dataesr/scientific-paragraphs-categorization huggingface dataset:
Radiomic Feature Extraction Seventy-three radiomic features (14 shape, 10 intensity, and 49 textural) (see Supplemental Table  1 ) compliant with the most up-to-date imaging biomarker standardization initiative (IBSI) benchmark  (22)  were extracted using homemade software (MIRAS v1. 0, LaTIM INSERM UMR 1101, Brest, France). Three different grey-level discretization methods [fixed bin number (FBN) with 64 bins, fixed bin width (FBW) with 0. 5 SUV or 10 HU, and histogram equalization with 64 bins] were considered for second-and higher-order textural features. Note that the FBN and FBW discretization schemes are IBSIcompliant, but the histogram equalization, although mentioned by the IBSI, is not yet a standard. Texture matrices were implemented in 3D following the merging strategy (i. e. , considering all 13 directions simultaneously).
It allowed us to focus on the question at hand without having to deal with harmonization issues  (26, 27)  since all patients had their PET/CT acquisition in the exact same PET/CT system, with no variability in the acquisition protocol or reconstruction settings. However, this means that our findings will need to be validated in external datasets, for which we will implement harmonization techniques for handling the multicenter nature of the data  (28). Although our cohort included both retrospectively (for training) and prospectively (for testing) recruited patients, the size of the test set was small as we could not include all available patients because a minimum follow-up duration was not reached and the prospective recruitment is still ongoing. This limited the statistical power for comparing the different results obtained with or without tumor delineation.
FUNDING This work was funded by the  French Ministry of Research  (PhD grant), the  INCa  and  DGOS  through a grant in the  PRINCE project  ( PRTK-2015 , # R16063NN ), the  PREDICT Innovative Training Network  funded by the  Marie Sklodowska-Curie Actions , part of the  EU's Horizon 2020 Programme  (grant agreement no. 766276 ), and the  POPEYE  project  under the frame of ANR (Agence National de la Recherche ;  ANR-19-PERM-0007 POPEYE ) and of  ERA PerMed  ( POPEYE T11EPA4-00055 ).
The use of X-rays to induce OH radicals from the radiolysis of water for X-ray footprinting are covered in two papers. In the first, new developments and the data collection pipeline at the NSLS-II high-throughput dedicated synchrotron beamline are described, and, in the second, the X-ray induced changes in three different proteins under aerobic and low-oxygen conditions are investigated and correlated with the absorbed dose. Studies in XFEL science are represented by a report on simulations of ultrafast dynamics in protic ionic liquids, and, lastly, a broad coverage of possible methods for dose efficiency improvement in modalities using electrons is presented. These papers, as well as a brief synopsis of some other relevant.
Typically the value of dose in an experiment cannot be measured, but can only be estimated from the particular beam conditions and sample constituents, so that reliable characterization of the required parameters (beam profile and flux) is important to be able to compare measurements taken under varying data collection protocols at different sites. Collecting X-ray crystallographic data from tiny macromolecular crystals is becoming more and more widespread thanks to increasingly intense synchrotron microfocus beamlines  (Yamamoto et al. , 2017) , and is facilitated by dedicated programs, such as the automatic system for high-throughput structure analysis ZOO  (Hirata et al. , 2019). The latter supports small-wedge synchrotron crystallography (SWSX) that consists of collecting small-wedge sub-datasets from a multitude of microcrystals mounted in a cryo-loop that are then merged into a full dataset.
In this issue, Hirata and coworkers systematically address the effect of absorbed dose on SWSX structure determination  (Baba et al. , 2021). In particular, they aimed to determine the optimal dose for sulfur-SAD (S-SAD) phasing. More than 400 small-wedge (10 ) subdatasets were collected at 100 K from densely packed lysozyme crystals (20 mm-sized) at five different doses  (1, 2, 5, 10, 20  MGy per sub-dataset) and three different wavelengths (1. 0, 1. 4 and 1. 7 A ˚), and the success of S-SAD phasing was monitored by calculating map correlation coefficients (CC map ). At all three wavelengths, CC map was highest at 5 MGy, implying that an optimal balance between diffraction intensity and radiation damage had been reached. This optimum-dose value is in line with the point of diminishing returns (about 3 MGy) for S-SAD phasing above which radiation-induced deterioration of data quality outweighs the gain in that data quality arising from increased multiplicity  (Storm et al.
, 2017). Progress in observing and understanding X-ray radiation damage at 100 K has also been reported in some other publications since the last JSR radiation damage special issue. These include a re-examination of the resolution and dose dependence of global damage (manifested in reciprocal space) by Atakisi and co-workers  (Atakisi et al. , 2019). Using datasets collected by other researchers from three different proteins, they suggest that these diffracted intensity decays can be explained by assuming a locally exponential dependence on dose, with a scattering-vector (q) dependence of D 1/2 (the dose required to halve the diffraction intensity) of D 1/2 (q) ' 1/q , where ' 1. 7. Factors affecting specific damage to particular amino acids (observed in electron density maps) have been investigated by Bhattacharyya and co-workers  (Bhattacharyya et al.
