dataesr/scientific-paragraphs-categorization huggingface dataset:
0168 (19) Å and 2. 0016 (18) Å, which is typical for a Jahn-Teller distortion. A sulfamerazine anion that is deprotonated at the N11 N atom is present in the structure (Fig. 1 ). To the best of our knowledge, a search in the Cambridge Structural Database reveal, that this is the first crystal structure where a sulfamerazinate anion act as a counter ion. A similar  et al. , 2002)  where a sulfathiazole acts as a deprotonated counter ion too. situation is observed in [Cu(en) 2 (OH 2 ) 2 ](Stz) 2 •2H 2 O (Anacona In the crystal strcuture of the title compound every complex cation is linked via O-H•••N, N-H•••O and O-H•••O hydrogen bonding to the counter cations, while the cations are interconnected via the N-H•••N interaction, which lead to the formation of a two dimensional network (Fig. 2 ).
The CLC Genomics Workbench version 6. 0. 1 (CLC bio, Aarhus, Denmark) was used to perform quality trimming and de novo assembly of the reads. The resulting contigs were reordered using Mauve version 2. 3. 1  (5)  and Rickettsia montanensis strain OSU 85-930 as a reference genome (GenBank accession no. CP003340. 1). Potential coding sequences (CDSs) were predicted using AMIGene  (6) , and the assignment of protein functions was performed by searching against the RickBase (7), GenBank, and Pfam (8) databases using BLASTp (9), while ribosomal RNAs, tRNAs, and other RNAs were identified using BLASTn, tRNAscan-SE version 1. 21  (10) , and RNAmmer 1. 2  (11) , respectively. The orthologous genes between R. tamurae and R. montanensis were identified using OrthoMCL  (12) , with a BLASTp E value cutoff of 1 ϫ 10 -5 and a default Markov cluster (MCL) inflation parameter of 1.
The R. tamurae chromosome exhibits a high level of synteny to R. montanensis, with the exception of two inversions of 896 bp and 3,766 bp, respectively. Furthermore, several genes are lacking in the R. tamurae genome, including genes for a spore coat protein-like protein, isopentenyl-diphosphate deltaisomerase (fni), site-specific DNA methylase (dam1), a proline/ betaine transporter (proP9_2), large extracellular alpha-helical protein, 3-hydroxyacyl-coenzyme A (CoA) dehydrogenase (fadB), competence protein F2 (comF2), PemK-like growth inhibitor, Sco 2 protein precursor, and two toxin-antitoxin pairs. Nucleotide sequence accession numbers. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under accession numbers CCMG01000001 to CCMG01000027 (BioProject PRJEB6744).
Acknowledgements I am grateful to  Dr. Pierre Moret ,  University of Toulouse , for the donation of the studied material and for sharing information about the ecology and habitat of the new species. I thank  Miss Michael M. Webber ,  University of Nevada, Las Vegas  for her review of an earlier version of the manuscript. Elise- Anne Leguin  ( MNHN ) for her assistance with the preparation of the photos and plates, and  Dr. Peter Schwendinger ,  Natural History Museum, Geneva  for the loan of the types of Chactas mahnerti.
C'est ainsi que les données collectées ne recouvrent pas tant le réel, mais expriment ce que l'on propose de faire de celui-ci. Les statistiques anthropométriques de Césare Lumbroso dans L'homme criminel sont édifiantes sur ce sujet tant ils se rapportent aux présupposés suspects de la physiognomonie 8. Si l'on veut un exemple plus proche de nous, on peut se demander ce qui impose que la base de données des homicides non résolus 9 dans les différents États américains comporte des colonnes correspondant à des données ethniques ou raciales des victimes, mais aucune rubrique sur la situation sociale de celles-ci. Est-ce la nature même de l'événement, en l'occurrence un acte criminel, qui oblige à cette structuration ou bien s'agit-il d'éléments d'une autre nature ? La réponse est évidente et Michel de Certeau  (De Certeau, 1990  : XLV) la donne d'une autre manière : « L'enquête statistique ne trouve que de l'homogène […] ».
