dataesr/scientific-paragraphs-categorization huggingface dataset:
To improve the quality of the predicted sRNAs, the free energy of the RNA secondary structures was computed by RNAfold from the Vienna package  [57]  and normalized by the length of each sequence. In order to support the evidence for the sRNAs predicted by SIGRS, the RNAspace pipeline  [54]  was used as a second method for sRNA prediction. The same input files of the set of known ncRNA genes and masked CDSs from the genomes described above were used.
In silico analysis of gene targets The novel sRNAs identified were analyzed by the Interacting RNA (IntaRNA) and RNAplex packages in order to find target genes for sRNA interaction. The IntaRNA software uses free energy of hybridation, target site accessibility and the presence of a seed to determine an RNA-RNA interaction  [22]. RNAplex was designed to quickly find possible hybridization sites for a query RNA in large RNA databases, using a slightly different energy model that reduces the computational time. It also has a length penalty that allows to focus the target search on short highly stable interactions. The input for both IntaRNA and RNAplex was the set of sRNAs, and the targets used were all CDSs from each of the three species: M. hyopneumoniae, M. flocculare and M. hyorhinis. The input sequences to screen for the targets comprised 150 nucleotides upstream of the start codon until 150 nucleotides downstream start codon of each annotated gene.
Funding Financial support for this study was provided by  Coordenação de Apoio de Pessoal de Nível Superior -CAPES  (Process no. 23038. 010041/2013¬13 ), CAPES-COFECUB Program  (Process no. 23038. 001507/2013-81 )and by the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no [ 247073]10. The publication costs for this article were funded by  CAPES  (process no. 23038. 010041/2013¬13 ).
The concordance index (c-index) was calculated for different models as a measure of the discriminatory ability that allows comparison of models. A model with a c-index=0. 5 has no predictive value, a model with a c-index =1 would allow a perfect prediction of patient outcome  (23). The concordance index was analyzed using the survConcordance function in the survival package for R. Associations between best overall response and biomarker categories were analyzed by Chi square and Fisher´s exact tests. Throughout the analysis, P-values <0. 05 were considered statistically significant. All statistical tests were two-sided. Analyses were carried out using SPSS Version 22 (IBM, USA) and R 3. 2. 1 (R Foundation for Statistical Computing, Vienna Austria).
Patients and treatments A total of 616 patients treated with pembrolizumab were included (Table  1 ). The median age was 60 years, and 40. 6% were male. Of 578 patients with sufficient data for classification according to AJCC 2009  (24) , 483 were assigned to the M-category M1c (83. 6%), 59 to M1b (10. 2%), 25 to M1a (4. 3%), and eleven patients had unresectable stage III disease (1. 9%). Treatment was mainly administered in the early access program (65. 3%; NCT02083484) or in the Keynote-002 study (17. 7%; NCT01704287)  (5). Fifty-four patients were treated with commercial pembrolizumab after FDA marketing approval (8. 8%), 42 patients were treated in the Keynote-001 study (6. 8%; NCT01295827)  (1, 4) , and nine were treated in the Keynote-006 study (1. 5%; NCT01515189). 97. 9% had received at least one prior systemic treatment including ipilimumab, while 2.
In the Keynote-002 trial, ipilimumab-refractory melanoma patients were randomized to receive two different doses of pembrolizumab or investigator-choice chemotherapy  (5). In the Keynote-006 trial, ipilimumab-naive patients were randomized to receive pembrolizumab using two different dosing schedules or ipilimumab  (3). Both trials showed a superiority regarding progression free survival (both trials) or OS (Keynote-006) of patients treated with pembrolizumab compared to alternative treatments. As all baseline data required for the proposed combination model including RLC and REC have been collected in the respective trial databases, a retrospective analysis of these data would be possible in principle. We are currently planning to perform such studies in collaboration with the sponsoring company.
