dataesr/scientific-paragraphs-categorization huggingface dataset:
Detection of aberrantly expressed protein complexes Detection of aberrantly expressed protein complexes was performed similar to the differential protein complex expression method described by Zhou et al. , 2019. Specifically, the quantified proteins were mapped to the protein complex database CORUM (v3. 0)  (Giurgiu et al. , 2019)  or to the mitochondria-related subset of HGNC gene groups by gene names. We considered the protein complexes of four subunits or more and with at least 50% of the subunits quantified. For each sample i and protein complex k, we computed yi,k, the mean deviation of observed versus expected protein intensities across all detected subunits (expressed in log2 fold-change and as estimated by PROTRIDER or LIMMA). For each protein complex k, we fitted by maximum likelihood a Gaussian on all yi,k with mean µk, and standard deviation k using the fitdistr function from the R package MASS  (Venables and Ripley 2002).
Data and code availability The proteomic raw data and MaxQuant search files have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository and can be accessed using the dataset identifier PXD022803. Code to reproduce the analysis is available via GitHub at github. com/prokischlab/omicsDiagnostics/. All data needed to reproduce the analysis including RNA count and split-read tables as well as the protein intensity matrix are available via Zenodo  (Smirnov et al. , 2021)   a, Multi-omic approach based on the integration of genomics (WES/WGS), transcriptomics (RNA-seq), proteomics, and phenotypic descriptors (HPO). We obtained DNA for WES/WGS from blood and RNA-seq and proteomics from fibroblasts (acquired by minimally invasive skin biopsy).
Acknowledgements This study was supported by a  German Federal Ministry of Education and Research (BMBF, Bonn, Germany)  grant to the  German Network for Mitochondrial Disorders  (mitoNET,  01GM1906D ), the  German BMBF  and  Horizon2020  through the  E-Rare project   GENOMIT  ( 01GM1920A ), the  ERA PerMed project PerMiM  ( 01KU2016A ), the  German BMBF  through the e: Med Networking fonds AbCD-Net  ( FKZ 01ZX1706A ), the  German Research Foundation/Deutsche Forschungsgemeinschaft  ( DI 1731/2-2 ), the  AFM-Telethon grant  (# 19876 ), the  Practical Research Project for Rare/Intractable Diseases  from the  Japan Agency for Medical Research and Development, AMED  ( JP19ek0109273 ,  JP20ek0109468 ,  JP20kk0305015 ,  JP20ek0109485 ), a  CMHI grant  ( S145/16 ), a  PMU-FFF grant  ( A-20/01/040-WOS ), the  Pierfranco and Luisa Mariani Foundation (CM23) , and the  Italian Ministry of Health  ( GR-2016-02361494 ,  GR-2016-02361241 ).
Yan, Y. , Wang, Y. , You, S. , Freeman, M. R. , & Yang, W. (2019). Quantitative proteomic analysis of prostate tissue specimens identifies deregulated protein complexes in primary prostate cancer. Clinical proteomics, 16(1), 1-18. CORUM: https://mips. helmholtz-muenchen. de/corum/ Zhou, B. , HGNC: https://www. genenames. org Simple ClinVar: http://simple-clinvar. broadinstitute. org GENOMIT: https://genomit. eu GENOMITexplorer: https://prokischlab. github. io/GENOMITexplorer/#readme. html online Resources Code to reproduce the figures: https://github. com/prokischlab/omicsDiagnostics/tree/master PROTRIDER: https://github. com/gagneurlab/OUTRIDER/tree/outrider2 Web interfaces: https://prokischlab. github. io/omicsDiagnostics/#readme. html Zenodo: https://zenodo. org/record/4501904 PRIDE: https://www.
LP variants This study P/LP variants ClinVar P/LP variants ClinVar VUS a 22 121 143 Proof of principle Unsolved WES/WGS cases Complete study cohort Validation (n=21) Discovery (n=100+6) Summary of multi-omics dataset (n=143) functional evidence 11% 39% (19/49) with downstream functional evidence on the complex level 36% 29% 64% 71% 89% 14/22 with protein 22% underexpression 79% RNA underexpression RNA + protein underexpression protein underexpression no functional evidence unsolved b Genomic Aberrant expression Aberrant expression GENOMIT variant N detected detected P/ type by RNAseq by Proteomics Missense 52 12% 94% 55% 61% 27% 63% Non-coding 12 100% 92% 2% 14% 5% 11% Frameshift 8 75% 88% 13% 10% 26% 1% Alleles Splice Nonsense Coding 5 2 2 100% 100% 0% 60% 100% 100% 7% 9% <1% 6% 2% 1% 9% 20% <1% <1% <1% <1% CNV 1 100% 100% 2% 1% 10% 5% mtDNA* 4 0% 0% 5% 5% <1% <1% Total 86 37% 87% 1.
