dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, if there are relatively few causal markers as we speculate, traditional rrBLUP would want to "push down" the causal marker effects. We now focus on the comparative analysis of different datasets rather than different methods, i. e. on the column-wise analysis, using the row Max in Fig  1  to compare best performances achievable on each of the data sets. We immediately notice that the highest accuracies of r 2 = 0. 57-0. 62 (mean r 2 of 0. 50, and median r 2 = 0. 56-0. 57) are achieved on the Maize dent data, for the two out of three traits: dent male flowering time (Tass_GDD6) and dry matter content (DMC), i. e. for those two datasets, genotypic information appears to be the most informative about the trait, i. e. these traits have high heritability. The second-best group of data sets, with respect to the best achievable accuracy, included: the Rice dataset for 'pericarp color' trait, where the best accuracy was r 2 = 0.
54 achieved by the Elastic Net, and the mean and median r 2 across all methods were 0. 38 and 0. 41, respectively, and the Maize dataset, for both flint male flowering time (Tass_GDD6) and dent plant dry matter yield (DM_Yield), where the best result of r 2 = 0. 48 was achieved by SVR Linear and by Epistasis rrBLUP, though the mean 0. 41 and median 0. 47 for the former trait are considerably better than the mean 0. 29 and median 0. 31 for the latter trait. Finally, Fig  6  summarizes feature-selection stability of the two best performing approaches we used, mRMR and Elastic Net. As described earlier, each of the 10CV folds produces a different subset of selected features. For each feature, we compute the number of folds when this feature was selected. Then, for a given number of folds k, we computed the number of features selected in at least k folds, and divided it by the total number of features selected across 10CV.
Moreover, another variable-selection method-Elastic Net, an example of the so-called sparse regression-also performed quite well overall, though it fell into the second-best group of methods, closely following the winner, mRMR. An intuitive explanation of good performance demonstrated by feature-selection approaches, such as mRMR and the Elastic Net, on specific datasets, can be potentially linked to observations made in the original paper by  [47] , and reinforced by more recent studies (see, for example, Chapter 3 in  [59] ). In  [47] , a simulation study compared univariate feature selection (subset selection regression) versus sparse regression (the LASSO approach) and versus the ridge regression (closely related to rrBLUP). It was observed that the univariate feature selection works best when there is a very small number of large effects, i.
In  Fig 7,  we plot the relevance scores of different features, ranked from best to worst, for each dataset. Note that for the Rice dataset, Pericarp color trait, there is clearly a small number of highly relevant features, followed by a large number of much less relevant ones, i. e. , we have a relatively small number of large effects; note that mRMR and especially the Elastic Net, indeed, works best on this data, outperforming rrBLUP and other competitors, as shown previously in a small subset of high-relevance (top-ranked) features, followed by the much lower-relevance majority; mRMR performs best here, clearly outperforming rrBLUP; however, the Elastic Net is also outperformed by mRMR, perhaps due to small (rather than moderate) number of large effects. On the other hand, on the two Pig datasets, practically all features have almost the same relevance scores, i.
e. fall into the last category, large number of small effects. Note that on these datasets, mRMR and the Elastic net are, respectively, only as good as, or worse, than rrBLUP. Overall, that standard rrBLUP approach, while performing fairly well, still fell into the large second-best category of methods, that also included methods such as Blasso, BayesCpi, Elastic Net, BayesA, BayesB, Epistasis rrBLUP, and SVR Radial. The remaining methods, including PCA-FOBA, SVR Sigmoid, SVR Linear, SVR Polynomial and FOBA algorithms, performs considerably worse than the second-best group, and ISIS appears to be particularly inaccurate on the data sets we considered. Finally, it still remains to be explored what data properties are essential for a specific method to work well (or poorly). One hypotheses we propose is that the discrete, rather than continuous, nature of the genomic data, where the variables are ternary, with a highly skewed distribution towards two out of three values being most frequent, may play an important role, affecting performance of some of the methods.