Acknowledgments The following people provided data to the project but are not represented in the author list: Steven Goetze:  Department of Dermatology, Friedrich Schiller University, Jena, Germany. Cornelia Mauch, Max Schlaak: Department of Dermatology, University Hospital of Cologne, Cologne, Germany. Frank Meiß: Department of Dermatology, University Medical Center Freiburg, Freiburg, Germany. Nadine Went: Department of Dermatology, Hannover Medical School, Hannover, Germany. Dirk Debus ,  Jasmin Jähner :  Department of Dermatology, Klinikum Nord, Nürnberg, Germany. Claudia Pföhler: Department of Dermatology, Saarland University Hospital, Homburg, Germany. Suvada Biserovic ,  Evelyn Dabrowski ,  Edgar Dippel :  Department of Dermatology, Ludwigshafen Hospital, Ludwigshafen, Germany. Ursula Dietrich: Department of Dermatology, University Medical Center Dresden, Dresden, Germany.
Introduction In recent years the construction of large bibliographic databases has made much of the published literature of biology available for searches. But, as any biologist who has performed online literature searches knows, the capabilities of the search engines indexing these databases are limited. Consider the following scenario: An investigator is interested in masticatory (chewing) muscles of mammals. She begins her work with a review of the literature. This turns out to be much more challenging than one might think. For example, the popular scientific literature search engines PubMed and Google Scholar return tens of thousands of hits for 'masticatory muscle. ' However, only a small subset of these is relevant to her work, and many relevant articles are missed, because the results are limited to articles that contain the phrase "masticatory muscle" or the words "masticatory" and "muscle" in the title, abstract or in some cases, the full text.
These and other primarily keyword index-driven search engines do not "know" what semantically a masticatory muscle is: a muscle that is active during mastication or, more broadly, feeding. To find papers that pertain to any of the masticatory muscles in mammals, the full list of those muscles would need to be entered in the query. Although such brute force methods are possible (e. g. ,  [1] ), they are tedious, prone to error by omission, and hardly available to anyone except those few with the requisite expert knowledge. In contrast, semantic searches, i. e. , ones that take advantage of computationally available expert knowledge, are currently not supported by most comprehensive literature databases. For comparative anatomy, and for many other domains of knowledge, this is, in part, because the specialized body of anatomical knowledge is currently not represented in a computable form that can be used by such tools.
These have been recently merged into a taxonomically broader community anatomy ontology known as Uberon;  [3, 6]. Uberon encompasses metazoan anatomy, with an emphasis on vertebrates, and is fully integrated with species-specific anatomy ontologies. Here we present the Mammalian Feeding Muscle Ontology (MFMO), a multi-species anatomy ontology focused on muscles and associated anatomical structures that participate in feeding and other oral/pharyngeal behaviors. The MFMO was developed to support the Feeding Experiments End-User Database (FEED) Project (  [15] ; https://feedexp. org/). FEED is a publicly-available, online repository for physiological data collected from in vivo studies of feeding behaviors (i. e. , oral/pharyngeal behaviors including mastication, biting, swallowing) of mammals.
The FEED database facilitates quantitative and comparative studies of feeding and other behaviors in a manner that heretofore would have been difficult, if not impossible, as demonstrated by the promising preliminary analyses of Vinyard et al. [16]  and Williams et al. [17]  across Eutherian mammals. To enable queries against FEED to return the most comprehensive, precisely focused, and relevant data possible to users, building FEED necessitated an ontological representation of the anatomical structures that participate in feeding behaviors, including relationships among muscles, their development, attachments, form and function. The resulting MFMO, which is incorporated into the search engine of the online FEED database, generalizes some of the concepts in Uberon  [3, 6] , and adds new terms and relations for muscle structures.
These relations are very useful. With them, one can search for all of the components in the lower limb, for example. But without other, biologically relevant relations, for example attached_to and innervated_by, such definitions are far too simplistic to be useful for contemporary biologists. Higher-level anatomical classes have been imported from Common Anatomy Reference Ontology (CARO;  [19] ) and the Biological Spatial Ontology (BSPO;  [20] ). Most relations have been imported from the subset of the Relations Ontology (RO) used in Uberon (  [6] , supp. 1; the 'innervated_by' relation is adapted from  [21] ; https://github. com/oborel/obo-relations). Because the MFMO is being used as a structured, controlled vocabulary and ontology module to maximize search capabilities within the FEED database  [15] , it was necessary to carefully refine the annotations and logical definition of each muscle class.