Cultural Heritage (CH) resources are partial, heterogeneous, discontinuous, and subject to ongoing updates and revisions. The use of semantic web technologies associated with 3D graphical tools is proposed to improve access, exploration, exploitation and enrichment of these CH data in a standardized and more structured form. This article presents the monitoring work developed for more than ten years on the excavation of the Xlendi site. Around an exceptional shipwreck, the oldest from the Archaic period in the Western Mediterranean, we have set up a unique excavation at a depth of 110m assisted by a rigorous and continuous photogrammetry campaign. All the collected results are modelled by an ontology and visualized with virtual and augmented reality tools that allow a bidirectional link between the proposed graphical representations and the non-graphical archaeological data.
Since the first photogrammetric surveys in 2014 using COMEX's (then prototype) ORUS3D underwater photogrammetry solution, we have used the Agisoft Metashape photogrammetric processing software, benefiting from their improvements over the years. Flexible and sufficiently efficient for our application, the Metashape software is highly customizable and can easily be automated thanks to the availability of Python and Java APIs. At the same time, it is easy to use, a crucial factor in a multidisciplinary project such as this one, where non-experts in topography must be able to manipulate and understand the results of the photogrammetric process. In practical terms, the University of Malta team is currently completely self-sufficient in terms of photogrammetric surveys. Photogrammetry has become one of the many tools that this team masters.
The geometry of recognizable artifacts (amphorae and grinding stone) was used to define the significant points, i. e. their centers of gravity, on which a rigid transformation was adjusted. The next step of the project was the formalization of an ontology modeling the multi- temporal surveys in 2D and 3D. This ontology considers the manufactured objects studied, as well as the method used to measure them, in this case, photogrammetry (photogrammetric data in the form of oriented photographs, cameras, 3D points and their projections, as well as camera distortion and precision estimators). The surveyed features are thus represented from the point of view of the measurement and are linked to all photogrammetric data that contributed to their measurement in space. A 2D and 3D web interface, accessible on the original project website, is available to access all these data and to perform semantic queries.
" (Shah and Musen, 2009 ). An ontology is a set of data elements within a domain that are linked together to denote the types, properties, and relationships between them. Ontologies can be used to cover different terminologies and to represent a clear specification of the different meanings. Hence, having an associated ontology where each term has a corresponding construct in the conceptual framework allows this distinction to be made in the conceptual model as well  (Shah and Musen, 2009). This type of conceptual framework along with the associated ontology is the optimal way to create a formal representation fitting different abstraction levels. We have developed an ontology to manage photogrammetry and an aligned domain ontology to manage heritage data related to the Xlendi wreck. A fine-grained description of these ontologies has been published by Ben Ellefi, where the archaeological part can be cited in  (Ben-Ellefi et al.
, 2018a) ,  (Ben-Ellefi et al. , 2018b)  and  (Ben-Ellefi et al. , 2019)  and the linked photogrammetry part in (Ben-Ellefi and Drap, 2019) as depicted in Figure  4. The ontology dedicated to the archaeological aspects used on Xlendi is aligned with modeling photogrammetry and the 'Arpenteur' ontology developed at the CNRS. Arpenteur is aligned itself with the well-known CIDOC-CRM ontology often used in the CH context  (Niccolucci, 2017) ,  (Niccolucci and Hermon, 2017)  and  (Gaitanou et al. , 2016). The Xlendi artifact dataset is made available as open data on the datahub under the name Xlendi Amphorae  (XlendiDataHub, 2020). Since an ontology enables the unambiguous representation of the entities and relationships among cultural heritage resources, it can guide the design of the knowledge bases that store the various experimental data as well as the measurement process in a knowledge manner.
