dataesr/scientific-paragraphs-categorization huggingface dataset:
Running Programs In order to fully execute a goal,  employs the high-level representation of the MALP program compiled via the "parse" option. The key point of this Prolog code is to extend each atom of the program with an extra argument, called truth variable, of the form TVi, which is intended to contain the truth degree obtained after the subsequent evaluation of the atom. In the case of a fact, the extra argument obviously contains its weight. For instance, "p(X) with 0. 5" is simply translated into the Prolog fact "p(X, 0. 5)". Fuzzy connectives are represented as predicates defined in the lattice associated to the program. For instance, the role of "&godel" is played by the Prolog predicate "and_godel". Since the fuzzy connective "&godel" is a binary operation, then its associated predicate "and_godel" has arity three: two parameters plus the result, returned in the third argument TV.
When compiling MALP rules, the last atom called in the body of the translated clause is the adjoint conjunction, which is intended to combine the truth degree of the body and the weight of the rule, in order to propagate the final truth degree to the head. For instance, given a MALP rule like "p(X) < prod q(X,Y) & godel r(Y) with 0. 8", the resulting translated Prolog clause would look like "p(X,_TV0):-q(X,Y,_TV1), r(Y,_TV2), and_godel (_TV1,_TV2,_TV3), and_prod(0. 8,_TV3,_TV0)". In order to execute a fuzzy program stored in a ". fpl" file,  needs firstly to load and compile it by using the "parse" option. Internally, while  analyzes the content of the file (following the DCG specification of the MALP syntax) it also generates two different Prolog representations of the fuzzy code: the high level representation coincides with a set of executable Prolog clauses as we have just described, while the low level representation allows  to draw execution trees as we will see in the next subsection.
Here we have an example of using the "parse" option, where note that  lists the content of any previously loaded ". pl" file (none in our case), the parsed MALP program and the generated Prolog code. >> parse. File to parse: 'P1. fpl'. No loaded files. ORIGINAL FUZZY-PROLOG CODE: y(peter) with 0. 4. y(mary) with 0. 8. h(peter) with 0. 9. h(mary) with 0. 3. e(peter) with 0. 5. e(mary) with 0. 95. c(X) < prod (h(X)|prod e(X)) &prod y(X) with 1. GENERATED PROLOG CODE: y(peter,0. 4). y(mary,0. 8). h(peter,0. 9). h(mary,0. 3). e(peter,0. 5). e(mary,0. 95). c(X,TV0):-h(X,_TV1), e(X,_TV2), lat:or_prod(_TV1,_TV2,_TV3), y(X,_TV4), lat: and_prod(_TV3,_TV4,_TV5), lat: and_prod(1,_TV5,TV0). While parsing,  creates a file tmp_fuzzy-prolog. pl to allocate the generated Prolog code, in order to allow the possibility of saving it afterwards into a new file by using the "save" option.
One important feature is that translated connectives (like "lat:and_prod/3") are prefixed by "lat:", which means that the Prolog interpreter will search their definitions in the "lat" module, a different name-space designed to avoid name collisions. The resulting Prolog code can be executed in any Prolog engine, with the only requirement that the associated lattice must be loaded in the corresponding module. This can be easily achieved with the following two goals: ?-lat:consult(lattice. pl). ?-consult(program. fpl). Moreover, goals are introduced in  by choosing the 'intro" option and they suffer a very similar translation process to program rules. So, it is easy to see that a fuzzy goal like "y(X) &godel h(peter)", is translated into the pure Prolog goal "y(X, _TV1), h(peter, _TV2), and_godel(_TV1, _TV2, Truth_degree)" (note that the last truth degree variable is not anonymous now) for which the Prolog interpreter returns the two desired fuzzy computed answers after selecting the "run" option (see Figure  4 ): [Truth_degree = 0.
Execution Trees Apart from the compilation method to Prolog code commented before, we have conceived a new low-level representation for the fuzzy code which is useful for building execution trees with any level of depth and offering too debugging (tracing) capabilities. For instance, after parsing the last rule of our program, we obtain the following expression (which is "asserted" into the database of the interpreter as a Prolog fact, but it is never executed directly, in contrast with the previous Prolog-based, high-level representation of the fuzzy code) whose components have obvious meanings: rule(7, head(atom(pred(c,1), [var('X')])), impl(prod), body(and(prod, 2, [or(prod, 2, [atom(pred(h,1),[var('X')]), atom(pred(e,1),[var('X')])]), atom(pred(y,1),[var('X')])])), td(1)).  is equipped with three options related with tracing tasks.