These include a phytochrome photosensory module (GAF domain) which showed severe structural radiation damage to the chromophore in synchrotron cryo-MX structures and whose RT structure solution by SFX now paves the way for radiation-damage-free structure determination of photoconversion intermediates of that module  (Burgie et al. , 2020). Another example is the SFX structure solution of a soluble methane monooxygenase hydroxylase complex with its regulatory component in its fully oxidized and its fully reduced states  (Srinivas et al. , 2020). Structure solution of this radiation-sensitive metalloprotein in its fully oxidized state has not been possible at synchrotron sources due to X-ray induced photoreduction. Srinivas et al. employed Fe K X-ray emission spectroscopy concomitant with SFX data collection to identify the redox states of the oxidized (diferric) resting state and the chemically reduced (diferrous) state.
In XFEL experiments, most of the doses thus far estimated in the literature do not represent the true situation in the experiment, since they do not include any time resolution. The diffraction process is much faster than most of the processes contributing to the dose, including that of photoelectron (PE) energy loss in the crystal. To address this challenge, the software program RADDOSE-XFEL has been developed to include a time stamp, and thus time resolution, for every interaction which contributes to the absorbed dose  (Dickerson et al. , 2020). It has been suggested that damage would be observed in SFX structures when one ionization per atom had occurred, at a dose of around 400 MGy. This was calculated to be the dose at which the number of free electrons in an average protein equals the number of atoms by the end of an XFEL pulse  (Chapman et al.
The fragments of the sample are then analysed by gel-electrophoresis or liquid chromatography mass spectro-metry to identify the points of modification, thereby allowing the susceptible regions of the protein or nucleic acid to be inferred. The method can give information on the dynamics of macromolecular folding as well as on protein-protein and protein-ligand interactions, and its applicability and contributions to structural biology have recently been reviewed by  Chance et al. (2020). To determine the X-ray dose, 'dose response curves' are collected prior to detailed data collection, but it should be noted that the dose in the XF field conventionally refers to the level of OH production rather than being the absorbed dose as in MX and SAXS. However, the number of OHs available for footprinting depends critically on the scavenging properties of the buffer which is employed, so that similar absorbed doses can result in very different experimental outcomes.
Typically, the sample solutions are oxygenated prior to irradiation, since dissolved molecular oxygen is necessary for most of the OH-mediated modifications that are typically observed. Two papers in this issue describe different aspects of XF data collection. Firstly Jain et al. present a detailed description of the new high-throughput XF beamline at NSLS II (XFP) which supplies X-rays to two endstations  (Jain et al. , 2021). One of these can provide smaller (down to 120 mm Â 450 mm) high-flux-density pink beams for irradiation of capillary flowed samples, producing very high OH concentrations with microsecond X-ray exposures, even in samples which have intrinsic scavenging ability (e. g. membrane proteins, live cells). The second endstation supplies a defocused larger beam with lower flux density for irradiating smaller proteins.
The various beamline components, sample delivery methodology and data collection protocols are covered in detail. Also described are experiments to probe the effect on the level of OH production when using 26 different reagents often used in sample buffers. As an indicator of OH presence in these solutions, the authors use the Alexa488 fluorophore OH dose reporter molecule, whose fluorescence is destroyed by OH and so OH levels can be correlated to the utilized exposure time and X-ray flux  (Gupta et al. , 2007). In the work reported here, the various buffers showed a range of responses. These results should allow better optimization of experimental practice, since the effects of the buffer on the irradiation necessary for successful outcomes is now more fully characterized. As mentioned above, XF measurements are usually carried out on aerobic solutions in order to enhance the apparent OH reaction rates.
95 A ˚resolution, observations of specific structural changes have been reported in regions affected by the radiationinduced alteration in redox states. The authors found they were able to minimize the damage by reducing the 'dosage' from to 50 to 2 frames  (Kato et al. , 2021). In electron diffraction (ED) from microcrystals (mED) at 100 K, there has also been a demonstration of the use of specific structural damage to phase a seven amino acid peptide from a single zinc atom. This atom was located in a difference Patterson derived from two datasets, the first being collected with an average incident fluence of 0. 17 e À A ˚À2 , and the second after an exposure of 0. 5 e À A ˚À2  (Martynowycz et al. , 2020). In a paper in this special issue, Zhang et al. collect and summarize the possible options for improving the dose efficiency in single-particle cryo-EM, giving a comprehensive overview of the current status of their development.