Sample Collection The Tr. anilis and Te. subulata specimens used in this study were collected on a 2011 expedition to Inhaca, an island off of the coast of Mozambique, as described in  Castelin et al. (2012). Specimens from this expedition were used to obtain venom ducts for NGS projects and to enhance existing phylogenetic reconstructions of the Terebridae. Specimens are dissected to extract venom ducts that are stored in RNAlater and brought back to The American Museum of Natural History for transcriptome research. Museum National d'Histoire Naturelle (MNHN) Museum voucher numbers and GenBank accession numbers are listed in supplementary table  S3 , Supplementary Material online.
Read Processing and De Novo Assembly Triplostephanus anilis, Illumina HiSeq generated 288,959,674 paired end reads of 100 bp length with 89. 43% bases having a quality score ! Q30. Raw read quality assessment was performed with FastQC to determine the need for base trimming and adapter removal (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/, last accessed June 10, 2015). Seqtk was used to trim bases from the reads using a default Mott algorithm based on phred score (https://github. com/lh3/seqtk, last accessed June 10, 2015) while Trimmomatic (http://www. usadellab. org/cms/?page=trimmomatic, last accessed June 10, 2015) was used to remove adapter contamination. Ultimately the quality control process yielded a total of 280,143,112 reads. For Te. subulata, paired end reads of 100 bp in length were also generated on the Illumina HiSeq platform, producing 176,799,164 raw reads with 92.
Basic Local Algorithm Search Tool Annotation Annotation of the Tr. anilis venom duct transcriptome for putative teretoxins was performed by running BLASTx for each of the four assemblies against a combined database of conotoxins downloaded from Conoserver (www. conoserver. org) and our in house database of teretoxin sequences. The same BLASTx search was performed for the Te. subulata Trinity assembly. After basic local algorithm search tool (BLAST) identification of putative toxins at a value of 1e-3 or lower, teretoxin contigs were subjected to open reading frame (ORF) finding using stand-alone getorf from EMBOSS (http://emboss. sourceforge. net/apps/cvs/emboss/apps/getorf. html). ORFs generated from sequences between two stop codons were translated with the standard genetic code. These translated ORFs were then validated based on the identification of a signal sequence, proregion (when present), and Cys frameworks.
All alignments of putative teretoxins to their closest BLAST hit were performed with MUSCLE  (Edgar 2004)  or ClustalW  (Thompson et al. 2002). All the RNA-Seq sequence reads used were submitted to NCBI SRA with BioProject ID 286256. The Tr. anilis Trinity DN assembly was used for a more general BLAST search against the 1) NCBI nonredundant (nr) protein database, 2) UniProtKB/Swiss-Prot (release 2014_03), and 3) UniProtKB/TrEMBL (release 2014_03). For this assembly, assignment of gene ontology (GO) terms was performed in two parts: phase one of annotation followed a bioinformatics pipeline (SFG) made freely available online by the Palumbi lab (http://sfg. stanford. edu/guide. html; De  Wit et al. 2012). After blasting against all three databases, a python script was run on significant BLAST hits in combination with a download of Uniprot flatfiles to extract gene names, general descriptions, and GO categories, which are then combined in a master annotation metatable.
Running this pipeline generated 18,390 annotated contigs with an e-value of 1e-6 or lower. The second phase of GO analysis incorporated the use of BLAST2GO (B2G) tools on the SFG results for further mapping, annotation and in particular, the use of GoSlim, which employs a reduced set of GO terms to provide a broad overview of the GO content which were used to identify posttranslational modification enzymes. The SFG annotation of the Tr. anilis Trinity DN assembly was mined for terms that could potentially relate to posttranslational modifications and Tr. anilis transcripts were extracted from these results. Putative Tr. anilis posttranslational enzymes were then blasted against the Te. subulata assembly to determine if similar transcripts were present in both species. BLAST hits with extremely low e-values (1e-30 or lower) and high query coverage were then aligned with either MUSCLE  (Edgar 2004)  or ClustalW  (Thompson et al.
