dataesr/scientific-paragraphs-categorization huggingface dataset:
Siemens Healthineers mostly operates with Microsoft Azure and, according to our interviewees, was also closing a deal with a Chinese cloud provider. Azure allows the company to manage all the data generated by its medical devices and provides powerful AI technologies as a service (for example, fast imaging analysis) 16. Meanwhile, Siemens's Digital Industries Software, 17 formerly Siemens PLM Software, operates with AWS since 2012. In 2017, AWS took over responsibility for developing part of Siemens's MindSphere. 18 Amazon Elastic Compute Cloud, Elastic Load Balancing and Amazon Relational Database Service are examples of the services rented for this purpose. Siemens's 'digital twin' technique approach to manufacturing digitalization would not be possible without the infrastructure provided by Amazon.
The former relies increasingly on the basic AI and data management services provided by AWS. AWS facilitates translation and automatic analysisvia machine learning techniques such as natural language processingof Siemens's employee data surveys 19 , contributes to its cyber-security 20 and provides a graph database and web-scale computing to enable online modelling. 21  Summing up, even if Siemens develops cutting-edge AI technologies in specific fields or niches, it remains a user of digital infrastructure, generic AI and data management services accessed as black boxes from tech giants' cloud. As stated by Frank Konopka, which as we mentioned is Senior Advisor for Digital Business and Digital Ecosystems at Siemens Healthineers, 'Google and the other big players have always developed tech that we need to base our technologies on top.
Furthermore, Frank Konopka's presentation included an in-detail explanation of how the Chinese tech giant Tencent has already developed an all-embracing healthcare platform seeking to control individuals' digital twins, accessing simultaneously to several data sources: from appointment booking and telehealth to personal health record management. This platform of platforms also includes a health insurance called Shuidi Huzhu and SoYoung, which is an aesthetic medicine marketplace. On this basis, Konopka warns of the dangers of US tech giantswhich unlike Chinese ones have a global reachdeveloping such a comprehensive digital healthcare ecosystem. In relation to direct competition, according to our interviews, Google is seen by Siemens as the biggest threat. Google's healthcare ventures are channelled through Google Health, Verily Life Sciences, Calicowhich focuses on ageing and age-related diseasesand DeepMind.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by  Institut Francilien Recherche Innovation Société (IFRIS/LabEX SITES) ;  Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación  ( PICT-2018-02562 ); and  Universidad de Buenos Aires  ( UBACyT 20020190 , 200019BA ).
We report the results of analyses of galactic outflows in a sample of 45 low-redshift starburst galaxies in the COS Legacy Archive Spectroscopic SurveY (CLASSY), augmented by five additional similar starbursts with Cosmic Origins Spectrograph (COS) data. The outflows are traced by blueshifted absorption lines of metals spanning a wide range of ionization potential. The high quality and broad spectral coverage of CLASSY data enable us to disentangle the absorption due to the static interstellar medium (ISM) from that due to outflows. We further use different line multiplets and doublets to determine the covering fraction, column density, and ionization state as a function of velocity for each outflow. We measure the outflow's mean velocity and velocity width, and find that both correlate in a highly significant way with the star formation rate, galaxy mass, and circular velocity over ranges of four orders of magnitude for the first two properties.
In this paper, we seek to significantly improve the usefulness of such data in two respects. First, we use the COS Legacy Archive Spectroscopy SurveY (CLASSY) atlas  (Berg et al. 2022, hereafter, Paper I) , which is a data set specifically designed to span a vast range in the most fundamental galaxy properties: stellar mass (and hence galaxy circular velocity), SFR, and metallicity (see details in Section 2). Reaching low mass is especially important since feedback effects should be stronger in these shallow potential wells. This makes CLASSY ideal for testing how the fundamental outflow properties (outflow velocities, column densities, ionization state, and metal, mass, momentum, and kinetic energy outflow rates) depend on the galaxy properties, thereby providing a crucial test of the theoretical models and simulations.