Furthermore, the use of ontologies will help in maintaining a strict distinction between observable data and an interpretation based on the data. The presented knowledge base is in the form of a Linked Open Data (LOD) dataset, also known as a knowledge graph  (Hogan et al. , 2020). Dedicated to the excavation of the Xlendi shipwreck, this dataset contains morphological data of the artifacts individualized at the site as well as all the geometric data that led to the restitution of the site over the years. The artifacts are classified into two main types of morphological categories: either the object has been seen and recorded on the site by photogrammetry, or it has been only partially seen and is defined by a set of geometric attributes consisting of measurements made by photogrammetry as well as others that are deduced based on previous observations.
The unobservable attributes are deduced from the objective measurements made by photogrammetry and from the hypothesis made by the archaeologists on the typology of the object. Deductions are based on numerous previous works done by the CNRS team  (Drap et al. , 2003) ,  (Drap, 2012). For the artifacts that have been brought to the surface, an exhaustive survey is carried out by photogrammetry and structured light scanning in order to deduce all the observable geometric attributes. Successive dives on the Xlendi wreck have resulted in seven temporal datasets corresponding to the survey dates listed in  (Ben-Ellefi et al. , 2018b). For each annual survey, a LOD dataset is generated containing all the geometric data involved in the calculation of the 3D model of the site: photographs, camera calibration, 2D points measured on the photographs, 3D points calculated from the 2D points, quality estimator of the 3D points.
This represents approximately 20 million triples per survey. The LOD dataset is published following the best practices of the semantic web  (Rudolph et al. , 2013 ) (Loscio et al. 2017) , and the principles of Linked Data (invented by Tim Berners-Lee): (i) URIs to identify "things" in your data, (ii) HTTP:// URIs for people (and machines) to search for them on the web, (iii) when a URI is searched, return a description of the "thing" in the W3C's semantic web format (typically RDF, RDF-Schema, OWL), (iiii) include links to related things. We used the Apache Jena Fuseki 2  as an open-source storage system for the different Xlendi LOD datasets. This storage system also offers an accessible SPARQL endpoint. (The URI at which a SPARQL Protocol service 3  listens for requests from SPARQL Protocol clients.
) A YASGUI-SPARQL client  (Rietveld and Hoekstra, 2016)  is made available online 4  allowing querying the Xlendi artifact dataset via a user interface. The LOD datasets are accessible from the Virtual Reality and Augmented Reality applications using the SPARQL protocol. Arpenteur Ontology visualization by http://www. visualdataweb. de/webvowl/#iri=http: //www. arpenteur. org/ontology/Arpenteur. owl Arpenteur Ontlology location : http://www. arpenteur. org/ ontology/Arpenteur. owl.
The VR tool is based on the most recent version of Epic Games's Unreal Engine (Unreal Engine, 2021). This choice was justified by Epic's acquisition of CapturingReality and their very good photogrammetry software, Reality Capture (Reality Capture, 2021). Indeed, the virtual reality engine Unreal Engine, proposed by EPIC, will integrate from this year, 2021, the support of very large 3D point clouds generated by photogrammetry. Until now, the visualization of a scene in Virtual Reality required a significant reduction in the number of points obtained, a meshing phase and a good texturing phase in order to obtain a sensation close to reality. This was done at the drastic expense of the geometry which was extremely reduced and simplified. The new approach is to visualize colored point clouds without any notion of surface or texture.
¥ We rely on Unreal Engine platform to deploy our VR solution, using a Vive HTC headset. Users can interact with the artifacts through its virtual laser pointer's selection feature; point the laser at an object (here amphora and grinding stones) to display related archaeological information. The archaeological data are retrieved from the LOD temporal datasets via the SPARQL protocol (an HTTP-based protocol for performing SPARQL operations against data via SPARQL Endpoints). Each artifact in Xlendi is identified by its unique name which allows to map the artifact from the VR to its correspondence in the LOD dataset. For example, Amphore A77 in the VR is the name of the OWL instance identified by the URI arp:Amphorae1029128976 where the information of this amphorae can be retrieved via the SPARQL query in Listing 1 and the response is in Listing 2.
