dataesr/scientific-paragraphs-categorization huggingface dataset:
Orthology prediction of assembled genomes For each assembly (species, technology and coverage level) we run OMA standalone (v2. 3. 3) on the UNIL HPC clusters using a SLURM scheduler. For this we collected all the species as depicted in Figure  2  using the OMA All vs All export function. Then we removed according to Figure  2  the relevant species, adding each time the assembly for mouse, yeast or thale cress in the set and run the orthology prediction with standard parameters (OMA v. 2. 2. 1). Thus for instance for the Illumina M. musculus 10X assembly we run OMA 7 times for all reference datasets with increasing distance to its closest relative. In total we run 126 different OMA runs with 7 variations of reference proteomes and 3 variations of technologies 3 coverage levels for A. thaliana and S.
Large yeast tree For the large yeast tree we extracted all yeast available datasets in the SRA November 2018 (406 species, Supplementary file 1) and applied Read2Tree (standard parameters) to 31 yeast species extracted from the OMA database (Nov 2018) using the marker export function and min tax availability of 0. 8 (3082 OGs). Selected species are available in the Supplementary file 3. Reads from the SRA database were mapped according to their sequencing methodology using Read2Tree. In order to compare our analysis to Shen et al 2018 we aimed to have as many species in common as possible. For this purpose we complemented our tree with additional sequences that we simulated from missing species in our tree that were present in the Shen et al 2018 tree. Simulations were conducted with iss (v1.
3. 0 https://github. com/HadrienG/InSilicoSeq, --model hiseq -n 600000). In order to map the species from the Shen et al (Shen et al. 2018) tree to ours we obtained for species / strains the taxid using ete3's NCBI interface  59. For species where automated mapping was not possible we obtained the taxid using the ncbi taxonomy interface (https://www. ncbi. nlm. nih. gov/Taxonomy/Browser/wwwtax. cgi).
Large yeast tree comparison Using all tax ids we retrieved the current uptodate ncbi reference taxonomy and the classification of each species. We then compared the three trees (NCBI, Read2Tree, Shen et al (Shen et al. 2018)) using the Robinson Foulds distance on the overlapping leafset. Additionally, we overlaid the Shen et al. classification on our tree. Finally, we compared the trees using the ancestral node that contains the highest number of monophyletic species given a specific grouping (order, family, phylum) extracted from the ncbi taxonomy information. All comparisons were conducted using custom python jupyter notebooks. Additionally, we collected data on GC content, input coverage to mapping ratio. Trees were visualised with ETE3  59.
Coronaviridae tree reconstruction Marker genes were exported from https://corona. omabrowser. org/ with at least four species. DNA sequences for these genes were obtained from the same resource. Four extra groups with intergenic regions from the SARS-CoV-2 reference genome were added using a custom script. SARS-CoV-2 samples were obtained from Nextstrain open (https://data. nextstrain. org/files/ncov/open/global/metadata. tsv. xz)  7. Different samples with SRA accession that spann all different clades were obtained with a custom python script. Reads were downloaded from the SRA database and trimmed. Read2Tree was applied to this dataset and all obtained reads were mapped to the marker genes. Read2Tree was run with standard parameters. Uninformative columns and rows were filtered from the final multiple sequence alignment and the tree was inferred using FastTree with default parameters.
| Model description We used a modified version of the land surface model ORCHIDEE-PEAT that was developed to simulate northern peatlands  (Qiu et al. , 2018). This version is called ORCHIDEE-PCH4, and simulates hydrology, surface energy, and C cycle processes resulting in CO 2 and CH 4 fluxes in northern peatlands using the parameterizations from  Qiu et al. (2018)  and a CH 4 module  (Salmon et al. , 2021). Photosynthesis and plant respiration are simulated for peatland vegetation type (represented as C3 graminoids) as described in section 2. 2. 1 in  Qiu et al. (2018). After senescence, litterfall goes into two litter pools (metabolic and structural) and three soil C pools (active, slow, and passive) after a series of decomposition processes following the CENTURY scheme  (Krinner et al. , 2005; Parton et al.
