dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSION Many HTP platforms have been established to perform large-scale experiments, where hundreds of genotypes are screened simultaneously. Although such platforms can generate large datasets, their usage is frequently limited because of the non-availability of convenient and suitable HTP data analysis pipelines. Hence, the sequential analytical pipeline (SpaTemHTP) presented here addresses these limitations and was developed to efficiently process data generated at outdoors platforms, that are characterized by an important amount of exogenous variation. The pipeline embeds a modular design to process raw HTP data for calculating spatially adjusted genotypic estimates, which is generally a complex procedure requiring knowledge about handling of spatial variability using mixed models. The significance of spatial adjustment in enhancing the quality of model estimates was also demonstrated through extensive testing of the pipeline for different species, traits, and experiments.
, some dataset might not require preprocessing or CPA. The genotype adjusted means data produced by the pipeline allows the user (breeder) to get quality data for immediate analysis (genotype comparison) or for further analysis like statistical genetics models (QTL, GWAS, or genomic prediction models). Hence, this automated procedure would also be extremely useful for larger data workflow processing strategy. Through CPA, we also showed the use of adjusted means to identify important time sections during an experiment, which provides a basis for further dissection of the genetic components in terms of G c × TW. For example, the G-BLUEs obtained at different OTW could be used in a multitrait QTL analysis that would help to model genetic effects that take into consideration the longitudinal nature of trait development.
Such criteria are advisable so that, following an outbreak, differences among isolates can be interpreted easily and accurately. The objective of this work was to investigate the molecular epidemiology of a well-characterized collection of OXA-48producing K. pneumoniae -the dissemination of which occurred in eight hospitals in Catalonia in 2012  (Argente et al. , 2019 )using different WGS-based typing methods and to compare them with classical MLST and PFGE. We used the following WGSbased typing methods: (i) core-genome MLST (cgMLST), based on two different schemes, SeqSphere+ and BIGSdb-Kp  (Snitkin et al. , 2012) ; (ii) whole-genome MLST (wgMLST), based on the SeqSphere+ scheme  (Zhou et al. , 2016) ; and (iii) core-genome single nucleotide polymorphism (cgSNP) analysis. In addition, we investigated and compared phenotypic data and genotypic antimicrobial resistance data obtained using PCR and WGS.
WGS After DNA extraction using the Ultraclean Microbial DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA, United States), WGS was applied to the 37 OXA-48-producing K. pneumoniae isolates. The libraries were prepared using a Nextera XT v. 01 kit (Illumina Inc. , San Diego, CA, United States) and sequenced on an Illumina MiSeq sequencer (Illumina Inc. , San Diego, CA, United States) to generate 2 × 250 bp paired-end reads. After sequencing, the reads were quality trimmed and de novo assembled using the CLC Genomics Workbench software v7. 0. 4 (CLC Bio, Aarhus, Denmark), with the default parameters, except for "removal of low quality sequence limit, " which was set to 0. 01 to increase the quality of the called bases. Assembly quality was assessed using QUAST v4. 5  (Gurevich et al. , 2013) , and a report including assembly metrics was generated (Supplementary Table  S1 ).
pneumoniae core genome (cgMLST) and 2,526 genes of the K. pneumoniae accessory genome (wgMLST; total of 4,891 targets), available in SeqSphere+, was used for the analysis (targets included in the schemes can be found at cgMLST. org). This scheme was developed using the seed genome K. pneumoniae subsp. pneumoniae NTUH-K2044 (GenBank accession no. NC_012731. 1; 15-JUN-2016) and comparing it with 30 query genome sequences that cover the genetic variability of the whole species complex, i. e. , K. pneumoniae, K. variicola, and K. quasipneumoniae, as previously defined  (Bialek-Davenet et al. , 2014). In addition, 61 plasmid sequences were used to exclude chromosomal homologs of plasmid genes. A detailed description of the scheme development is available in the software. In addition, a cgMLST complex type (CT) function was used to identify isolates with very similar cgMLST profiles 2.
