the nine sub-cellular locations of Gram-negative and Gram-positive Eubacteria, and Archaeal prokaryotes, combining these predictions with results of BLASTP searches (E < 1 × 10 -9 ) against reference sets of proteins of known subcellular localization and model HMMs for transmembrane-spanning α-helices and signal peptide cleavage sites. A Bayesian network is then used to combine predictions from all modules and generate a weighted localisation prediction based on the performance accuracies of each prediction module.