dataesr/scientific-paragraphs-categorization huggingface dataset:
Ablation results in terms of each of the classes in the dataset. The average± is the standard deviation of each metric for each of the 4 classes. The experiments to examine the impact of each of the branches (b 1 , b 2 , b 3 ) in the multi-input classifier were performed in a fully supervised (FS) way in order to analyze the effects only of the translations performed by the GAN. The ablation result corresponding to branch b 1 is equivalent to the baseline (ResNet-101) result since the inputs from CSi-GAN are not used.
on xxxx x, xxxThis work was supported by the  ATLAS  project. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  813782. This work was also partially supported by  French State Funds  managed by the  Agence Nationale de la Recherche (ANR)  through the  Investissements d'Avenir Program  under Grant  ANR-11-LABX-0004  ( Labex CAMI)  and Grant  ANR-10-IAHU-02  ( IHU-Strasbourg ).
Introduction: Non-baumannii Acinetobacter species are increasingly isolated in the clinical setting and the environment. The aim of the present study was to analyze a genome database of 837 Acinetobacter spp. isolates, which included 798 non-baumannii Acinetobacter genomes, in order to define the concordance of classification and discriminatory power of 7-gene MLST, 53-gene MLST, and single-nucleotide polymorphism (SNPs) phylogenies. Methods: Phylogenies were performed on Pasteur Multilocus Sequence Typing (MLST) or ribosomal Multilocus Sequence Typing (rMLST) concatenated alleles, or SNPs extracted from core genome alignment. Results: The Pasteur MLST scheme was able to identify and genotype 72 species in the Acinetobacter genus, with classification results concordant with the ribosomal MLST scheme.
Genome dataset Bacterial genomes included in the analysis were manually selected from the PubMLST database 1 until January 2022. In detail, we selected 39 A. baumannii complete genomes assigned to international clonal lineages ICI, ICII, and ICIII, which corresponded to Pasteur ST1, ST2, and ST3, respectively, and to additional epidemic clonal lineages assigned to Pasteur ST10, ST25, ST32, ST52, ST78, and ST79  (Gaiarsa et al. , 2019). Furthermore, we collected from the National Center for Biotechnology Information (NCBI) high-quality complete genomes and, when not 1 https://pubmlst. org/organisms/acinetobacter-baumannii/ available, scaffolded sequences of all species into the Acinetobacter genus. The "parameters" to consider the genomes of "high quality" were: N50 ≥ 10,000 bp; number of contigs ≤1,000; identification of the correct species through rMLST  (Jolley et al.
, 2012). The Acinetobacter selected genomes were typed by A. baumannii Pasteur MLST scheme  (Diancourt et al. , 2010)  and rMLST  (Jolley et al. , 2012)  using the BIGSdb software available at https://pubmlst. org/organisms/acinetobacter-baumannii/  (Jolley et al. , 2018). The characteristics of the genomes were included in Supplementary Table  S1  and available at https://pubmlst. org/ bigsdb?db=pubmlst_abaumannii_isolates&page=project&project_id=8.
Phylogenetic and statistical analyses The allelic profiles of Pasteur and ribosomal MLST schemes were extracted from all the genomes, and then the sequences were aligned using Muscle  (Edgar, 2004)  to generate the neighbor-joining trees using the BIGSdb software  (Jolley et al. , 2018). The core genome single-nucleotide polymorphisms (SNPs) were detected using the tools of PARSNP v1. 1. 2 (Treangen et al. , 2014) , and the SNP alignment was performed considering the ascertainment bias using the Lewis correction  (Lewis, 2001). In detail, each genome was aligned to the reference genome NC_010611. 1 of ACICU, and the alignments were then concatenated using Muscle  (Edgar, 2004). The maximum-likelihood phylogenies of 574 genomes belonging to the A. baumannii group  (A. baumannii, A. pittii, A.
seifertii, A. lactucae, and A. nosocomialis)  were performed using the concatenated alleles of the Pasteur and ribosomal MLST schemes and a reference phylogeny using genome-wide data (a core genome of 372 highquality genes and an alignment of 17,072 SNPs). The phylogenies of Pasteur MLST, ribosomal MLST, and core SNP alignments were inferred through the GTR-GAMMA model at 100 bootstrap replicates using RAxML v. 8 (Stamatakis, 2014). GTR's GAMMA model was used for its ability to optimize the transition/transversion speed ratio and the α parameter of the gamma rate heterogeneity distribution  (Stamatakis, 2014). The phylogenetic trees and annotations were visualized using the iTol v6 software. 2.