Second, the CLASSY data, by design, are high signal-to-noise ratio (S/N) spectra and cover a wide wavelength range in the UV that encompasses many interstellar lines that span wide ranges in ionization state and optical depth. Both points enable us to analyze the data more rigorously than before, leading to more robust measurements of outflow properties. The structure of this paper is as follows. In Section 2, we introduce the CLASSY project and briefly describe the observations. In Section 3, we go through various data reduction processes. In Section 4, we present analyses to isolate the blueshifted absorption lines for galactic outflows, and we also discuss the ancillary parameters. In Section 5, we present the major results for the observed outflows, including covering fraction, column density, and ionization state as a function of velocity, and we also derive outflow rates of metals, mass, momentum, and kinetic energy.
Observations CLASSY is a Hubble Space Telescope (HST) treasury program (GO: 15840, PI:  Berg) , which provides the first high-resolution high-S/N rest-frame far-UV (FUV) spectral catalog of 45 local star-forming galaxies (0. 002 < z < 0. 182). These galaxies were selected to span a wide range of important galaxy properties, including stellar mass (log M å ∼ 6-10 M e ), SFR (∼0. 01-100 M e yr -1 ), metallicity (12+log(O/H) ∼ 7-9), and electron density (n e ∼ 10 1 -10 3 cm -3 ). For each galaxy, CLASSY completes its FUV wavelength coverage (1200-2000 Å observed frame) by utilizing the G130M+G160M +G185M/G225M gratings of HST/Cosmic Origins Spectrograph (COS). Overall, CLASSY combines 135 orbits of new HST data with 177 orbits of archival HST data to complete the first atlas of high-quality rest-frame FUV spectra of the proposed 45 galaxies.
Data Reduction After the observations, all data were reduced locally using the COS data-reduction package CalCOS v. 3. 3. 10. 19 These include both new data from CLASSY itself (GO: 15840) and all archival data that were included in CLASSY. Therefore, the whole CLASSY data set was reduced and processed in a selfconsistent way. The details of the data reduction have been presented in Paper I and Paper II, including spectra extraction, wavelength calibration, and vignetting. Given the final reduced and coadded spectra for each galaxy Paper I, we analyze the galactic outflow properties from various absorption and associated emission lines in this paper. Several additional steps in the reduction of the data are necessary, and are discussed in this section. In Section 3. 1, we discuss the fits of the stellar continuum for each galaxy, which are used to remove the starlight contamination of the spectra.
In Section 3. 2, we discuss other systematic effects in the analyses of outflows. To enlarge the sample size at the highest SFRs, we have added five Lyman break analog (LBA) galaxies from  Heckman et al. (2015)  that were not already in the CLASSY sample but also had HST/COS observations. We have checked that these LBAs satisfy all selection criteria of the CLASSY sample. We processed and analyzed these data in exactly the same way as the CLASSY data. This gives us a total sample size of 50 galaxies. HST/COS G130M and G160M gratings have the original spectral resolutions R ∼ 20,000, which is more than necessary for our outflow analyses. Therefore, for all spectra, we resample them into bins of 0. 18 Å (R ∼ 6000-10,000 from the blue to red end) to gain a higher S/N.
In both cases, the ratio increases rapidly at lower velocities (slopes ∼-1. 6 to -1. 7). See further discussion in Section 5. 4. 4. (D. Fielding, private communication). For the β = 1 model, N H increases from about 2 × 10 20 to 8 × 10 20 cm -2 as the individual cloud masses increase from 10 2 to 10 6 M e. For the β = 5 model the corresponding range is ∼1-2 × 10 21 cm -2. For our sample we find a median value of N H = 5 × 10 20 cm -2 , which agrees best with the β = 1 model and massive clouds. In summary, at least for parameters appropriate to an M82level starburst, our data are most consistent with the models with β = 1 (a fast wind that has not been contaminated) and with relatively massive clouds (∼10 5 -10 6 M e ). In the future, it will be interesting to compare our data to new models that have been tuned to cover the ranges of the CLASSY sample in V cir , SFR, and starburst size.
Conclusion We have reported here the results of our analysis of starburst-driven galactic outflows of warm ionized gas. This was based on data for 45 galaxies taken from CLASSY, augmented by five additional starbursts with COS data of similarly high quality and under the same selection criteria. The properties of the outflows were based on fitting the UV resonance absorption lines, and in particular, using five Si II multiplets and the Si IV 1393, 1402 doublet to derive the column density and covering fraction of these ions as a function of outflow velocity. CLOUDY models were used to derive total Si column densities and these were converted into H column densities using the metallicities derived from the nebular emission lines. The key parameters obtained from this analysis are the mean outflow velocity (V out ) and the FWHM out of the blueshifted absorption lines, the total Si and H column densities (N Si and N H ), and the outflow rates of Si (  M Si,out ), mass (  M out ), momentum (  p out ), and kinetic energy (  E out ).
