dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other side, the differences in computational time between the three levels of discretisation are almost negligible. Therefore, it can be stated that the interpolation algorithm is almost not impacted by the size of the background mesh h. Also, the computational time of the Julia package when using a discrete SDF does not differ much from that of Abaqus. For a more complete analysis, these computational times are compared with those obtained when using a discrete surface in Abaqus. In fact, in Abaqus analytical surfaces are described by mathematical functions and, thus, efficient contact algorithms can be exploited. As a result, the total computational cost is very low. However, analytic rigid surfaces can only be created in Abaqus for simple geometries. If, on the contrary, a triangulated mesh is used, Abaqus is forced to use a standard contact method and, depending on the total number of triangular elements, its computational time can become significantly higher than that shown by the Julia package with a discrete SDF (Table  7 ).
Conclusions In summary, we have designed and implemented an open-source, Julia-based package of an implicit 3D corotational formulation for  frictional beam dynamics based on  [25]. The performance of the optimised Julia-based package has been compared to the original Matlab code exhibiting a great improvement in terms of computational time (33× faster in complex 3D contact problems). Moreover, the same tests have been performed in Abaqus and the results have shown a good agreement, although Julia is slightly faster than Abaqus (2. 7× faster in complex 3D contact problems). The code of EndoBeams. jl is easily readable, which represents a convenient aspect for further development of the package. The current version of the algorithm is well-suited to applications in cardiovascular biomechanics, in particular the simulation of cerebral stents deployment.
jl package leaving room for further optimisation. In particular, it was observed that the most time-consuming part of the code in complex environments remains the computation of elemental contributions (Appendix B). This part of the code could be parallelised by dividing the mesh into disjoint domains and by computing the contributions of each domain in parallel threads before assembling them in the tangent stiffness matrix. A mesh colouring technique could be used to ensure that no adjacent elements belong to the same domain. Nevertheless, the effects of code parallelisation are only significant when the number of mesh elements is very high and would probably not be appreciated in examples such as the braided stent. Finally, a variety of approaches is proposed in literature to achieve real-time simulation, which is necessary for surgical training simulators or visualisation aids in operating room, in particular combining FE simulations and machine learning models  [59] [60] [61] [62].
In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition.
This has opened up the way for the many different spectrum-centric and peptidecentric data analysis strategies available today  [11] [12] [13] [14] [15] [16]. The latest reduction in complexity or "chimericy" of DIA spectra encompasses continuously scanning the quadrupole as is done with SONAR  17  and Scanning SWATH  18  and combining quadrupole selection and ion mobility separation, as is done with diaPASEF  19. Unsurprisingly, machine learning is taking center stage in mining the various resulting data architectures  [20] [21] [22] [23] [24] [25] [26] [27]. Here, we created a comprehensive dataset on a single benchmark experimental design adapted from Navarro et al. 28. It contains a ground truth that serves as a quality control for bioinformatics algorithm development and evaluation. This sample was acquired with adequate replicates on many of the current day instrumental platforms -partially in nano flow LC and partially in capillary flow LC -by most of the available acquisition strategy families, covering all commonly measured ion coordinates (Figure  1 ).
Far from being complete, it still is the most comprehensive repository of its kind for algorithmic development and validation, both at the level of identification and quantification. Instead of being yet another way of attaining the highest number of identified peptides, we hope it becomes a resource for compatibility assessment and data analysis quality control. Above all, it is a snapshot of current day completeness of our digital image of the protein world. Orbitrap QE HF-X, Waters Synapt G2-Si and Synapt XS and Bruker timsTOF Pro. The complete dataset was made publicly available to the proteomics community through ProteomeXchange with dataset identifier: PXD028735. In addition, a system suitability workflow (AutoQC) was incorporated on each instrument using commercial E. coli lysate digest which were acquired at multiple timepoints throughout each sample batch.