A cluster alert distance of 15 cgMLST allele differences and a cluster alert quality threshold of at least 90% good cgMLST targets were used to detect closely related isolates. These thresholds were based on retrospective analysis of well-defined outbreaks and out-group isolates with the same MLST/MLVA/PFGE profiles. Distance matrices describing pairwise allele differences (ignoring missing values) are included in Supplementary Data Sheet S1 (cgMLST and wgMLST, SeqSphere+). Furthermore, another parameter named the genetic distance was calculated for paired isolates as the proportion of allele differences, obtained by dividing the number of allele differences between the two genomes by the total number of called genes shared by those two genomes. We considered previously described genetic distance thresholds, 0.
Antimicrobial Resistance Genes and Plasmid Analysis In addition to WGS typing, resistance genotypes and plasmid families were investigated in the collection of OXA-48-producing K. pneumoniae and compared with those obtained previously  (Argente et al. , 2019). Genome assemblies were uploaded to the Center for Genomic Epidemiology to extract information on antimicrobial resistance genes (ARGs) and plasmid replicons for K. pneumoniae using ResFinder 2. 1 and PlasmidFinder 1. 3, respectively  (Zankari et al. , 2012; Carattoli et al. , 2014). The de novo assembled genomes were annotated using Prokka 1. 12 (Seemann, 2014) and compared with reference K. pneumoniae plasmid pOXA-48 NC_019154. 1 using BLASTn analysis. Circular representation of compared genomes was performed with the Circular Genome Viewer (CGView) Comparison Tool program  (Grant et al.
Analysis of Isolates Using the BIGSdb-Kp cgMLST Scheme We obtained identical seven-gene MLST STs for all isolates when using the BIGSdb-Kp database to those previously determined using other WGS analysis tools, confirming the high reproducibility of the seven-gene MLST scheme from genomic assemblies. All ST405 isolates had ≤2 allelic mismatches (mostly 0 allelic mismatches) (Table  2 ; figure in Supplementary Figure  S1 ). The three ST101 isolates presented between 2 and 5 allelic mismatches, whereas ST14, ST17, and ST1233 isolates were genetically unrelated with more than 480 allelic mismatches (figure in Supplementary Figure  S1 ). We also calculated the genetic distance for this scheme; all paired isolates had a genetic distance below 0. 0032, except for CARB115-A1 and CARB058-A3, which had a genetic distance of 0.
Antimicrobial Resistance Genes From WGS Data Compared to PCR and Phenotypic Results The ResFinder tool from the Center for Genomic Epidemiology detected the presence of multiple resistance genes implicated in β-lactams, aminoglycosides, and quinolone resistance as follows. All isolates were positive for bla OXA-48 , except CARB077, and for one of the following bla SHV genes: bla SHV-1 (n = 4), bla SHV-11 (n = 4), bla SHV-42 (n = 1), and bla SHV-76 (n = 31) (Table  1 ). In addition, 78. 4% (29/37) of the isolates contained bla CTX-M-15 ; 56. 4% (22/37), bla OXA-1 ; and/or 81% (30/37), bla TEM-1 genes (Table  4 ). We observed some discrepancies with results previously obtained by PCR and Sanger sequencing  (Argente et al. , 2019). In three isolates (CARB009, CARB050, and CARB056), the bla OXA-1 gene was detected by PCR but not by WGS, whereas in two isolates (CARB039 and CARB044), the bla CTX-M-15 gene was not detected by PCR but detected by WGS.
DISCUSSION WGS has a higher discriminatory power than do conventional typing methods and allows for a more precise outbreak analyses. This is especially important when analyzing the emergence and evolution of antimicrobial resistance, such as the threat of a worldwide spread of carbapenem resistance. Previous studies described the molecular epidemiology of carbapenemase-and extended-spectrum beta-lactamaseproducing K. pneumoniae isolates by WGS using a gene-by-gene comparison and/or SNP analysis  (Snitkin et al. , 2012; Lee et al. , 2014; Marsh et al. , 2015; Mathers et al. , 2015; Onori et al. , 2015; Zhou et al. , 2015; Kluytmans-van den Bergh et al. , 2016; Pérez-Vázquez et al. , 2016; Zhou et al. , 2016; Taboada et al. , 2017). Here we combined both approaches, describing for the first time the cgMLST scheme included in SeqSphere+ Ridom software.
