dataesr/scientific-paragraphs-categorization huggingface dataset:
Peptides were considered diagnostic when, after BLAST search  [61]  against the entire nrNCBI protein database, they were assigned to a single species, or to a limited number of species among which only one can be considered plausible, based on the nature of the samples, such as the size of the skin element, or their geographic origin. For example, peptides equally present in cattle (Bos taurus), water buffalo (Bubalus bubalis) and yak (Bos mutus) were considered diagnostic for cattle. For samples lacking at least one species-diagnostic peptide, i. e. sample 9 and 11, species identification was attempted based on a set of peptides  [43, 62] , only compatible with one species (Fig. 2  and Table  S5  and S6 in File S1). Two skin samples were identified as bovine (sample 2 and 10), six as sheep (Ovis aries, sample 3-7 and 9), and three as goat (Capra hircus, sample 1, 8, 12), while for one sample (sample 11), identified as ovine, it was not possible to detect any marker to discriminate between sheep and goatskin (Table  2 ).
The preparation of modern comparable material from known species with the same procedure, enabled the recovery of a higher number of diagnostic peptides (Tables  S4  and S5  in File S1) for each sample. This is in full agreement with the recent origin of the material and its storage in favourable conditions. Only a limited number of the species-specific peptides identified (Fig. 2  and Table  S5  in File S1), were previously reported in literature describing ancient samples  [43, [46] [47] [48] [49] 63]. The MS-based approach recovers additional information of particular interest for archaeological reconstruction and the understanding of the exploitation of natural resources in antiquity. An example is the secure identification of peptides uniquely assigned to bovine haemoglobin foetal subunit beta (UniProt accession number: P02081) in sample 10 (Fig.
Although publicly available protein lists currently used to assign peptide sequences to spectra are only complete, or significantly extended, for a relatively few mammal species, the number of mammal proteomes extensively covered is rapidly increasing. During the preparatory stage of our work, we observed, and took advantage of, the inclusion of extended protein lists for both sheep (O. aries) and goat (C. hircus) in public databases. This clearly demonstrates the rapid progress in this field. An example of the immediate implications of these contributions is that, until recently, the MS-based discrimination of the hairless skin and bone remains of sheep and goats was solely based on a single, relatively long, collagen peptide  [43, 62]  (Fig. 2 ), now this discrimination can be achieved on the basis of a much longer list of species diagnostic peptides as reported for sample 1 and 12 (Table  S3 , S4 and S5 in File S1).
Sample no. Despite the novel, reliable results providing secure species assignment, the identification of archaeological skin garments based on MS-based ancient peptide sequencing also comes with certain limitations. In particular, reference protein databases are still incomplete, as exhaustive protein lists are at the moment only available for a limited number of species. This shortcoming, however, will eventually become less of an issue in the near future, as the rapid progress of genome-sequencing projects will soon make reference proteomes available for an increasing number of species, enabling even more secure species identification and higher taxonomical resolution. MS-analysis remains a (minimally) destructive approach, requiring sophisticated equipment and laboratory facilities. Consequently, it cannot be immediately available for all archaeological skin samples, and its diagnostic value is limited to the analysed skin element, which is only one among the many elements used to assemble a skin garment.
Conclusions The aim of this study was to compare established, morphologybased methods for species identification of archaeological skin objects from bogs with MS-based ancient peptide sequencing. The three methods adopted, in some cases, gave inconsistent results. Microscopy was challenged by general problems caused by degraded and partial fibres of archaeological material, while MS yielded secure peptide signals indicating that this method is suitable for application in the archaeological context examined. It thus represents a promising approach for future archaeological skin garment species identification. Although public databases of protein sequences are not yet complete, they already enable the determination of the most common domesticated species. Microscopy, on the other hand, holds the advantage of being relatively inexpensive, non-destructive, and easily applicable to a large number of samples, and sometimes, the sole option when dealing with mineralised samples.
Acknowledgments We thank  Antoinette Rast-Eicher  for kindly performing SEM analysis and for providing the necessary comparanda for this study. We thank the  National Museum of Denmark and Mogens Andersen , the  Natural History Museum of Denmark , for help and permission to sample prehistoric and historic skins. We thank  Marie-Louise Kampmann , at the  Center for GeoGenetics , for her help with sample preparation and  Maria del Carmen Avila Arcos  and  JoseAlfredo Samaniego , at the  Center for GeoGenetics , for their support in data analysis. We also thank  Sidsel Frisch , from the  DNRF's Center for Textile Research , for designing figures,  Cherine Munkholt , from the  DNRF's Center for Textile Research  for linguistic comments to the manuscript, and  Roberto Fortuna , from the  National Museum of Denmark , for providing photos of the skin objects.