LC-MS/MS In this section, a detailed description of the different LC-MS/MS parameters is given for each LC-MS/MS instrumental setup applied to generate this comprehensive dataset. All instruments were operated according to the lab's best practice, i. e. not necessarily the best attainable, but rather most realistic data quality. Sample load was chosen based on LC setup (nano flow = 1 µg on column vs capillary flow = 5 µg on column) and instrument sensitivity. Thus, differences in absolute number of identified peptides and proteins can be attributed to sample load, LC flow rate, MS instrumentation, operator's choices and search algorithmic compatibility; direct conclusions on MS instrument performance can therefore not be drawn from this dataset. As a rule of thumb, data-dependent acquisition (DDA) methods use high energy fragmentation spectra (MS2) of narrow mass selections for identification and use the area under the curve of the precursor (MS1) for quantification.
c. Scanning SWATH GP 1Da A Scanning SWATH beta version was installed on the Analyst TF control software in collaboration with Sciex Research. Scanning SWATH Q1 calibration was confirmed by directly infusing a tuning solution (ESI Positive Calibration Solution for the SCIEX X500B System -P/N: 5049910 ) and by acquiring a pre-built calibration batch (SSCalibration. dab). Afterwards, the calibration was verified by i) running a verification calibration batch and inspect the data in PeakView. Calibration of the Q1 after initial calibration was controlled automatically by internal recalibration of each data file at run time removing the need for subsequent calibration events. The gas-phase fractionation approach usually acquired in PAcIFIC, were also acquired by Scanning SWATH because this uniquely allows to apply DIA annotation algorithms for library building of subsequent full mass range DIA acquisition.
Data Records Data record 1. The mass spectrometry DDA and DIA-MS proteomics data including instrument raw files (. wiff,. raw,. d) have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier, PXD028735  34, 35. For every instrument, a separate Sample and Data Relationship File (SDRF) and an Investigation Description File (IDF) have been uploaded to ProteomeXchange. Both the SDRF and IDF file formats are relatively new in Proteomics and were developed in a collaboration between EuBIC and the Proteomics Standards Initiative (PSI)  36, 37. These files are used to annotate the sample metadata and link the metadata to the corresponding data file(s) and thus will improve the reproducibility and reanalysis of this comprehensive benchmark dataset.
Note that the timsTOF Pro AutoQC samples were acquired using the 120min gradient similar to the actual hybrid proteome samples. System suitability assessment was performed by monitoring peptide-identification free metrics (i. e. retention time, peak area, mass accuracy, etc. ) extracted with the vendor neutral Panorama AutoQC framework  38, 39. To isolate a set of peptides that can be used for this, triplicate AutoQC samples acquired on each instrument were peak picked using MSConvert (version 3. 0. 20070) and the corresponding. MGF files were searched against an E. coli FASTA database using Mascot Daemon (v2. 7). The searches were performed with following settings: (i) 20 ppm peptide mass tolerance, (ii) 50 ppm fragment mass tolerance and (iii) two allowed missed cleavages. The peptide and protein identification results were exported as Mascot.
DAT file and imported into Skyline Daily (version 21. 1. 1. 160). The five highest ranked proteins were retained in the target list and after importing one of the AutoQC. raw files, we manually verified and removed each precursor with co-eluting peptides and low MS1 signal intensity before a Skyline file was saved as template file. Finally, a configuration file for each setup was created with the AutoQC Loader software (version 21. 1. 0. 158) which leads to the automatic import of every sample, with the pattern "AutoQC" in the. raw file or folder structure, in the Skyline template. The data and skyline reports were published to the PanoramaWeb folder "U of Ghent Pharma Biotech Lab -LFQBenchmark across Instrument Platforms" containing six subfolders for each instrumental platform. For each instrument, peak area, retention time and mass accuracy were manually checked by plotting these metrics in Levey-Jennings plots.
