dataesr/scientific-paragraphs-categorization huggingface dataset:
Primer design was performed using Primer 3 software. Specific primer pairs were designed with an overlapping intron when possible, using known trout sequences in nucleotide databases (GeneBank and INRA-Sigenae). Database Accession Numbers and the sequences of forward and reverse primers used for each gene are provided in Table  3. For the RT-qPCR, total RNA (1μg) was reverse-transcribed to cDNA with the SuperScript III RNase H reverse transcriptase kit (Invitrogen, Carlsbad, CA, USA) using oligo dT Primers. Real-time PCR was performed in the iCycler iQ TM (BIO-RAD, Hercules, CA, USA). Quantitative PCR analyses for gene expression were performed on 10μl of the RT reaction mixture using the iQ TM SYBR 1 Green Supermix (BIO-RAD, Hercules, CA, USA). The total volume of the PCR reaction was 25μl containing 200nM of each primer.
Statistical analysis and data mining Tanks were used as the experimental unit for data on growth, body lipid content and fatty acid profile. Individual fish were the experimental unit for data on plasma parameters and gene expression, since no tank-related effect was observed during the experiment. Data for biometric parameters, lipid content and fatty acids are presented as mean ± standard deviation (SD). Data were analyzed statistically using the R software (version 2. 14. 0) and the Rcmdr package. The normality and the homogeneity of variance of the variables were tested with Shapiro-Wilk's and Levene's tests, respectively. When both conditions were satisfied, a one-way ANOVA (p-value <0. 05) was performed to assess the effects of the diets. The variables with non-parametric distribution (some fatty acids) were normalized with an arcsin transformation.
If the criteria (normality and homogeneity) were still not met, a non-parametric test was used for the analysis. Data from microarray analyses were normalized and analyzed statistically using GeneSpring software (12. 6, Agilent). Data were scale-normalized using the median value of each array to identify genes differentially expressed between conditions. Differentially expressed genes were obtained by one-way ANOVA (diet, p-value <0. 05). For all genes found to be differentially expressed, GO ontologies were obtained using the Expression Analysis Systematic Explorer (EASE) software, version 2. 0  [33]. Significant GO enrichment was tested using EASE software, with Benjamini-Hochberg correction (score <0. 05). Data from RT-qPCR were analyzed by one-way ANOVA (diet, p-value <0. 05) followed by a Tukey's post hoc test (p-value <0.
Acknowledgments The authors would like to thank  P. Maunas  and  N. Turonnet  from the  INRA experimental facilities of Lees Athas (Pyrenees Atlantique, France)  for fish rearing and  F. Sandres ,  F. Terrier  and  A. Lanuque  for manufacturing the experimental diets and rearing fish ( INRA, Donzaq, France ). We are grateful to  A. Le Cam  and  J. Montfort  ( INRA, Rennes, France ), and  M. Cluzeaud  and  A. Herman  ( INRA, Saint-Pee-sur-Nivelle ) for technical assistance.
Post-translational modification (PTM) serves as a regulatory mechanism for protein function, influencing stability, protein interactions, activity and localization, and is critical in many signaling pathways. The best characterized PTM is phosphorylation, whereby a phosphate is added to an acceptor residue, commonly serine, threonine and tyrosine. As proteins are often phosphorylated at multiple sites, identifying those sites that are important for function is a challenging problem. Considering that many phosphorylation sites may be non-functional, prioritizing evolutionarily conserved phosphosites provides a general strategy to identify the putative functional sites with regards to regulation and function. To facilitate the identification of conserved phosphosites, we generated a large-scale phosphoproteomics dataset from Drosophila embryos collected from six closely-related species.
We built iProteinDB (https://www. flyrnai. org/tools/iproteindb/), a resource integrating these data with other highthroughput PTM datasets, including vertebrates, and manually curated information for Drosophila. At iProteinDB, scientists can view the PTM landscape for any Drosophila protein and identify predicted functional phosphosites based on a comparative analysis of data from closely-related Drosophila species. Further, iProteinDB enables comparison of PTM data from Drosophila to that of orthologous proteins from other model organisms, including human, mouse, rat, Xenopus laevis, Danio rerio, and Caenorhabditis elegans.
