dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, it should be noted that R-GCN provides satisfying results as well. Except in a very few cases (e. g. outperformed by ComplEx on Codex-S and WN18RR in terms of Sem@1), R-GCN showcases better semantic awareness than semantic matching models. Most of the time, R-GCN also provides comparable or even higher semantic capabilities than translational models. In particular, Sem@K values achieved with R-GCN are similar to the ones achieved with TransE and TransH (e. g. on FB15K237-ET and YAGO3-37K, see Tables  8a  and 8c ) while the latter models are actually outperformed by R-GCN in terms of Sem@K[ext]. It appears clearly on Codex-M and WN18RR (Tables  9b  and 9c ), although the conclusion holds for all datasets. Fig. 6. Sem@K[base] comparisons between KGEMs on the 4 benchmarked schema-defined KGs.
A C T Limited-angle computed tomography is often imposed by in-situ experiments combining tomography with sample environments. The missing projection data causes artifacts in the tomographic reconstruction. We demonstrate that the correction of these numerical artifacts can be achieved by restoring the missing projections using an iterative reconstruction scheme. The reconstruction is regularized using segmentation, and thresholds determined from the histogram of reconstructed gray levels. The missing projections are simulated by forward projection and incorporated into the original measured dataset to give a complete angular span. This scheme typically converges within a few iterations. Results are presented for several measurements using parallel-beam synchrotron X-ray tomography and 165 degrees of valid projection data.
Computed tomography at extreme conditions Computed tomography (CT) is a powerful non-destructive technique for 2D or 3D sectional imaging, based on numerical reconstruction of the object from a series of projections  [1, 2]. Tomography reconstruction consists of solving the inverse Radon transform from a set of projections of the sample at different angles. The most common algorithm is Filtered Back Projection (FBP) which can be implemented on GPUs for faster computation. Several software packages are available for reconstructing tomography data  [3] [4] [5] [6] [7] [8]. At PSICHE we currently use PYHST2  [9]  for FBP, driven by an interface layer of Python scripts called Tomodata  [10]. Projection images can be formed using laboratory or synchrotron X-ray sources, but other radiation types may also be used (neutrons, electrons, etc).
Limited-angle tomography In limited-angle CT, some of the data that would typically be required to perform the reconstruction are not available. Therefore, the reconstruction using a given algorithm will contain artifacts or be of reduced quality or accuracy when compared to a reconstruction using a complete dataset. It can be due to either only a few projections can be acquired, or because they are blocked by part of the instrument, the sample environment, or strongly absorbing components of the sample itself (metal artifacts)  [16, 17]. In the specific case of the UToPEC used at a synchrotron source, the data are characterized by a well sampled (angular) sinogram over 165°, with 15° of data completely obscured by the columns. This differs from much of the literature which comes from medical applications.
For efficient exploitation of the results the reconstructed volumes should be available to beamline users immediately after their beamtime. These volumes should be corrected of as many artifacts as possible to avoid later difficulties. The iterative reconstruction scheme presented here has been developed respecting the particular constraints of a tomography beamline serving a wide user community. The selected algorithm must be computationally efficient, in order that data can be processed during a user experiment using the available resources. It is considered more important that the algorithm can be easily adapted to each new experiment than to have necessarily the best possible performance on a particular sample type. For these reasons, machine learning methods were not selected. Indeed, machine learning (ML) model training for a volume is a significant computing demand and it must be done for each new type of sample.
Iterative reconstruction scheme For synchrotron CT, the X-ray illumination can be considered to be an extended parallel beam. A 2D detector is used to acquire radiographs. A tomography data set is thus composed of radiographs collected over a 180° rotation of the sample. In the case of a laboratory instrument with a conical beam geometry 360° of sample rotation is typically acquired. A data set of radiographs can be denoted as I r ( , ) the intensity measured by the detector, with r the 2D coordinates and θ the sample rotation. Intensity of the beam I r ( ) 0 without sample and the detector dark current d r ( ) are measured, and a standard flat field correction used to calculation the transmission t: t r I r d r I r d r ( , ) ( , ) ( ) ( ) ( ) 0 = Projections are line integrals of the local attenuation coefficient f x ( ) of the sample for X-ray photons going through the sample and hitting the detector at position r, for a given rotation angle.