For almost every instrument a few outliers were detected, as can be expected on a dataset of over 600 LCMS runs. Fortunately, most of these can be explained by inspecting the raw data and by personal communication with the technicians acquiring the respective datasets. Figure  2  illustrates one such case. More specifically, two AutoQC samples display a near-complete loss in peak area in the TripleTOF6600+ DDA dataset. Indeed, these were caused by (a) a wrong vial in the sample tray and (b) an empty vial. When these two samples are removed from the QC plot, a more coherent perspective on the variation in standard deviation is seen in the Levey-Jennings plot. Other instances that we have already found include (i) a significant shift in standard deviation in peak area reported for both the Orbitrap, timsTOF Pro and Synapt XS dataset because AutoQC samples were incidentally acquired in two different acquisition methodologies, i.
e. in DDA and DIA; (ii) For the timsTOF Pro, a drift in retention time was seen, indicating LC related technical variation which could have been caused by e. g. too short column equilibration times; (iii) In the Orbitrap QE HF-X AutoQC data one peptide (EEAIIK) was undetectable in all the AutoQC samples acquired in AIF. Manual inspection of the acquisition in Skyline (easily accessible through the Panorama QC pipeline) surfaced that it fell out of the precursor m/z range (351. 7053) that was acquired. As expected in such a massive MS proteomics experiments, it seems that for every instrument some outliers were recorded, most of which have explanations common to the field. Above all, this demonstrates the necessity of a performant system suitability workflow to increase the reproducibility and quality of LC-MS/MS proteomics datasets  40.
Usage Notes A comprehensive dataset such as the one presented here is inspired by the bioinformatics need to cope with the recent expansion of novel acquisition strategies and data dimensions. Apart from being a repository for validating both performance and compatibility of (new) bioinformatic pipelines, it can serve as a reference for general proteomics courses (e. g. Skyline tutorials, SWATH/DIA course) and be applied for training and validating machine/deep learning algorithms. As such, it is intended to facilitate our understanding of the impact of instrumentation on the perspective that is generated on protein biology and to a larger extent to help unify the field of proteomics. To demonstrate the applicability of this data repository, we assessed the impact of instrumentation on the most conventional data format acquired by all instruments i.
e. DDA. Therefore, for every instrumental platform, triplicate Human, Yeast and E. coli DDA runs were peak picked with MSConvert (version 3. 0. 21285) and exported as a Mascot. MGF file (Figure  3A ). MS Convert software was chosen as it is vendor-independent and contains each vendor's implementation for peak picking, with the exception of UNIFI i. e. Waters. Therefore, Progenesis QI for Proteomics (version 4. 2. 7207) was used for the Waters DDA data. By doing so, the MS1 precursor space was aligned in the retention time dimension before peak picking. Subsequently, all MS/MS spectra were exported as. MGF file for peptide identification. A standard search with carbamidomethylation of cysteine as fixed modification was performed using a database containing the Human, Yeast and E. coli protein sequences (downloaded from Uniprot on 19/01/2021) and with following parameters: i) mass error tolerances for the precursor ions and the fragment ions were set at 20 ppm and 50 ppm, respectively; ii) enzyme specificity was set to trypsin, allowing up to one missed cleavage.
Next, the results were exported as. DAT file and imported into Skyline to create a non-redundant spectral library with BiblioSpec. Afterwards, the. BLIB file was converted to a. dlib and. msp file format respectively using EncyclopeDIA  29. The resulting. msp file was converted using a Python conversion tool (speclib_to_mgf. py) built-in MS²PIP, to create a peptide record file (. PEPREC) and. MGF file. Next the proportion (amount) of peptide identifications overlapping between the different instruments was assessed using a custom Python script (Figure  3B ). Since each instrument analysed the same commercial protein digests, a large overlap in peptide identifications would be expected. However, while the timsTOF Pro, TripleTOF 6600+ and Orbitrap QE HF-X roughly identify a similar number of peptide sequences (approximately 40,000 over nine LCMS runs), the overlap in identified sequences is in the order of 50%, with that between the Orbitrap QE HF-X and the TripleTOF5600 and 6600+ being overall 10% higher than the overlap with the timsTOF Pro.