Analysis of antimicrobial resistance genes The antimicrobial resistance genes were detected using abricate 3 based on the ResFinder 4. 0 database  (Bortolaia et al. , 2020). The presence of the gene and percentage of identity were indicated in the matrix in Supplementary Table  S2. The threshold identity of 80%, the minimum length of 80% matches, and the coverage value of more than 90% were selected for each gene. The following acquired resistance genes were analyzed: class A, B, C, D β-lactamase, folate pathway inhibitors, rifampicin, aminoglycoside, chloramphenicol, colistin, macrolide, quaternary ammonium salts, and tetracycline resistance genes (Supplementary Table  S2 ). The multiple alignment and phylogeny of class D β-lactamase genes were performed using Clustal W  (Thompson et al. , 1994) and RAXML v.
Acinetobacter genus database The database consisted of 837 genomes assigned to 72 distinct species in the Acinetobacter genus, which were identified at genus and species levels using the ribosomal MLST scheme  (Jolley et al. , 2012)  and showed a genome size range of 2. 85-4. 85 Mpb (Supplementary Table  S1 ). The Pasteur MLST scheme  (Diancourt et al. , 2010)  was able to assign an allelic profile and an ST to all 837 genomes belonging to 72 species in the Acinetobacter genus; while, the ribosomal MLST scheme assigned an ST to 806 out of 837 genomes because from 1 to 20 alleles could not be identified in the genomes belonging to  A. baumannii, A. nosocomialis, A. bereziniae, A. pittii, A. radioresistens, and A. seifertii. In addition, the rMLST scheme identified paralogues in 11 out of 53 loci paralogous in 57 Acinetobacter spp.
(Supplementary Table  S1 ). The genome-wide distances of the 3 https://github. com/tseemann/abricate whole database analyzed using the minimum spanning tree (MST) with the Pasteur MLST scheme showed that the most abundant species were A. pittii (n = 282), A. nosocomialis (n = 175), A. seifertii (n = 61), A. baumannii (n = 39), and A. lactucae (n = 14), while 263 genomes were assigned to other 67 species of Acinetobacter genus (Figure  1 ). The 574 genomes assigned to the above five species have been considered the most clinically relevant species and were included in the A. baumannii group  (Cosgaya et al. , 2017). Among genomes within the A. baumannii group, the genetically closest species were A. pittii with A. lactucae or A. seifertii, showing 5 and 6 locus variants (LVs) genome-wide distance, respectively.
Antimicrobial resistance genes in Acinetobacter spp. genomes We analyzed the presence of antimicrobial resistance genes in the 837 genomes assigned to 72 distinct Acinetobacter species. The ResFinder software identified 188 genes encoding for resistance to 12 antimicrobial categories (Supplementary Table  S2 ). The presence of class A, B, C, and D β-lactamases in the neighbor-joining tree of the 837 genomes assigned to 72 distinct Acinetobacter species is shown in Figure  4. The class A β-lactamase genes bla CARB-8,-2,-14,-16 were found in 1 A. nosocomialis, 4 A. pittii, 1 A. towneri, and 1 A. bereziniae genomes; the class A β-lactamase gene bla SCO-1 was found in 1 A. radioresistens genome. In addition, class A β-lactamase genes bla TEM-1A,-1B,-1D were found in 9 of 39 A. baumannii genomes; bla PER-1,-2 ESBL genes were found in 1 A.
radioresistens genomes in our database showed the bla OXA-23 gene, which reinforced the evidence that A. radioresistens is the progenitor of the bla OXA-23 gene and the source of carbapenem resistance for A. baumannii  (Poirel et al. , 2008). Moreover, in agreement with previous data showing that the bla NDM type is the most common type of metallo-beta-lactamase contributing to carbapenem resistance in clinical isolates of A. baumannii  (Zarrilli et al. , 2013; Gaiarsa et al. , 2019)  and other Acinetobacter spp. (Yang et al. , 2012; Yamamoto et al. , 2013; Sung et al. , 2015; Pfeifer et al. , 2020; Alattraqchi et al. , 2021) , we found bla NDM type genes in the genomes of 12 species of the Acinetobacter genus.
