dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally , they gratefully acknowledge the local observers, local authorities,  Sagarmatha National Park  and the  Cho-Oyu trekking agency  with its staff and porters. This work was funded by the  Agence Nationale de la Recherche  (references  ANR-09-CEP-0005-04/PAPRIKA  and  ANR-13-SENV-0005-03/PRESHINE ), Paris, France. It was locally approved by the  Bilateral Technical Committee of the Ev-K2-CNR Association (Italy)  and the  Nepal Academy of Science and Technology (NAST)  within the Ev- K2-CNR/NAST Joint Research Project. It is supported by the  Department of Hydrology and Meteorology, Kathmandu, Nepal. Edited by: Uwe Ehret Reviewed by: two anonymous referees.
007 (Figure  2A ). Single qRT-PCR assay was then applied to validate miR-150 results using a combination of three miRNAs for normalization (miR-93, miR-222 and miR-30e). The normalized Cq value from single qRT-PCR assays of plasma samples showed high consensus with the normalized Cq value from the panel (p < 0. 001, Rho = 0. 913). miR-150 was further analyzed in tissue samples using paired tumor and tumor free samples from 15 SCCT patients; 8 of the patients included in the plasma analysis and an additional 7 patients. In contrast to the decreased plasma levels in tumor patients, there was significant up-regulation (p-value 0. 02) of miR-150 in tumor tissue compared to tumor free tissue adjacent to the tumor (Figure  2B ). There was no correlation between plasma and tissue miR-150 in the 8 patients for whom both sets of data were available.
miRNA profiling in plasma Twenty microliters of RNA extracted from plasma from 13 patients and 13 controls was sent on dry ice to Exiqon Services (Vedbaek Denmark) for miRNA analysis with real-time PCR panel. The panel used was miRCURY LNA Universal RT microRNA PCR Human panel I. Briefly 10 μl RNA was reversed transcribed in 50 μl using the miRCURY LNA Universal RT microRNA PCR, polyadenylation and cDNA synthesis kit (Exiqon, Vedbaek, Denmark). cDNA was diluted 50x and assayed in 10 μl PCR reactions according to the kit protocol. Amplification was performed in a LightCycler® 480 Real-time PCR system (Roche) in 382 well plates, and amplification curves analyzed using Roche LC software, both for determination of Cq (by the 2 nd derivative method) and for melting curve analysis. All samples went through a quality control of RNA isolation and cDNA synthesis using Exiqon´s RNA spike-in kit.
Statistical analysis For multivariate data analysis we used Simca -P 14 (MKS Data analytics Solutions, Umea, Sweden). Multiple Experiment Viewer (MeV) (http://www. tm4. org/ mev. html) with the LIMMA package was used to identify significantly differentially expressed miRNAs from the panel and also circulating proteins from the protein panel. Spearman correlation was used for statistical correlations between miRNA panel results and results from single assays and for correlations between plasma and tissue miRNA. Wilcoxon test was used for evaluation of tissue expression of miR-150 in paired tissue samples. The predictive analytical software SPSS was used for the above statistical analysis and receiver operating characteristics (ROC) and area under the curve (AUC) analysis to test how well individual factors separate the groups.
All graphs were prepared in Graphpad Prism 7 (Graphpad prism software). IL13; interleukin 13 miRNA; microRNA Midkine; neurite growth-promoting factor 2 MIP 1b; macrophage inflammatory protein 1b MMP3; metalloproteinase 3 NT-3; Neurotrophin-3 OSCC; Oral squamous cell carcinoma PCA; Principal component analysis PEA;Proximity extension assay ROC; receiver operating curve SCC-Ag; squamous cell carcinoma antigen SCCHN; Squamous cell carcinoma of the head and neck T; Tumor TF; Tumor free TIE2; TEK Receptor tyrosine kinase SCCT; Squamous cell carcinoma of the tongue VCAM; vascular cell adhesion molecule.
ACKNOWLEDGMENTS AND FUNDINGS This study was supported by grants from the  Cancer Research Foundation in Northern Sweden, Lion´s Cancer Research Foundation ,  Umeå University , the  Swedish Cancer Society  Contract number  16 03 36  and  Västerbotten County Council  and project grant  MEYS-NPSI-LO1413  ( Ministry of Education, Youth and Sports, Czech Republic ) and  GACR P206/12/G151  ( Grant Agency of the Czech Republic ).