This research has made use of the HSLA database, developed and maintained at  STScI, Baltimore, MD, USA. We also thank the anonymous referee for the insightful comments on the paper. Facilities:  HST (COS) ,  LBT (MODS) ,  APO (SDSS) ,  KECK (ESI) ,  VLT (MUSE, VIMOS). Software: astropy (Astropy Collaboration et al. 2018) BEA-GLE (Chevallard & Charlot 2016),  CalCOS (STScI) , dustmaps (Green 2018), jupyter (Kluyver et al. 2016), MPFIT (Markwardt 2009), Photutils (Bradley et al. 2021), python ( Van Rossum  & Drake 2009), pysynphot ( STScI Development Team 2013 ).
The Gaussian (NIST) atomic database (Kramida et al. 2018). (2). Vacuum wavelengths in representing the NUV light profile in the dispersion direction of this galaxy is units of Angstroms. (3). Oscillator strengths. (4). Einstein A coefficients in shown in blue. The approximate LSF used in our fits is shown in black, which units per second. (5). Energies from lower to higher levels in units of electronvolts. is the convolution between the blue and red curves. This galaxy has a relatively large NUV size in our sample, so the differences between the two LSFs are noticeable. See discussion in Section 4. 2.
These functionalities are enabled for any RNA-or DNA-targeting nucleases, including Cas9, Cas12, and Cas13. We illustrate the general applicability of our tools by designing optimal gRNAs for three case studies: tiling CRISPRbe library for BRCA1 using the base editor BE4max, tiling RNA-targeting libraries for CD46 and CD55 using CasRx, and activation of MMP7 using CRISPRa. The crisprVerse ecosystem is open-source and deployed through the Bioconductor project to facilitate its use by the CRISPR community (https://github. com/crisprVerse).
For developers, crisprDesign is built on top of a modular package ecosystem that implements the gRNA design tasks (see Table  4  in the Methods section), allowing the same code to be easily re-used outside of CRISPR applications and gRNA design. Table  1  includes a comparison to ten commonly-used gRNA design softwares: multicrispr  [Bhagwat et al. , 2020] , CRISPRseek  [Zhu et al. , 2014] ,  CHOPCHOP [Montague et al. , 2014] , CRISPOR  [Concordet and Haeussler, 2018] , CCTop  [Stemmer et al. , 2015] , GUIDES  [Meier et al. , 2017] , Cas-Designer  [Park et al. , 2015] , FlashFry  [McKenna and Shendure, 2018 ], E-CRISP  [Heigwer et al. , 2014]  and CRISPick; see the Methods section for a detailed description of the criteria used for benchmarking. While several of the features implemented in crisprDesign are also available in other tools, crisprDesign provides the most complete gRNA design solution across nucleases and modalities.
, 2014 , Wessels et al. , 2020 , Moreno-Mateos et al. , 2015 , Labuhn et al. , 2018]. In addition, the required inputs, data structures, and terminology are not consistent across software and algorithms, increasing the likelihood of user error. Finally, several of the algorithms are currently not bundled up into easy-to-use packages, limiting their accessibility and therefore their usage. To resolve this, we created a general and harmonized framework for on-target and off-target prediction of gRNAs, implemented in our R package crisprScore. The philosophy behind crisprScore is to abstract away from the user the language, implementation, and complexity of the different algorithms used for prediction. It uses the Bioconductor package basilisk  [Lun, 2021]  to seamlessly integrate and manage incompatible Python modules in one user session.
To add genomic context to the on-and off-targets, a TxDb object can be provided to the addSpacerAlignments function. The TxDb object is a standard Bioconductor object to store information about a gene model, and can easily be made from transcript annotations available as GFF3 or GTF files. Gene annotation columns are added to the off-target table for different contexts: 5' UTRs, 3' UTRs, CDS, exons, and introns. Finally, users can add the MIT and CDF off-target specificity scores  [Hsu et al. , 2013a , Doench et al. , 2016a]  implemented in crisprScore to characterize the likelihood of nuclease cleavage at the off-targets.
