dataesr/scientific-paragraphs-categorization huggingface dataset:
The influence of an alternative anchor (see  Fig. 7, top panel, and Sect. 3. 5. 3) proved to be small for most ACSMs. However, there are outliers with larger differences in the factor contributions (e. g. #7, #12, TOF) which indicates that by testing a set of reference profiles, if possible, an improvement of the individual source apportionment can be reached. The source apportionment of the ToF-ACSM data produces clearer diurnal trends due to less scatter in the time series and higher temporal resolution compared to the Q-ACSM data. This facilitates source identification. In this study, however, for a clear separation of all four factors without the extra information of HR fitted spectra, the additional controls (e. g. possibility to introduce anchor spectra) of the ME-2 package were necessary for the source apportionment of both, ToF-ACSM and Q-ACSM data.
To avoid negative m/z 12 in future data sets, the waiting time between quadrupole scans should be increased in the DAQ software. -Anchor profiles constructed from the studied data set are preferable to database profiles. These profiles can often be extracted from solutions with additional factors (e. g. this study) or from separate PMF on parts of the data set with high fractional contributions of a factor (e. g. period with nearby forest fires or high primary traffic emissions). -The PMF results of short-term, high-resolution AMS measurements overlapping with long-term ACSM measurements can provide useful constraints on the source apportionment of the ACSM data set (e. g. number of factors, special features in a profile). -If no profiles can be extracted with the methods described above, it is advised to try and compare different database anchor profiles (e.
Conclusions The ACTRIS ACSM intercomparison taking place for about 3 weeks (end of November to December 2013) at the SIRTA site in Gif-sur-Yvette near Paris provided great insight into the comparability of ACSM instruments, especially in terms of mass concentrations (part 1 of this study), mass spectra and source apportionment. Future exercises of this kind are encouraged. In this study, factor analysis source apportionment was performed on the data sets of 15 co-located aerosol mass spectrum analysers (13 × Q-ACSM, 1 × ToF-ACSM, 1 × HR-ToF-AMS) operated in parallel. To minimise external influence, operation (e. g. same operator of all source apportionments, use of the same software versions) and instrumentation (e. g. same calibration equipment) were harmonised. In each case four specific factors were identified: HOA, COA-like, OOA and BBOA sources, having features consistent with previous AMS studies at a nearby site  (Crippa et al.
Acknowledgements. This work was conducted in the frame of the  ACTRIS programme (European Union Seventh Framework Programme  ( FP7/2007-2013 ), grant agreement no. 262254 ). The authors acknowledge the  French Agency of Environment and Energy Management  (ADEME grants  1262C0022  and  1262C0039 ), the  CaPPA (Chemical and Physical Properties of the Atmosphere)  project ( ANR-10-LABX-005 ) funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir) , the  EU-FEDER CORSiCA, Eurostars E!4825  and KROP, financed by the  Slovenian Ministry of Economic Development and Technology , and ChArMEx projects. J. G. Slowik acknowledges support from the  Swiss National Science Foundation (SNSF)  through the  Ambizione programme  ( PZ00P2_131673 ). V. Crenn acknowledges the DIM R2DS programme for his postdoctoral grant.
Genotypic data Illumina bovine high-density (HD) SNP genotypes were available for 2660 dairy and beef bulls, which included 770 Holstein-Friesian, 927 Charolais, and 963 Limousin animals. All animals had a genotype call rate greater than 95 %. Genotypes that were available on both the sire and son(s) were used to confirm parentage. The presence of opposing homozygous genotypes for the 1929 sire-son pairs was examined to determine Mendelian inconsistencies. Among the 777 962 high-density SNPs, 3554 autosomal SNPs with a Mendelian error rate greater than 2 % were discarded. The genotypes of sire-son pairs were set to missing for the remaining SNPs for which sporadic Mendelian inconsistencies were found. In addition, 1574 SNPs with a GenTrain score (Illumina clustering algorithm) less than 0.
