dataesr/scientific-paragraphs-categorization huggingface dataset:
to generate ab initio gene predictions for each genome, after initially training with a transcriptome generated by running StringTie (Pertea et al. 2015) on the aligned RNA-seq data and proteins taken from D. mojavensis and D. melanogaster. Second, we used PASA (Haas et al. 2003) within the funannotate pipeline (https://github. com/nextgenusfs/funannotate) to generate gene predictions after trimming, normalizing, and aligning the raw RNA-seq reads described above.
In the textile composite drape process, the material bending behavior plays a critical role in the onset and development of wrinkles. The bending moment is linked with the membrane force in the classical shell element which leads to the insufficient for the textile composite draping simulation. Specific to the textile composite, a hypoelastic approach is proposed within the stress resultant shell. A bending stiffness independent of membrane stiffness is introduced to calculate the stress moments in the finite element analysis. This approach is implemented in the commercial software Abaqus, which makes it possible for any user of this software.
98, p = 0. 99) (Fig. 5b  & c ). Multivariate RDA demonstrated significant difference between the group of samples (faeces and rectal swabs combined) collected at the same time compared to the group collected at different times (p = 0. 001); and samples categorised as collected during antibiotics, after antibiotics or not on antibiotics (p = 0. 001); but not based on sample type (p = 0. 995) (Fig. 6 ). To confirm the observed relationships and to account for unknown data distributions with the selected variables, permutational manova (adonis) with Bray-Curtis distance metric was used and confirmed the finding, with time p-value < 0. 05, antibiotics p < 0. 05 but sample type p = 1. Antimicrobial resistance (AMR) gene detection was undertaken on assembled sequences. Swabs compared to faeces had no significant difference in total contig length (p = 0.
Analytics Basecalling was performed using bcl2fastq v2. 17. 1. 14 (Illumina, USA). For trimming and demultiplexing, and quality of assessment bcl2fastq and Trimmomatic v0. 36  [18]  were used. Trimmomatic removes the adapters as well as low quality bases (<Q30) from the beginning and ends of the reads. Average length after trimming was 127 bp. Using CLC Genomics Workbench version 8. 0. 2 (Qiagen, Germany), all reads were mapped against the human reference genome (NCBI Accession number GCA_000001405. 1). Unmapped reads were uploaded to and bioinformatically analysed using One Codex (One Codex, USA) with the 2019 database. One Codex uses a k-mer based analysis against a curated in-house database. Sequences are identified by exact alignment using k-mers (k = 31 bp). Overlapping k-mers in a read are matched to the most specific organism possible, classification is to the lowest common ancestor taxon, and k-mers are aggregated across a given read to ensure specific and consistent taxonomic assignment  [19, 20].
8. 84  (Zakrzewski et al. , 2016) , and the default parameters. The OTU. biom output from One Codex was used for overall statistical analysis and the rolled up taxonomic level data for the specific interrogation of the key organism groups of interest. Calypso is a web-application specifically designed to compare and interpret taxonomic information from microbial metagenomic sequence compositional data. The software allows the user to select from several statistical analysis methods and graphical displays which can be applied to the uploaded data. For this study the output from reads assigned to taxons from One Codex (One Codex, USA) and meta-information prepared by the authors for each sample was uploaded into Calypso  [21]. Uploaded read data was normalised using the default parameters which is set to include the top 3000 taxa, and exclude samples with less than 1000 sequence reads and taxa with less than 0.
01 percent relative abundance across all samples. Total sum normalisation and square root transformation (Hellinger transformation) was used to render the compositional data suitable for standard statistical analysis  [22]. The software was used to perform abundance determination and commonly used statistical testing and visualisation of taxonomic information, including diversity estimates using multiple metrics, regression, analysis of variance (ANOVA), Wilcoxon signed-rank testing, principal component analysis (PCA), canonical correspondence analysis (CCA), redundancy analysis (RDA) and multivariate analysis  [21]. In addition, Calypso was used to determine false discovery rate (FDR) and Bonferroni correction, which were applied to counter potential statistical type I errors due to multiple comparisons.