Ce n'est pas seulement une métaphore pour indiquer que toute chose peut disposer d'un double numérique à partir duquel on le considère, puisqu'il existe désormais aussi des vêtements intelligents. Cf. par exemple, le projet ANR Homo Textilus. Cf. http://www. agence-nationale-recherche. fr/projet-anr/?tx_lwmsuivibilan_ pi2[CODE]=ANR-11-SOIN-0007.
A consequence of the current lack of analysis is that it is not fully clear what a system can actually learn from the available data sets. In the light of the above considerations, the purpose of this paper is to carry out a deep analysis of Textual Entailment (TE) data sets. We investigate the relations among two relevant aspects of semantic inferences: the logical dimension, i. e. the capacity of the inference to prove the conclusion from its premises, and the linguistic dimension, i. e. the linguistic devices that are used to accomplish the goal of the inference. With respect to other studies -see, for instance,  Garoufi (2007)  and  Sammons et al. (2010)  -that have annotated and investigated TE datasets, we take a data oriented and neutral approach. As an example, we do not assign a polarity to single linguistic phenomena, and we do not impose specific categorizations on positive and negative entailment, rather we expect to derive such distinctions from observations.
In the paper we systematically analyze a data set of TE pairs according to two relevant dimensions: (i) the nature of the inference, using the traditional logical view on arguments (Section 3); (ii) the linguistic phenomena involved in the inference (Section 4). In both sections we first provide the necessary background, and then we apply the analysis to a TE data set that we use throughout the paper. Section 5 presents a novel approach aiming at producing inference data sets where single linguistic phenomena are isolated one at a time. Through the decomposition of an initial RTE pair we obtain all the atomic pairs involved in the inference process, each tagged with the corresponding phenomenon. We show that the fine-grained analysis allowed by atomic pairs is a powerful investigation tool, which sheds new light on the relations between the polarity of a certain linguistic phenomenon and the occurrence of that phenomenon in both positive and negative pairs.
Inference data sets This section first presents the current status of RTE data sets, then describes other data sets used by the community for semantic inferences, and finally introduces the data set we have used for the analysis carried out in this paper. In 2005, the PASCAL Network of Excellence started an attempt to promote a generic evaluation framework covering semantic-oriented inferences needed for practical applications, launching the Recognizing Textual Entailment challenge  (Dagan et al. , 2005) ,  (Dagan et al. , 2006) ,  (Dagan et al. , 2009) , with the aim of setting a unifying benchmark for the development and evaluation of methods that typically address similar problems in different, application-oriented, manners. As many of the needs of several Natural Language Processing (NLP) applications can be cast in terms of TE, the goal of the evaluation campaign is to promote the development of general entailment recognition engines, designed to provide generic modules across applications.
e. the goal of each problem is expressed as a question). With respect to RTE pairs, here the problems are designed to focus on a broader range of semantic and inferential phenomena, including quantifiers, plurals, anaphora, ellipsis and so on, as shown in Example 1. 4 (fracas-022: monotonicity, upwards on second argument) 10. ( Even if the FraCas test suite is much smaller when compared to the number of annotated pairs in RTE data sets, and it is less naturalseeming (i. e. it provides textbook examples of semantic phenomena, the corpus). This task is situated in the summarization application setting, where i) H's are based on Summary Content Units  (Nenkova et al. , 2007)  created from human-authored summaries for a corpus of documents about a common topic, and ii) the entailing sentences (T's), are to be retrieved in the same corpus from which the summaries were made.