Phenotypic data Predicted transmitting abilities (PTA) and their associated reliabilities for all 2660 dairy and beef bulls were obtained from the Irish Cattle Breeding Federation database from the December 2013 national genetic evaluation. Direct and maternal PTA for calving difficulty and direct PTA for perinatal mortality were obtained from this evaluation; there is currently no maternal component estimated for perinatal mortality in the Irish national genetic evaluations. Heritability estimates used in the national genetic evaluations were equal to 9 % for direct calving difficulty, 2 % for maternal calving difficulty, and 2 % for perinatal mortality. Irish genetic evaluations across dairy and beef breeds use multi-trait and multi-breed models. Ireland has a long history of crossbreeding including a substantial use of purebred beef sires on dairy cows (36 % of dairy cows in 2013) and also of purebred beef sires on crossbred beef cows.
In Ireland, calving difficulty is subjectively scored by producers on a linear scale of 1 to 4, where 1 = no calving assistance; 2 = slight assistance (assistance by one person, without needing to use a calf puller); 3 = considerable assistance (assistance by one person using a calf puller or more than one person); 4 = veterinary assistance (including caesarean). Perinatal mortality is recorded as a binary variable by producers, which indicates whether the calf died within a 24-h period after birth. It is a legal requirement in Ireland to record all animal deaths, including stillbirths. Phenotypic data were edited as described in detail by Purfield et al. [13]. PTA for each trait were deregressed and only sires with an adjusted reliability (i. e. reliability after removing parental contribution) of more than 30 % were retained.
Sequence analysis Whole-genome sequence data from the 1000 bull genomes project  [25]  (http://www. 1000bullgenomes. com/) (run 4) were available for 311 Holstein-Friesian bulls, of which 64 also had high-density genotypes. The 1000 bull genomes project identified 35. 2 million SNPs across the bovine genome. The average genome coverage was 11. 0X. Sequence data for the Holstein-Friesian animals used in our GWAS analysis were imputed for significant regions of the genome that were identified to be associated with calving performance in the Holstein-Friesian population analysed here. Significant regions were chosen based on clear peaks of association identified in the high-density GWAS with -log 10 p-values greater than 8 (chromosomes 18, 2 and 4) or else based on results from previous studies which suggested the presence of QTL that affect calving performance on the chromosomes with peaks (chromosomes 6 and 10)  [12, 26].
53 × 10 -5 ; q = 2. 53 × 10 -6 ; BF = 220. 36); however, no candidate gene was identified in this region. The four strongest associations in the SSR analyses were detected within a 20-kb region around position 37. 85 Mb on chromosome 4 (Bonferroni corrected p = 2. 25 × 10 -6 to 5. 99 × 10 -6 ). Using imputed sequence data within the interval between 32. 85 and 42. 85 Mb, the same region with the strongest association was identified around 37. 85 Mb [See Additional file 2: Figure  S1 ]. The strongest SNP association (p = 3. 71 × 10 -12 ) was located in an intron of the PCLO gene. Additional candidate genes were identified within this 10-Mb region that contained intronic SNPs with p-values less than 2. 51 × 10 -7 , among which were the SEMA3D, HGF, a novel gene ENSBTAG00000019276, CROT and KIAA1324-like genes.
Two missense variants located in the PCLO gene and one in the KIAA1324-like gene had a p-value less than 0. 005. SIFT scores were not available for the missense variants in the PCLO gene but the variant within the KIAA1324-like gene was classified as tolerated (SIFT = 0. 38). SSR and the Bayesian analyses identified associations with maternal calving difficulty in the Charolais breed on chromosomes 2, 3, and 7 (Fig. 3b  and [See Additional file 1: Table  S1 ]). However, after adjustment for multipletesting in the SSR analysis, no association remained significant. Similarly, in the Limousin breed, no associations remained significant after multiple-testing adjustment, although strong SSR associations were detected on chromosome 5. In the Charolais dataset, three SNPs were detected on chromosome 3 in the region around 90.