0. 1  [25]  with the default settings and ResFinder database (2020-Dec-13) available at the time  [26]. Quality of assemblies were assessed using QUAST v4. 6. 3  [27]. The number, length and N50 for all contigs were determined. Contigs were not excluded based on length and all contigs were utilised for gene detection, however quality of gene detection was further managed through Abricate  [25]. Abricate is a bioinformatic tool for the mass screening of contigs for acquired antimicrobial resistance genes by using DNA sequence matching against the selected antimicrobial resistance gene database. Results can be combined into a matrix with the presence of genes indicated by the percentage coverage of the gene. The default settings were used with a cut-off for gene coverage a minimum of 80% and the same for percentage identity.
Resistance gene detection was based on the presence or absence of genes, which included counting two detections of the same gene in a sample as a single detection and two different genes for the same resistance gene type as a single gene type in a sample, was counted as single gene type detection. For example tet  (32) _1 and tet(32)_2 were counted as two genes but rolled up into a single gene type detection, tet  (32). Gene class allocations were based on information from the Resfinder database  [26, 28] , Abricate v1. 0. 1, and NCBI MicroBIGG-E  [29]. Statistical analysis for contigs and resistance gene differences were calculated using Microsoft 365 Excel ® with the Data Analysis Toolpak Add-in. Values were compared and tested using t-Test and p-value of < 0. 05 was considered statistically significant.
Acknowledgements We are grateful to  Rudma Tek Pty Ltd  for the assistance with the illustrations for this article. Funding for this research was received from the  Brisbane Mater Foundation  (Betty McGrath Fellowship for  Dr S. Schlebusch ), the  Study, Education and Research Committee, Pathology Queensland, Queensland Health  (sequencing costs), and the  University of Queensland Centre for Clinical Research  (sequencing costs). The authors would also like to thank the Morrison Group at the  Translational Research Institute, University of Queensland, Brisbane, Australia ; and staff at RBWH  ICU ,  Mater Pathology  and the  Public Health Microbiology laboratory, Forensic and Scientific Services , for their support.
Funding Funding for conducting this research was received from the  Brisbane Mater Foundation  (Betty McGrath Fellowship for  Dr S. Schlebusch ),  Brisbane, Australia ; the  Study, Education and Research Committee, Pathology Queensland, Queensland Health  (sequencing costs), and the  University of Queensland Centre for Clinical Research  (sequencing costs),  Brisbane, Australia. The funding bodies had no input in the design of the study and collection, analysis, interpretation of data, or writing of the manuscript. Funding provided by the  University of Queensland  was internal departmental funding and there was no input in the design of the study and collection, analysis, interpretation of data, or writing of the manuscript beyond the affiliated authors.
Operationally, this approach uses Python modules for the creation and manipulation of MOCs (mocpy;  [18] ,  [19] ) and for use of the HEALPix tessellation  [20]  (cds-healpix-python;  [21] ), combined with the astroplan package  [22]  to select the HEALPix indices at a given order and to set up a new MOC map that represents the visibility with all of these constraints taken into account. Note that the complete code is reported in a public GitHub repository  4  which indicates all of the Python modules necessary for this analysis. A video demonstration 5  is also provided focusing on the Aladin functionalities used through the paper.
The ligo. skymap python package The ligo. skymap package is a set of Python tools for reading, writing, generating, and visualising gravitational-wave probability maps from LIGO and Virgo. From ligo. skymap, it is possible to invoke the command-line ligo-skymap-contour-moc  11. The method returns a credible level encoded in a MOC data structure of an allsky probability map in FITS format (see Subsection 2. 2). The method is based on mocpy. Recently, the mocpy version 0. 10. 0 builds the credibility regions with an algorithm that has various options, including one which produces the same results as Aladin Desktop, and others that allow for different handling of the region boundaries; see from multiordermap fits file method  12. Mocpy is a Python library allowing easy creation, parsing and manipulation of sky areas encoded in a MOC data structure  [32].