This paper investigates this result by using a different six-year data set of laser altimetry to characterize the penetration effect on the retracking algorithm and the retrieved height, effect we call the penetration bias. Most of the studies based on the estimation of surface elevation changes have managed solely to reduce the induced noise but have not been able to estimate the absolute errors. Because the laser altimeter aboard the Ice Cloud and land Elevation Satellite (ICESat) is free from the systematic errors associated with radar altimetry over ice sheets, we decided to compare both measurements to characterize the radar altimeter's biased height. The goal of this paper is to understand and help correct the penetration bias by exploiting the entire temporal data set from ICESat and the initial results from SARAL/AltiKa.
To do so, we chose a crossover analysis between GLAS and RA-2. The ENVISAT and ICESat satellites observed the ice sheets at the same time, from 2002 to 2010 for ENVISAT and from 2003 to 2009 for ICESat. ENVISAT has a 35-day cycle, while ICESat has a 91-day cycle with 30-to 35-day campaigns. The elevation difference between ICESat and ENVISAT has been previously investigated, notably by  Brenner et al. (2007) , who found a 40 cm value with a 98 cm standard deviation (std) for flat surfaces and a 0. 05 m value with a 25 m std for high slopes  [3]. Brenner et al. found that the elevation difference is retracker-dependent  [3]. We will discuss this later in the paper. The data we use are the height retrieved from ENVISAT with the Ice-2 algorithm, the height retrieved from ICESat with the Goddard Space Flight Center (GSFC-4) algorithm (the only algorithm used for ICESat data).
The backscatter, the leading edge width and the trailing edge slope are all retrieved from ENVISAT with the Ice-2 algorithm. The first step in our processing is the correction of the slope effect for the six-year data from ENVISAT. Before performing the crossover analysis, we correct the RA-2 observations for the slope effect to compare more accurate radar altimetry elevations. Unlike oceanic surfaces (as mentioned in the Introduction part), ice sheets are sloped surfaces, over which the slope decreases from the coasts to the domes. Due to the slope and the 10-to 20-km footprint, the radar altimeter does not sample the surface well. The measured point is not necessarily located directly at the nadir, but is shifted in an upslope direction and is the closest point to the satellite. This shift induces a non-negligible bias that can add up to several meters to the retrieved height; this bias is known as slope-induced error  [20].
In addition, one cannot exclude the difference between both satellites footprints: the aperture of 1. 35° explains a part of the measurement bias, which will be higher on the coasts indeed. As the error budget increases as the slope does, we do not take into account the highest slopes in our analysis. The data corrected from the slope error over flat surfaces is accurate enough to process it further so we deduce the crossover points between each measurement from both altimeters, the second step in our methodology. Seventeen consecutive ICESat cycles were processed and compared to the measurements of the ENVISAT cycles (from cycle 20 to cycle 83, i. e. , from September 2003 to November 2009) interpolated at the crossover intersection. The largest difference between each crossover point is 17 days, and the differences feature a uniform distribution and a std and a mean of eight days.
We examined six years of observations, which has never been performed. The ICESat data are publicly available from the National Snow and Ice Data Center (NSIDC) website. We used the Level 2 product GLA12 (Antarctica and Greenland Surfaces). Several articles have been published on the biases involved with laser altimetry, especially the intercampaign bias associated with the Gaussian-Centroid (G-C) offset that potentially affects the elevation trend  [24, 25]. Because we do not compute any elevation trends in this paper (we study the quantitative differences between ENVISAT and ICESat elevations), it is not necessary to take this error into account. In Figure  2a ,b, the crossover analysis is schematized (a) and the sampling plotted (b). In Figure  2a , we illustrate how the crossover method works, and we analyze the measurements provided by both altimeters at the same location.