Qiu has been supported by the ANR (CLAND Convergence Institute; ANR-16-CONV-0003). Elodie Salmon has been supported by Horizon 2020 (CRESCENDO grant no. 641816), and Labex VOLTAIRE (ANR-10-LABX-100-01). Bertrand Guenet has been supported by the grant (Holistic management practices, modelling and monitoring for European forest soils [H2020 grant agreement No. 101000289]). Eugénie S. Euskirchen acknowledges funding provided by the US Geological Survey, Research Work Order 224 to the University of Alaska Fairbanks, the Bonanza Creek Long-Term Ecological Research Program funded by the National Science Foundation (NSF DEB-1026415, DEB-1636476) and the NSF Long-Term Research in Environmental.
Funding information Academy of Finland , Grant/Award Number:  337550 ;  Agence Nationale de la Recherche , Grant/Award Number:  ANR-10-LABX-100-01 ,  ANR-16-CONV-0003  and  ANR-18-MPGA-0007 ;  H2020   Societal Challenges , Grant/Award Number:  101000289  and  641816 ;  National Science Foundation , Grant/Award Number:  DEB-1026415 ,  DEB-1636476  and  LTREB-2011276  thawrelated drying. Here, we optimize a version of the Organizing Carbon and Hydrology.
DATA AVA I L A B I L I T Y S TAT E M E N T All the model simulation data are stored at zenodo. org (10. 5281/ zenodo. 6817633). Observation data used for the model optimization is available as follows: (1) FI-SAR & FI-LAK: https://doi. org/10. 5281/ze nodo. 6967198, (2) US-BZF: https://doi. org/10. 17190/ AMF/ 1756433, (3) US-HEA: https://doi. org/10. 6073/pasta/34c20956a48edb45 07add832b98a7fa6 and https://doi. org/10. 6073/pasta/2959f51f7 9d067ce01d2c74a8d5fbb75, (4) RU-CHE: https://doi. org/10. 18140/ FLX/1669654 and https://doi. org/10. 18140/ FLX/1669655, and (5) US-BES: https://doi. org/ 10. 18739/ A20Z7 0Z1H.
Chunjing Qiu has been supported by the ANR (CLAND Convergence Institute; ANR-16-CONV-0003). Elodie Salmon has been supported by Horizon 2020 (CRESCENDO grant no. 641816), and Labex VOLTAIRE (ANR-10-LABX-100-01). Bertrand Guenet has been supported by the grant (Holistic management practices, modelling and monitoring for European forest soils [H2020 grant agreement No. 101000289]). Eugénie S. Euskirchen acknowledges funding provided by the US Geological Survey, Research Work Order 224 to the University of Alaska Fairbanks, the Bonanza Creek Long-Term Ecological Research Program funded by the National Science Foundation (NSF DEB-1026415, DEB-1636476) and the NSF Long-Term Research in Environmental.
The Rayonix LX170 HS WAXS detector is deployed outside the detector tube. All detectors are hardware synchronized with the data acquisition via a timeframe generator as previously described  (Narayanan et al. , 2018) , and a desired combination can be selected for simultaneous image acquisition. The main characteristics of these detectors are listed in Table  1. The spatial resolution tabulated corresponds to the Gaussian FWHM point-spread function (PSF), which was measured by means of an attenuated beam of 20 mm cross section centred on the pixel. The apparent beam profile was fitted to a Gaussian function convoluted with the actual beam A rear view of the different detectors installed inside the wagon. The desired detector is selected by translating the table to bring it in front of the fibrous carbon window.
Detector data acquisition All detectors are integrated into the beamline control software, SPEC (Certified Scientific Software), via LIMA (Library for Image Acquisition) device servers. The LIMA framework developed at the ESRF provides a unified control interface for 2D detectors. It adopts the concept of image generation and implements common processing tasks for feature extraction and file saving  (Petitdemange et al. , 2018). LIMA consists of a core library and a multitude of hardware plugins, which are built on top of the software development kit (SDK), providing direct access to detector control and data readout. Strongly optimized for high-performance imaging applications, the LIMA servers usually run on dedicated detector control backends with essential processing and input/output capabilities.