This regularization method is simple and powerful, avoiding computationally intensive morphological operations, and can be automatized using various well-established criteria like Otsu, intermode, etc  [29] [30] [31] [32]. Similarly, the positions and widths of peaks in the histogram can be fitted in order to estimate the instrumental broadening of the gray levels (noise), and this information used to define gray level ranges for segmentation  [33]. By using an algorithm to determine the thresholds used, the thresholds can evolve from iteration to iteration. This is important because as the missing projections are replaced, the gray levels in the reconstruction change. Initially, with missing data, the variation in gray levels (contrast between phases) is underestimated with respect to the values that would be reconstructed from a complete dataset.
The design of the assembly is specifically described in  [36]. Note that the gasket and furnace are outside of the reconstructed volume (local tomography condition). Four major phases are visible in the reconstruction: Aluminum (gray level ∼0. 5), hBN (∼0. 1), steel (> ∼4) and pores/air (< 0) (see histogram Fig. 2(B) ). A small amount of NaCl has a similar attenuation coefficient to aluminum. In this situation the analysis of the reconstructed volume is supposed to reveal the number of pores and their size. The limited-angle data poses a particular problem because the streak artifacts around the steel (highly attenuating) particles produce dark regions that have similar apparent gray level to the pores. A simple thresholding segmentation thus fails to correctly identify the pores. In this situation we performed the iterative reconstruction, using sequential regularization in two steps.
It explains the absence of hBN in the histograms shown Fig. 6(B ). Pores and steel particles are then segmented using threshold values halfway between the aluminum peak and the extremes of the histogram. Pores and particles are then counted, and the results in terms of numbers of particles are shown in Fig. 6 (A4). We can observe that the very large number of spurious pores identified in the limited-angle dataset is not seen in the corrected dataset, suggesting that the correction has a useful effect.
Discussion To compare our method to existing strategies, we applied the iterative total variation algorithm implemented in PyHST2 to our experimental dataset  [9]. Fig. 7  presents the result of TV correction obtained with different regularization parameters. In the first situation Fig. 7 (A), the correction effect is too weak and only reduces the noise in the reconstructed slice. (B) corresponds to the best compromise we could obtained, in which noise is strongly reduced and the streak artifacts are slightly reduced. If the regularization parameter is further increased (C), aluminum homogeneity and streak reduction is better, but the smallest pores and steel particles are removed. Moreover, the geometrical detail on the edges is not improved. Our method is more effective at correcting artifacts without suppressing real details.
Conclusion To conclude, a simple and easily adaptable iterative correction for limited-angle tomography data has been presented. This correction is simple to control, being based on the histogram of reconstructed graylevels. It modifies the original dataset as little as possible, by replacing only the missing part of the projection dataset. The reconstruction algorithm used remains a standard filtered back projection, to minimize the risk of unanticipated effects in the output. The user can choose to apply more than one phase of data correction. In a multiphase sample, the strongest artifacts are observed around the most contrasting interfaces. By correcting these first, the remaining artifacts from more weakly contrasting interfaces can be corrected in a subsequent step when the gray levels are less perturbed.
The resourcecode Marine Data Toolbox is a python package developed within the Resource-CODE project, to facilitate the access to a recently developed Metocean hindcast database  (Accensi et al. , 2021) , and to a set of state-of-the-art methods for data analysis. This toolbox provides developers with a set of standard functions for resource assessment and operations planning. The advanced statistical modelling tools provided together with the embedded high resolution wave hindcast database provide the developers with a set of standard functions for resource assessment, extreme values modelling and operations and maintenance planning. Suitable for users not familiar with netCDF files handling or statistical analysis development, it is however designed to fulfil expert metocean analysis requirements.