Physically based multilayer snowpack models suffer from various modelling errors. To represent these errors, we built the new multi-physical ensemble system ESCROC (Ensemble System Crocus) by implementing new representations of different physical processes in the deterministic coupled multi-layer ground/snowpack model SURFEX/ISBA/Crocus. This ensemble was driven and evaluated at Col de Porte (1325 m a. s. l. , French alps) over 18 years with a high quality meteorological and snow dataset. 7776 simulations were evaluated separately accounting for the uncertainties of evaluation data. The ability of the ensemble to capture the uncertainty associated to modelling errors is assessed for snow depth, snow water equivalent, bulk density, albedo and surface temperature. Different sub-ensembles of the ESCROC system were studied with probabilistic tools to compare their performance.
The major difficulty in building such an ensemble is that it requires isolating the uncertainty originating from the snowpack model itself from the other uncertainty sources. Therefore, following the conclusions of  Raleigh et al. (2015)  and similarly to  Essery et al. (2013) , our strategy is to build and evaluate our ensemble on one specific well instrumented site where we assume that input meteorological errors are low compared to model errors. It also allows to follow  Essery et al. (2013)  recommendations in terms of evaluation data because numerous snow variables are available over a long period. Sect. 2 summarizes the dataset used to force and evaluate a new multi-physical version of the Crocus snowpack model called ESCROC (Ensemble System CROCus) and presented in Sect. 3. The evaluation methodology (Sect.
Forcing data The 18-year long snow and meteorological dataset from the Col de Porte observatory (CDP, 1325 m altitude, 45. 3 o N, 5. 77 o S) is used to force and evaluate ESCROC from 1993 to 2011. A complete description of the dataset is provided by  Morin et al. (2012). The CDP site is a grassy meadow characterized by its mid-altitude and located in the Chartreuse massif in the French Alps. Snow is usually present from December to April, but snow melt and rainfall events can occur any time in the snow season. Wind is usually low, so that drifting events are unusual. The quality and completeness of this dataset have allowed to drive and evaluate various snowpack models (e. g. Magnusson et al. , 2015; Decharme et al. , 2016, a complete  list is available at http://www. umr-cnrm. fr/spip. php?article533).
All the forcing variables of the Crocus model are directly measured: incident shortwave and longwave radiations, 2 m air temperature and specific humidity, 10 m wind speed and precipitation rates. Precipitation phase is manually assessed using all possible ancillary information. The only modification applied to the original published dataset is a bias correction of the incident longwave correction. Indeed, a break was identified in the time series in December 2010, associated with a replacement of the sensor. A -10 Wm -2 homogeneous correction is applied before December 2010 and a +10 Wm -2 homogeneous correction is applied from December 2010 onwards. These values were estimated from the concomitant evolution of the monthly mean difference between these data and the longwave incident radiation simulated by the  Ritter and Geleyn (1992)  radiative transfer scheme included in SAFRAN meteorological reanalyses  (Durand et al.
IntroductIon During the late 19th century, an intriguing phenomenon was discovered by  Newcomb (1881) : The first significant digit (leftmost nonzero digit) of seemingly random numbers often fails to follow a flat distribution with an equal proportion of 1s, 2s, … , 9s, as one would expect, but instead follows a decreasing distribution, with more 1s than 2s, more 2s than 3s, and so forth. The same phenomenon was later rediscovered and detailed by  Benford (1938). According to what is now referred to as Benford's law or Newcomb-Benford law (NBL), the distribution of the first significant digit X of a "random" number follows a logarithmic law given by P(X = d) = Log(1+1/d), where Log stands for the base 10 logarithm and d stands for a digit (in the range of 1-9; see Table  1 ). Many real-world datasets approximately conform to NBL  (Hill, 1998; Nigrini, 2012).
Nevertheless, his and Diekmann's studies certainly suggest that using Benford's law to detect fraud is questionable in general since humans may be able to produce data confirming to NBL, in which case a Benford test will yield many undetected frauds, lacking sensitivity. As mentioned above, not all random variables or real-world datasets conform to NBL (and when they do, it is generally only in an approximate manner). Because many real-world datasets do not conform to NBL, a Benford test used to detect fraud not only may have low sensitivity but may also have low specificity.