MOC data visualisations MOC maps can be visualised in various environments and libraries. The MOC files can be viewed in the Aladin Desktop or in a dedicated Jupyter Notebook  [35]  using ipyaladin  14. The files may also be viewed in JavaScript applications in which Aladin Lite  [36]  is embedded. Dedicated tutorials for plotting a general MOC map using the popular Matplotlib library  [37]  are available in the mocpy repository  15. The property of shading the areas inside the MOC or the property of displaying only the edges are well-suited to display/manage many overlapping planes (see Subsection 5. 5).
The ingestion tool adopted by CADE is based on the drizzling library  18  , and applies an algorithm where the surface of pixel intersection is computed as described in  [46]. The service delivers VO-compatible data, accessible through the Aladin software. With the all-sky map of Galactic reddening loaded into Aladin Desktop, the MOC is computed from a pixel range as follows: select Coverage ⇒ Generate a spatial MOC based on ⇒ The current image, HEALPix map or HIPS. The MOC generation window (shown in the right panel of Figure  2 ) offers three options. • Plane: the dropdown menu lists the image files loaded in the Aladin Desktop session. To preselect a plan, click on its name in the Aladin Stack. • Pixel range: a range of pixel values. • MOC resolution: the dropdown menu lists the MOC-resolution options and the corresponding pixel resolutions.
Searching for reference images To search for convenient reference images, we explore the public surveys from the Aladin data collections tree  19. When one or more image resources have been chosen, the spatial coverage related to the resource is loaded as a MOC map by checking the box space cov. (see Figure  5 ). This functionality permits further operations to be done with the new MOC map. As a first step, we choose the PanSTARRS DR1 20  coverage and then we intersect it with the BAYESTAR gravitational-wave sky localization of GW190425 pre-processed as discussed in the previous Section 4. 2. The MOC map of the PanSTARRS DR1 dataset has been generated by CDS as part of the ingestion of the PanSTARRS data into the CDS HiPS node. The MOC was generated using Aladin/HipsGen v10. 138. 4. The options available in this window are described in Section 4.
Computing the visibility of MOC regions from observatory sites The Python module astroplan  [22]  is used to compute the visibility of a MOC from different observatories by specifying the location and the airmass constraints. We consider a multi-messenger team composed of three astronomical research observatories: 1) Haleakala Observatories in Hawaii, USA 2) Paranal Observatory in Chile 3) Siding Spring Observatory (SSO) in Australia. With astroplan we define three Observer classes. Observer is an astroplan container class for information about an observer's site 22. Using the methods at night, we verify if it is night at the time of the GW190425 at Haleakala Observatories, otherwise, we ask for the nearest twilight astronomical evening with twilight evening astronomical method. 21 https://l.
Retrieving the reference images The images available via the Aladin data collections tree are HiPS (Hierarchical Progressive Survey,  [50] ) data sets that comply with the IVOA HiPS standard  [25]. The HiPS network is made up of 20 nodes hosted at different sites, and a listing of all of the data sets published in the network (more than 900 data sets) is available  24. The provenance of each HiPS data set is included in the standardised metadata in the properties file of each data set. Cut-out images, in the FITS (Flexible Image Transport System) format, may be extracted from HiPS data sets using the online hips2fits service  [51]   25. This provides a very efficient and flexible way to generate cut-outs from many surveys, which can be useful for many purposes albeit with a number of caveats.
Note that the extracted FITS files do differ from the original data because they have been converted to HiPS which involves re-sampling onto the HEALPix grid, and then extracted with user-defined sampling into a FITS cut-out by hips2fits. These transformations would need to be taken into consideration when making scientific measurement from these FITS files. The tiling patterns to split the sky areas proportional to the instrument footprints can also be generated using the Aladin Desktop functionality (from File ⇒ Load Instrument FoV ⇒ Server selector) or dedicated packages; GWsky 26  , teglon 27  , gwemopt  28  , GW-Localization-Tiling 29  , sky tiling  30  , to mention a few. Three screenshots taken from the video tutorial associated with the present paper compose Figure  5. 4. The server selector window, in which the instrument field-of-view can be chosen/added, is shown in the top panel.