Phylogeny of the Terebridae Terebrid sequences of nuclear gene 28S and three mitochondrial genes 16S, 12S, and COI were downloaded from GenBank, concatenated and aligned with MUSCLE  (Edgar 2004). The model general time reversible (GTR) with gamma-distributed rate across sites and a proportion of the sites invariable (GTR + G + I) was selected as the best model of sequence evolution for each partition, implementing the AIC with ModelTest 3. 7  (Posada and Crandall 1998). The combined molecular data set was analyzed in RAxML 7. 4. 2 (Stamatakis 2006 ) using the maximum likelihood (ML) optimality criterion, with GTR + G + I, and a partition scheme allowing individual optimization of parameters for all four genes. Support values were estimated with a rapid bootstrap algorithm with 1,000 pseudoreplicates.
Phylogenetic Reconstruction of Putative Venom Toxin Homologs Terebrid transcripts that exhibited homology to toxin families (conopressin/conophysin and actinoporin) and putative orthologs downloaded from GenBank and provided by von Reumont et al. (  2014 ) from selected taxa spanning the Metazoa were aligned with MUSCLE  (Edgar 2004). The best fitting model of protein evolution was selected using ProtTest 2. 4  (Abascal et al. 2005)  following the Akaike Information Criterion (AIC) and used for phylogenetic tree reconstruction under two optimality criteria: ML and Bayesian inference (BI). ProtTest 2. 4 identified Whelan and Goldman + I + G as the best model of protein evolution for the actinoporin data set and Jones, Taylor, and Thorton (JTT) + I + G for the conopressin/conophysin data set.
ML analyses were performed with RAxML 7. 4. 2 (Stamatakis 2006 ) on both data sets and support values estimated with a rapid bootstrap algorithm with 1,000 pseudoreplicates. BI analyses were also performed on both data sets with MrBayes 3. 1. 2 (Ronquist et al. 2012). Four Markov chains of 10,000,000 generations each were started from a random tree and run simultaneously, with a sampling frequency of one tree every 100 generations (samplefreq = 100). The consensus trees were calculated after discarding the initial 25% of trees as burnin (burninfrac = 0. 25). Different chordate taxa were selected as outgroups for both trees. The topologies of the trees generated for each data set under the two optimality criteria are congruent and thus, for convenience, only the ML tree is shown with both posterior probabilities and bootstrap values indicated above each supported node.
Identification and Characterization of Putative Teretoxins Using BLAST BLAST searches of the venom duct transcriptomes against an in-house local database of cono-and teretoxins created using the Conoserver database and teretoxins identified in the Holford lab yielded 84 putative teretoxins for Tr. anilis, and 55 putative teretoxins for Te. subulata (fig. 4 ). It should be noted that discrepancies in teretoxin diversity between the Tr. anilis and Te. subulata might be due to the different depth of sequencing of both trasncriptomes. The transcripts were analyzed for the presence of canonical peptide features as identified in conotoxins, namely the presence of an N-terminal signal sequence, an intervening propeptide region ending with several basic residues identifying a cleavage site, and a C-terminal Cys-rich mature peptide  (Terlau and Olivera 2004).
The most prevalent teretoxin transcripts identified in Tr. anilis and Te. subulata assembled sequences fall into four different Cys frameworks, VI/VII, VIII, IX, and XXII, of varying number and pattern (fig. 4B ). Even though the number of Cys frameworks varies between Tr. anilis and Te. subulata, comparative analysis of the types of Cys frameworks highlight strong similarities between the two venoms. Representative alignments of teretoxin transcripts of selected Cys frameworks found in Tr. anilis and Te. subulata transcriptomes along with their closest BLAST hit are illustrated in figure  5. For the alignments shown, Cys frameworks are conserved between teretoxins and conotoxins; however, the intervening residues are largely variable, again supporting the claim that teretoxins may have molecular functions distinct from conotoxins (fig.
2014). In contrast, similar to conotoxins, relatively few teretoxin transcripts are reported for frameworks XI, XIII, and XV all of which contain eight Cys in different conserved patterns  (Kaas et al. 2010). Framework XI (I superfamily), composed of three subfamilies, is one of the best characterized conotoxin families in terms of pharmacology, with some I conotoxins functioning as Na + channel agonists and K + channel modulators  (Kauferstein et al. 2004 ). The representative framework XI sequence shown, Tan. 11. 1, has its closest BLAST hit to a teretoxin identified from Te. guttata, and as such differs significantly in sequence from its conotoxin counterparts (fig. 5B ). Framework XIII conotoxins are extremely rare and have only recently been assigned to a novel G superfamily  (Aguilar et al.