No threshold has been defined when using cgMLST from BIGSdb-Kp, but clearly, genetically related isolates are nearly identical (≤2 mismatches) when using this scheme. The analysis using the scgMLST BIGSdb-Kp scheme therefore provided complementary information that may be easier to interpret given the lower variation observed using this scheme. We used two different thresholds in the gene-by-gene approach, a cluster alert distance of 15 from SeqSphere+ software (see text footnote 2) and a genetic distance of 0. 0035 for cgMLST analysis described by Kluytmans-van den  Bergh et al. (2016). Both proved to be useful for the description of genetic relatedness. In addition, the latter could be adopted independently of the cgMLST scheme used, which would also facilitate the interpretation of results, since it considers the proportion of shared genes by two isolates that are being compared.
DATA AVAILABILITY STATEMENT The datasets generated for this study can be found in the ENA. DATA SHEET S1 | CgMLST distance matrices for pairwise comparisons of 37 OXA-48 K. pneumoniae isolates. DATA SHEET S2 | VCF files for ST101, including SNP, indel, and structural variation calls information generated by Harvest software suuite (parsnp)  (Treangen et al. , 2014).
The interpretation, using the Diaxonhit software, combined the intensity of the reactions obtained and the number of positive antigens for each family. The qualitative results provided were positive, negative or indeterminate in favour of or against an infection. In cases where the test was positive, indeterminate or invalid, it was repeated once. If the test was again indeterminate, it was excluded from the analysis. If the serum was positive for all the antigens tested (non-specific cross-positive reactions), it was considered invalid and excluded from the analysis according to the supplier's recommendations.
Simplifications enabled by default; disable with --postprocess-comp=0. Acceptance adjusted according to --tba (default), or --ba. --complement={best,spot,pldi} is supplied. In that case, the semi-det. automaton is built as a TBA. Both routes used unless --complement={spot,pldi}. are hosted on GitHub (https://github. com/mklokocka/seminator). The package contains sources of the tool with two user-interfaces (a command-line tool and Python bindings), a test-suite, and some documentation.
[6, Section 5. 3] (implemented in Seminator 2). Both variants take a TBA as input and produce a TBA. The options --tba and --ba apply on the final complement automaton only. The seminator tool can now process automata in batch, making it possible to build pipelines with other commands. For instance the pipeline ltl2tgba <input. ltl | seminator | autfilt --states=3. >output. hoa uses Spot's ltl2tgba command to read a list of LTL formulas from input. ltl and transform it into a stream of TGBAs that is passed to seminator, which transforms them into semi-deterministic TGBAs, and finally Spot's autfilt saves into output. hoa the automata with 3 states or more. Python bindings form the second user-interface and are installed by the Seminator package as an extension of Spot's own Python bindings.
It offers several functions, all working with Spot's automata (twa graph objects): semi determinize() implements the semi-determinization procedure; complement semidet() implements the "pldi" variant of the NCSB complementation for semi-deterministic automata (the other variant is available under the same function name in the bindings of Spot); highlight components() and highlight cut() provide ways to highlight the nondeterministic and the deterministic parts of a semi-deterministic automaton, and its cut-transitions; seminator() provides an interface similar to the command-line seminator tool with options that selectively enable or disable optimizations or trigger complementation. The Python bindings integrate well with the interactive notebooks of Jupyter  [17]. Figure  3  shows an example of such a notebook, using the seminator() and highlight components() functions.
We have also generated 500 random LTL formulas of each category. The scripts and formulas used in those experiments can be found online, 2  as well as a Docker image with these scripts and all the tools installed. 3 All experiments were run inside the supplied Docker image on a laptop Dell XPS13 with Intel i7-1065G7, 16 GB RAM, and running Linux. Fribourg plugin for GOAL (na)  [1, 30]  GOAL (gc) 20200506  [28]  Owl (ltl2ldgba) 19. 06. 03  [11]  ROLL (replaces Buechic) 1. 0  [20]  Seminator (seminator) 1. 1  [ 5]  Spot (autfilt, ltl2tgba) 2. 9 Table  2. Comparison of semi-determinization tools. A benchmark set marked with x + y consists of x formulas for which all tools produced some automaton, and y formulas leading to some timeouts. A cell of the form s (m) shows the cumulative number s of states of automata produced for the x formulas, and the number m of formulas for which the tool produced the smallest automaton out of the obtained automata.