The physiological roles of phosphorylation at Ser235/236 of rpS6 remained unclear until genetic approaches abolishing the phosphorylation sites were applied in model organisms  (Meyuhas, 2015). These examples highlight how conservation can illuminate phosphosite function. Model organisms can play essential roles in the elucidation of the functions of posttranslational modification of highly conserved sites. Mass spectrometry (MS)-based proteomics is a powerful approach for large-scale identification and characterization of phosphorylation sites. Three large-scale Drosophila melanogaster phospho-proteomic datasets have been generated over the past years using MS. Two datasets were generated from cultured cells  (Bodenmiller et al. , 2007; Hilger et al. , 2009)  and one was generated from embryos  (Zhai et al.
, 2008). Because the coverage of each dataset is limited, and to further characterize the breadth of phosphorylation in Drosophila, we generated a new dataset for Drosophila melanogaster and five closely-related species: D. simulans,  D. yakuba, D. ananassae, D. pseudoobscura, and D. virilis. To facilitate the use of this dataset, we built an online resource, iProteinDB, integrating our data with other large-scale PTM data  (Bodenmiller et al. , 2007; Hilger et al. , 2009; Zhai et al. , 2008)  and curated PTM annotations for Drosophila and other model organisms. At iProteinDB, users are able to align PTM data for any protein of interest from multiple resources, including data from the six Drosophila species, other model organisms, and human cells. Additional relevant information, such as disease-related protein variants, sub-cellular localization, and protein abundance during Drosophila development, is also provided at iProteinDB.
Generation of phosphoproteomics data Pre-larval embryos of mixed sex and age from each of the six Drosophila species were collected. Since different species develop at different speeds, the timing of collection was different for each species. Flies were enticed to lay eggs by incubating in the dark on grape juice plates. Proteins from embryos lysed in 8 M urea were digested with trypsin and separated into 12 fractions by strong cation exchange chromatography. Phosphopeptides were purified with titanium dioxide microspheres and analyzed via LC-MS/MS on either an LTQ-Orbitrap or Orbitrap Fusion instrument (Thermo Scientific). SEQUEST was used for spectral matching. Peptides were filtered to a 1% FDR. Proteins were filtered to achieve a 2% final protein FDR (final peptide FDR near 0. 15%) and a probability-based scoring method was used to assign the localizations of phosphorylation events  (Beausoleil et al.
Predicting the probability of phosphorylation We aligned the phosphorylation sites identified in our datasets from 6 Drosophila species based on orthologous relationships predicted by OMA  (Altenhoff et al. , 2018; Altenhoff et al. , 2011; Altenhoff et al. , 2015). For each proteome, we assign the probability of a phosphoacceptor (S+T (together) and Y) to be phosporylated, using a two-step approach. First, we scan each proteomes to find the kinase specificity of each phosphoacceptor, using NetPhorest  (Horn et al. , 2014). This provided 40 scores for kinases specificity for a given region. Then, a support vector machine algorithm (SVM-light) was trained on each of the six species, using all 40 scores. We extracted the surrounding region of sites that are detected to be phosphorylated, and they received an initial score of 1 (positive dataset).
Comparison of PTM data across major model organisms and human Orthologous relationships of Drosophila melanogaster proteins to major model organisms including human, mouse, rat, X. tropicalis, zebrafish and C. elegans were obtained using DIOPT (release 7) with a DIOPT score of 3 or higher. Protein sequences of the best orthologous genes based on DIOPT score and each non-redundant isoform of Drosophila melanogaster gene were aligned using MAFFT (vs 7. 305B). Observed PTM sites, conserved phosphorylation sites, domain and disease-related protein variants are annotated on the aligned protein sequences (Figure  1 ). To compare specific PTM sites, the sequence of a sliding window of five amino acids surrounding the identified phosphosite was extracted and compared across species. The number of identical amino acids was counted and the percent of identity was calculated by dividing the number of identical amino acids over the window length.