Data sets for this task are therefore very different from the previous edition of the challenge, since there are no predefined T-H pairs. 10 In the example, P and Q are respectively the premises and the question from the original source problem. The H element contains a sentence which is, as nearly as possible, the declarative equivalent to the question posed in the Q element. B. MacCartney (Stanford University) converted FraCas questions into declarative hypothesis: http://www-nlp. stanford. edu/ ~wcmac/downloads/fracas. xml quite different from the kind of inferences that can be found in real data), it is worth mentioned here. Another available inference data set that we are aware of is the Microsoft Research Paraphrase Corpus 11  , that contains 5800 pairs of sentences which have been extracted from news sources on the web, and then manually annotated as paraphrase/semantic equivalence.
g. Microsoft Paraphrase, WMT2008 development data set -Europarl section 14  , pairs of sentences where the first comes from Ontonotes and the second from a WordNet definition, and so on). In both competitions, most of the approaches implement Machine Learning methods, that try to exploit training set data for learning. Since the work we present in this paper focuses in particular on Textual Entailment, the data we consider for our analysis include a sample of pairs extracted from RTE-5 data set  (Bentivogli et al. , 2009b). More specifically, in order to compare our results with the literature, we created our reference data joining the data sets annotated by  Sammons et al. (2010)  (composed of 210 pairs from RTE-5 test set: 107 entailment, 37 contradiction, 66 unknown) and by  Bentivogli et al.
Validation criteria applied to RTE pairs In the light of the definitions provided in the previous section, we annotated our RTE-5-SAMPLE data set with respect to the argument evaluation criteria described in Section 3. 1. In general, in TE we assume the fact that: i) if T and H refer to an entity x, the reference is the same (reinforcing the relevance criterion), and ii) T (i. e. the premise) is assumed to be true (criterion 1 is always satisfied). According to the second evaluation criterion (i. e. validity and inductive probability), TE pairs are annotated as deductive (Example 1. 5, pair id=414), inductive (Example 1. 6, pair id=194), abductive (Example 1. 7, pair id=224) or not valid (i. e. invalid argument, contradiction) (Example 1. 8, pair id=11). Inductive arguments have also been annotated according to the subcategories of inductive reasoning following  Nolt et al.
On the contrary, (almost) all contradiction pairs are invalid arguments (the premises do not support the conclusion). Unknown pairs can be either inductive arguments with a low inductive probability (i. e. 12%), abductive arguments (i. e. 16%), arguments committing the fallacy of relevance (i. e. 28%), or arguments committing the fallacy of suppressed evidence (44%). In general, abductive arguments are very infrequent in RTE data set, and can result both in entailment or in unknown pairs. As introduced in Section 3. 1, relevance is an essential criterion, even if simplifying assumptions have been made by RTE organizers (i. e. the same meaning of entities mentioned in T and H is assumed). The criterion of total evidence relates to the problem of background knowledge, since incomplete arguments require new evidence both to validate or invalidate the conclusion.
e. contradiction and unknown pairs (Columns C and U, respectively). A number of remarks can be made on the data presented in Table  3. Both macro categories and fine-grained phenomena are well represented but show a different absolute frequency: some have a high number of occurrences, whereas some others occur very rarely. To highlight the main features and the points of strengths of our annotation strategy, we compare it with two relevant works in the literature, i. e. Garoufi (2007)  and  Sammons et al. (2010). In  Garoufi (2007) , a scheme for manual annotation of textual entailment data sets (ARTE) is proposed, with the aim of highlighting a wide variety of entailment phenomena in the data. ARTE views the entailment task in relation to three levels, i. e. Alignment, Context and Coreference, according to which 23 different features for positive entailment annotation are extracted.
Each level is explored in depth for the positive entailment cases, while for the negative pairs a more basic and elementary scheme is conceived. The ARTE scheme has been applied to the complete positive entailment RTE-2 test set (400 pairs, i. e. 100 pair of each task), and to a random 25% portion of the negative entailment test set, equally distributed among the four tasks (100 pairs, i. e. 25 pairs of each task). Reasoning is the most frequent feature appearing altogether in 65. 75% of the annotated pairs: this indicates that a significant portion of the data involves deeper inferences. The combination of the entailment features is analyzed together with the entailment types and their distribution in the data. More recently,  Sammons et al. (2010)  carried out an annotation work that is very similar in spirit to the approach proposed in  Bentivogli et al.