Conclusion We have presented Seminator 2, which is a substantially improved version of Seminator 1. 1. The tool now offers a competitive complementation of TGBA. Furthermore, the semi-determinization code was rewritten and offers new optimizations that significantly reduce the size of produced automata. Finally, new user-interfaces enable convenient processing of large automata sets thanks to the support of pipelines and batch processing, and versatile applicability in education and research thanks to the integration with Spot's Python bindings.
To prevent therapists from using the instrument as a reason for not using eMental health interventions, the members of the focus group decided that the tips should never suggest not to use eMental health. Instead, the tips should encourage therapists to think outside the box to involve difficult-to-engage patients in blended care or to delay the use of eMental health to a later point. The final activity of the third phase was the development of a working prototype of the instrument to enable therapists to test it with several patients. A digital prototype was created based on the needs and wishes of the therapists that were identified in phase 1. The prototype was made in Qualtrics, as this software provides tailored advice per chosen answering option.
• To use the instrument, therapists had to use the Qualtrics prototype, which was considered inconvenient as they often could not retrieve the link, which was e-mailed to them. They indicated that it would be easier to integrate the instrument in one of the existing systems they used, among which were the platform of the eMental health intervention and the electronic patient file. Consequently, the importance of integrating the instrument in these systems was emphasized to prevent the use of the instrument as an additional time-consuming activity. In the focus group, the project team decided that the first part of the FfBC instrument should be integrated in a to-be-developed web-based welcoming module, which is expected to be followed by all patients that start the treatment. The questions of the second part of the FfBC instrument have to be integrated into an existing system that has all existing questionnaires that are used in treatment to ensure that the FfBC is used in the same way as other questionnaires that are used in forensic mental health care and to ensure that they do not require any additional work.
3 times higher than the remainder of the rectangular field-of-view. The headlight source lies within this band. For CS(5-4), a simple single-resolution clean was applied to the 12-m data within the same mask as for the CO(2-1) emission. The 7-m and Total Power data were not added in this case because no emission was detected in these datasets. The deconvolved image was also convolved with a Gaussian to reach an angular resolution of 1. 0.
In each case, we plot the line ratio in the comparison source normalized to the value estimated for the headlight cloud, so that it indicates a line ratio identical to the headlight cloud. From the top to bottom panel, we plot the data for entire galaxies, and for disk and center regions separately. We do not show data for the inter-arm or diffuse extended regions. This is why the galactic "average" values can sometimes be lower than both the disk and center values. The line ratios used in this plot and quoted in Table  5  are derived using different methods and on datasets with a range of resolution and sensitivities, and the values should therefore be interpreted with some caution. As described in Section 3. 3, the values for headlight cloud are determined using the peak brightness of a Gaussian fit to line profiles extracted at a spatial scale of ∼ 140 pc, whereas the values for the EMPIRE galaxies are ratios calculated using the integrated intensity of an average line profile from within a kpc-scale aperture.
2). To be consistent with the measurements of the headlight cloud in Section 3. 3. 1, the line ratios were defined using the peak brightness of CO(1-0) and CO(2-1) data cubes that had been smoothed to common (round beam) resolution of 3. 2. We restricted our measurement to pixels where the signal-to-noise at the line peak was greater than 5 in both tracers, and we rejected clouds where our analysis box contained less than 10 valid pixels. We list the cloudscale CO(2-1)/CO(1-0) measurements for NGC 628's ten most luminous clouds in Table  2 , and we plot all the cloud-scale measurements of CO(2-1)/CO(1-0) versus the cloud luminous mass in Fig. 7. From this analysis, it is clear that kpc-scale averages hide genuine local variations in CO(2-1)/CO(1-0): we find a mean cloud-scale value of CO(2-1)/CO(1-0) in NGC 628 of 0.