So, consider the partially ordered multi-adjoint lattice in Figure  9  for which the conjunction and implication connectives based on the Gödel logic described in Section 2. 1 conform an adjoint pair but with the particularity now that, in the general case, the Gödel's conjunction must be expressed as ( ) ( ) & , inf , x y x y  G , where it is important to note 1 Here we also assume that several versions of the classical conjunction operation have been implemented with different arities. that we must replace the use of "min" by "inf" in the connective definition. To this end, observe in the Prolog code accompanying Figure  9  that we have introduced five clauses defining the new primitive operator "pri_inf/3" which is intended to return the infimum of two elements. Related with this fact, we must point out the following aspects:  Note that since truth degrees α and β -or their corresponding representations as Prolog terms "alpha" and "beta" used for instance in the definition(s) of "members(s)/1"-are not comparable, any call to "leq (alpha,beta)" or "leq(beta,alpha)" will always fail.
 However, goals "pri_inf(alpha,beta,X)" and "pri_inf(beta,alpha,X)", instead of failing, successfully produces the desired result "X = bottom".  Note anyway that the implementation of the "pri_inf/1" predicate is mandatory for coding the general definition of "and_godel/3". As a final example, we can also define the so called Borel algebra based on the union of intervals (where for instance [ ] ( ) 0,1  is the union of intervals between 0 and 1  [8]  [26]) as a Prolog program for being used into  as follows:  A member of this algebra is a list of pairs representing disjoined intervals.  The top element is the point 1 (interval from 1 to 1), and the bottom one is the point 0 (interval from 0 to 0).  A union of intervals, U , is less or equal than other union of intervals U ′ if for each I U ∈ , there exists another interval I U ′ ′ ∈ , such that I I′ ⊆.
For instance, we can represent the linguistic variable distance, with values { } In the following example, we design a touristic application to compute the best destination for vacations. The database includes some cities and relevant information relating them (kind of weather, good sights, distance from our hometown). It is clear that some of this information has a very fuzzy taste, so the choice of a fuzzy language is desirable. We can fuzzify the crisp distance using the linguistic variable defined above as follows. nice_weather(madrid) with 0. 8. nice_weather(istanbul) with 0. 7. nice_weather(moscow) with 0. 2. nice_weather(sydney) with 0. 5. many_sights(madrid) with 0. 6. many_sights(istanbul) with 0. 7. many_sights(moscow) with 0. 2. many_sights(sydney) with 0. 6. crisp_distance(madrid, 250).
( ) { } 0. 38, 4 ; info X peter for goal " ( ) c X ", with meaning that we 4 steps to prove that the query is true at a 38% degree when X is "peter". One step beyond, we will also see that if instead of the number of computational steps, we are interested in knowing more detailed data about the set of program rules and connective definitions evaluated for obtaining each solution then, instead of  it will be mandatory to use a new lattice  based on strings or labels (i. e. , sequences of characters) for generating the Cartesian product ×  . In [25] we show not only that  is a complete multi-adjoint lattice, but also that the concatenation of strings, usually called "append" in many programming languages, plays the role of a conjunction connective in such lattice. In order to be loaded into  , we need to define again the new lattice as a Prolog program, whose elements will be terms of the form "info(Fuzzy_Truth_Degree, Label)" as shown in Figure  13  (we only list some representative clauses).
Conclusion In the recent past, several fuzzy extensions of the popular pure logic language Prolog have been designed in order to incorporate on its core new expressive resources for dealing with uncertainty in a natural way. However, real tools for putting in practice the power of such languages are not always available to a wide audience. To reduce this gap, the experience acquired in our research group regarding the design of techniques and methods based on fuzzy logic in close relationship with the so-called multi-adjoint logic programming approach, has motivated our interest for putting in practice all our developments around the design of the  environment, which offers comfortable resources for programmers trained in declarative languages. In a first stage, we have showed that our tool is able to execute MALP programs via a "transparent compilation process" to standard Prolog code which, to the best of our knowledge, constitutes the first initiative to put in practice the integral development of this kind of fuzzy programs.