Source of other data sets or tools Protein information of 6 Drosophila species was obtained from FlyBase (ftp://ftp. flybase. net/releases/FB2017_03/). Protein information of human, mouse, rat, X. tropicalis, zebrafish and C. elegans were obtained from RefSeq (https://www. ncbi. nlm. nih. gov/refseq/). Other high throughput datasets of Drosophila melanogaster were obtained either from online resources (Phosida: http://141. 61. 102. 18/phosida/index. aspx and Phosphopep: http://www. phosphopep. org/) or corresponding supplemental tables of relevant publications  (Zhai et al. , 2008). The protein annotation of Swiss-prot and TrEMBL was downloaded from the UniProt FTP site (http://www. uniprot. org/downloads). Orthologous relationships were obtained from OMA (https://omabrowser. org/oma/home/) and DIOPT (http://www.
flyrnai. org/diopt). Protein domain annotation of Conserved Domain Database (https://www. ncbi. nlm. nih. gov/Structure/cdd/cdd. shtml) was extracted from the RefSeq release files (. gbff files). Kinase motifs were predicted using the API of Scansite3 (http://scansite3. mit. edu/#home). PTM annotation of orthologous genes other than Drosophila was obtained from PhosphoSitePlus (https://www. phosphosite. org/staticDownloads. action).
Implementation of the online resource There were several steps involved that process the information and populate the back-end database of iProteinDB. After downloading data from relevant sources, such as UniProt, PhophoSitePlus and various publications, the extraction of relevant information was accomplished with in-house parsers written in Perl and Python. The redundancy of protein sequences was consolidated and a collection of distinct protein sequences from each Drosophila species was assembled based on FlyBase genome release (D. mel r6. 16, D. ana r1. 05, D. pse r3. 01, D. sim r1. 04, D. vir r1. 02, D. yak r1. 03). Since different resources annotate data based on different genome releases, we synchronized the data from various sources by mapping the original data (peptides) to the non-redundant protein collection of recent FlyBase genome release (see above) using the SeqIO interface of BioPython.
Once filtered and updated, the data were then uploaded into a MySQL database, which is currently hosted by the Harvard Medical School (HMS) Research Computing group. To display the data, we created a web-based application with PHP and a PHP framework called Symfony (version 2. 6). Several client-side functions rely on JavaScript and AJAX, while some tabular displays use a jQuery plugin called DataTables. js, which allow for sorting and paging functionalities within the tables. This web application is also hosted by the HMS Research Computing group.
We next compared the propensity score with the phosphoproteomics data from other sources. We found a strong correlation between the propensity score and the chance that a predicted site was phosphorylated as supported by independent datasets (Supplementary Figure  3a ). We also compared the frequency of phosphorylation among the six Drosophila species with experimental data for orthologous human proteins. Not surprisingly, phosphorylation sites conserved among the six Drosophila species were more likely to be reported as phosphorylated at the corresponding sites in orthologous human proteins. This correlation was more prevalent for those sites with greater than 50% amino acid similarity between Drosophila and human orthologs (Supplementary Figure  3b ). We estimated the false negative rate for each of the six Drosophila species by selecting those sites that are 100% identical (considering eleven amino acid peptides comprising the phosphosite plus five amino acids upstream and downstream) among all six species and for which phosphorylation was observed in at least two species.
The false negative rate is estimated to be the percent of the sites that are not covered by the data in each species. For example, 86% of these sites for Drosophila melanogaster are covered by at least one of the 4 datasets, and/or UniProt annotation so the false negative rate is about 14% while there is only 1 dataset for each of the other 5 Drosophila species, and therefore, the false negative rate is relatively higher, 44% to 79% (Supplementary Figure  3c ).
Integration of phosphoproteomes from other resources We built the iProteinDB database to store phosphoproteomics data generated by our group and other large PTM datasets. Other datasets were obtained from the supplemental table of the original publications  (Zhai et al. , 2008)  or the relevant websites  (Bodenmiller et al. , 2008; Bodenmiller et al. , 2007; Gnad et al. , 2011). Original data were mapped to the same version of the FlyBase proteome annotation (FB2017_03) and then integrated with our data in iProteinDB. The information of PTM sites and the score/peptide from the original source are stored and made available at the iProteinDB website. To compare PTM data across species, we integrated orthologous relationships of Drosophila species as predicted by OMA  (Altenhoff et al. , 2018) , the orthologous relationships among major model organisms predicted by DIOPT  (Hu et al.