Conclusion The data presented herein analyze a genome collection of isolates assigned to 72 distinct species of Acinetobacter genus. The non-baumannii Acinetobacter genomes database, which has been validated by rMLST and Pasteur MLST, represents a useful tool for genome sequencing-based identification at the species level and typing of Acinetobacter spp. isolates. The phylogenies of A. nosocomialis, A. lactucae, A. pittii, and A. seifertii genomes belonging to the A. baumannii group demonstrate the presence of distinct clades in the A. pittii and A. seifertii genomes, while the A. nosocomialis genomes are highly heterogeneous. "In silico" analysis of antimicrobial resistance in isolates assigned to 72 distinct species of the Acinetobacter genus shows the presence of carbapenemases and resistance genes to several antimicrobial classes in A.
Based on these drug costs, the model estimated the total annual cost per patient per single treatment. The treatment cost per patient for the first year and the subsequent years are reported in Table  1. The estimated annual costs were combined with the epidemiological data to estimate the 10-year cumulative cost of the two scenarios. The difference between these two  indicate the potential budget impact associated with the application of the new ELITA guidelines in Europe, over the next 10 years, considering all HBV transplanted patients. Additional analyses were performed to assess the following: 1. The impact of HBIG price to the budget impact results, applying the lowest and highest price reported within the 6 European countries used to estimate the HBIG price, 2. The variations in the number of incident LT patient, 3.
We obtain annotated logic programming when we consider regular Horn CNF formulas and N is a lattice  [25]. Annotated logic programming is an appropriate logical formalism to manage locally inconsistent, incomplete and uncertain databases. In the area of satisfiability testing, one of the problems that has attracted more interest in recent years is the Maximum Satisfiability problem (MaxSAT)  [9, 28]. Whereas SAT is the problem of deciding if there exists a truth assignment for a given CNF formula that evaluates the formula to true, MaxSAT is the problem of finding a truth assignment that minimizes the number of unsatisfied clauses in a CNF formula. In practice, SAT is used as a generic problem solving formalism for decision problems and MaxSAT for optimization problems. The development of highly competitive MaxSAT solvers (e.
We believe that studying Regular MaxSAT resolution is worthwhile due to the utilization of regular signs. Apart from enabling to deal with infinite truth value sets, regular signs contribute to produce simpler and more efficient variable elimination algorithms for Regular MaxSAT. Thanks to the notion of polarity and the structure of regular signs, Regular MaxSAT resolution proofs are simpler than Signed MaxSAT resolution proofs because the inference rules exploit such features. Additionally, detecting intersections among signs does not require intricate operations; simple comparisons of numbers suffice. Regular signs also simplify the data structures as they can be represented by recording the lowest and greatest values of the sign. Regular CNF formulas are also significant because they are the language behind the so-called order encoding  [7, 37] , which has demonstrated to be more efficient than other encodings to model, for example, cardinality constraints.
Summary  Our results provide new information about water balance at the surface of steep alpine rock slopes.  The combined application of the S2M-SAFRAN dataset with the CryoGrid model is a powerful tool to study cryogenic and hydrologic processes in high alpine landscapes.  Sublimation is systematically the main process of snow mass loss in our study site.  Snowmelt occurs between late spring and late summer, and most of it does not reach the rock surface due to a formation of an impermeable ice layer at the base of the snowpack.  The annual effective snowmelt that is available for infiltration is highly variable and ranges between 0. 05 and 0. 28 m in our study site.  Effective snowmelt is the main source of water for infiltration at elevation >3600 m a. s. l.  Below 3600 m, direct rainfall is becoming more dominant and water availability increases rapidlymaking this transition elevation highly sensitive to climate change, as snowmelt-dominated permafrostaffected slopes experience an abrupt increase in water input.