Statement of Need The resourcecode python package allows to retrieve and analyse time series of Metocean parameters and spectra extracted from the companion hindcast database. This database consists in a high-resolution unstructured grid with more than 300 000 nodes, spanning from the South of Spain to the Faeroe Islands and from the Western Irish continental shelf to the Baltic Sea. At each node, 39 wave parameters and frequency spectra are available with a hourly time-step. Directional spectra are also available, on a coarser grid over the area covered. This data has been extensively validated against both in-situ and satellite remote sensing data  (Accensi et al. , 2021). However, this database is very large (more than 50Tb) and can not easily be downloaded by the end users. The resourcecode python package objectives are twofold: preparing data harvesting from the database, which is often one of the most time-consuming steps, and providing the user with unified, state-of-the-art methods for analysing the data extracted.
Key Features of the resourcecode python package The resourcecode python package provides a wrapper for easy fetching of data from the ResourceCODE database, by calling the Cassandra API. The database itself represents more than 50Tb of data, preventing standard user to download it as a whole. Hence, this toolbox is intended to provide access to time series of sea-state parameters, as simply as the following code snippet: import resourcecode client = resourcecode. Client() data = client. get_dataframe_from_criteria( """ { "node": 134939, "parameter": ["hs","fp","dp","cge"] } """ ) In the example above, we used resourcecode to fetch data from node 134939, for the entire time period, and for some parameters: significant wave height   , peak wave frequency   , Mean direction at peak frequency   and Wave Energy Flux.
The resourcecode package will automatically construct the proper Cassandra request and will process the data internally to output a pandas data frame (McKinney (  2010 )) workable in-memory. The different tasks that can be handled via this package, both for data management and statistical modelling include : • Data management: - -A WEC case study is included, extensible with user-provided characteristics; -PTO optimization (as in  Payne, Babarit, Pascal, & Perignon (2021) ).
Acknowledgements The  ResourceCODE  project has received support under the framework of the  OCEANERA-NET   COFUND  project, with funding provided by national/ regional sources and co-funding by the  European Union 's  Horizon 2020 research and innovation program. The authors also acknowledge the following people for their significant contribution to the toolbox, the web site and this paper:  Frank Bessou ,  Olivier Giorgis ,  Noé Gaumont ,  Baptiste Garcia ,  Nicolas Chauvat  and  Arthur Lutz  from  LOGILAB ,  Mickael Accensi ,  Charlie Andre  and  Glenn Judeau  from  IFREMER ,  Matthew Storey  from  EMEC  and the three reviewers of the JOSS paper for fruitful discussions.
Extraction of sea-state parameters from Cassandra web-service; -Extraction of both 2D and 1D spectral data; -Accessing database configuration: *  nodes location and spectral data availability; *  coastlines, islands ans mesh triangles; *  bathymetry and bottom roughness; *  List of output variables; -Easy to use pandas data frame (filtering, aggregating,…); -Facilities to plot 2D and 1D spectral data; -Data conversion: *  Directional spectra → Frequency spectra → Sea-state parameters; *  Zonal/Meridional components to Intensity/Direction. • Statistical modelling: -Environmental conditions assessment; -Extreme values modelling (using pyextremes 1 ); -2D and 3D environmental contours (as in Raillard, Prevosto, & Pineau (2019)). • Weather windows for O&M: -Model based (as in Walker, van Nieuwkoop-McCall, Johanning, & Parkinson (2013)); -Empirical estimates.
The TVB-AdEx Model The TVB-AdEx model is a biologically informed, brainscale cortical model built using The Virtual Brain (TVB) platform. Using MRI scans, one can divide a scanned brain into different anatomical regions and estimate the strength of the connections between regions using tractography analyses  (Schirner et al. , 2015). From this data one can build the TVB-AdEx model as a network of AdEx mean-field models, each mean-field model describing the neuronal activity of one of the anatomical regions in the scanned brain. The strength of the interactions between the mean-field models are determined by the connectivity matrix, also called connectome, obtained from the MRI tractography analyses. The parcellation used for this model divides the brain into 68 regions (berlinSubjects/ QL_20120814 from https:// zenodo.