Perinatal mortality Perinatal mortality in cattle is defined as calf mortality shortly before, during, or after parturition within a 24-h period. Since no maternal genetic effects are estimated in the national genetic evaluations, our analyses are based on PTA for direct mortality. Both SSR and Bayesian analyses detected the strongest associations on chromosomes 4 and 26 for the Holstein-Friesian breed ([See Additional file 1: Table  S1 ] and Fig. 4a ). Nine SNPs had a BF greater than 30. 1, which provides strong evidence for an association; combining these SNPs together accounted for 0. 13 % of the genetic variation in perinatal mortality in the Holstein-Friesian population. We did not perform an analysis with imputed sequence data since no association remained significant after adjustment for multiple-testing in the Holstein-Friesian dataset.
Coastal sandy environments are extremely dynamic and require regular monitoring that can easily be achieved by using an unmanned aerial system (UAS) including a drone and a photo camera. The acquired images have low contrast and homogeneous texture. Using these images and with very few, if any, ground control points (GCPs), it is difficult to obtain a digital surface model (DSM) by classical correlation and automatic interest points determination approach. A possible response to this problem is to work with enhanced, contrast filtered images. To achieve this, we use and tune the free open-source software MicMac.
Why Using an UAS? An unmanned aerial system (UAS) including a drone and a photo camera could be a good alternative for coastal monitoring as it offers high frequency, relatively low cost photo-analyzing information on the nature and geometry of the coast (coastline position, dunes' plant cover) and elevation data describing the morphological beach evolution (e. g. , erosion, accretion areas) by DSMs difference. In this article we present a method for working on low contrast images taken by drone on sandy regions. Using these images we obtain DSM using the free open source software MicMac  (Pierrot-Deseilligny, 2015). DSMs differences obtained from images at different periods of time allows simple regular monitoring of these extremely dynamic regions.
MicMac Software MicMac-Apero is free open-source software, distributed under the CeCILL-B license, implemented at IGN (Institut National de l'Information Géographique et Forestière, France). Apero (Aerotriangulation Photogrammetrique Experimentale Relativement Operationelle) is used for images orientation processes and MicMac (Multi-Images Correspondances par Méthodes Automatiques de Corrélation) computes multi-image sub-pixel correlation. MicMac's fields of application include three-dimensional modeling in archeology, architecture for cultural heritage and geology and surveying for environmental applications  (Guérin et al. , 2012 , Lisein et al. , 2013) , as well as two-dimensional displacement measurements  (Rosu et al. , 2015). From now on, we will use the general name MicMac when speaking of MicMac-Apero software.
Local Contrast Enhancement Technique We used the free open source software MicMac and tuned it in order to be able to obtain satisfactory results using this kind of low contrast images. MicMac, in its current implementation, uses the SIFT++ algorithm for tie-points detection. SIFT++ is a lightweight C++ implementation of SIFT detector and descriptor originally created by David Lowe  (Lowe, 1999 , Lowe, 2004). This implementation produces interest points and descriptors which are very similar to David Lowe's implementation. But, according to Pierrot-Deseilligny (2015), the current implementation of is not fully invariant to scaling or translation in radiometry, which may be a problem when working on low contrast images, as the information may be assimilated to noise and therefore impossible to extract.
RESULTS Contrast enhancement technique may lead overenhancement of noise contrast in the image. Tests were conducted on images with and without contrast enhancement, on raw and jpg images to see if the information is valid or if noise prevails. Raw images are uncompressed -large file size with no loss of data from the camera sensor -images. Jpg images are on the contrary compressed -fairly small in file size with loss of information. Raw images have 12 bits per channel, jpg ones only 8 bits per channel. The dynamic range, defined as a ratio between the smallest and highest pixel intensity, of a raw image is higher than the one of a jpg image.
High-throughput experimental platform technologies ranging from genomic sequencing to proteomic and metabolomic profiling are now being used for molecular characterization of clinical phenotypes  [13] [14] [15] [16] [17] [18] [19]. A variety of datasets have become available e. g. in Array Express/ Gene Expression Omnibus (GEO) for transcriptomics, Human Proteinpedia for proteomics, or in large data consolidation platforms such as GeneCards  [20]. In regard to disease specific omics data, valuable general sources in oncology include TCGA (http://cancergenome. nih. gov/ ), Oncomine  [21] , and OMIM  [22]. Though omics profiling has provided an abundance of data, technical boundaries involving incompleteness of the individual molecular catalogues together with the static representation of cellular activity limits the insights on molecular processes and their interaction dynamics  [23] [24] [25].