Mapping the VR to the LOD datasets via the SPARQL protocol is the master key in the presented knowledge-based VR system. Hence the mapped resources in the VR system can be queried in the knowledge. A realistic representation of diving at this depth has been made possible using multiple features of the Unreal Engine. Nevertheless, users can modify the level of underwater visibility effects to display the site in its entirety, something that divers can only dream about doing. This is indeed an aspect that we wanted to highlight, it is a tool to study and understand the site, but also to experience sensations close to those experienced by a diver going to the site. The three-dimensional VR navigation technique was designed to represent as close as possible a diver's movements at the Xlendi site.
l'Observatoire, 75014 Paris, France. Email: karim. tararbit@inserm. fr Funding information AREMCAR (Association pour la Recherche et l'Etude des Maladies Cardiovasculaires) association; French Ministry of Health (PHRC  2004 and 2008)   (Aristotle Basic Complexity: ABC) scores have been developed for those who undergo surgery. Population-based studies for assessing the predictive ability of these scores are lacking. Objective: To assess the predictive ability of RACHS-1 and ABC scores for the risk of infant mortality using population-based cohort (EPICARD) data for newborns with structural CHD.
Hence, the extent to which the predictive ability of the scores may be applicable specifically to surgical procedures for structural CHD (exclusively) is not known. We emphasize however that our study was designed to assess the predictive ability of these scores for an outcome that was not the target for these scores (ie, overall infant mortality and not postsurgical mortality) and for a study population for which these scores were not specifically designed (ie, structural CHD requiring surgery and not all cardiovascular abnormalities including in particular the large proportion of cases of patent ductus arteriosus that require surgery). The objective of our study was to assess the predictive ability of RACHS-1 and ABC scores for the prediction of infant mortality, using data from a large, prospective, population-based cohort of newborns with structural CHD.
Among these, 443 could be assigned both a RACHS-1 and an ABC Score and the models which included only the RACHS-1 or ABC as predictive variables were based on data from these 443 newborns. One infant had missing information on the covariates (other predictor variables noted below). Hence, the models that included both the RACHS-1/ABC scores and the additional predictive variables were based on data from 442 infants. The outcome variable was infant (≤1 year) mortality. The main predictor variable was the maximum RACHS-1 (or ABC) score, which was assigned by a neonatologist with training in pediatric cardiology (SK). Double coding of the procedures, blinded to the outcome, was conducted by SK. For newborns who had several surgical interventions, the surgical procedure with the highest RACHS-1 (or ABC) score was used to assign the maximum RACHS-1 / ABC for the infant.
| Principal findings Using data from a prospective, population-based cohort study of 443 newborns with a structure congenital heart defect who had undergone curative (non-palliative) surgery, we assessed the predictive ability of two commonly used predictive scores of pediatric cardiac surgery, RACHS-1, and the ABC scores. We assessed the predictive ability of the two scores using both the most commonly used indices of predictive ability, namely discrimination (area under the ROC curve) and calibration (Hosmer-Lemeshow test for lack of fit), as well as, a clinically meaningful and intuitive measure of predictive ability, the Tjur's Coefficient of Discrimination. To our knowledge, this is the first study to use Tjur's coefficient in our field. We found that both the models based on the RACHS-1 and ABC scores had adequate calibration but that the models based on RACHS-1 score had better discrimination.
databases for the period 2002 to 2007 and a Bayesian model that adjusted for small denominators. Each procedure is assigned a numeric score ranging from 0. 1 to 5. 0 based on the estimated mortality rate. Procedures are also sorted by increasing risk and grouped into five categories that were chosen to be optimal with respect to minimizing within-category variation and maximizing between-category variation. Both the Aristotle Comprehensive Complexity score and the STS-EACTS have been shown to have better predictive ability than the ABC Score and comparable or better predictive ability compared with the RACHS-1 score. Nevertheless, the RACHS-1 and the ABC scores are the most common ones used in our population and evaluated in the literature. All the existing scores are intended to predict postoperative (hospital) mortality and not infant mortality.
Mineral synthesis and characterization Chukanovite was synthesized as detailed in the supplementary material. For phase identification of the initial precipitate and its redox reaction products in the presence of Tc or oxygen, XRD measurements were performed with a powder X-ray diffractometer Rigaku MiniFlex 600 (Tokyo, Japan), using Cu Kα radiation and a Bragg-Brentano geometry (in θ-2θ geometry) with a step size of 0. 1°2θ and a scanning speed of 2°per min. To reduce the risk of chukanovite and/or Tc IV oxidation during the measurements, Kapton tape capped low-background airtight sample holders were used. The end products of reoxidation experiments were characterized using sample holders without protection from air. Rietveld refinement analysis was performed with the program PDXL 2 (Rigaku).