Data analysis: Quantification. Next, we used the benchmark design to verify to what extent the annotated peptides and proteins were found in their respective ratios (Figure  1 ). Apart from being a benchmark target for quantification algorithms, this ground truth can also be considered a very rough empirical estimation of the false discovery rate (FDR)  1. At the peptide level the spread of especially Human peptides into other ratios is increasingly visible in the order TripleTOF 6600+ < Orbitrap QE HF < timsTOF Pro, which alludes to increasing misannotation (Figure  3G ). While ratio estimation is known to become compromised with decreasing intensities of the MS1 features selected, the relative position of these peptides on the y-axis argues against this intensity explanation. Unfortunately, the exported ion counts of the timsTOF Pro differ at least 1-2 orders of magnitude compared to both the Orbitrap and the TripleTOF 6600+ and more advanced (conversion) algorithms are needed to compare these.
Additionally, recent annotation tools have doubled the number of peptides that can be extracted from the same DIA data, an asset not expected for novel DDA annotation algorithms  21, 28. We would therefore like to invite the developers of all current bioinformatics tools to make use of this comprehensive dataset to benchmark their performance for each instrument individually and adapt their algorithms to increase the performance on all. This dataset was compiled from different labs and therefore also captures differences in instrument usage peculiar to the field, yet it all can be assessed in the Panorama QC metrics that are also publically available. Especially a better understanding of the detectable and annotatable ion space will move the field forward and help researchers make informed decisions on the best acquisition strategies for their application or biological question under investigation.
Acknowledgements This research was funded by grants from the  Research Foundation Flanders (FWO)  awarded to BVP (grant number:  11B4518N ),  RG  ( 1S50918N ), and  MD  ( 12E9716N ). Hans Vissers  is acknowledged for his assistance with data conversion and formatting. This work was supported in part by the  French Ministry of Research  with the  Investissement d'Avenir Infrastructures Nationales en Biologie et Santé program  ( ProFi, Proteomics French Infrastructure  project;  ANR-10-INBS-08 ).
SZ observations are performed using the high angular resolution NIKA2 camera, observing at 150 and 260 GHz with respective angular resolutions 18" and 11"  [3]. Among its goals are the measurement of the Y 500 -M 500 scaling relation  [4]  and of the mean pressure profile of the ICM. These studies require the extraction of individual pressure profiles for each cluster in the sample of the program. As a consequence, a fast and robust software able to provide an accurate pressure profile measurement must be used. In this proceeding, we present the PANCO2 (Pipeline for the Analysis of NIKA2 Cluster Observations) software, that has been developed in order to fill the needs of the NIKA2 SZ.
Simple spherical simulations The first step of our validation is running PANCO2 on a simulated NIKA2 map of spherical mock clusters. We model clusters representative of the low-mass, high redshift and highmass, low-redshift part of the NIKA2 SZ Large Program sample  [1, 2]. We generate realistic gNFW pressure profiles for both of these clusters. We then use the forward modeling algorithm described in section 2. 2 to create realistic maps from their pressure profiles, including NIKA2 beam smearing and transfer function filtering, and realistic -albeit uncorrelatedresidual noise. These maps are cropped to the NIKA2 field of view, corresponding to the size of the largest angular scales that are recovered from NIKA2 cluster observations. We run PANCO2 on the resulting maps to reconstruct the clusters pressure profiles, and compare these results with the known true profile of each cluster.
The analysis is performed on both clusters, using both a gNFW and binned pressure profile model. We present the results for both model adjustments on the low-mass, highredshift cluster in figure  2. The results are very similar for each of the four studies, showing excellent agreement between recovered and true pressure profiles, from the angular resolution of NIKA2 to its field of view. Typical running times are around 10 minutes for a binned model fit and 30 minutes for a gNFW, using 30 walkers on 30 parallel threads. The data used for this analysis will be released with PANCO2.