Going on deeper, we have next proposed a second compilation way which produces a low-level representation of the fuzzy code, thus enabling the possibility of drawing "derivation trees" (which offer too debugging/tracing tasks) and opening the door to new program manipulation techniques such as fold/unfold-based program optimization, program specialization by partial evaluation, etc. in which we are working nowadays after being formally matured in our research group. Moreover, our tool offers the possibility of managing in a single pack, i. e. "project", fuzzy programs with its associated multi-adjoint lattices modeled in Prolog, together with additional clauses and scripts helping to manage and increase the power of this programming tool. Our philosophy is to friendly connect this fuzzy framework with Prolog programmers since our system (which can be easily installed on top of many Prolog platforms) translates the fuzzy code to classical clauses, admits the definition of sophisticated lattices for modeling advanced notions of truth degrees collecting proof traces on fuzzy computed answers and simplifies the implementation of modern, real-world applications where fuzzy logic plays an important role.
Methods Exome sequencing. Genomic DNA was extracted from blood leukocytes and prepared as described previously  (35)  for capture. Amplification and sequencing were performed on the Illumina Genome Analyzer II (index case) or Hi-Seq platform (parents) (Illumina) as 54-or 75-bp paired-end reads respectively. BWA software package 0. 5. 9, Picard v1. 46, SamTools v0. 1. 16 (36) , Dindel v1. 01  (37) , Genome Analysis Toolkit (GATK) v1. 1-20 (38) , and Variant Effect Predictor (VEP) v2. 1  (39)  were used to map sequence reads to the genome reference sequence (NCBI build 37), to calculate read quality, to call single nucleotide variants (SNVs) and insertion/deletions, and to annotate against Ensembl build 64. Variants were filtered to include only those with a minimum depth of 4× to a maximum depth of 1200× and were reported in variant calling file (VCF) format v4.
1. For the index case, 6. 4 Gb of sequence was produced at a mean depth of 60, with 80. 4% coverage at a depth greater than or equal to 10, using high mapping quality alignments only (i. e. , mapping quality ≥ 30). Equivalent figures for the father and mother were 8. 8 Gb, 95% and 86. 1%, and 8. 9 Gb, 97% and 91. 0% respectively. Raw exome sequence data from the trio are available from the European Genome-phenome Archive (EGAN00001101658, EGAN00001001829, EGAN00001001831). De novo and compound heterozygous mutation analysis. Exome data for P1 and her parents were merged, and variants with a high posterior probability of being de novo (cutoff value 0. 8) were extracted using DeNovoGear v0. 5 (https://sourceforge. net/projects/denovogear/)  (40). Python scripts were used to extract potential compound heterozygous mutations.
Cells were harvested at 2-hour intervals and processed as described above. Data were collected using a BD FACS Canto and analyzed with FACS DIVA software. (Applied Biosystems; Life Technologies) and run on ABI 3730 DNA Sequencers (Applied Biosystems). Sequence analysis was performed using the Mutation Surveyor software package v2. 3 (LLC; SoftGenetics). For P2, all exons and exon-intron boundaries of NSMCE2 were sequenced using Sanger sequencing as above (for primer sequences, see Supplemental Table  1 ). Haplotype analysis. Twenty-four SNPs were selected from Hap-Map release 27  (42)  to capture the variation within the 2 linkage blocks surrounding the p. Ser116Leufs*18 and p. Ala234Glufs*4 mutations (chr8: 125672673-126874287). These were genotyped along with the microsatellites D8S1179 and D8S266 in both probands, their families and 54 ethnically matched controls predominantly from the west of Great Britain from the 1000 genomes GBR panel (http:// www.
1000genomes. org/about#ProjectSamples). SNP genotyping was performed using the iPLEX Gold Assay (Sequenom Inc. ) according to the manufacturer's instructions, while microsatellites were amplified by PCR using FAM-labeled forward primers, diluted, mixed with CC5 Internal Lane Size Standard 500 (Promega),and run on an ABI 3730 DNA Analyser (Applied Biosystems). Allelic determination was undertaken using GeneMapper software (Applied Biosystems). Of the 24 SNPs, 3 were monomorphic in our data set. Analysis was performed on the 21 polymorphic SNPs using SHA-PEIT2 (http://www. shapeit. fr/) to phase the genotypes and calculate the most likely haplotypes, and the microsatellite markers were used to further define the haplotypes where ambiguous. Patient cell culture. Epstein Barr virus-transformed LCLs were generated from primary lymphocytes using standard procedures and maintained in RPMI 1640 supplemented with l-glutamine, antibiotics (penicillin streptomycin), and 15% FBS.