Beyond BLAST Identification of Putative Teretoxins As terebrids and their peptide toxins are in the preliminary stages of investigation and currently have very low representation in public databases, it is important to have alternatives to BLAST as a means to identify putative peptide toxin transcripts. To this end, an in house software program, termed Pepticomb, was developed and used to analyze the Tr. anilis and Te. subulata transcriptome assemblies. Pepticomb is largely based on source code kindly provided by the developers of the conoprec tool found on the Conoserver website (www. conoserver. org; Kaas et al. 2008 Kaas et al. , 2012)). In stepwise fashion, Pepticomb examines a set of contigs for the presence of a signal sequence, looks for a proregion that terminates in a basic residue cleavage site, and uses regular expressions to mine for permutations of Cys frameworks in the mature peptide with a limited number of intra-Cys residues set by the user.
Running this program on the Tr. anilis and Te. subulata assemblies successfully identified all the putative teretoxins already identified via BLAST, as well as additional candidate teretoxins not identified by BLAST (supplementary table  S2 , Supplementary Material online). These additional candidates include peptides of frameworks I, IX, VI/VII, VIII, XIII, XIV, and XXII, which are Cys frameworks already present in the putative teretoxins identified via BLAST. Pepticomb results also included candidate teretoxins with unknown frameworks (supplementary table S2, Supplementary Material online). Two unknown frameworks with 10-and 12-Cys were found in Tr. anilis, whereas three 8-Cys, three 10-Cys, five 12-Cys, and four 14-Cys unknown frameworks were found in Te. subulata. Interestingly, one of the ten-Cys unknown frameworks (C-CC-C-C-C-C-C-C-C) was found in putative toxins from both Tr.
anilis and Te. subulata. The surprising number of novel frameworks for Te. subulata transcriptomes compared with BLAST results suggests there are many more teretoxin Cys frameworks to be discovered. Given the inherent potential for misassembly or chimeric transcripts in de novo assembly, it is difficult without strong BLAST support to present the additional putative teretoxins identified via Pepticomb as high confidence transcripts. As such, these results are not included in the final count of 139 putative teretoxins found in Tr. anilis and Te. subulata transcriptomes identified via BLAST homology, and are pending further verification by proteomic analysis and other methodologies. Nonetheless, Pepticomb is an important tool for exploring the frontiers not captured by BLAST, and is extremely useful for organizing and validating putative toxin transcripts that are generated from BLAST results.
These results were included to demonstrate that there is a significant amount of transcripts not identified by BLAST when working with nonmodel systems. In such instances, alternatives to BLAST techniques, such as Pepticomb and the use of the profile Hidden Markov Model to aid in the identification and classification of terebrid gene superfamilies may yield more robust results, as was recently demonstrated for conotoxins  (Laht et al. 2012; Robinson et al. 2014). Similar to other conoidean venom peptides, the abundance of teretoxins found in our Tr. anilis and Te. subulata transcriptomes is believed to stem in part from high rates of gene duplication, while the hypervariable mature peptide region likely results from strong diversifying selection at specific loci, leading to significant allelic variation  (Duda and Palumbi 2000; Chang and Duda 2012).
Acknowledgments The expedition in Mozambique was carried out at  Estaca ˜o Marı ´tima de Biologia da Inhaca under a Memorandum of Cooperation between Museum National d'Histoire Naturelle (MNHN, Paris ) and the  Faculty of Science of University Eduardo Mondlane (Maputo) , and the authors thank  Prof. Amalia Uamusse  for generously hosting our research project. Authors wish to specially thank  Philippe Maestrati  and  Jose Ŕosado , and the rest of participants in the Inhaca, Mozambique expedition:  Magalie Castelin ,  Virginie He  ´ros,  Pierre Lozouet ,  Ellen Strong ,  Alexander Fedosov ,  Laurent Charles ,  Emmanuel Vassard ,  Gabriel Albano ,  Sergio Mapanga ,  Arlindo Fernando Machel ,  Daniela de Abreu ,  Mizeque Julio Mafambissa , and  Mito Nhaca. The authors are also thankful to  Mgavi Brathwaite  ( Program Manager  and  Academic Adviser ,  MS in Bioinformatics, NYU Polytechnic School of Engineering ) and  Ekta Sharma  (graduate student,  MS in Bioinformatics, NYU Polytechnic School of Engineering)  for their help and to  Quentin Kaas  for sharing conoprec source code.