Two field-of-view footprints are plotted in blue and orange (1 • × 1 • ). The sky position centred at the field-of-view instrument can be taken opening the properties window associated with the current field-of-view instrument loaded in the Aladin stack. That is depicted in the middle panel. Finally the cut-out images can be obtained by filling out the form of the hips2fits web server as shown in the bottom panel. Note, thumbnails for a list of targets or coordinates are also readily available through the submission of queries from dedicated Python scripts.
Encoding time information in (visibility) MOCs Operationally, the addition of time information in a spatial MOC can be achieved using the from spatial coverages method in mocpy or managing the Properties in the Aladin Desktop. This is accomplished by selecting the menu bar the item called Coverage ⇒ Generate a Space-Time MOC based on ⇒ The selected Space MOC. The resulting new data structure is indicated with STMOC (Space and Time MOC). While a detailed description of STMOC is beyond the scope of this paper, we provide a brief outline based on  [24]. The temporal coverage (TMOC) of a data set can be expressed using a discrete time axis where the unit element has a duration of 1 microsecond. Using the same hierarchical approach as the spatial MOCS, the time axis is organised into orders of increasing temporal duration, with each order being a factor of two longer than the previous one.
It aims to efficiently organise the multi-messenger effort provided by worldwide collaborations (see instruments reporting  33  ) in order to minimise unnecessary overlap. In this paper, we have shown various applications for managing the 2D sky maps from source localizations of gravitational-wave transient events when the enclosed probability areas are encoded with MOC data structures. We have demonstrated that the MOC data structure to plan multi-messenger observations proves to be a very flexible and interoperable methodology to satisfy many research demands in multi-messenger science (data comparison, data access and data visualisation). In the Python implementation of MOC, excellent performances are obtained thanks to the core functions written in Rust programming language. Visualisation and operations of MOCs are fully integrated in the VO standard and traditional tools as Aladin Desktop and Aladin Lite.
Dedicated software are also developed to share proprietary images or footprints in the HiPS/MOC data structure  34  in order to apply the FAIR (Findable, Accessible, Interoperable, Reusable) principles in the context of the IVOA ecosystem. In more detail, we have investigated an interoperable approach to determine the observability of MOC cells based on the geographic coordinates of groundbased observatories. Before identifying the visibility MOC area, the sky localizations can be processed in order to optimise follow-up strategies to increase the chance to identify the electromagnetic counterpart of a gravitational-wave event. Here we have simulated two basic examples: removing or identifying the highest extinction areas and collecting information querying VO data providers to search for reference image catalogues.
In the first operation, we perform a subtraction between the initial gravitational wave sky localization and a dust Galactic map. In the second operation, the subtracted sky map is intersected with the reference image catalogue chosen from the Aladin data trees. The computation speed (generally a few milliseconds, see Section 1. 6. 4 in  [17] ) is exceptionally high for the MOC operations. This aspect makes them more efficient than using polygons to confine complex and irregular sky regions. The visibility of the resulting sky map is quantified from three astronomical sites composing our ideal multi-messenger network. Finally, we also encoded to the MOC map the temporal information adopting the STMOC data structure. An observation campaign that makes use of visibility (ST)MOCs can easily quantify the observation time between the various observers as soon as an alert has been launched and reshape it in real time meeting the current observational requirement.
Acknowledgements. The research leading to these results has received funding from the  European Union 's  Horizon 2020 Programme  under the  AHEAD2020  project (grant agreement n. 871158). This work has been partly supported by the  ESCAPE  project (the  European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures ) that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the Grant Agreement n. 824064.
Beam detectors During the crystal-collimation tests, the SPS beam instrumentation and other detectors developed ad hoc were used to continuously monitor the circulating beam parameters, the loss rate and the trajectories and shape of the deflected halo. The Beam Current Transformer (BCT) was used to measure the intensity of the circulating beam. In the SPS there are two different types of BCTs. The fast BCT measures the intensity of a single bunch every five RF buckets, that is every 25 ns. The BCTDC measures the intensity of the entire beam every turn. Their information was combined to monitor the beam status during data taking and to normalize loss rate measurements during the offline analysis. The Beam Position Monitor (BPM) provides information on the centroid position of the circulating beam.