Akshay. Bhat is an employee of Mosaiques Diagnostics. Bernd Mayer is the managing partner of emergentec biodevelopment GmbH, Austria. Paul Perco, Andreas Heinzel and Irmgard Mühlberger are employees of emergentec. These issues do not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials. On the effector level, proteins interact and co-operatively form specific molecular processes and pathways. Intermolecular interactions include various types being represented as networks (graphs) with molecular features denoted as nodes (vertices) together with their interactions (edges). A large number of biological pathway resources has become available, including KEGG  [26] , PANTHER  [27] , REACTOME  [28]  and AmiGO  [29]  described in PathGuide (http://www. pathguide.
org/), all displaying well-defined human molecular metabolic and signalling pathways together with disease-specific pathways (e. g. pathways in cancer). Molecular features being identified as associated with bladder cancer can be interpreted on the level of such pathways, adding to a functional interpretation of molecular feature sets characterizing the phenotype. To add to our understanding of muscle-invasive bladder carcinoma (MIBC), we derived a phenotype-specific network model (interactome) by integrating omics signatures characterizing MIBC, reported in scientific literature and databases. Our procedure incorporated scientific literature screening and signatures from omics profiling, resulting in 1,054 protein-coding genes being associated with MIBC, further consolidating to 286 genes on the interactome level.
Data sources for characterizing bladder cancer pathophysiology For consolidating molecular features associated with muscle invasive bladder cancer, NCBI PubMed, Web of Science, Google Scholar and the omics repositories Gene Expression Omnibus (GEO)  [30]  and ArrayExpress  [31]  were queried. The keywords for the literature search included "bladder OR urothelial OR transitional cell" AND "neoplasm OR tumor OR carcinoma" AND "muscle" AND "invasÃ OR aggressÃ OR progressÃ OR inflammation" (Database version of April, 2014). By construction this search query focused specifically on muscle invasive bladder neoplasm. For extracting protein-coding genes associated with these publications gene-2-pubmed as provided by NCBI was used  [32]. The list of publications relevant to bladder cancer muscle invasion was isolated from the complete list of papers indexed in PubMed along with the associated gene IDs (ftp://ftp.
Interaction data and induced subgraph Protein interaction information was obtained by querying IntAct  [33] , BioGRID  [34] , and Reactome  [28]  leading to a total of 233,794 interactions covering 13,907 protein-coding genes within the human interactome (Databases in version of April, 2014). Mapping the MIBC associated molecular features on this consolidated interaction network  [13]  provided an MIBCspecific induced subgraph. MIBC associated features not connected to at least another such feature were disregarded from further analysis.
Protein coding gene selection based on literature mining From the set of MIBC-associated protein-coding genes, each gene symbol was evaluated for being a member of the MIBC pathway set. The evidence of identified pathways and extracted genes involved in MIBC was assessed based on the level of annotation depth, defined as the number of individual studies identifying such protein-coding genes as involved in MIBC. Specifically, such evidence was derived from metadata available in PubMed. Gene-2-pubmed was used for linking the molecules contained in the induced subgraph to publications relevant to bladder cancer muscle invasion. The quality of publications obtained for each molecule was assessed based on manual reviewing. Only papers where a direct link of the molecule to bladder cancer muscle invasion was proven were retained.
Data Mining Mining of published articles and omics repositories led to a collection of 285 references after manual screening (Fig. 1 ). This screening was performed to discard duplicated studies retrieved from the varying repositories as well as articles not explicitly focusing on muscle-invasive bladder carcinoma. All molecular features were converted to their official gene symbol by using the UniProt ID Mapping service  [37]. The resulting set of references yielded in total 1,279 proteins of which 1,054 were unique protein-coding genes associated with MIBC (S1 Table ). For collecting specifically proteins involved in MIBC, we further screened these 285 articles with the keywords ("molecular and biomarker") to retrieve 122 proteins that had a tag "biomarker" mentioned in these articles (S2 Table ).