61 m, a median of -0. 63 m and a std of 2. 77 m. The plot is coherent, the measurement error is lower, and there are fewer outliers in the data set when the slope is less than a given value, as the lower std value and nearly identical mean and median prove. Consequently, due to the large uncertainties induced by the slope and the sparser sampling, we decided to remove the coasts and the Peninsula from the processing of the elevation differences between ICESat and ENVISAT and focus on a more reliable and valid data set. If we do not take into account the residual slope error, the roughness at various scales, such as sastrugi or megadunes, and the penetration biases still affect the waveform  [27]. In 2001, Arthern et al. showed the influence of surface undulations and microwave penetration using ERS1 and ERS2 measurements.
Focus on Four Specific Locations To understand the retrieved height error, we attempt to link it with the waveform parameters to determine whether the penetration effect influences one or more waveform parameters, as has been previously suggested. The consensus up to now is that the variations in the backscatter are linked to the variations in the height  [19]. The innovative idea here is to investigate whether the bias on the retrieved height is associated with the leading edge width (because the height is deduced from this parameter), and we did observe similar patterns through time. Moreover, Legrésy and Rémy showed that the leading edge is influenced by penetration and roughness at a micro-scale  [17]. We selected data that were within 15 km of each location. We then analyzed the relationship between the leading edge width and the elevation difference.
Moreover, the Ka bandwidth is 480 MHz instead of 320 MHz; thus, the leading edge will be better sampled. In addition, we benefit from less penetration into the snowpack; theoretical calculations estimate less than one meter of penetration depth instead of 5 to 10 m in the Ku-band  [31]. For the first time ever, AltiKa observes the surfaces at nadir in a simultaneously active/passive manner. The main drawback is a poor return signal due to the effects of clouds and rain, but the models predict a low data loss rate  [32]. AltiKa provides continuity in the observations, and it has proven essential to understanding the ice sheets' dynamics and evolution, improving the electromagnetic models, and discerning the interaction of microwaves with the snowpack  [25]. Figure  8  shows the difference between the mean profile from ENVISAT in 2006 and the along-track profile from SARAL in September 2013, when a maneuver to get SARAL on the same orbit as ENVISAT was performed.
Conclusions A six-year data set from ICESat and ENVISAT was processed to better understand a known bias that has been less studied and is still under investigation: the penetration bias on the retrieved height. The comparison between six years of observation from ICESat and ENVISAT was made because a study had already been conducted over a shorter period to assess the accuracy of laser altimetry  [3]. This paper presents the longest period of comparison between laser altimetry and radar altimetry in the Ku-band. Similar patterns between the leading edge width and the elevation difference fluctuations are observed, suggesting a direct influence of the penetration effect over the leading edge width, i. e. , the deduced height. To confirm this observation, a simple linear model between the leading edge width and the elevation difference between both altimeters was calculated and first performed for flat surfaces before applying it to the entirety of Antarctica, save for the more sloped areas, which are more complex to analyze and do not have a data set as dense as the flatter areas.
As such, they impact many, if not all, cellular processes and their deregulation is causative of many human malignancies, including cancers  [8] [9] [10] [11]. A plethora of researchers have now implicated miRNAs in the initiation and progression of primary tumors, as well as in metastasis formation  [12] [13] [14]. More than 12,600 publications related to miRNA and cancer are listed in the NCBI PubMed database and their number exponentially grows. Advantageously, cancer cell types tend to have a highly specific cellular repertoire of miRNAs  [15] [16] [17] [18]. The expression levels of miRNA can be monitored in a variety of human specimens, including fresh or formalinfixed paraffin embedded (FFPE) tissues  [19, 20] , as well as in almost all human body fluids  [21] [22] [23] [24] [25]. Moreover, recent studies revealed that specific miRNA expression levels in biological fluids are associated with chemotherapy responses  [23, 26, 27].
Operating under the knowledge that mRNAs are predominantly targeted by miRNAs in their 3   [93] , the first comprehensive database of mutations from whole-genome sequencing of cancer samples obtained by extracting mutations specific to cancer samples when compared to matched normal samples. The database provides the community with germline and somatic mutations in miRNAs and target sites that have the potential to functionally altering miRNA regulation. Importantly, the database stores experimental information about the impact of the mutations on miRNA function and their association with cancer. Bioinformatics analyses of SNVs in miRNA target sites are critical for predicting functionally impactful mutations, but the development of bioinformatics approaches has only recently started to be the focus of concerted genomewide efforts.