Inspired by the major-allele reference genome indices provided by the Bowtie team (see https://github. com/BenLangmead/bowtie-majref), we created two new human genomes to be used throughout our ecosystem that represent the major allele and the minor allele using dbSNP151 (see Methods). The blue box stores the genomic coordinates in GRCh38 to represent the target protospacer sequences using a GRanges object. By convention, we use the first nucleotide of the PAM sequence (in the 5 to 3 direction) as the representative genomic coordinate of protospacer sequences. The pink box stores sequence information of the protospacers and PAMs. The yellow box represents global metadata used for creating the GuideSet, including a formal CrisprNuclease object, the reference genome of the target protospacers, and gene model used for annotation.
, 2019] , addGeneAnnotation reports whether or not the gRNA cut site precedes any downstream in-frame ATG sequences, following the rules of  Cohen et al. [2019]. Additionally, to maximize gene knockout based on protein domains  [He et al. , 2019] , we include Pfam domain annotation  [Bateman et al. , 2004]  via the biomarRt package  [Durinck et al. , 2005]. For CRISPRa and CRISPRi applications, addTssAnnotation indicates which promoter regions are targeted by each gRNA, as well as the location of the target cut site relative to the TSS. This allows the user to easily select guides in the optimal targeting window. To further put the gRNA targets into biological context, users can access thousands of genomic annotation datasets through the Bioconductor AnnotationHub resource. The resource includes common sources such as Ensembl, ENCODE, dbSNP and UCSC.
Where appropriate, those annotations are in the GenomicRanges format, which make them directly compatible with the GuideSet object used to represent gRNAs in our ecosystem. By leveraging overlap operations on GenomicRanges, users can identify which gRNAs are present or absent in a given set of annotated features by using a few lines of code. For example, users can ask AnnotationHub whether a gRNA is targeting repeat elements to avoid cutting-induced toxicity, or whether a gRNA targets the region upstream of an annotated Cap Analysis of Gene Expression (CAGE) peak for CRISPRa applications. Additionally, the rtracklayer Bioconductor package  [Lawrence et al. , 2009]  provides functionalities to easily read genome annotations that are stored in the commonly-used WIG, BED, bigWig and bedGraph formats.
Functional annotations in crisprDesign improve gRNA selection We illustrate how functional annotations implemented in crisprDesign can improve gRNA selection by focusing on two functionalities: addConservationScores and addGeneAnnotation. We assessed the addConservationScores function using the large-scale CRISPRko fitness screening dataset from Project Achilles  [Meyers et al. , 2017]. We obtained normalized log fold changes (LFCs) measuring gRNA dropout over time (see Methods). In fitness screens, gRNAs targeting essential genes should deplete over time, and are therefore expected to have negative LFCs. Therefore, gRNAs targeting essential genes can be used to investigate determinants of gRNA activity. We downloaded basewise phyloP scores  [Pollard et al. , 2010]  from the UCSC genome browser.
This is in line with previous results  [Schoonenberg et al. , 2018 , DeWeirdt et al. , 2022 , Veeneman et al. , 2020]. gRNAs targeting non-essential genes serve as negative controls and show no dropout, irrespective of the conservation scores, as expected (Figure  S3c ). Next, we sought to understand how gRNA position within the CDS of the target gene impacts gRNA activity. Given that most gRNAs in Project Achilles were located in the first 50% of the target CDS by design, we obtained a different screening dataset; we downloaded data from a genome-wide fitness screen performed in HCT116 cells (Hart2015 dataset, see Methods). We used the addGeneAnnotation function in crisprDesign to annotate gRNAs with a position percentage of the target CDS. We used the Ensembl canonical transcript of the target gene as the representative CDS.
Next, we obtained and processed 5 human genome-wide fitness screen datasets from published studies (Table  3 ), each performed using a different gRNA library. For each dataset and gRNA, a LFC between later and earlier time point samples was calculated to quantify gRNA dropout over time. gRNAs targeting essential genes are expected to drop out and can be used for benchmarking purposes. To investigate the relationship between gRNA activity and gRNA ranking, we considered for each gRNA library the subset of gRNAs targeting a common reference set of essential genes  [Hart et al. , 2014]. For each gene and tool, we identified the top 15 ranked gRNAs based on the tool-specific in silico ranking. In Figure  3a , we show the distributions of LFCs in the Hart2015 dataset based on two groups: red lines show the distributions of the top 15 ranked gRNAs across genes, and green lines show the distributions of remaining gRNAs.