Online data reduction In order to handle the high data rate from these fast detectors efficiently, a new data reduction pipeline based on Python Fast Azimuthal Integration (PyFAI)  (Kieffer & Karkoulis, 2013)  has been developed. Through a graphical user interface (GUI) accessible via SPEC, different correction steps can be activated. Depending on the type of detector, main corrections involve dark-image subtraction, flat-field Table  1  Main specifications of the SAXS, USAXS, WAXS and XPCS detectors for 12. 4 keV X-rays. The spatial resolution (FWHM) was measured using a 20 mm attenuated beam and fitted to a Gaussian profile. The maximum counts of the CCD detectors are in analogue-digital units (ADU) per pixel. For the Eiger detectors, the PSF is roughly a boxcar function of width the same as the pixel size, and the indicated value is an analogous Gaussian FWHM.
Eiger2 4M Rayonix    , 2015). Plugins are written in Python and loaded dynamically. Each job controls one plugin that is executed asynchronously within its own thread, making use of the multiple processors of the system. In this way, the plugin for data reduction can simultaneously process several data sets, composed of hundreds of individual frames, each data set coming from a different detector (usually the SAXS and WAXS detectors). This processing runs while acquisition continues in parallel via SPEC/LIMA, and thus the reduction step needs to be compatible with the acquisition speed. The reduction plugin, performing intensity correction, image warping and azimuthal regrouping, is based on pyFAI for the numerical part and uses the graphics processing unit (GPU) to reach the required performance.
Normalized 2D patterns are masked, azimuthally regrouped and averaged to obtain the 1D SAXS profiles, which are saved in individual HDF5 files. For high-throughput operation, the whole sequence is performed in one step as depicted in Fig. 5 , and saving of normalized and regrouped images is optional. Required parameters for correction are automatically retrieved from the metadata of the recorded frames or from input parameters transmitted as JSON dictionaries from SPEC. When reprocessing is required (e. g. remasking or refinement of beam coordinates), these jobs can also be run offline using a command line tool or the SAXSutilities2 program  (Sztucki, 2021). Unlike the online data analysis server, these processing tools are currently not multi-threaded and plugins are run directly for data re-processing.
Data visualization The 1D and 2D data outputs from the data reduction pipeline can be visualized using the SAXSutilities2 software, which has been rewritten in Python  (Sztucki, 2021). This software is available as a complete binary package (based on pyinstaller; https://github. com/pyinstaller/pyinstaller) containing all required libraries for Windows and some Linux distributions. This package features the visualization of 2D images Figure  5  The work flows involved in the data reduction pipelines for SAXS and XPCS. The MCS unit is presently the C216 compact PCI module. in the ESRF data format (EDF) and HDF5 files (as implemented at the ESRF) in different reference coordinate systems  (Boesecke, 2007) , finding the beam centre, SAXS/ WAXS distance calibration using different calibration standards (such as parabromobenzoic acid, silver behenate, alumina and silicon), and creation of software masks for unusable regions in the scattering patterns.
In addition, 2D image operations like averaging, subtraction, and conversion between EDF and HDF5 files are available. A specific interface allows offline data reduction based on the Dahu plugin and older Saxs programs package  (Boesecke, 2007). The latter also allows averaging of I(q) over a restricted azimuthal angular range for the analysis of oriented samples. One of the main applications of SAXSutilities2 is for displaying multiple 1D data sets, optionally after background subtraction, in different plot modes used in small-angle scattering. Data sets can be easily sorted and selected by their file name or metadata (like sample description or sample-to-detector distance) saved in the header of all files. The 1D tool feature of the program includes, among others, averaging, background subtraction, rebinning of the q scale, merging of data sets recorded at different sample-to-detector distances, conversion between HDF5 and ASCII data types, and creation of 2D maps of a large set of 1D data from spatial scans.
In addition, the intensity autocorrelation functions calculated by the Dynamix program can be visualized. Selected features of the program are displayed in Fig. 6. The gap between the modules of the Eiger2 4M detector is a nuisance when investigating oriented samples, notably in fibre diffraction. The missing data along an azimuthal sector appear as slots in the corresponding averaged scattering profile. One way to patch these gaps is by making a second acquisition after displacing the detector along the diagonal. This approach is not convenient in time-resolved investigations or for radiation-sensitive samples. Therefore, a feature has been added to rotate the 2D scattering pattern by 180 with respect to the beam centre and patch the gaps, as illustrated in Fig. S2. This procedure still leaves some holes at the intersection of horizontal and vertical gaps, which can be further patched by mirroring the image.