It was used to ensure that the circulating beam orbit was under control in each run. Moreover, a similar detector was mounted on the jaws of the LHC-type collimator to provide information on the distance of the beam centroid from the mid-point of the two jaws. 42 Its main use was to precisely monitor the orbit stability during data taking and eventually to correlate unwanted losses to possible orbit jitter. The Timepix inserted in the Roman pots was a hybrid pixel detector developed by the CERN-based European Medipix2 Collaboration. 43 It consisted of a CMOS readout circuit (Timepix) bump-bonded to a 300 µm thick silicon pixel detector with a matrix of 256 × 256 square pixels of 55 µm pitch. Each cell in the matrix contained a charge-sensitive preamplifier, a single threshold discriminator, a timebased logic and a 14-bit pseudo-random counter with overflow control logic.
44 The 2230004-15 Int. J. Mod. Phys. A 2022. 37. Downloaded from www. worldscientific. com by GERMAN ELECTRON SYNCHROTRON @ HAMBURG on 06/15/22. Re-use and distribution is strictly not permitted, except for Open Access articles. Timepix allowed to acquire data inside a configurable time window called shutter. When a particle generated a signal over a selected threshold, the pixel was triggered generating a hit signal. Every pixel could be configured independently in three different modes. In the Medipix counting mode, each pixel counted the number of hits inside the shutter. In the Time of Arrival (TOA) mode, each pixel counts the number of clock cycles from the first hit to the end of the shutter signal. Finally, in the Time over Threshold (TOT) mode, each pixel counted the number of clock cycles with an over-threshold signal inside the shutter.
Observations during the initial exploratory runs The first UA9 runs performed in 2009/2010 in the SPS with proton beams of 120 GeV were dedicated to explore the necessary experimental procedures and, at the same time, to identify issues requiring improvement. Angular and linear scans were performed with the first series of four crystals, namely STF9, QMP15, 2230004-25 Int. J. Mod. Phys. A 2022. 37. Downloaded from www. worldscientific. com by GERMAN ELECTRON SYNCHROTRON @ HAMBURG on 06/15/22. Re-use and distribution is strictly not permitted, except for Open Access articles. QMP25 and STF18. The extensive analysis of the recorded data is discussed in Refs. 52 and 55. In Table  3 , the performance of the crystal-assisted collimation recorded in this period is shown with a single representative result selected for each crystal.
Curve 3 shows the multi-turn simulation results based on a Monte Carlo crystal routine  59  and on the SixTrack code to transport the particles in the SPS. The diffractive scattering of protons was accurately included. In this case, the calculated width of the overall angular shape is smaller than that of the experimental data, the losses in channeling orientation are also smaller than the measured ones, while the loss value in the VR region is in better agreement. In the central plot (in green) of Fig. 26 , the beam loss profile recorded by the BLM5 in the HD area is shown. The beam loss reduction was R BLM5 = 8. 3, thus smaller than downstream the crystal as in the previous experiments. The green triangles, connected by a dotted curve, are the result of a simulation based on the crystal routine in Ref.
59 and on the SixTrack code. The agreement with the data is satisfactory in channeling orientation and fair in other angular ranges. The right plot of Fig. 26  shows the beam loss shape as a function of the crystal orientation observed by BLM7. The angular dependence is the same as downstream the crystal. The beam loss reduction in channeling instead is considerably larger, R BLM7 = 18. 1. The reduction values of the proton losses on the beam pipe obtained by the simulation with the SixTrack code are 9. 1 and 20. 1 at the crystal and at BLM7, respectively. The large loss reduction at the BLM7 was essentially due to the reduced flux of off-momentum particles emerging from the TAL1 that could reach the BLM7 area. These particles were predominantly lost near the first dispersion maximum and the BLM5, where the betatron phase advance from the TAL1 is about 90 •.