g. , speech synthesis, speech recognition) -Image/video processing -Human-computer interaction (HCI, e. g. , tools for conversational systems) • The potential for short-and mid-term development of LTs, insofar as this potential can be approximated by the current availability of resources that can be used as training or evaluation data. The availability of data was investigated with regard to a small number of basic types of resources: -Text corpora -Parallel corpora -Multimodal corpora (incl. speech, image, video) -Language models -Lexical resources (incl. dictionaries, wordnets, ontologies, etc. ) We measured the LT support for 87 national, regional and minority European languages with regard to each of the dimensions mentioned above based on their respective coverage in the ELG catalogue.
e. , 3%, 10% and 30%) were informed by an exploratory k-means 4-cluster analysis based on all data per application and resource type, in order to investigate the boundaries of naturally occurring clusters in the data. The boundaries of the clusters were then used to define the bands per application area and resource type. The overall level of support for a language was calculated based on the average coverage of all dimensions investigated. The ELG platform harvests several major LR/LT repositories 3  and, on top of that, more than 6,000 additional LRTs were identified and documented by language informants in the ELE consortium. These records contain multiple levels of metadata granularity as part of their descriptions. At the time of investigation, the ELG catalogue comprised more than 11,500 metadata records, encompassing both data and tools/services, covering almost all European languages, both official and regional as well as minority ones.
It should be noted that due to the evolving nature of this extensive catalogue and differing approaches taken in documenting records, certain categories of metadata captured are not yet at the level of consistency required to carry out a reliable cross-lingual comparison at a granular level. For example, information provided on corpus size, annotation type, licensing type, size unit type, and so on, still varies across records for many languages, while numerous gaps exist for others. As the ELG catalogue is continuously growing, the comprehensiveness, accuracy and level of detail of the records are expected to improve over time. For the purposes of a high-level comparison, the results presented here are based on relative counts of entries in the ELG for the varying types of data resources and tools/services for each language.
Results and Findings As discussed above, our analysis takes into account a number of dimensions for data and tools/services. Table  1  reports the detailed results per language per dimension investigated and the classification of each language into an overall level of support. The best supported language is, as expected, English, the only language that is classified in the good support group. French, German and Spanish form a group of languages with moderate support. Although they are similar to English in some dimensions (e. g. , German in terms of available speech technologies and Spanish in terms of available models), overall they have not yet reached the coverage that English has according to the ELG catalogue. All other official EU languages are clustered in the fragmentary support group, with the exception of Irish and Maltese, which have only weak or no support.
Users of Language Technologies Commercial users were those respondents representing companies in the sector of Information and Communication Technologies (ICTs) and eCommerce (e. g. , Megabyte Ltd, A Capela group, Telecats), energy (e. g. , Shell, Menai Science Park Ltd) and business services (e. g. , Spencer Stuart, Inuits, Projectus grupa). They also included respondents from the following groups: self-employed language professionals (e. g. , translators); professionals working on different economic sectors (e. g. , banking, health); independent professionals/consultants; professionals working in public administration; media and publishing professionals. Academic users included researchers, data scientists, university professors, language teachers, lecturers, and Master's and PhD students.
More details on this survey and the community consultation methodology are presented in Chapter 38 (p. 229 ff. ). In total, 21,108 complete responses were collected. However, as the collection of survey responses through commercial online services is known to present some known issues that can render results unreliable  (Lawlor et al. 2021) , closer inspection revealed a number of flags indicating unreliable responses. These responses were filtered from the dataset, and as such, a final 20,586 responses were analysed. Respondents provided profiling questions and were asked to list all of the languages they speak. Of particular interest in our examination of the current situation is the response to question 6 "Please rate all the types of software applications, apps, tools or devices you use for your language(s)".
The list of eight tools presented was: Search apps (e. g. , Google, Bing); personal assistant apps (e. g. , Siri, Alexa); proofreading apps (e. g. , spelling and grammar checkers, autocorrect); translation apps (e. g. , Google Translate, DeepL); automatic subtitling (e. g. , news report, YouTube); language learning apps (e. g. , Babbel, Rosetta Stone); chatbots (e. g. , for customer support) and screen readers. The aim of this question was to understand the perception of the average EU citizen and LT user of the quality of the tools that they use for each language they speak. The ratings were based on a 5-point Likert scale, i. e. , respondents had the option of rating 1-star (poor) through to 5-stars (excellent) for each of the eight tools presented, and for each language they had selected in the previous question.