, 2008). The minima in the scattering profiles move to lower q with time, indicating the growth of these self-assembled structures. Control measurements indicated no change in the scattering profile with the X-ray exposure for this duration (0. 25 s), thereby demonstrating the reliability of the results. However, there is an important difference in the shape of the profiles in the low-q region, which suggests the formation of more elongated structures than spherical unilamellar vesicles. Fig. 11(b)  displays the best description of the initial time data obtained with a cylindrical shell structure using the SasView program  (Doucet et al. , 2018). As their growth progresses, the profile gradually  Multispeckle ensemble-averaged intensity-intensity autocorrelation functions [g 2 (q, t)] from a dilute colloidal suspension of silica particles with a mean radius of 300 nm.
This secondary scattering is a general problem when there is a vacuum window in front of the detector. In this case, it becomes visible below 10 À4 of the maximum intensity and even more significant below 10 À5. This artefact is mainly observed when I(q) decays steeply and in the presence of sharp oscillations in the high-q region of the pattern. For a slower decaying profile with no well defined oscillations, this parasitic contribution will be hidden beneath the actual I(q). To correct for this excess intensity, it is sufficient to make two measurements with the smallest and largest beamstops. The two profiles can be joined together using the SAXSutilities2 program to obtain the corrected scattering profile. This correction procedure has already been applied for the data shown in Fig. 7.
Acknowledgements The authors express their deep gratitude to the  ESRF Accelerator and Source Division  for the construction of the EBS, to various service groups ( Survey and Alignment, Vacuum, Optics, Mechanical Engineering, Safety, Building and Infrastructure, Detectors and Electronics , and Software), and in particular to  M. Ruat ,  M. Kocsis ,  F. Sever ,  J. Leonardon ,  D. Martin ,  R. Homs-Regojo ,  L. Claustre ,  P. -A. Douissard ,  B. Rousselle ,  F. Andrei ,  B. Picut ,  G. Malandrino ,  R. Barrett  and  C. Allais , for invaluable technical support, and to the  AVS company (Spain)  for servicing of the detector tube. P. Paleo  and  Y. Chushkin  are profusely thanked for the development of the Dynamix XPCS package, and  L. Matthews  for involvement in the beamline operation. The  ESRF  is gratefully acknowledged for financial support and the provision of synchrotron beam time.
A related topic is the problem of ranking differentially expressed genes across two conditions  [20]. Following the seminal work on gene set enrichment methods  [40] , the template of gene set enrichment analysis (GSEA) consist of three steps, namely computing an enrichment score for each gene, estimating its statistical significance, and performing a correction for multiple hypothesis testing. Setting aside the issue of correlations between genes, such methods combine feature selection and clustering (one cluster per cell state/condition)  [15] , but do not address the question of connecting two sets of genes using an interaction network. Finally, yet another related challenge is pathway enrichment analysis  [34]. Given a set of experimentally determined genes and a database of pathways, the goal here is to find pathways whose genes are over-represented in the gene set of interest.
e. from X to P and from P to X. Third, instead of using a single restart rate  [42] , we study a filtration (sequence of nested sets) of genes retrieved, in tandem with so-called saturation indices revealing accessibility scales in the PPIN. To accommodate this rationale, we present a novel analysis technique based on random walks with restarts and absorbing states. Recall that in a Markov chain, an absorbing state is a state which is never exited. Since stationary distributions are irrelevant in this context  [11] , we resort to hitting probabilities for the nodes on the target set P. As we show, doing so yields scores for pairs of genes in S × T and T × S, from which a ranking of genes in X is defined. As a case study, we use a dataset of differentially expressed genes involved in the regulation of a cancer drugs pharmacodynamics  [26].
In such single-cell analyses performed with isogenic cells treated together, the stable differences in gene expression between cells from the two groups, namely predicted sensitive and predicted resistant, are small and otherwise confounded in gene expression noise. In addition, the short treatment necessary for the cell response prediction does not induce a detectable genomic response (Mendeley data Dataset https://doi. org/10. 17632/m289yp5skd. 1  [30, 26] ) that would confound the functional role of the differentially expressed genes between the two groups with gene induction, as it is often the case in other studies. Sets X and P. The list of differentially expressed genes obtained in this study constitutes our set X [26, Table  S2 ]. The criteria used to define the two groups of single-cells compared in the differential expression analyses giving X, is the activation rate of caspase-8.