Acknowledgements We thank  Ian Carmichael  and  Edward Snell  for their constructive comments on this summary. The 11th International Workshop on X-ray Radiation Damage to Biological Samples, at which most of the work in this special issue was presented, would not have been possible without the invaluable support of the Swiss Light Source at the  Paul Scherrer Institute in Switzerland  who hosted it, and the hard work of the Local Organizing Committee of  Florian Dworkowski ,  Karol Nass  and  Vincent Olieric  with wonderful administrative support from  Sonia Reber. We are most grateful to them for their enthusiasm and efforts, especially given the strictures imposed by the pandemic.
Compared to Wikipedia, Wikidata is a single domain website with the possibility to view the information in multiple languages. Translation plays a significant role in Wikidata. Unlike Wikidata items, Wikidata properties are influenced less by translation bots and require a meaningful amount of human effort. The study of Wikidata property creation and translation is, therefore, very essential. Since the inception of Wikipedia, several research works have focused on the information flow among different language Wikipedias. The attention has now shifted to the way information on Wikidata is created and translated. The focus of this article is the Wikidata properties. WDProp is a web application created to understand and obtain an integrated view on the various multilingual aspects of Wikidata properties, from their proposition to their use on multiple domains.
OpenSym 2021, September 15-17, 2021, Online, Spain aspects around them. Wikidata properties play a major role in describing knowledge, across domains. Unlike Wikidata items, they cannot be simply created but requires discussion and voting. Depending on the need, the properties are proposed by the Wikidata contributors. Such propositions are discussed by the community members, where some may point to the availability of existing properties and others agreeing to the need for the creation of the property. Every property proposition undergoes voting and if a property has achieved a sufficient number of supporting votes, the property may be created and made available for use. A newly created property may not be available in all the languages and may have one or more translations, based on the initial translations proposed by the property proposer.
This makes it difficult for users who do not speak or comprehend any of the available translations. The role played by bilingual or multilingual speakers is therefore very important. It is also difficult to use the properties, immediately after their creation, if their usage is not properly described and documented. For this purpose, Wikidata WikiProjects for different domains have been created, which curates the properties and documents the usage for describing Wikidata items belonging to the given domain. The problem of finding relevant properties has also garnered some attention in the research community. There have been several works on predicting properties [3, 18] for describing domain knowledge, including a recent work on their prediction from Wikipedia (e. g. , Wiki2Prop [10]). It is important to state here that such property suggestions or predictions may require some validation by the contributors and hence their translations cannot be overlooked.
Wikidata is highly evolving. It is difficult to find and track the latest number of Wikidata supported languages, datatypes, properties, translation statistics, etc. Some contributors are interested in a subset of curated information and the associated translation statistics. In this article, we discuss the development of WDProp as a solution to these problems. Section 2 gives a brief introduction of Wikidata, particularly focusing on properties. Some works on Wikidata properties are presented in section 3. Section 4 presents the development of WDProp and the associated results. Starting from some coarser analysis, it shows the results related to some fine-granular analysis. Finally, section 5 concludes the article and presents the future course of action.
Translation of properties may aid to extracting structured information from Wikipedia articles to populate infoboxes [9] and Wikidata 26   27. Finally, it is equally important to point the role played by Wikidata WikiProjects [7, 8] in curation of properties. The focus of WDProp proposed in this article is on Wikidata properties and the multilingual aspects around the properties. The goal is to navigate and explore properties by languages, datatypes, WikiProjects, etc.
WDPROP: FROM COARSER ANALYSIS TO FINE-TUNED ANALYSIS One approach for analysis of Wikidata items and properties is to download Wikidata datadump 28  with the commit history for the study. Another approach is to make use of SPARQL endpoints and Wikidata Mediawiki API, especially when the focus is on a small subset of information. Multilingual Wikidata Property Translation Flow Dataset [2] was created using the latter approach. Datadumps, complete or selective are quite useful for coarser analysis and may provide some useful insights [6, 11]. Table  1  shows the details of Multilingual Wikidata Property Translation Flow Dataset. This dataset contains the translation flow of labels, descriptions, and aliases of Wikidata properties collected on July 7, 2019. The dataset consists of four columns: timestamp of the translation, the identifier of the property translated, the language of translation, and the type of translation (label, description, or alias).
Measure The above dataset can also be used to study the property translation flow for a given property as shown in Figure  4. It took a period between 2013 and 2019 for the translation of the labels (violet dots) of the property P17 (country) to be available in multiple languages. The first descriptions (light blue dots) were subsequently translated and the first aliases (red dots) were added after a long time. Check the distances between the violet, blue and red dots for some languages. By focusing on modifications (i. e, changes made after the first translations), a modified form of this visualization can be used to detect possible vandalisms. However, the above analysis is limited to a snapshot of data at a given time. Considering the rapidly evolving nature of Wikidata, there is a need to have some real-time view of Wikidata properties.