mRNA was extracted using TRIzol (Invitrogen). One microgram of mRNA from each group was transcribed using Superscript II First Strand Synthesis System (Invitrogen). Specific primers were then used to amplify a region across exons 1-3 for nsmce2 sp1 and across exons 2-4 for nsmce2 sp2 as well as control primers for Agpat2 to check equal loading. Primers are detailed in Supplemental Table  3. Embryos were cultured in egg water at 28. 5°C for 48 hours before scoring phenotypes. Live fish images were captured using a Leica M80 stereomicroscope, and body length was calculated by the Leica Application Suite program. Embryos were assigned to categories defined with reference to the SD of the length of a population of uninjected WT fish from the same mating. The experiment was carried out 6 times.
Data shown are representative. For rescue experiments capped fulllength human NSMCE2 mRNA (either WT, S116Lfs*18, A234Efs*4, or SUMO LD) was transcribed in vitro following the manufacturer's manual (mMessage mMachine; Invitrogen). RNA was purified using a Megaclear column (Ambion) and quantified spectrophotometrically and the size verified on an agarose gel. 100 picograms of mRNA were comicroinjected with nsmce2/mms21 MO at the 1-to 2-cell stage and the phenotype scored after 48 hours.
Acknowledgments I. Barroso ,  R. K. Semple ,  D. B. Savage , and  S. O'Rahilly  were supported by the  Wellcome Trust  (grants  WT098051 ,  WT098498 ,  WT091551 , and  WT095515 , respectively), the  Medical Research Council Centre for Obesity and Related Disorders , and the  United Kingdom National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre. M. O'Driscoll  is supported by  Cancer Research UK , the  UK Medical Research Council and Leukaemia Lymphoma Research (United Kingdom). Funding for human studies was also provided by the  EU/EFPIA Innovative Medicines Initiative Joint Undertaking  (EMIF grant no. 115372 ). Thanks to  Alan Lehmann  and Elaine Taylor and  Oskar Fernandez-Capatillo  for providing antibodies. We are grateful for access to exome sequence data from the CoLaus cohort, sequenced through partnership between the  Wellcome Trust Sanger Institute , the CoLaus principal investigators, and  GlaxoSmithKline.
Introduction Infection with Mycobacterium tuberculosis is responsible for extensive morbidity and mortality worldwide with approximately 8. 8 million new cases and 1. 1 million deaths reported in the year 2010  [1]. The importance of TB as a major public health problem has dramatically escalated with the emergence of the multi-drug resistant tuberculosis (MDR-TB), defined as a combined resistance to Rifampicin and Isoniazid  [2]. Iraq is one of the countries with the highest incidence among the Eastern Mediterranean region with TB accounting for 56/100,000 population, and the estimated MDR-TB cases among new pulmonary TB cases notified in 2010 is 210 , whereas the estimated number among retreated pulmonary cases is 160 (57-260)  [3]. These data are being extracted from the cases registration system that is based on clinical findings and microscopy.
Genotyping of the isolates The isolates were characterized by two genotyping methods, spoligotyping and MIRU-VNTR. Spoligotyping was performed as previously described  [18]. MIRU-VNTR genotyping was performed by PCR-amplification of a panel of 15 MTB MIRU loci using primers described in the MIRU-VNTR standard protocol  [17]. Mycobacterium bovis P3 and H37Rv were used as positive controls and autoclaved with double distilled water as a negative control. The results were compared with updates of the international spoligotype database of the Pasteur Institute of Guadeloupe which provides information on the shared-type distribution of M. tuberculosis spoligotypes at the worldwide level  [18]. At the time of the comparison, the updated SITVIT2 version contained more than 90,000 patterns from more than 160 countries of patient origin.
MIRU-VNTR profile matching a pre-existing profile was classified as MIRU-VNTR international type (MIT). Newly created MIT results either after matching of an orphan profile in the study with another orphan profile present in the database or when two or more isolates within the study had the same new MIRU-VNTR profile. Additionally, MIRU-VNTR clusters arise when two or more isolates within the study had an identical profile.