, 2011) , and PTM data for other species from PhosphoSitePlus (https://www. phosphosite. org) (Hornbeck et al. , 2012; Hornbeck et al. , 2015). The subcellular localization annotation and human disease related protein variants were integrated from UniProt (https://www. uniprot. org/), whereas protein domain annotation information was integrated from the National Center for Biomedical Information (NCBI) Conserved Domain Database (https://www. ncbi. nlm. nih. gov/cdd). Information about protein abundance during Drosophila development was also integrated from a recent publication  (Casas-Vila et al. , 2017). Altogether, iProteinDB covers 168,997 individual PTMs for Drosophila, of which 70,013 (41%) were observed in Drosophila melanogaster (Figure  1a ). 62,239 (89%) of the Drosophila melanogaster PTM data collected in iProteinDB are phosphorylation sites, covering 8,068 unique proteins and 3,937 genes (Figure  1b ).
e. the original scores and peptide sequences. A table summarizing all predicted sites based on data from closely related Drosophila species is provided with a link to detailed information and multiple sequence alignments. Also indicated in this tab is subcellular localization annotation from UniProt for each phosphoprotein and kinase predicted to act on individual sites, as identified using ScanSite3  (Obenauer et al. , 2003). 2. ) Predicted ortholog tab. Users can find a table of the best ortholog candidates for major model organisms based on DIOPT ortholog predictions  (Hu et al. , 2011). Multiple sequence alignments were performed based on the protein sequences of orthologous genes. The sequences of all the aligned Drosophila phosphosites, over a sliding window of five residues, were compared to the corresponding sequences of each orthologous gene and a similarity score was calculated by pair-wise comparison.
Of the three phospho-acceptor residues, serine has the highest percentage of phosphorylation (Supplementary Figure  5 ) while phospho-tyrosine has the highest probability of residing within a defined protein domain and the highest sequence similarity with human orthologs (Figure  1d ). Further analysis showed that the sequence identity of PTM sites between human and Drosophila melanogaster correlates with the probability that the associated phosphorylation event has also been observed in human cell phospho-proteomic datasets (Figure  5a ). We observed an enrichment of UniProt human disease related variants located proximal to phosphosites conserved with Drosophila melanogaster. For example, the enrichment p-value of disease related variants is 4. 3*10 -10 by Fisher exact test for the phosphosites with 80% or higher identity between human and Drosophila sites.
Conclusion Drosophila melanogaster is one of the most-studied model organisms. Current PTM resources, such as PhosphoSitePlus  (Hornbeck et al. , 2012; Hornbeck et al. , 2015) , dbPTM  (Huang et al. , 2016; Lee et al. , 2006)  and Phospho. ELM  (Diella et al. , 2004; Diella et al. , 2008; Dinkel et al. , 2011) , have comprehensive coverage for human, mouse, and rat, but have very limited coverage for Drosophila. Resources like PHOSIDA  (Gnad et al. , 2011) , PHOSPHOPEP  (Bodenmiller et al. , 2008; Bodenmiller et al. , 2007)  and dbPAF  (Ullah et al. , 2016)  provide largescale PTM data for Drosophila genes but are focused on only one or, at most, a few datasets. We generated a large-scale proteomics dataset of six closely related Drosophila species, made the data available, and integrated it with literature annotation and other large datasets for Drosophila melanogaster.
Acknowledgments We would like to thank the members of  Perrimon laboratory, Gygi laboratory, Drosophila RNAi Screening Center (DRSC)  and  Transgenic RNAi  Project (TRiP) for helpful input on the project. Particularly we would like to thank  Dr. Stephanie Mohr  for helpful suggestions during manuscript preparation and  Mr. Aram Comjean  for advice during the resource implementation. The DRSC is supported by  National Institutes of Health (NIH) National Institute of General Medical Sciences  grant  R01 GM 067761  (to N. P. ). The  National Institutes of Health  supported this work ( 5R01DK088718 ,  5P01CA120964 ,  5R01GM084947  and  5R01GM067761 ). R. S. is a Special Fellow of the  Leukemia and Lymphoma Society. N. P. is a  Howard Hughes Medical Institute  investigator.