WDProp was developed to understand and obtain an integrated view on the different aspects of properties, their creation, and translation. The focus here is on obtaining real-time statistics, on a much fine-granular basis, i. e. , instead of focusing on the global analysis of properties (as seen above), the users can focus on a single property or a combination of curated properties. WDProp provides a visual interface to such information. Property translations can be analyzed in different ways. There are two major translations: property labels and property descriptions. Properties may also have some aliases in certain languages. Whether all the aliases in one language need to be available in other languages is an open question. For a given language, the language community may wish to ensure the translation of all the property labels and descriptions.
For some generic properties like country (P17), the goal of the Wikidata community members is to ensure the translation in all the supported languages. WDProp separates property translations in the following manner: • translated and untranslated property labels • translated and untranslated property descriptions • translated and untranslated property aliases A Wikidata contributor may find one or more of the above pieces of information relevant, some of whom may wish to obtain the latest translation statistics, and some others looking for properties not yet translated.
Development WDProp was developed using basic web technologies like HTML, Javascript, and CSS. It makes use of the SPARQL queries and Wikidata MediaWiki API to obtain the latest information from the Wikidata servers. It, therefore, does not require any installation since it contains a collection of HTML, Javascript, and CSS files. This simple approach ensures that it can be tested on any modern browser, including desktop and mobile devices. It is also available on Toolforge 29  , for users wishing to use links that can be shared with others.
Results WDProp can be used to analyze the collaborative approach to the development of multilingual ontology [12] and to obtain the real-time information related to multilingual aspects of Wikidata properties, especially their translation [13]. It provides the following information and features: • Bookmarkable links: This feature ensures that the links are bookmarkable and use Wikidata supported codes and identifiers for obtaining the information related to languages, properties, datataypes, etc. • List of supported languages: see Figure  5  • Translation statistics of labels, descriptions and aliases of Wikidata properties: see Figure  6  • List of properties: see Figure  8  • Compare translation statistics among different languages: see Figure  7  • Use of references and equivalent properties: see Figure  9.
Discussion Wikidata is highly evolving and new properties are being regularly proposed and translated. The WDProp interface has also undergone several changes during the last couple of years to incorporate these changes. Take, for example, WDProp initially displayed all the properties on the properties page [14], less than 5000 properties at that time. But this number has now increased to around 9,000 properties. So now the interface shows only the first 100 properties obtained from the SPARQL query. However, the user now has the SPARQL query link to obtain the complete list. SPARQL query links and links to Wikidata Mediawiki API calls have now been integrated into all the pages on WDProp. This enables the users to verify, use and even modify these API calls to suit their needs. Some commonly used properties also face vandalism.
CONCLUSION AND FUTURE WORKS With the growing usage of mobile devices and internet penetration across the world, web developers and content producers are looking for ways to ensure the availability of information in the local languages of the readers. Considering the number of languages, it is not easy for a few developers to produce multilingual information. Wikipedia and Wikidata have long shown how such multilingual information can be produced collaboratively and in an open manner. However, considering the disparity in the number of contributors in different languages, we find that not all languages are equally represented on these websites. This article focused on Wikidata properties and their translations and discussed the design and development of WDProp for this study. It also demonstrated how the web application can be used to perform granular analysis on Wikidata properties and their curation.
Tissue damage dramatically alters how cells interact with their microenvironment. These changes in turn dictate cellular responses, such as stem cell activation, yet early cellular responses in vivo remain ill-defined. We generated single cell and nucleus atlases from intact, dissociated, and injured muscle and liver, and identified a common stress response signature shared by multiple cell types across these organs. This prevalent stress response was detected in published datasets across a range of tissues, demonstrating high conservation but also a significant degree of data distortion in single-cell reference atlases. Using quiescent muscle stem cells as a paradigm of cell activation following injury, we captured early cell activation following muscle injury and found that an essential ERK1/2 primary proliferation signal precedes initiation of the Notch-regulated myogenic program.
1D ). A similar pattern was observed for the down-regulated genes (Fig. S2A ). Importantly, we uncovered a conserved transcriptional core composed of 98 genes that was induced in at least 7 of the 10 main cell types (Fig. 1E ; S2B, supplementary Table  1 ). The average expression of this gene set was designated as the Stress Index and was validated in our snRNA-seq dataset (Fig. S2C ). Gene ontology analysis of the stress core was enriched for terms linked to MAPK signaling, response to growth factors and cytokines, and initiation of transcription (Fig. 1F ). Consistently, phospho-ERK1/2 showed rapid induction of MAPK activation at 15 min of muscle digestion (Fig. S2D ). Based on the broad modifications that we observed in muscle and liver cells during dissociation, we extended our analysis to a multi-organ murine single-cell atlas (Tabula Muris;  Schaum et al.