25 GHz processors for a total of 128 cores, 256GB DDR4 of RAM and 1TB NVMe for memory (Jusuf supercomputer configuration, 2022). A profiling of a five-seconds TVB-AdEx simulation, together with the corresponding feature extraction pipeline, showed that running the script required less than 600 MB of RAM at any point in time, less than one GB of static memory for I/O operations, and approximately 6 min of execution time (see Fig. 8  in the Supplementary Information). Thus, it was possible to run 128 simultaneous simulations on each JUSUF node, increasing significantly the simulation and analysis speed.
, 2008; Aru et al. , 2019). Rather, the TVB-AdEx model relies on simplified representations of incoming subcortical effects of transient and noisy inputs, modeled as aff and k drive , respectively, from Eq. 4. Therefore, future advances should include corticalsubcortical loops, particularly, the explicit representation of thalamo-cortical connections in TVB-AdEX. Additionally, this multi-scale cortical model assumes that each region shares the same parameters as the others. For instance, one could try to make use of an acetylcholine receptor density map to obtain a heterogeneous distribution of b e parameters, similarly to what is done in  Herzog et al. (2023). Future iterations of the TVB-AdEx model will therefore need to run even more quickly to efficiently scan parameter space. Finally, most current empirical functional connectivity studies use fMRI data  (Barttfeld et al.
Acknowledgements Research supported by the  CNRS  and the  European Union  ( Human Brain  Project  H2020-785907 ,  H2020-945539 ). We thank  Kevin Ancourt ,  Thierry Bal ,  Frederic Chavane ,  Nima Dehghani ,  Matteo di Volo ,  Sami El Boustani ,  Nicolas Hô ,  Trang-Anh E. Nghiem ,  Denis Paré  and  Yann Zerlaut  for support and collaboration. We want to thank  Sandra Diaz  and  Michiel Van der Vlag  for their invaluable help in executing the simulations at the JUSUF supercomputer.
Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrapclass and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations.
Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.
Currently available top-down identification workflows differ substantially in their strategies for managing the challenges presented by top-down data. ProSight PD is a contemporary implementation of the 2003 ProSight PTM  12  algorithm built around a Poisson match-scoring model published in 2001. 13 ince its genesis, ProSight PD has sought to leverage protein annotation to curtail the larger search space of top-down identification; UniProtKB XML provides considerable information beyond naked sequences, such as observed or predicted signal peptide cleavage sites, coding SNPs, and modified residues with known mass shifts. MetaMorpheus, a search engine that operates in both top-down and bottom-up modes,  14  also makes use of UniProtKB XML databases but adds the capability to supplement UniProtKB PTMs with those discovered through bottom-up identifications in the same sample.
The spectral alignment algorithm matches deconvolved fragment ions to sequence-derived mass ladders, treating regions bearing PTMs as mass gaps in these alignments. It was originally deployed in top-down identification with MS-TopDown in 2008  15  and was followed by the publication of MS-Align+ in 2012. 16 Three contemporary top-down identification algorithms have built upon these foundations. The 2016 TopPIC Suite  17  was created with spectral alignment at its core, featuring built-in detection of mass shifts that are not pre-specified by the user. Also in 2016, the pTop software  18  (part of pFind Studio: http://pfind. org) incorporated machine learning to improve precursor deconvolution. MSPathFinderT was introduced in 2017 as part of the Informed Proteomics Suite,  19  incorporating HUPO-PSI formats for reading spectra (mzML  20  ) and writing identifications (mzIdentML  21  ).