GeneralIzed Benford'S law Several researchers (e. g. ,  Leemis, Schmeiser, & Evans, 2000)  have studied conditions under which a distribution seems more likely to satisfy NBL. Fewster (  2009 ) provided an intuitive explanation of why and when the law applies and concluded that any dataset that smoothly spans several orders of magnitude tends to conform to NBL. Data limited to one or two orders of magnitude would generally not conform to the law. To pursue the question of why many data conform to NBL further, the conservative version of the NBL may be a better starting point than the mere first-digit analysis. Recall that in the conservative version, a random variable X conforms to NBL if Frac(Log(X)) ~ U([0,1[).
Method Participants were randomly assigned to one of four groups: cities (n = 41), numbers (n = 44), stars (n = 36), or tuberculosis (n = 48). In each group, participants were informed that a series of 30 numbers had been selected at random from a dataset, and were instructed to produce what they thought would be a credible outcome-that is, to supply 30 plausible numbers. In the cities group, the list was the set of popula-.
Method The experiment was performed online using a Google Form (https:// www. google. com/forms/about/). We used country population data, as this was believed to grant a somewhat larger variation in familiarity. Participants were asked to produce series of only 20 data points to lower the risk of tiredness. Participants were randomly assigned to one of two groups (no time pressure or time pressure condition). Each group included 62 participants. They were informed that they would have to supply a list of 20 values that could be the numbers of inhabitants of 20 randomly selected countries in the world. They were asked to try to guess what these populations could be. They were told that a true sampling would be performed for comparison with their answers. In the no time pres-sure condition, the instruction was to be "as accurate as possible, taking as much time as needed.
" In the time pressure condition, the instruction was to be "as fast and accurate as possible. " The former condition is known to conduce to superior cognitive effort than the latter (e. g. ,  Maule & Edland, 1997). Self-reported data on level of expertise in the field of country populations were also collected using a 6-point Likert scale, from 0 = absolutely naïve about country populations to 5 = expert in country populations. This measure serves as an indication of the participants' familiarity with the material. Sixty-two true samples of 20 country populations were selected from real-world data (found at http://data. okfn. org/data/core/ population#data) to be compared with participants' productions.
In an attempt to show that the roots of NBL ubiquity should not be looked for in the specific properties of the logarithm, Gauvrit and Delahaye (2008, 2009) defined a generalized Benford's law (GBL) associated with any function f as follows: A random variable X conforms to a GBL associated with function f if Frac(f(X)) ~ U([0,1[). The classical NBL thus appears as a special case of GBL, associated with function Log. Testing several mathematical and real-world datasets, Gauvrit and Delahaye (2011) found that several of them fit GBL better than NBL. Of 12 datasets they studied, six conformed to the classical NBL, while 10 conformed to a GBL with function f(x) = π × x 2 , and nine with square-root function. On the other hand, none conformed to GBL with function Log o Log. These findings suggest that a GBL associated with the relevant function-depending on the context-might yield more specific or sensitive tests for detecting fraudulent or erroneous data.
tions of the 5,000 most populated US cities. Numbers corresponded to the dataset of numerical constants published by Simon Plouffe and described in the experiment as an extensive encyclopedia of mathematical constants. Participants assigned to the stars group were told that the dataset was the list of distances from earth to all known visible stars expressed in light-years. Last, the tuberculosis group dealt with the set of known country-wise incidences of tuberculosis as measured in 2012. Real samplings of 30 numbers from the corresponding databases were also performed. The set of populations of the biggest US cities came from an online dataset (http://factfinder2. census. gov/). Numbers were randomly selected from the Simon Plouffe database of numerical values (http://www. plouffe. fr). The distances to the stars were read from the HYG2.
, were computed. The two last functions were selected on the basis of previous studies indicating that they led to satisfying fits with several numerical datasets(Gauvrit & Delahaye, 2009). The deviation from GBL was measured by the Kolmogorov-Smirnov statistic D-that is, the maximum difference between the cumulative distribution function of Frac(f(X)) and the cumulative distribution function of U([0,1[). D thus serves as a proximal measure of conformity to GBL. This statistic is a classical measure of distance between distributions that grounds the classical Kolmogorov-Smirnov test.
Association analysis with BMD. The first approach used in our study was to identify functional variants within microRNAs involved in bone metabolism. The minor allele frequency (MAF) of many of the miR-SNPs found in databases has not been assessed in European population; therefore, a validation step was necessary. In this regard, SNPs in microRNA sequences were firstly validated as polymorphic in patients from BARCOS subcohort  8. Of the 53 miR-SNPs tested, 14 were validated in this cohort. However, only 5 SNPs had a MAF > 0. 01 and were finally genotyped in the OSTEOMED2 cohort for their association analysis with LS (lumbar spine) BMD and FN (femoral neck) BMD (Table  1 ). SNP rs6430498 in the miR-3679 was significantly associated with FN BMD in a log-additive (p value = 0. 036, Beta coefficient [95% CI] = -0.