The MFMO ontology is maintained in a GitHub repository, where it is available for examination and download in Web Ontology Language (OWL) format (http://purl. obolibrary. org/ obo/mfmo. owl). Each class is in the MFMO namespace, and is uniquely identified by a URI of the form: http://purl. obolibrary. org/obo/MFMO_nnnnnnn. MFMO is maintained and edited in Protégé (v. 5. 0; http://protege. stanford. edu). We import images using the Image depiction Protégé plugin (https://github. com/balhoff/image-depictions-view). The MFMO uses an ontology 'Continuous Integration server approach' (http://bio-ontologies. knowledgeblog. org/405) as described in Haendel, et al  [6]. To promote expert review, the annotations and logical definition of each class are posted as tracker items for review by the FEED and Uberon communities in the GitHub repository for Uberon (https://github.
Structure of the MFMO To populate the MFMO we first considered taking a slice of the multi-species anatomy ontology, Uberon  [3, 6] , containing all of the classes for muscles and associated structures required for FEED, but that approach proved to be very inefficient. The initial version of Uberon was created through a system in which generalized classes were extracted from existing species-centric anatomy ontologies. Many of the textual definitions for muscles were extracted from Wikipedia via the DBpedia database  [3, 22]. As a result, we found that most of the textual definitions for anatomical structures in Uberon were human-centric and the computable definitions too simple to make each one unique, necessary, and sufficient across mammals. Thus, this effort required creation of a new class for each muscle in the MFMO.
Methods for Use-Cases The value of the MFMO was assessed by asking competency questions  [23]  in three use-cases. We used the Description Logic Query function within Protégé (DL Query; http://protegewiki. stanford. edu/wiki/DLQueryTab) and the ELK reasoner  [24]  to assess the logical structure of the MFMO itself. We searched for trials (single physiological recordings) within the FEED Database to demonstrate the powerful search capabilities made available by building an anatomy ontology such as the MFMO into a database. Finally, we created Textpresso for FEED-a Textpresso demonstration site for the FEED project (http://textpresso-dev. caltech. edu/FEED/). As explained in  [25] , a "competency question (CQ) is a natural language sentence that expresses a pattern for a type of question that people expect an ontology to answer.
The answerability of CQs hence becomes a functional requirement of the ontology. " To test the competency of the MFMO "use-cases" the questions were formed in the familiar language of comparative biology to judge if the answers are appropriate in three settings: 1) in the MFMO ontology itself; 2) in the FEED online database; and 3) in Textpresso sites into which the MFMO is incorporated. Results of the CQs in MFMO are compared to similar searches in two popular databases, PubMed (http://www. ncbi. nlm. nih. gov/pubmed) and Google Scholar (https://scholar. google. com/), accessed through the University of Illinois, Chicago. Searches were performed between February and January of 2016. To enable the FEED database, which is not integrated with any kind of reasoning capability, to utilize the MFMO for searching its records, we developed a tool that takes an OWL ontology (MFMO in our case) as input and, using the ELK reasoner, generates its transitive closure, which includes not only the asserted, but also all implied relationships, including those implied by recursion.
In addition to computing the subclass hierarchy closure, the tool precomputes closures along selected transitive properties, such as "part of. " The tool outputs the result, a pre-reasoned ontology, in OWL format, which is then imported by an ontology import component that is part of FEED. The tool is available freely in source code form from GitHub (https://github. com/NESCent/feed-ontology-closure). Textpresso is a text-mining system that facilitates powerful searches of a corpus of scientific literature (see  [26]  for a detailed description of the Textpresso system and construction of Textpresso sites). Textpresso allows users to query the full text of papers using keywords or semantically related categories (a shallow ontology; "bags of words") or both, thus allowing highly specific queries for information retrieval from the literature.