This restriction helped in discarding general articles containing gene symbols that were not associated to the muscle-invasive phenotype. The same set of 285 articles was used to collect all pathways connected to bladder carcinoma. Thus, 11 pathways reported in the literature to be associated with bladder cancer were obtained (S3 Table ). The largest number of features associated with MIBC resulted from transcriptomics with a total of 716 gene symbols. Metabolites were mapped to protein coding genes using the Human Metabolome Database (HMDB)  [38]  and provided 329 gene symbols. The miRNAs from the transcriptomics studies were mapped to their respective gene targets using the service from miRbase  [39, 40]. In addition, DNA-methylation studies provided 77 gene symbols respectively. DNA-mutation studies reported 35 gene symbols.
The present study intended to characterize muscle invasive bladder carcinoma by incorporating scientific literature screening and signatures from omics profiling further linked in an interaction context, resulting in a set of 286 protein-coding genes. Such analysis on the level of networks and pathways was chosen with the expectation that miscellaneously found phenotypic features consolidate on a pathway level, under the assumption that they are functionally linked and collectively affect the disease phenotype. High-throughput DNA sequencing can yield erroneous data  [46]. MS based proteomics experiments generate enormous datasets that need to be carefully assessed  [47]. Biological pathway databases play an essential role in annotating protein-coding genes resulting from high-throughput profiling approaches.
There are approximately 547 pathway database resources available as listed in PathGuide (http://www. pathguide. org/). Albeit there are several well curated and reliable pathway database resources as also described by our group  [48] , significant efforts have been taken to expand biological pathway coverage beyond any single pathway data source. This is frequently carried out by integrating different sources in order to build high quality integrative pathway models without sacrificing data quality. However, biological data integration from heterogeneous sources has been challenging due to variability at the syntactic and semantic level. Syntactic variability is due to heterogeneity of molecular feature and pathway data formats, representation schemas and retrieval methods. Semantic variability is due to incompatible pathway names, signalling event representations and molecular identifiers.
For example, different pathway databases may choose to provide information on post-translation modifications, interacting proteins within a complex, or cellular location. Hence all these limitations have inhibited the growth of high quality integrative pathway models  [49] [50] [51]. Another issue that arises when aiming to integrate data from different omics platforms is that conflicting results can be obtained. For example in some muscle invasive tumours presented in  [52] , transcriptomics analysis proved that the mRNA level of EGFR is up-regulated, whereas proteomic analysis did not show differential expression at the protein level. One explanation for such discrepancy may be translational regulation. In KEGG, biological pathway categorization is currently available for several human key cellular processes  [13].
Mapping MIBC-specific features (corrected on the level of the induced subgraph utilizing protein interaction information) to KEGG and performing enrichment analysis provided a total of 15 pathways (4 novel and 11 cited in published studies). 68 of 122 literature-mined protein candidates of relevance in muscle-invasive bladder cancer were identified as members of the identified pathways. This enabled to comprehensively rank pathways allowing the shortlisting of terms being individually discussed in the specific context of MIBC. We focused on expanding our knowledge on muscle invasive urothelial neoplasm affected at the molecular level by comprehensively mapping available molecular datasets to pathways to build an interactome network utilizing public domain data sources. By differentiating the pathways based on previously described pathways and novel ones we obtained 11 modules that were known in context of bladder cancer muscle-invasion and 4 novel pathways.
On the other hand, the two pathway terms GnHR receptor and T cell receptor signalling pathways found as enriched on the basis of the 72 gene symbols being multiply identified in, were not retrieved from the analysis resting on the full set of 286 features being derived from the induced subgraph. Data evidence and selection biases clearly affect results of such integrated analysis demanding strict quality control of input data sets as followed in our study. Apparently, each individual functional context highlights specific aspects of bladder cancer pathophysiology, but only providing limited characterization of clinical outcome on the cohort level. In summary, automated data retrieval from the literature resulted in a first collection of molecular features associated with MIBC, and, complementing with omics profiling data, allowed augmenting a mechanistic (pathway) map linked to MIBC.