Phylogenetic tree and data analysis Data obtained from spoligotyping and MIRU-VNTR were used to construct a minimum spanning trees (MSTs) with the Bio-Numerics software version 3. 5 (Applied Maths, Sint-Martens-Latem, Belgium), and Spoligoforests with SpolTools software (available at: http://www. emi. unsw. edu. au/spolTools). Spoligoforests were drawn as directed and not necessarily connected graphs using a hierarchical layout and a Fruchterman-Reingold algorithm to determine the parent to descendant links between the spoligotype patterns. Contrarily to the Spoligoforests, MSTs are connected undirected graphs which link all the strains together with the fewest possible linkages between nearest neighbors. MSTs have been constructed with spoligotype patterns as well as MIRU-VNTR patterns. The discriminatory power and allelic diversity were calculated using h value and Hunter Gaston Discriminatory Index (HGDI) using the equations: h = 1 À Rxi 2 where xi is the frequency of the ith allele at each locus  [19, 20].
Spoligotyping analysis Reproducible spoligotype profiles were obtained from all the 134 isolates. The spoligotype profiles obtained were compared using the SITVIT2 database, and the strain lineages were identified. In this database, SIT (shared international type) designate spoligotype patterns shared by two or more patients' isolates, whereas ''orphan'' designates patterns reported for a single isolate. The description of spoligotype patterns observed among the 134 isolates, corresponding Spoligotype International Type (SIT) designations, genotypic lineages, and their worldwide distribution in the SITVIT2 database is summarized in Table  1. Spoligotyping yielded 39 patterns; 111/134 (82. 2%) were grouped in 16 clusters vs. 23/134 (17. 2%) isolates that were unique. Comparing results with the international database categorized 128 (95.
No association was found between gender and shared types or clusters. Comparing the distribution of the spoligotype patterns reported in this study with strain isolates in neighboring countries revealed several notable findings (Supplement Table  S2 ); SIT1144/T1, the predominant Spoligotype in Iraq (n = 20, 14. 93% of all cases) is not recorded in any of the neighboring countries, and it showed the following distribution pattern in SITVIT2 database (n = 24 strains in total: Iraq n = 20, USA n = 2, Venezuela n = 1, Greece n = 1), the distribution proportion of SIT127/(H4/Ural-2) sublineage is 17% in Iran, 3% in Iraq, and 0. 7% in Saudi Arabia (0. 66%), thus, SIT127 may have dispersed first in Iran, followed by Iraq, and Saudi Arabia. Considering the total number of SIT284 strains in SITVIT2 database (n = 167), the global proportion of those strains is more predominant in Bulgaria (23.
95%), followed by Turkey (20. 96%) and Saudi Arabia (13. 77%). However, this new Iraqi study revealed that, considering the proportion of SIT number per country, SIT284 is present in Iraq in a greater proportion than its neighboring countries. In addition, this study showed that there are other similar lineages in circulation among Iraq, Iran, Saudi Arabia and Turkey, such as CAS1_delhi. Distribution of spoligotype lineages and sublineages in this study is summarized in Table  2. It is noteworthy that 92. 6% of all M. tuberculosis were attributed to 4 lineages, Table  1  -The description of spoligotype patterns observed among the 134 isolates, corresponding Spoligotype International Type (SIT) designations, genotypic lineages, and their worldwide distribution in the SITVIT2 database. * A total of 33/34 SITs matched a preexisting shared-type in the database, whereas 1 SIT (SIT3776 * , n = 1 isolate) was newly created after a match with an orphan strain from Iran.
Note that orphan patterns (n = 5) are shown at the bottom of the table, and mentioned as Orph 1-5, which correspond to the following strain numbers: Orph 1 (3284); Orph 2 (5760); Orph 3 (5953); Orph 4 (6005); Orph 5 (6320). ** Clade designations according to SITVIT2 using revised SpolDB4 rules; ''Unknown'' designates patterns with signatures that do not belong to any of the major clades described in the database. *** Clustered strains correspond to a similar spoligotype pattern shared by 2 or more strains ''within this study''; as opposed to unique strains harboring a spoligotype pattern that does not match with another strain from this study. A unique strain matching a preexisting pattern in the SITVIT2 database is classified as SIT, whereas in case of no match, it is designated as ''Orphan'' (n = 5, shown at the bottom part of the Table ).