To create the Textpresso for FEED implementation, we compiled a corpus of 1636 articles in the domain of vertebrate functional evolutionary biology, primarily focused on the mammalian head and neck. PDFs were collected from the digital libraries of several members of the FEED Working Group and focus on the evolution, functional morphology, and developmental biology of the head and neck in mammals. No strict criteria were used for inclusion. Many articles were excluded because the text could not be interpreted by a text reader. The Textpresso for FEED corpus will be expanded to over 8000 articles in the near future. Going forward, it may be possible to automate the process of identifying and adding new literature to the corpus as it is published. We created an ontology for the site by combining the MFMO, the Oral/Pharygeal Behavior Ontology (OPBO, an ontology of oral/pharyngeal behaviors, an ongoing project of the FEED Working Group; https://github.
Results The type of data returned by a query is dependent upon the data in the database, so results of queries in the use-cases are not precisely the same. Queries of the MFMO in Protégé return only the muscles (classes) themselves. Queries of the Textpresso sites return published articles, as do queries of PubMed and Google Scholar. Queries of FEED return trials (single recordings) in the database. All of the websites cited herein were active at the time of submission. We invite our readers to explore the sites and create their own queries.
The MFMO facilitates searches for a muscle and synonyms The MFMO includes synonyms for muscles. The MFMO underlies the FEED Database, thus recordings from any of the muscles in FEED can be retrieved by searching for a given muscle or any of its synonyms. The current FEED database is small (1069 trials as of 1/1/2016, and most of the data are, at present, electromyographic (EMG) recordings from trigeminal muscles. Even so, the competency of the ontology is apparent. If we ask FEED to find all recordings from the 'lateral pterygoid muscle,' FEED returns 31 trials whether we ask for the 'lateral pterygoid muscle' or its synonym, the 'external pterygoid muscle' (S2 Fig). In contrast, PubMed and Google Scholar yield very different results when asked for papers that contain the text 'lateral pterygoid muscle' or 'external pterygoid muscle.
' Searches return 773 papers for lateral pterygoid muscle and only 110 papers for external pterygoid muscle in PubMed. In Google Scholar the same searches return approximately 26,000 papers for lateral pterygoid (S3 Fig)  and 25 ,700 papers for external pterygoid (S4 Fig). The reason that these searches return different numbers of papers for the same muscle is that these search engines do not synonymize the terms "lateral pterygoid muscle" and "external pterygoid muscle. " FEED returns appropriate answers to the questions; search engines on the large databases do not.
Discussion The use-cases described above demonstrate how the knowledge encoded in the MFMO enables databases such as FEED and Textpresso to index their content such that it can be retrieved in response to, and consistent with, language and terminology familiar to comparative anatomists. One can ask, for example, which muscles that develop from the second branchial arch are attached to the hyoid apparatus, because the requisite knowledge about developmental origin and attachments is encoded in the MFMO by means of logical axioms. We can also infer the answer to the question "from which branchial arch is the posterior digastric muscle derived?" The MFMO infers that the posterior digastric muscle is derived from the second branchial arch, because it is innervated by the facial nerve. This type of inference is an essential part of the logic of comparative anatomy.
FEED is a "proof-of-concept" database that is meant to grow as the data on mammalian and reptilian feeding grows. Analyses are currently underway that will identify the most critical taxonomic gaps in FEED. It is our hope, and a goal of the FEED project, that our colleagues who study mammalian feeding will also begin to use FEED and upload some of their data to the site. The MFMO is of central importance as it facilitates the computational comparisons that allow meaningful analyses of phenotypic diversity. Use of the MFMO in conjunction with the orthogonal ontology on oral/pharyngeal feeding behaviors (the OPBO) that is incorporated into FEED permits the integration of anatomical, physiological, and behavioral data so that the links between structure, function, and behavior can be explored in a systematic way across users and across disciplines.
But the use-cases elaborated above show that such succinct, generic, logical definitions as in the MFMO can be powerful as well. Interestingly, in practice students of anatomy learn quickly that a muscle can often be identified without examination of the details of the muscle attachments. In contemporary textbooks, the basic attachments, innervations, and functions of muscles are often summarized in large tables (e. g. ,  [34] ), Table  8. 21). Much progress has been made toward the development of a methodology for encoding phenotypic variation in ontologies and, using these new methods, the creation of databases with descriptions of taxa and individual specimens (e. g. ,  [36, 37, 38] ). Knowledge of anatomy encoded in machine-interpretable ontologies with concise logical definitions is not only powerful for intelligent information retrieval, but also will play a foundational role in transforming the rich descriptions of phenotypic variation into fully computable logic expressions based on the so-called 'Entity-Quality' (EQ) formalism  [39].