Trends with Galactocentric Radius In Fig. 10 , we show the kpc-scale structure of NGC 628, highlighting the location of the headlight cloud. We plot the gas and stellar structure in NGC 628 as a function of galactocentric radius. Each point in Fig. 10  shows mean properties of NGC 628 estimated within a 1 kpc aperture (see  Leroy et al. 2016; Sun et al. 2018; Utomo et al. 2018, for details). The apertures cover the galaxy. The measurements are taken from  Sun et al. (in prep. ). The measurements shown in Fig. 10  are derived from multi-wavelength archival data for NGC 628. In addition to the PHANGS-ALMA CO (2-1) maps, we use THINGS HI 21 cm moment-0 map  (Walter et al. 2008)  to trace atomic gas distribution, S 4 G dust-subtracted 3. 6 µm image  (Sheth et al. 2010; Querejeta et al. 2015)  to trace stellar mass distribution, and GALEX-NUV and WISE band 3 images (presented in  Leroy et al.
Acknowledgements. We thank the anonymous referee for a prompt and helpful report that strengthened our manuscript. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2012. 1. 00650. S and  2013. 1. 00532. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. We thank  J. R. Goicochea  for useful discussions. CH, AH and JP acknowledge support from the  Programme National "Physique et Chimie du Milieu Interstellaire" (PCMI)  of  CNRS/INSU  with  INC/INP  co-funded by  CEA  and  CNES , and from the  Programme National Cosmology and Galaxies (PNCG)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES.
The work of AKL, JS, and DU is partially supported by the  National Science Foundation  under Grants No. 1615105 ,  1615109 , and  1653300. FB acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement No  726384 ). APSH is a fellow of the  International Max Planck Research School for Astronomy and Cosmic Physics at the University of Heidelberg (IMPRS-HD ). SCOG acknowledges support from the  DFG  via  SFB 881  " The Milky Way System " (sub-projects  B1 ,  B2  and  B8 ). JMDK gratefully acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  via the  ERC Starting Grant MUSTANG  (grant agreement number  714907 ). JMDK and MC gratefully acknowledge funding from the  German Research Foundation (DFG)  in the form of an  Emmy Noether Research Group  (grant number  KR4801/1-1 ).
g. contradicting experimental results, alternative hypotheses). The main use cases of audit trails for KRs based on hierarchies of graphs include version control for updates in schema-aware graph databases presented in  [4]  and updates to the knowledge corpora provided by the bio-curation framework KAMI  [15]. An audit trail system with the desired capabilities heavily relies on: (1) the existence of an efficient semantic representation of object transformations (or deltas), which frees us from the necessity to store the state of the object at each point of its transformation history; (2) the reversibility of transformations, which guarantees that any sequence of transformations can be 'undone'; and (3) the existence of sound means for composing a pair of successive transformations, which allows us to efficiently store and switch between different versions of the same object.
There exists an extensive body of work on versioning for database systems  [23] , including graph databases 1   [5, 17]. Such systems are usually divided into snapshot-and delta-based classes (also called copy and log), where the first store snapshots of the entire database at different points of the revision history, while the second preserve the transformation log. In this context, our system falls into a rather hybrid category: while the audit trail stores a representations of transformations (logs), these logs themselves consists of subgraphs, i. e. graph patterns that are affected as the result of transformation. A different classification of versioning systems for graph databases divides them into those that provide the revision history from the point of view of individual nodes and edges (or relationships), and those that use the graph point of view  [5].
However, only limited data are available on how and why PY varies inter-and intra-specifically in natural ecosystems  [24]. Legumes are thus a model example for studies of PY dormancy patterns in relation to environmental variations. The study of Rubio de Casas et al. [25]  showed a latitudinal gradient in PY dormancy in legumes. Thus, PY dormancy increases from regions with long growing seasons (e. g. , tropical climate) in lower latitudes to regions with a seasonal climate in higher latitudes. However, there are some studies of intraspecific PY dormancy variation along environmental gradients in several legume species  [10, [26] [27] [28] [29]  that are in disagreement with the results of Rubio de Casas et al. [25]. Medicago truncatula (barrel medic) is an annual, diploid, self-fertile species with a natural geographic distribution across the Mediterranean Basin.