X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine-structure (EXAFS) spectra were collected in fluorescence mode at the Tc K-edge (21,044 eV) using a 13-element Ge-detector (Canberra, USA). The X-ray beam was monochromatized by a Si(111) double-crystal monochromator. XANES and EXAFS data analysis was performed with standard procedures using WinXAS  (Ressler, 1998) , after energy calibration against the K-edge of Mo (20,000 eV) and correction for the deadtime of the fluorescence detector using SIXPack  (Webb, 2005). The threshold energy (E 0 ) was defined as the first inflexion point of the absorption edge, determined as knot of the second derivate of the absorption coefficient. Extraction of the EXAFS function was conducted by a cubic-spline fit from 2 to 14. 25 Å -1 using the auto-spline function of WinXAS to determine the optimum number of splines.
The k 3weighted EXAFS function was then Fourier-transformed using a Bessel window with parameter 3 across the k-space from 2. 0 to 14. 25 Å -1. The theoretical phase shifts and backscattering amplitudes for shell fit were obtained with FEFF8. 2  (Ankudinov and Rehr, 1997) , using crystal structures of suited crystalline references as given below. The shift in threshold energy (ΔE 0 ) was varied as a global parameter in the fit procedure. Further analyses of the XAS data were conducted with iterative target transformation factor analysis (ITFA) using the ITFA software package  (Rossberg et al. , 2003)  and self-organizing maps (SOM) (see below). 2. 4. Self-organizing maps (SOM) for XAS analysis SOM are biologically inspired artificial neural networks (ANN) which were developed in the 1980s  (Kohonen, 1982; Turing, 1952).
, 2014). To perform these calculations, we developed the dedicated software SPECSOM, which can be downloaded at http://www. esrf. eu/UsersAndScience/Experiments/CRG/BM20/ Software. In this approach, the rectangular-shaped SOM consists of 900 nodes (i. e. neurons), each node containing a weight vector which consists of an EXAFS spectrum (X-map), the fractions of the Tc-species (Y 1 -map), and entries for n additional parameters (Y 2, …n -maps). In the present case six additional parameters (pH, E h , Tc VII initial, Tc loading, carbonate, contact time) were used, thus in total seven SOMs are fused. Fig. 1  shows the architecture for three fused SOM. Note that also other ANN architectures, such as multilayer perceptrons, were applied in recent XAS studies aimed for the extraction of the radial pair distribution function (RPDF) of ferric and austenitic phases of bulk iron  (Timoshenko et al.
, 2004). In fact, our software SPECSOM produces two-dimensional maps of the input space via self-organized clustering, which can in turn be used to elucidate relationships between spectra and their metadata, e. g. physico-chemical parameters like pH. From the EXAFS spectra and the corresponding physico-chemical parameters (Table  S1 ) a training set of 27 prototype vectors was generated. Based on the iterative target transformation factor analysis (ITFA) result we introduced three additional vectors as prototypes, representing the spectra of the Tc endmember species. For all prototype vectors the entries for all unknown parameters, i. e. fractions, were set to an initial guess of zero. Before starting the training of the network, all entries of the weight vectors were initialized with random numbers.
We chose a palm tree as the pictogram for the REST state (i. e. , the NOINPUT command), a calculator for the MENTAL SUBTRACTION task (i. e. , HEADLIGHTS command), and arrows pointing to the left and right for, respectively, LEFT and RIGHT-HAND motor imagery (i. e. , turning LEFT and RIGHT commands). and MENTAL ROTATION were screened for the last task. MENTAL SUBTRACTION was chosen as our pilot felt more comfortable with this task. This phase enabled the pilot to apprehend the practice of Mental Tasks and to familiarize himself with the online feedback provided by the BCI classifier. During each of these sessions, he practiced in closed-loop with a 2-class MT-BCI whose classifier was trained on the data of the two first runs of that session. This phase also enabled us, the experimenters, to identify which MTs to train in a more systematic and controlled way in the subsequent phase.