By combining whole-genome sequencing data from The Cancer Genome Atlas pan-cancer data set with Argonaute crosslink immunoprecipitation (AGO-CLIP) data, Hamilton et al. [94]  defined a set of miRNA target sites derived from AGO-CLIP that were mutated in cancer. The algorithm developed by Hamilton et al. was then used to identify thousands of SNVs in miRNA binding sites. By combining these datasets with mRNA expression, they highlighted expression changes correlating with mutations. Four out of six tested mutations successfully exhibited experimentally strong evidence of miRNA binding and regulation. An alternative approach for highlighting mutations that impact miRNA regulation is through analyses of the transcriptome. In  [95] , the authors identified 73,717 SNVs in UTRs from transcriptome data of non-small-cell lung cancer samples.
This set of SNVs was processed to predict mutations affecting miRNA secondary structure and target sites. The computational analysis highlighted 490 SNVs with potential effects on miRNA target sites; the SNVs in turn are associated with genes enriched in molecular mechanisms of cancer. In the recent years, it has become apparent that mutations in miRNAs and miRNA target sites that play a critical role in cancer development. As an increasing number of cancer whole-genome sequence datasets will become publicly available to the community in the near future, it is critical to develop dedicated computational tools for the identification of mutations altering miRNA-mediated regulation. This will enable the community to better understand the underlying causes of carcinogenesis at the level of miRNA regulation and promises to significantly contribute to the vision of personalized diagnosis and therapeutic treatment.
The secretion of miRNAs represents a potent mode of intercellular communication that can, for instance, create a favorable context for the implantation of metastasis and the formation of secondary tumors  [99, 103]. The miRNAs circulating in body fluids also present the remarkable characteristic of being extremely stable, though the mechanistic basis of this resistance to degradation remains largely unclear  [104]. One reason could lie in the fact that circulating miRNAs are packaged in exosomes or other microvesicles present in body fluids as well as associated with (lipo)proteins (HDL and Argonaute 2)  [105] [106] [107]. Similar to mutations, several miRNA profiles in various human specimens and cancers have been collected and made publicly available in dedicated databases: PhenomiR  [108] , oncomiRDB  [61] , PROGmiR  [109] , miRò  [110] , and miRandola  [111].
It is worth mentioning that a clinical trial using a miR-34 mimic is already in progress  [152, 153]. In preclinical studies, it was reported that the injection of miR-34a mimic extended the survival of tumor-bearing mice  [58]. Another study demonstrated that systemic administration of a miR-34 in a pancreatic xenograft cancer model significantly inhibited tumor growth and induced cancer cell apoptosis  [154]. In May 2013, the Mirna Therapeutics Company initiated a phase I study to evaluate the safety of MRX34, a liposome-formulated mimic of miR-34, in patients with unresectable primary liver cancer and advanced or metastatic cancer (ClinicalTrials. gov Identifier: NCT01829971). Likewise let-7 mimics are in preclinical development stages at Mirna Therapeutics. In addition to these chemical and synthetic procedures, miRNA expression levels can also be adjusted through dietary manipulations.
[92][91],Bruno et  al. used SNP data to create the miRdSNP database, which stores disease-associated SNPs located in the 3  UTRs of genes and are supported by the literature after manual curation of publications stored in PubMed[92]. The most recent version of miRdSNP (v. 11. 03) stores 175,351 SNPs in 3  UTRs with 630 disease-associated SNPs for 204 diseases (including ∼30 cancers). While previous studies have mainly been focused on SNPs, an increasing number of studies provide access to patient-specific somatic SNVs. Last year, Bhattacharya et al. created SomamiR.
SNAI3 is aberrantly reactivated in breast cancers While the SNAI1 and SNAI2 genes are reported to be frequently reactivated in numerous carcinomas (breast, esophageal, colon, kidney)  [32] [33] [34] [35] , the status of the related SNAI3 gene has remained unclear. To address this question, SNAI3 expression was assessed by qRT-PCR in a cohort of primary human tumors (n = 44) encompassing four different carcinoma types (colon, lung, ESCC, kidney), as compared to healthy tissues or normal cell counterparts. SNAI3 transcription was barely detectable in colon and lung cancers and weakly induced in ESCC and kidney cancers (data not shown), but significantly induced in primary non-metastatic breast tumors from untreated patients, as compared to immortalized mammary epithelial cells (HMEC-hTERT) (FC.