More widely enabling semantics-aware searching is a future challenge for the broader scientific and publisher community. The FEED and Textpresso sites with the MFMO installed make semantic searches possible, but they also do more than that. Both FEED and Textpresso enable the utilization of "dark data. " "Dark data" are defined as "any data that is not easily found by potential users" ([51], page 281). FEED is a repository for data on the physiology of feeding that resides in laboratories around the world. FEED will enable comparative studies across disparate species from these data. Textpresso and other projects such as the Paleobiology Database (https://www. paleobiodb. org/), attempt to make portions of the "dark" published literature of science available online in searchable form. As argued by Wächter et al.
[52] , the literature holds the answers to many questions that a researcher might ask, "but a classical literature search cannot answer the questions directly. " Literature search indexes, such as PubMed and Google Scholar, lack the ability to utilize domain knowledge for query expansion, reduction, and filtering. In other words, tedious enumeration of concepts relevant to queries is required (e. g. , listing all of the masticatory muscles), as well as expert knowledge of the subject. Conversely, if the domain knowledge is encoded in a computationally interpretable form in an ontology, databases can utilize it to expand queries based on logical entailments of query expressions, and ontology-powered query answering is then available to experts and non-experts alike. The MFMO encodes the broad consensus of comparative anatomy knowledge about the head and neck muscles involved in feeding across Eutheria.
Acknowledgments Gowri Ramachandran  and  Matt O'Neill  provided support to help organize and enter data on muscle anatomy and terminology. Kun Peng  and  Elizabeth Ro  performed data entry the Textpresso for FEED project. Work on the MFMO is supported by the  National Science Foundation  ( DBI-1062350  to  Robert Druzinsky ). We thank  Wasila Dahdul  for all of her advice and support. We would like to thank  Kathleen Smith ,  Allen Rodrigo ,  Susan Alberts , and the  National Evolutionary Synthesis Center (NESCent ;  NSF EF-0905606 ). NESCent hosted FEED Workshops in May, 2012, and May, 2014. Chris Mungall  was supported by the Director,  Office of Science, Office of Basic Energy Sciences , of the  U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. Paula Mabee  and  Wasila Dahdul  acknowledge funding from the  National Science Foundation  ( DBI-1062542 ) and the  Phenotype Ontology Research Coordination Network  ( DEB-0956049 ).
This work received support from the following sources:  National Science Foundation  (nsf. gov) to RED DBI-1062350; to SHW DBI-1062327; to CEW DBI-1062333; to RZG  DBI-1343588 ; to CJV DBI-1062332; to PMM  DBI-1062542  and  DEB-0956049 ;  National Evolutionary Synthesis Center  (https://www. nescent. org/;NESCent;  NSF   EF-0905606 ). NESCent hosted FEED Workshops in language familiar to biologists. Further, it provides a template for muscles that will be useful in extending any anatomy ontology. The MFMO is developed to support the Feeding Experiments End-User Database Project (FEED, https://feedexp. org/),a publicly-available, supported by the Director,  Office of Science, Office of Basic Energy Sciences , of the  U. S. Department of Energy  under Contract No. DE-AC02-05CH11231  (http://energy.
Our problem Conventional tools, designed for linear sequences, could yield an incorrectly high genetic distance between closely related circular sequences. Indeed, when sequencing molecules, the position where a circular sequence starts can be totally arbitrary. Due to this arbitrariness, a suitable rotation of one sequence would give much better results for a pairwise alignment, and hence highlight a similarity that any linear alignment would miss. A practical example of the benefit this can bring to sequence analysis is the following. Linearized human (NC_001807) and chimpanzee (NC_001643) MtDNA sequences, obtained from GenBank  [16] , do not start in the same region. Their pairwise sequence alignment using EMBOSS Needle  [17]  (default parameters) gives a similarity of 85. 1 % and consists of 1195 gaps.