In the interest of space, Figure  9  presents only the languages for which language reports were produced (see Chapters 5-37) and only shows responses from the perspective of each language, as opposed to each tool. Due to the large size of the dataset and the varying proportion of responses for each language, the figures presented here are based on the calculation of the median score (purple) and the mode (blue). Tools that were not available or used by a respondent did not receive a score. In these instances, the tool was assigned a rating of zero, as a penalty for lesser-used tools across all languages. This explains the low scores for languages such as Serbian, Luxembourgish and Icelandic, which either have very few available or low-rated existing LTs. To some degree, the results reflect the trend presented for the technological DLE scores of the relevant languages (see Chapter 3) in terms of the quantification of the technological factors of the DLE Metric.
5. 2 software (Nanion) and the Biomek v1. 0 interface (Beckman Coulter). Prior to recordings, dissociated cells were shaken at 200 RPM in a cell hotel reservoir at 10 • C. After cell catching, sealing and whole-cell formation, protocol application, compound administration and data acquisition were all performed sequentially and automatically. The intracellular solution contained (in mM): 110 CsF, 10 CsCl, 10 NaCl, EGTA and 10 HEPES (pH 7. 2, osmolarity 260 mOsm) and the extracellular solution, in which cells were incubated, was used. Whole-cell experiments were done at -100 mV holding potential and at room temperature (18-22 • C), while currents triggered at either + 10 mV test potential were sampled at 20 kHz. Stimulation frequency was set at 0. 2 Hz. Each JzTx-34 analog was prepared at various concentrations in the extracellular solution, itself supplemented with 0.
Chemical shift assignments and structure calculations Classical procedures were followed to assign chemical shifts, with CcpNmr2. 1 software  [34]. NMR constraints were deduced from the volume integration of NOE correlations and introduced into the iterative calculation process of the Aria2. 3 software  [35]. In each run, structures were calculated per iteration, and 250 structures were kept for the final iteration in water. 0nly NOE-derived constraints were introduced in the calculation, all combinations of disulfide bridges pairing being allowed with the ambiguous intersulfur constraints option of ARIA. Quality of the final structures were evaluated using Procheck3. 5  [36]  software, and ten representative structures were selected according to their agreement with experimental and energetic data (residual NOE violation, Ramachandran plot, energy values …).
Structure of JzTx-34 The structure of JzTx-34 was determined by NMR (PDB access code 8CIQ). The chemical shift resonances are particularly dispersed attesting that JzTx-34 is well folded and indicating also that aromatic residues disturb the chemical shifts of surrounding residues. Note that the triad of tryptophan residues W 5 /W 31 /W 33 is responsible of out-of-range chemical shifts for a series of close protons, for example L 6 (δ), H 18 (β), L 19 (α and δ) and W 31 itself (β1 and indole). Other protons such as W 25 (α), P 26 (α) and H 28 (β and δ) showed atypical chemical shifts too, influenced by Y 27 and W 25 itself. All the chemical shifts for 1 H, 15 N and 13 C were assigned except in a limited number of cases for 13 C δ (K 24 ), 13 C α (W 31 ) and 13 C α (W 33 ). They are accessible in the BioMagResBank resource (http://www.
Note the higher AUC values reached by the D 14 A mutation. Note also that higher concentrations of JzTx-34 E 4 A (333 nM and 1 μM) lead to reduction in AUC stimulation probably because of peak current reduction possibly by binding onto another channel site. H 18 A, E 20 A, W 25 A, W 31 A and W 33 A mutants, which allowed the determination of high-resolution NMR structures (PDB codes 8CJP, 8CJQ, 8CJR, 8CJS and 8CJT, respectively). Poor quality spectra were obtained for the W 5 A, L 6 A, L 19 A, R 22 A and V 30 A mutants indicating a folding deficiency, potentially linked to alteration in the disulfide-bridging patterns adopted by these analogs. As stated earlier, chemical synthesis of wt JzTx-34 was prone to produce two eluting peaks with the most hydrophobic one possessing the best activity.