(Caspase-8 is a protein of the receptor-mediated apoptosis pathway and of the TRAIL mechanism of action  [35]. ) Therefore, in our case study the set P is a set of regulatory proteins of the extrinsic apoptosis signaling pathway. The sets X and P are of size 320 and 49, respectively. Altogether, these sets X and P represent a unique dataset to assess the benefit of our approach in ranking genes based on their likelihood to impact drug response.
PPIN. A number of interaction databases coexists, each with specific features, in particular a trade-off between exhaustivity and confidence. We use MINT due in particular to the compliance with the protein naming standards  [7]. The PPIN was constructed from interactions downloaded from the MINT website https://mint. bio. uniroma2. it/. Only proteins with the species label Homo Sapiens were downloaded. The interacting proteins are identified in UniProtKB format. The resulting network is called the MINT network in the sequel. This initial graph, containing 11,672 vertices representing proteins, and 52,839 edges representing protein -protein interactions, is edited as follows. First, the PPIN being disconnected, we focus on its largest connected component, encompassing 11,427 vertices. Second, we remove all self-interactions.
Implementation We compute hit scores (Def. 3) using the C++ Marmote library (  [19]  and https://wiki. inria. fr/MARMOTE/Welcome). The whole pipeline is implemented in the Genetrank package of the Structural Bioinformatics Library (  [6]  and http://sbl. inria. fr), see https://sbl. inria. fr/doc/Genetrank-user-manual. html. The running time of one instance i. e. computing the hit scores at a fixed r, depends on the sizes of the PPIN, and of sets X and P. These latter two sets matter most since a Markov chain is generated and then evaluated for every source. The most time-consuming work is the calculation of the hitting probabilities, which in theory can be performed exactly with matrix inversion, at a practical cost of order n 3. We selected from the Marmote library the iterative method which approximates the result with iterations of order m, with m = |E| the number of edges.
Secondly, within a cell type, differential analyses between treated versus untreated samples lacks specificity over genes at the origin of the cell response versus the genes induced by the drug in resistant cells (not to mention that sensitive cells are rarely recovered in experiments). Moreover, the subsequent analyses such as gene set enrichment analysis and pathway-based analyses  [40, 41, 43] , dependent on prior knowledge of the gene functions and their interactions, often determined with the aforementioned experimental designs. Gene annotations themselves (from Gene Ontology (GO) database for example) might hinder gene-based drug sensitivity predictions, by introducing biases related to errors or incompleteness due to unknown function, protein moonlighting  [37] , or technical and biological inherent limits  [24, 39].
Transcriptome analyses. RNA is extracted from one half of skin biopsy with the RNeasy Lipid Tissue kit (Qiagen, Hilden, Germany) using TissueLyser II (Qiagen, Hilden, Germany), following the manufacturer's protocols. Transcriptome analysis is performed with RNA sequencing (RNAseq), in outsourcing by Macrogen (Seoul, Korea), in order to identify DEGs and alterations in biological molecular pathways, to confirm the genetic variations identified with WES. Genomic and transcriptomics data integration. Single-omics datasets, such as genomic and transcriptomic, could fail to fully untangle the complexity of a disease, so HS can be better understood by integrating the multi-omics datasets. In detail, omics data integration will allow a broad characterization of the molecular mechanisms, an identification of key molecular pathways involved in HS susceptibility, onset, clinical course and severity.
The advantage of the self-surveillance/diagnosis approaches is their ability to provide the patient the information fast, without the need to visit a dermatologist. However, this is also a major shortcoming, since the algorithms have various degrees of accuracy in detection or classification, and furthermore can be biased based on the dataset that was used to train them. Coupled with the abundance of misinformation and unvetted applications, this calls for a very cautious handling of such applications, which should ideally only be used together with a professional medical counsel. As patients are typically not equipped to critically assess and evaluate the information found online, the doctors should work with them to educate them and to inform them where to find appropriate resources  60.