Conclusions We have presented PANCO2, a new software to perform pressure profile measurements from thermal SZ observations. The forward modeling MCMC approach used enables accounting 2 https://music. ft. uam. es for various millimeter observational artifacts to deliver unbiased pressure profile estimates and confidence intervals. This was demonstrated using simulated cluster maps, showing good agreement between underlying and recovered pressure profiles on radial ranges covered by observations. PANCO2 is currently being adapted to be able to take as input SZ data from any instrument, so that it will be usable for all current and future millimeter surveys and pointed observations. This software will be made public in the coming months.
Acknowledgements This work is supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ). 291294). F. R. acknowledges financial supports provided by  NASA  through  SAO Award Number   SV2-82023  issued by the  Chandra X-Ray Observatory Center , which is operated by the  Smithsonian Astrophysical Observatory  for and on behalf of  NASA  under contract  NAS8-03060.
Also, it has been proven that humans and animals from the same household can share similar K. pneumoniae lineages. 2 n the other hand, there are still only a very few and fragmented WGS-based studies on this bacterial species in the animal sector (in contrast to Escherichia coli). Consequently, advanced phylogenetic information on MDR K. pneumoniae in veterinary medicine regarding human K. pneumoniae datasets remains highly limited. This study aimed to fill this gap by providing a new comprehensive understanding of the phylogenomic relatedness of the major ESBL/AmpC/carbapenemase-producing K. pneumoniae lineages/sub-lineages recognized in animals and humans.
Genome sequencing Bacterial genomic DNA was extracted using the NucleoSpin® Microbial DNA Kit (Machery-Nagel, Hoerdt, France) according to the manufacturer's instructions. DNA concentration and purity were respectively determined using the Qubit R 3. 0 Fluorometer (Thermo Fisher Scientific, Illkirch, France) and the NanoDrop 1000 (Ozyme, Saint-Cyr-l'Ecole, France). Library preparation was performed using the Nextera XT DNA library preparation kit and 2 × 150 paired-end sequencing was performed using the NovaSeq 6000 Illumina technology (Illumina, San Diego, USA). Illumina adapter sequences were removed and reads were quality trimmed using trimmomatic version 0. 39. 23 De novo assemblies were generated with Shovill version 1. 0. 0 (https://github. com/tseemann/shovill), and the quality of assemblies was assessed using QUAST v4.
Genome sequencing analysis STs and core genome MLST (cgMLST) were assigned using the online BIGSdb K. pneumoniae database available on the Institut Pasteur MLST and whole-genome MLST website (https://bigsdb. pasteur. fr/ klebsiella/). 4 New alleles and STs were submitted to the MLST database. Kleborate and BIGSdb analytical tools were used to detect capsular loci (KL types), K. pneumoniae virulence determinants and heavy metal tolerance genes. AMR genes were assessed using Kleborate (https://github. com/katholt/Kleborate) and ResFinder, whereas plasmid replicons were obtained using PlasmidFinder (http://www. genomicepidemiology. org). In addition to the Southern blot hybridizations, plasmid location of AMR genes was evaluated for all isolates based on the co-localization of resistance and plasmid incompatibility group genes on the same contig.
Clonal relationships Two datasets were defined, one including French animal isolates recovered in this study, and a second comprising French animal isolates belonging to ST11 and its single-locus variant (SLV) ST4850, ST15 and ST307 together with the whole genome sequences from humans belonging to the same STs and retrieved from the European Survey of Carbapenemase-Producing Enterobacteriaceae (EuSCAPE) study,  25  as well as additional animal isolates from different countries also belonging to these STs. 26 ll assemblies were annotated with Prokka version 1. 12 using default settings,  27  and for both datasets, a pangenome was determined and core gene alignments were generated using Roary v. 3. 11. 0 with a protein BLAST identity of 80% and a core definition of 90%. 28 Subsequently, recombination was removed with Gubbins v2.