In this regard, other studies have demonstrated that miR-SNPs in pre-miRNA sequences lead to altered levels of mature miRNA in vivo even though changes in its secondary structure are not predicted by RNAfold program  14. In our study the AA genotype for both rs6430498 and rs12512664 showed higher expression levels of miR-3679-3p and miR-4274 in hOBs, respectively. Moreover, these miRNAs were overexpressed in OP bone samples. Consistently with miR-3679-3p overexpression in OP fractured bone, transfection results suggest that miR-3679-3p negatively regulates matrix mineralization by osteoblasts which could lead to an OP phenotype. Further supporting these results, the A allele for both SNPs was associated with lower FN BMD levels. In overall, predicted targets and pathways involving miR-3679-3p belong to cell cycle, differentiation and tissue development, suggesting a dysfunction in bone remodeling in the osteoporotic bone.
org website  24  (http://www. microrna. org), TargetScan Human (http://www. targetscan. org) and PicTar (http://pictar. mdc-berlin. de). The algorithm used is based on sequence complementarity, binding energy of the miRNA-target duplex, evolutionary conservation of the target site sequence and target position in aligned UTRs of homologous genes. Some listed miRNAs target more than one bone-related gene, as for example, miR-130b, miR106b, miR148b and miR-93 that target both ESR1 and TGFB2 genes. These miRNAs were located in ESR1 target miRNAs in order to simplify the Table  1. Next, miRNAs with the highest expression levels in primary human osteoblasts (hOB) were selected (Table  1 ) according to results obtained in a previous miRNA array performed in our group (GEGSE74211; http://www. ncbi.
nlm. nih. gov/geo/query/acc. cgi? acc = GSE74211)  1. Finally, SNPs in the pri-miRNA sequences were searched using the Ensembl (www. ensembl. org), UCSC Genome Browser on Human Dec. 2013 (GRCh38/hg38) Assembly  25  (http://genome. ucsc. edu/), and HapMap (www. hapmap. org) databases. Only those SNPs with a minor allele frequency (MAF) in Utah residents with ancestry from northern and western Europe (CEU) > 0. 01 were included in the study; SNPs lacking published MAF were validated in participants from the BARCOS cohort  8  (included in OSTEOMED2) by means of PCR-RFLP, genotyping or Sanger sequencing. Polymorphism genotyping. DNA was obtained from peripheral blood collected in EDTA tubes and genotyping was performed in LGC Genomics platform using KASPar v4. 0 genotyping systems. To ensure genotyping quality, a random sample (5% of the total number of samples) was also genotyped in a separate control plate.
There was 100% concordance between these results. Human osteoblasts culture. Human primary osteoblasts (hOB) were obtained from trabecular bone of patients who underwent knee or hip replacement due to osteoarthritis. Bony tissue was cut up into small pieces, washed in phosphate buffered solution (PBS, Gibco by Life Technologies; Paisley, UK) to remove non-adherent cells, and placed on a 140 mm culture plate. Samples were incubated in hOB medium: Dulbecco's Modified Eagle Medium (DMEM; Gibco; Invitrogen, Paisley, UK), supplemented with 10% fetal calf serum (FBS; Sigma-Aldrich; St. Louis, USA), 100 U/ml penicillin/streptomycin (Sigma-Aldrich; St. Louis, USA), 0. 4% fungizone (Gibco by Life Technologies; Paisley, UK), and 100 ug/ml ascorbic acid (Sigma; Steinheim, Germany). Alkaline Phosphatase (ALP) activity and osteocalcin gene expression were measured in order to confirm the osteoblastic phenotype (data not shown).
cDNA from total bone was diluted x8 and cDNA from hOBs was diluted x16; in both cases, 2 µl were assayed in 10 µl PCR reactions in 384-well plates using MiScript Syber Green PCR kit according to the protocol. The sequence of the mature miRNAs selected, according to the mirBase web site, was used as a forward primer and the Universal primer as a reverse. U6 snRNA was used as the reference gene for normalization. Amplification was performed in a QuantStudio 12 K Flex Real-Time PCR (Applied Biosystems), and the Expression Suite software was used both for determination of relative quantification (RQ) (by 2 -ΔΔCt method) and for melting curve analysis. Bioinformatics analyses of BMD-associated miRNAs. Gene target prediction was assessed using the following six programs: PicTar (http://pictar. mdc-berlin.