2017 also saw the publication of the TopMG software,  22  added to TopPIC Suite to identify more heavily modified proteoforms. In practice, most top-down laboratories develop expertise in a single top-down search engine and do not consider others. One of the key questions driving this study is how much more information might be obtained from existing data if more than one identification approach is used. The four algorithms compared here are: MSPathFinderT from Informed Proteomics, ProSight PD, pTop, and TopPIC. They can either be operated using included graphical user interfaces (all but MSPathFinderT) or via MASH Suite,  23  a universal interface for top-down proteomics software. Previously published comparisons between proteoform identification software have generally been part of papers that introduce a new algorithm;  [16] [17] [18] [19] 22  as a result, they may have inadvertently featured data types or evaluation methods that show the new algorithm in a disproportionately positive light.
In this study, data from ThermoFisher Orbitrap-class and Bruker maXis Q-TOFs are used to compare the performance of four top-down proteomics software workflows. Various datasets from ThermoFisher instruments were used, including Sulfolobus islandicus (S. islandicus) and Escherichia coli (E. coli) cell lysates, brain tissue from male and female Danio rerio (D. rerio), H. sapiens serous ovarian tumors and de-fatted milk from Bos taurus (B. taurus) with clinical mastitis. To compare the performance for top-down deconvolution in data from Bruker Q-TOF instruments, this study relied on the analysis of data from B. taurus milk (maXis I), Sus scrofa (S. scrofa) cardiac material, and Homo sapiens (H. sapiens) embryonic kidney samples (both maXis II). With these datasets, the estimated PrSM yield and orthogonality of each search engine were assessed.
■ EXPERIMENTAL PROCEDURES ThermoFisher Orbitrap Data Sets PXD003074 SULIS. Vorontsov et al. characterized lysine methylation and N-terminal acetylation of S. islandicus LAL 14/ 1 via top-down and bottom-up techniques on a ThermoFisher Orbitrap Fusion Tribrid instrument (Table  1 ). 24 The proteome was separated by strong anion exchange into six fractions analyzed once in ETD and once in HCD. The six ETD fractions included in this analysis (see Supporting Information Tables S1-S3 for a complete list of raw files for this and other sets) averaged 2111 MS/MS scans per raw file, while the six HCD fractions were approximately double that number, averaging 4003 MS/MS scans per raw file. The ETD data yielded an interquartile range of precursor charges from +8 to +16, while the HCD data yielded +7 to +14.
The maximum precursor   25  The six EThcD LC-MS/MS replicate experiments analyzed for this study averaged 7435 MS/MS scans per raw file. The interquartile of precursor charges from TopFD deconvolution ranged from +5 or + 6 to +14 (with a maximum Z of +41). The same sequence database was used for this data set as for the S. islandicus data above, with the SULIS sequences representing unlabeled decoy sequences. PXD020342 DANRE. Xu et al. 26  employed capillary IEF-MS/MS rather than RPLC-MS/MS to investigate sexual dimorphism in brain tissue of zebrafish (D. rerio). 27 The four size exclusion chromatography fractions for each sex were analyzed in triplicate to produce a total of 24 raw files on a Q-Exactive HF. We omitted the first fractions from consideration because they contributed few PrSMs.
Unlike the other studies, the zebrafish data incorporated iodoacetamide to alkylate cysteines after the reduction of disulfides. The 18 HCD experiments averaged 1576 MS/MS scans per raw file for female samples and 1969 for male samples. After TopFD processing, the precursor charge interquartile range spanned from +4 to +8 for female brain samples and from + 4 to +12 for the male samples. The UniProtKB zebrafish reference proteome, UP000000437, contained 47,204 sequences when downloaded during September, 2021. An additional 27 raw files under the same PXD accession examined similar samples by bottom-up methods, which enabled the creation of a parsimonious subset database via MSFragger 3. 0 28 and IDPicker 3. 1  29  containing 9432 sequences. PXD031744 BOVIN. To create reference spectra for a phospho-proteoform mixture, we analyzed four samples of defatted B.