Top ranked gRNAs from CRISPick and crisprDesign show greater activity than lower ranked gRNAs, as indicated by a negative shift in the red distributions with respect to the green distributions. We repeated the analysis for each dataset, and summarized the performance of the top ranked gRNAs at discriminating active gRNAs by calculating the difference in means between the green and red distributions (∆ LFC). Results are shown in Figure  3b. Higher ∆ LFCs indicate better performance, and results indicate that both CRISPick and crisprDesign perform well across all datasets. 3. 2 Designing gRNAs targeting BRCA1 for the base editor BE4max CRISPR base editors are deaminases fused to CRISPR nickases to introduce mutations at loci targeted by the gRNAs without introducing double-stranded breaks (DSBs)  [Gaudelli et al.
Best ranked gRNAs Other Best ranked gRNAs Other Essential genes: Non-essential genes: Figure  3. Comparison of CRISPRko Cas9 gRNA rankings for protein-coding human genes. We designed and ranked gRNAs targeting all protein-coding human genes (Ensembl release 104) using tools that provide gRNA rankings: CCTop, CHOPCHOP, FlashFry, CRISPick and crisprDesign. To compare gRNA ranking performance across tools, we obtained gRNA LFCs from 5 genome-wide CRISPRko fitness screening datasets, listed in Table  3. In these fitness screens, active gRNAs targeting essential genes are expected to drop out and show negative LFCs. To investigate the relationship between gRNA activity and gRNA ranking, we considered for each gRNA library the subset of gRNAs targeting a common reference set of essential genes  [Hart et al.
, 2014]. For each gene and tool, we identified the top 15 ranked gRNAs based on the tool-specific in silico ranking. a LFC distributions in the Hart2015 dataset for gRNAs targeting essential genes (solid lines) and gRNAs targeting non-essential genes (dotted lines). Red lines show the distributions of the top 15 ranked gRNAs across genes, and green lines show the distributions of remaining gRNAs. For essential genes, top ranked gRNAs from CRISPick and crisprDesign show greater activity than lower ranked gRNAs (red distributions are negatively skewed). As expected, there are no differences for gRNAs targeting non-essential genes. b We repeated the analysis described in a for each dataset. We summarized the performance of the top ranked gRNAs by calculating the difference in means between the green and red distributions (∆ LFC), for essential genes only.
A higher ∆ LFC indicates better performance. For each method and dataset, a t-test was performed to quantify the difference in LFCs between the top ranked gRNAs and the remaining gRNAs. Corresponding p-values are reported above the bars ( * : p-value < 0. 05; * * : p-value < 0. 01; * * * : p-value < 0. 001. ) The first step consisted of designing all possible guides targeting BRCA1 using the findSpacers function in crisprDesign. The BE4max BaseEditor object from crisprBase was used to store nucleotide-and positionspecific editing probabilities (see Figure  4a ), which inform the editing window of interest for each of the gRNA targets. Next, using the function getEditedAlleles, we generated and stored all possible editing events at each gRNA (see Figure  4b ). The function also adds a score for each edited allele that quantifies the likelihood of editing to occur based on the editing probabilities stored in the BaseEditor object (see Methods).
We then added a transcript annotation to each gRNA using an Ensembl TxDb object as input. This adds a list of targeted isoforms to each gRNA, as well as transcript context (CDS, 5 UTR, or 3 UTR). We observed in the screen that gRNAs targeting a higher number of isoforms, and gRNAs located in CDS, lead to higher activity (Figure  5b , and Figure  S4 ). Different thresholds of the nonsense score were used to label a gRNA as nonsense or not. d Relationships between gRNA dropout from the BRCA1 BE4max dataset and several on-target activity scores. gRNAs that are not predicted to induce a nonsense mutation are colored in grey, and the size of the dots is proportional to the magnitude of the mutation score. The horizontal dotted lines at -0. 5 represent a cutoff to classify a gRNA as active or not.