This work was supported by the  National Science Foundation  ( 1247550 ), the  Camille and Henry Dreyfus Foundation ,  National Institutes of Health  ( MD007599 ), and  PSC-CUNY   Enhanced Collaborative Grant  ( CIRG2064 ) to M. H. W. G. Q. support provided by The  National Institutes of Health -National Institute of Allergy and Infectious Diseases  ( AI107955 ). Y. K. funding provided by  Russian Foundation for Basic Research  grant ( 14-04-00481 ) and the  MNHN as part of its program  of visiting scientists. A. V. , J. G. , and M. E. W. support provided by The  Graduate Center of the City University of New York Science Scholarship. A. V. acknowledges additional funding from The  Weissman School.
Our language is context-based which allows it to be easily adapted to any resource naming systems e. g. , Linux and Android (with SELinux) or PostgreSQL. Moreover, by abstracting low-level functionalities (e. g. , deny write to a file) through capabilities, our language remains independent from the security mechanisms. These capabilities can then be combined into security and assurance properties in order to provide high-level functionalities, such as confidentiality or integrity. Furthermore, we propose a global architecture that receives these properties and automatically configures the security and assurance mechanisms accordingly. Finally, we express the security and assurance policies of an industrial environment for a commercialized product and show how its security is enforced.
A. Security Policy and Enforcement Because of the ever increasing adoption of Cloud Computing platforms, many researches have been done to improve their security. As stated in  [6] , a security policy language is required to allow the tenants to express their security requirements. Indeed, this makes sense that the tenants define their security as they are the ones that know the best their infrastructure and its security requirements. Some of works related to security policy languages are specific to a programming language and require the modification of the application sources. Ponder2  [7]  is a distributed object management system. The Ponder2 language can express security and management policies for distributed systems. It is declarative and object-oriented and can be used to declare different types of policies.
Consequently, it can only be used on Java application augmented with the Ponder2 solution. Same for the A4Cloud project described in  [8]  and its associated language, A-PPL. Furthermore, this solution focuses on privacy and accountability, but does not address other classes of security, such as isolation. Works such as  [9] ,  [10]  also strength the need of combing multiple security mechanisms to provide an end-to-end and cross-layer security. VESPA  [10]  is one of such architecture for protecting cloud infrastructures using a policy-based management approach. However, this work is oriented toward the use of automatic computing to create self-protection loops. Consequently, they lack a language allowing the tenants to express their security. Nonetheless, a combination with our works could be a a way for future work.
B. Security Mechanisms Many different security mechanisms exist, providing a wide range of features. We present some of them that we use in our solution. SELinux  [16]  is a Linux Security Module (LSM) providing MAC (Mandatory Access Control). PAM  [17]  provides authentication management support for Linux. Iptables  [18]  is a standard Linux firewall. We use the tunnel functionality provided by OpenSSH  [19]  to secure the communications between the machines. Even if not used in this paper, cryptographic solution (such the one presented in  [20]  for the security of medical records or even homomorphic encryption  [21] ) could be added to the list of the security mechanisms that our solution takes into account. Indeed, the solution we propose is mechanism-agnostic.
SEE For instance, if the SE E receives the integrity property Integrity(configApp, adminRoot) defined in the previous section, it can enforce it using several security mechanisms. If this property is enforced using SELinux, then the SE E generates a SELinux module that forbids any write operation to the files labeled configApp that does not come from a user labeled adminRoot. The SE E also provides secure communication capabilities, especially for the case of properties between multiple systems. Thus, the two sides (i. e. , the selected mechanisms applying to the two contexts) of the communication must use compatible mechanisms to enforce a property. For instance, let us consider the property Confidentiality_Tunnel(hostClient, tunServer). The server allows the connection of the user through the defined port, and the client sets up the tunnel.