Towards total evidence The underlying idea is to create atomic pairs, i. e. T-H pairs where a phenomenon relevant to the inference task is highlighted and isolated, 23 on the basis of the phenomena which are actually present in the RTE T-H pairs. As claimed before, one of the advantages of testing the proposed methodology on RTE data consists of the fact that the actual distribution of the linguistic phenomena involved in the entailment relation emerges. In Section 4. 1 we proposed a classification of the phenomena we detected while analyzing a sample of RTE pairs, July 2013 and we decided to group them using both fine-grained categories and broader categories. Grouping specific phenomena into macro categories would allow us to create specialized data sets of atomic pairs representing those phenomena, containing enough pairs to train and test TE systems.
Different rules can be needed in order to formalize the variants in which the same phenomenon occurs in the pairs. For example, the following entailment rules both formalize the phenomenon of apposition (syntax): a) Pattern: X Y ⇔ Y X Constraint: APPOSITION(Y,X) b) Pattern: X, Y ⇔ Y is X Constraint: APPOSITION(Y,X) Given such basic concepts, the procedure for the creation of atomic pairs we propose consists of a number of steps carried out manually. We start from a T-H pair taken from the RTE data sets and we decompose T-H in a number of atomic pairs T-H i , where T is the original Text and H i are Hypotheses created for each linguistic phenomenon relevant for judging the entailment relation in T-H. The procedure is schematized in the following steps: 1. individuate the linguistic phenomena which contribute to the entailment in T-H 2.
35 atomic pairs, among which 1. 29 are entailment pairs and 1. 05 are contradiction pairs. This means that the methodology is productive for both positive and negative examples. As introduced before, in 54 out of 75 unknown examples no atomic pairs can be created, due to the lack of specific phenomena relating T and H (typically the H contains information which is neither present in T nor inferable from it). For the 11 pairs that have been decomposed into atomic pairs, we created an average of 1. 8 atomic pairs, among which 1. 19 are entailment and 0. 61 are unknown pairs. This analysis shows that the only source of negative atomic pairs are the contradiction pairs, which actually correspond to 20% of RTE-5 data set. Overall, the study showed that the decomposition methodology we propose can be applied on RTE-5 data.
Related work This section presents a number of studies that analyze RTE data sets from the point of view of linguistic phenomena. An attempt to isolate the set of T-H pairs whose categorization can be accurately predicted based solely on syntactic cues has been carried out in  Vanderwende et al. (2005). The aim of this work is to understand what proportion of the entailment pairs in the RTE-1 test set could be solved using a robust parser. Two human annotators evaluated each T-H pair of the test set, deciding whether the entailment was: true by syntax ; false by syntax ; not syntax ; can't decide. Additionally, annotators were allowed to indicate whether the recourse to information in a general purpose thesaurus entry would allow a pair to be judged true or false. Their results show that 37% of the test items can be handled by syntax, broadly defined (including phenomena such as argument assignment, intra-sentential pronoun anaphora resolution); 49% of the test items can be handled by syntax plus a general purpose thesaurus.
The lexicalsyntactic level additionally captures syntactic relationships and transformations, lexical-syntactic inference patterns (rules) and co-reference. They manually annotated a sample from the RTE-1 data set according to each model, compared the outcomes for the two models as a whole as well as for their individual components, and explored how well they approximate the notion of entailment. It was shown that the lexicalsyntactic model outperforms the lexical one, mainly because of a much lower rate of false-positives, but both models fail to achieve high recall. The analysis also showed that lexical-syntactic inference patterns stand out as a dominant contributor to the entailment task. Clark et al. (2007)  agree that only a few entailments can be recognized using simple syntactic matching, and that the majority rely on a significant amount of "common human understanding" of lexical and world knowledge.
e. the FATE corpus) and discuss experiments conducted on this basis. Bentivogli et al. (2009a)  focus on some problematic issues related to resolving coreferences to entities, space, time and events at the corpus level, as emerged during the annotation of the data set for the RTE Search Pilot. Again at the discourse level,  Mirkin et al. (2010b), and Mirkin et al. (2010a)  analyze various discourse references in entailment inference (manual analysis on RTE-5 data set) and show that while the majority of them are nominal coreference relations, another substantial part is made up by verbal terms and bridging relations.