) encountered in the acquisition of images. In addition, the DST manages a formalism of disjunction between the sources during the DSmT simultaneously manages a disjunction and a conjunction between the sources. Next we realized the algorithms and related codes that we implemented in the MATLAB environment. Our contribution lies in taking into account the imperfections (inaccuracies and uncertainties) linked to source information through the use of mass functions based on a simple Gaussian distribution support model in order to model each focal element independently of the others and to evaluate the belonging of a pixel to a class with respect to the majority of elements representing said class. The resulting results show that the DST approach is relatively satisfactory for the unsupervised classification of mineral surfaces and aquatic surfaces while it is not satisfactory for vegetated surfaces according to all proposed models.
So, a question arises: How to take advantage of these indices taking into account their imperfections in order to improve the characterization of the Earth's surface state? To answer this concern we propose to use information fusion to take into account and manage any imperfections related to the images associated with the indices. The general objective of the study is to contribute to the unsupervised classification of satellite images by merging information by developing an unsupervised classification approach, based on the use of spectral indices, as well as theories of  Dempster-Shafer (DST)  and Dezert-Smarandache (DSmT), to characterize vegetated surfaces, aquatic surfaces and mineral surfaces. It is a specific way, first to model the guiding elements of the classification, then to carry out several classifications by implementation of the algorithms and programming codes developed under the MATLAB environment and finally to evaluate and decide on the chosen model for classification.
Material The equipment used consists of software, positioning tools and data. With regard to software, it was first used ENVI 4. 7 to preprocess ASTER images, then MATLAB to develop classification models based on the use of segmented NDVI, MNDWI and NDBaI spectral indices, lastly the DST and the DSmT for the classification of aquatic, mineral and vegetated surface conditions. The positioning tools are composed of a 2. 5 m resolution Garmin GPS to determine and locate the coordinates of the classification entities, fixed points and outcrops and three (3) topographic maps (Leaves Gagnoa, Bouaké and Dimbokro) for the identification of the localities of the experimental study area, in the forestsavanna transition zone, in the center of Côte d'Ivoire. The data for this study are of two types: Field data and remote sensing data.
Field data consists of geographic coordinates of fixed points and outcrops. The remote sensing data used are from the ASTER sensor and are rectified satellite images of the AST_L1A_00301102004105832 scene. This sensor has 14 bands with a broad spectral region covering the visible and the near infrared (VNIR-Visible and Near Infrared: Band 1, Band 2 and Band 3N), the medium infrared (SWIR-Short-Wave Infrared: Band 4, Band 5, Band 6, Band 7, Band 8 and Band 9) and thermal infrared (TIR-Thermal Infrared: Band 10 to Band 14). The spatial resolution associated with these images is 15 m in the visible and the near infrared, 30 m in the medium infrared and 90 m in the thermal infrared.
Packaging In order to benefit from the totality and quality of spatial resolutions and spectral resolutions, these ASTER satellite images have been subject to georeferencing, resampling and geometric correction to create a compatible database, based on 14 bands. First geo-referencing was carried out for each band with the k nearest neighbors method; then the geometric correction was made from 100 bitter points, chosen uniformly covering the ASTER scene considered, with the bi-linear method; finally, the re-sampling, at a step of 15 m with the bilinear method, is carried out for the bands of SWIR (bands 4, 5, 6, 7, 8 and 9) and TIR  (bands 10, 11, 12, 13 and 14). Georeferencing and geometric correction make it possible to superimpose said satellite images on other georeferenced media in the same coordinate system.
Conclusion In this study, the aim was to propose an unsupervised classification approach, based on the use of the NDVI, MNDWI and NDBaI spectral indices and the Dempster-Shafer and Dezert-Smarandache theories, to characterize vegetated surfaces, aquatic surfaces and mineral surfaces. We used as an experimental study area, in the forestsavanna transition zone in central Côte d'Ivoire, ASTER satellite images, ENVI 4. 7 and MATLAB software, a GPS, topographic maps and geographical coordinates of fixed points and outcrops The methodology first consisted of conditioning the ASTER satellite images, then developing and implementing DST and DSmT models for unsupervised classification and finally evaluating these models in order to remember that one. The resulting results show that the DST approach is relatively satisfactory for the unsupervised classification of mineral surfaces and aquatic surfaces while it is not satisfactory for vegetated surfaces according to all proposed models.