de), TargetScan Human (http://www. targetscan. org), miRDB (http://mirdb. org), MiRanda (http://www. microrna. org), DIANA-TarBase (http://diana. imis. athena-innovation. gr), and miRTarBase (http://mirtarbase. mbc. nctu. edu. tw). The DIANA-mirPath web-based computational tool  26  was used to identify molecular pathways potentially altered by the intersection of miRNAs differentially expressed in fractured bone. miRNASNP database  10  , RNAstructure (http://rna. urmc. rochester. edu/RNAstructureWeb) and RNAfold software (http://rna. tbi. univie. ac. at/cgi-bin/RNAfold. cgi) were used to predict the effect of the variants on the miRNA secondary structure. These predictions are based on the assumption that variants can destabilize the hairpin and therefore reduce the mature miRNA levels. Cell transfection.
Statistical methods. Hardy-Weinberg equilibrium (HWE) was calculated using the chi-square test. Multiple linear regression models were fitted to assess the association between genotyped SNPs and BMD. Log-additive, dominant and recessive models were tested for each SNP association analysis. Potential confounders considered for adjustment were densitometer devices, body mass index (BMI) and age. Association analyses were performed using R software version 2. 13. 2 with the SNPassoc, foreign, gdata and multtest packages. Mann-Whitney U test was performed for OP and non-OP group comparisons in the SPSS v. 12. 0 for Windows. This test was also used for treatment comparisons between miRNA mimics or inhibitors and their respective controls. Linear regression was used to analyze the association between miRNA expression levels and sample genotypes.
Acknowledgements We would like to thank  Navarro-Casado L. (lead researcher) and  Navarro-Martínez  A. (collaborator researcher) of  Albacete cohort, C. Hospitalario Universitario de Albacete ;  Ricardo Usategui-Martín  and  Tatiana Carranco-Medina  for their supporting in sample collection from  Hospital Universitario de Salamanca  and to  Layla Panach  (B. Sc. ) who is a predoctoral fellow (F. P. U. Program) in  INCLIVA (Valencia)  for the technical assistance. We thank to  Garcia Fontana B ,  Luque Fernandez  I from  UGC Endocrinologia y Nutrición, Hospital Universitario San Cecilio , for their collaboration in sample collecting. The authors thank  Elaine M. Lilly, PhD , for helpful advice and critical reading of the manuscript. This work was supported by  RETICEF  ( RD12/0043/0022 ),  CIBERFES  ( CB16/10/00245 ) and the Grant  FIS PI10/01537 ,  PI12/02775 ,  PI12/02582 , and  PI13/00116  ( Carlos III Health Institute, Science and Innovation Ministry ); FIS and Grants from the  Generalitat de Catalunya  ( DIUE 2014 SGR 775 ), " 664367/FOCUS" project (EU Health Programme ;  2014-2020 ) and  FEDER  funds have supported this study.
ANNOTATION DATABASES During early collection and documentation of midwater organisms from submersibles, there was no standardized procedure for quantifying the observations made during the dives, nor was there a central database for archiving the observations. As a result, most of these early records are essentially unavailable for further study. In contrast, and from the outset, MBARI established and has maintained protocols for annotating organisms seen from the ROV and entering them, along with associated oceanographic data, into a central database. This procedure and database have become the Video Annotation and Reference System (VARS;  Schlining and Jacobsen Stout, 2006). The database has been a source of detailed data for hundreds of publications (Figure  2 ). VARS is built upon a foundation of relational databases (ROV dives, animal concepts like species within a taxonomic hierarchy, discrete observations, and oceanographic parameters such as temperature, salinity, and oxygen concentration).
To facilitate the use of these databases, the bioluminescence and biodiversity research group has created Python and R scripts to retrieve and collate records spanning the history of MBARI undersea observations. For example, with a single command, we can retrieve all the instances of cephalopods consuming cnidarians (three instances), or make a histogram of the collection depths of all specimens of the ctenophore Bathyctena chuni (37), or retrieve the water-property data for the last 10 dives of a particular chief scientist. These computing tools are being used to rapidly generate the core data sets for publications spanning large temporal and spatial scales, including a summary of all the predator-prey interactions seen  (Choy et al. , 2017) , and the spatial and phylogenetic distributions of the ability to emit light among water column animals  (Martini and Haddock, 2017 ; see below.