A relevant consequence of this fact is that the polarity of most of the phenomena is not predictable from the logical judgments, with an evident impact on the possibility to learn it from the available annotated RTE data sets. On the base of these findings we suggest that future developments should exploit the decomposition approach on specialized data sets, composed of atomic pairs. In several respects the work we have presented in this paper is incomplete. It opens the way to further research in this direction. Particularly, we think that much more investigation and empirical experiments would be necessary in order to better determine the relations between linguistic phenomena and logical judgments in semantic inferences. Our hope is that these future data oriented studies will support computational approaches by e.
Identification of protein domains is a key step for understanding protein function. Hidden Markov Models (HMMs) have proved to be a powerful tool for this task. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in sequenced organisms. This is done via sequence/HMM comparisons. However, this approach may lack sensitivity when searching for domains in divergent species. Recently, methods for HMM/HMM comparisons have been proposed and proved to be more sensitive than sequence/HMM approaches in certain cases. However, these approaches are usually not used for protein domain discovery at a genome scale, and the benefit that could be expected from their utilization for this problem has not been investigated. Using proteins of P. falciparum and L.
Introduction With the continuous improvement of genome sequencing technologies, an increasing number of new genomes are emerging everyday, enhancing basic knowledge on the diversity of organisms and providing valuable data to understand their biology and evolutionary relationships. A survey of the Uniprot database indicates, however, that this knowledge is highly unbalanced, with most of sequenced Eukaryotes being related to Plant and Unikont super-groups (see Figure  1 ). Since functional annotation tools have been developed based on this wealth of unbalanced data, they show limits when applied to the exploration of divergent genomes  [1, 2]. This is especially true for protein domains, as illustrated in Figure. 1 Domains occupy a key position among the relevant annotations that can be assigned to a protein.
Protein domains are sequential and structural motifs that are found in different proteins and in different combinations and, as such, are the functional subunits of proteins above the raw amino acid level  [3]. Protein domain composition provides strong clues regarding protein function. Indeed, two thirds of mono-domain proteins having the same domain also have the same function. Likewise, 35% of multi-domain proteins having one common domain present similar functions, while this rate increases to 80% when they share two common domains  [4]. Protein domains also provide meaningful information for comparative genomics  [5, 6]  as well as for studying protein-protein interactions  [7]. Several approaches and databases have been developed to define and identify domains. One of the most widely used domain schemes is the Pfam database  [8].
The Pfam 26. 0 release offers a large collection of 13672 domain families. Each family in Pfam is represented by a Hidden Markov Model (HMM) of a multiple sequence alignment  [9]. HMMs model both the conserved positions and gaps (insertions and deletions) of the multiple alignment  [10]. HMMs are classically used as sequence/profile approaches to recognize homology and decipher family membership. When analyzing a new protein sequence, each Pfam HMM is used to compute a score measuring the similarity between the sequence and the domain using HMMER software  [11]. If the score is above a given threshold provided by Pfam (each domain has its own recommended score threshold), then the presence of the domain can be asserted in the protein. However, when applied to organisms showing high evolutionary distance from the classical models which served in the construction of the HMMs, this strategy may miss several domains  [12, 13].
It enables comparison of an HMM built on a protein alignment against an HMM database like Pfam. Given a query sequence, HHPRED first generates a multiple sequence alignment (MSA) of related sequences through an iterative approach like PSI-BLAST. This MSA is then transformed into a query HMM which is compared against the HMM database. HHPRED is one of the best performing methods for fold recognition and domain boundary prediction  [15] [16] [17]. However, although the HHPRED approach is widely used in the proteinstructure prediction community to identify remote homologues with known 3D-structure, it is seldom used to annotate a whole new genome and to help predict the function of its proteins. For this task, sequence/profile comparison remains the gold standard method. Here we use HHPRED to help annotate proteins of two main human pathogens, the kinetoplastid Leishmania major, that causes a cutaneous form of leishmaniasis, and the apicomplexan Plasmodium falciparum which is responsible for the deadliest form of human malaria.