Expression vectors The cDNAs encoding human wild-type SNAIL1 (GenBank NM_005985), SNAIL2 (GenBank NM_003068), and SNAIL3 (GenBank NM_178310) were generated by PCR and subcloned into the pBabe-Puro (Addgene) or pPRIP-Puro retroviral vector  [51]. HA-tag was inserted in the C-terminus of SNAIL1 or SNAIL3 by PCR-mutagenesis. The c-MYC pBabe retroviral construct was generously provided by Martin Eilers (Wu ¨rzburg University, Germany).
Immunoblot analysis Cells were washed twice with a PBS-0. 5% EGTA solution and lysed in RIPA buffer (150 mM NaCl, 1% NP40, 100 mM Tris pH 7. 5, 0. 1% SDS, 0. 5% DOC, 1 mM EGTA) supplemented with complete protease (Roche) and phosphatase inhibitors (Sigma). After clarification by centrifugation at 14 000 rpm for 20 min at 4uC, cell extracts were subjected to SDS-PAGE. Proteins were revealed with mouse monoclonal anti-E-cadherin (clone 36, Becton Dickinson), anti-b-catenin (clone 14, Becton Dickinson), anti-fibronectin (clone 10, Becton Dickinson), antivimentin (clone V9, Dako), anti-N-cadherin (Becton Dickinson), anti-phospho-ERK1/2 (9106S, Cell Signaling), anti-GAPDH (6C5, Biodesign), rabbit monoclonal anti-active caspase 3 (ab32042, Abcam), or rabbit polyclonal anti-SNAIL1 (ab17732, Abcam), anti-SNAIL2 (G-18/SC-10436, Tebu-bio), anti-SNAIL3 (HPA016757, Sigma), anti-ERK1/2 (#9102, Cell Signaling), anti-AKT (8272, Cell Signaling), anti-phospho-AKT (Ser473) (#4058, Cell Signaling), anti-c-MYC A14 (sc-789, Santa Cruz Biotechnology), anti-HA Y11 (Santa-Cruz) and horseradish-peroxidaseconjugated secondary antibodies (Dako).
Gene expression analysis in established human cell lines Total RNA (Figures  2, 3  and 4 ) was extracted with the RNeasy minikit (74106, Quiagen) according to the manufacturer's recommendations. Reverse transcription was performed from 1 mg total RNA with the Dynamo cDNA synthesis kit (F-470L, Thermo Scientific). The reverse transcription product was diluted 1:10 and used as cDNA template for qPCR analysis. SYBR green quantitative PCR was carried out in a CFX96 Real-time PCR detection system (Biorad). PCR mixtures contained SsoAdvanced SYBR Green supermix (1725264, Biorad) and 200 nM primers. The HPRT1 housekeeping gene was used for normalization. Realtime PCR intron-spanning primers were designed with the Primer3 software. The following combinations of primers were used: SNAI1 59-GCTGCAGGACTCTAATCCAGA-39 and 59-ATCTCCGGAGGTGGGATG-39, SNAI2 59-TGGTTGCTT-CAAGGACACAT-39 and 59-GTTGCAGTGAGGGCAAGAA-39; SNAI3 59-CCACAGGGTCCCCAACTAC-39 and 59-GAG-CAGGCACCATTGATTTC-39; ZEB1 59-AACTGCTGGGAG-GATGACAC-39 and 59-TCCTGCTTCATCTGCCTGA-39, ZEB2 59-AAGCCAGGGACAGATCAGC-39 and 59-GCCA-CACTCTGTGCATTTGA-39, HPRT1 59-TGACCTTGATT-TATTTTGCATACC-39 and 59-CGAGCAAGACGTT-CAGTCCT-39.
Microarray analysis Microarray processing and data analysis were performed at the ProfileXpert core facility (Lyon, France). Gene expression profiles were analyzed with a whole human genome microarray containing 47231 probes (HumanHT-12 v4 Expression BeadChip; Illumina Inc. , USA). Total RNA (500 ng) was amplified and biotin-labeled with the Illumina TotalPrep TM RNA Amplification Kit (Ambion Inc. , USA). Hybridization was performed with 750 ng biotin-labeled cRNA on each BeadChip. The standard Illumina scanning protocol was used to scan the arrays with the iScan (Illumina Inc. , USA). Data were normalized by quantile normalization with Genome Studio Software 2010 (Illumina Inc. , USA). The complete set of raw and normalized files is available at the GEO database under accession number GSE40690.