dataesr/scientific-paragraphs-categorization huggingface dataset:
Likewise, as we shall see, it is also possible to investigate the discursive dimension also with respect to classical information design and data visualization. That is why, from this theoretical perspective, we would like to introduce a semiotic reflection on what we have called "discourse of data"  (Manchia 2020a). Regarding data and information visualizations, it is possible on the one hand to consider both the dataset construction procedures and the definition of relevant information paths as rhetorical in themselves, as oriented to a specific argumentative purpose; on the other hand, it is possible to analyze the visual and communicative strategies used to construct, both from an informational and an expressive point of view, an image that is just as effective. From this point of view, taking into account Bruno Latour's reflections on the construction of scientific discourse, through a continuous dialogue with Greimas's narrative semiotics, as well as considering both the semiotic reflection on the role of images in scientific discourse in the essential work of Françoise Bastide and Paolo Fabbri, and the methodological framework of the Greimasian visual semiotics, we propose the investigation, from a semiotic perspective, of both big data collections and data (or direct) visualizations based on them.
The whole article is a technique of visualization for the public, for the scientific community concerned; the experimental conditions concern a technique of visualization more limited to the researcher' s own usage. (Bastide 1990 (Bastide [1985b]]  In this final stage of translation, images-photographs, tables, graphs, visualizations-play a key role, and it may be worthwhile to attempt an "iconography" of them  (Bastide 1990 (Bastide [1985b]] ), within the methodological framework of Greimasian visual semiotics  (Greimas 1984; Floch 1985). In our vision, Bastide's position is thus doubly interesting when transposed from the scientific discourse to the "discourse of data. " On the one hand, it allows us to investigate how the so-called data become data, that is, through what procedures researchers construct datasets as starting points for their work, somehow narratively governing the standing out, in the discourse, of such information, through the affirmation of given categories of analysis to the detriment of others.
Among the data fed into the project are photos shared on Instagram, posts with images from Twitter, images from Google Street View, Foursquare check-ins, cab ride data, and others. Data was collected extensively along Broadway, on both the left and right sides, and then channelled into multiple layers (the horizontal layers), each of which identifies a distinct survey category (Fig. 6 ). Not all information is transposed in its entirety, via direct visualization: that is, of some datasets only a schematic graphical representation is provided, through diagrams or graphs of more or less classical layout, according to the rules of the most classic data visualization. Fully displayed, however, and in the true spirit of media visualization, are the images (visual data) collected in the field-and this is what we will focus on here.
Likewise, in Visualizing Vertov, the multiple paths for the user to actualize from the virtualities offered by the source database would not be possible if they were not based on the choices necessary to create the database itself. Returning once again to Françoise Bastide and her seminal investigation of devices of visualization, the entire database, insofar as it is structured and organized, is itself, too, the result of a specific "making-to-see", which is framed within the peculiar research perspective of the data scientist or researcher and takes its point of departure from it. A similar critical position toward the pure virtuality of datasets is now increasingly evident, and in various fields of research, for example among policy and data scientists. To give a few examples, scholars currently working on the topic of data gaps point out how the so-called "raw data" are in fact the result of selection filters and structuring practices.
The main themes are, specifically: 1. Nature; 2. Universe; 3. Animal Kingdom; 4. Society; 5. Hope; 6. Happiness; 7. Science; 8. Technology. Each macroseries is then divided into three more specific subseries (for example, 1. Nature includes the three subtopics 1. 1. The disappearance of the Aral Sea; 1. 2. Human impact on the environment, 1. 3. The effects of climate change), for a total of 24 subseries arranged in horizontal rows and running, linearly, along all three walls (fig. 9 ). The visual modulations present on the expression plane, as might have been expected, are not developed in parallel with the themes of the macroseries, but are exploited in a very fine and always varying way to represent the development of each individual dataset. The only exception are some color scales, which are always identical and used to represent the continents according to two different categorizations, one by geographic affinity and the other by cultural affinity (identical to the former but with the addition of another color for North Africa and the Middle East, an affinity that is clearly cultural and not geographic).
Thus, subject to the geographical references and their fixed color coordinates, a different regime of information visualization applies in each of the 24 datasets: from series to series, different plastic categories become relevant, and on all levels (chromatic, eidetic and topological). A curiously very nonfunctional visualization work, according to the parameters of classical data visualization, in which Tufte's data-ink ratio is completely disregarded. And to this great visual complexity it is difficult, based on this first description, to find an explanation according to the principle of transparency and perfect equivalence between data and its visualization. Why all this complexity, then, in the visual transposition of information? To understand why, it is necessary to take a closer look at The Room of Change not only as an informational artifact, but as a complex visual artifact, as well as on its simultaneously informational and expressive nature, well summarized by the label of "data-driven wallpaper.
Thus, from the May 24, 2020 visualization to the February 21, 2021 visualization, both the extent of the reference dataset (from 100,000 to 500,000 data points) and the way in which data is designed have changed. What was previously described, for example, as "Theresa Elloie, 63, New Orleans, renowned for her business making detailed pins and corsages. "  (Grippe 2020 ) is now nothing more than an anonymous black dot, with no more name, age, or biographical profile, in a graphic "with nearly a half-million dots running down the length of the page and across three of its six columns"  (Coleman 2021). This is a radical change that New York Times readers, in comments on the Facebook post announcing the new front page on February 21, 2021, did not fail to remark on, with comments such as "So my father in-law is basically a dot to some.
Indeed, more than the individual dots, which are still clearly identifiable even if no longer narrated as in the previous visualization, the observer's eye is struck by the horizontal bands in which, as in a typographic screen, the blackness thickens without any form --any data-being clearly distinguishable. The purely functional strategy adopted in the visual representation of data fades into the tragic impossibility of quantifying such a large number in such a short period, as if to remark on the fallibility of exact, live recounting of massive amounts of data, and at the same time the need we still have to make the invisible visible. In this case, the visualization of such a large database, precisely because of the arduous task it sets itself, proves capable of making one feel rather than see, transforming the information receiver into an observer and a pathemic subject as well.
Across immediacy and transparency. Conclusions In this paper we have tried to consider visualization no longer as a mere display of data but as the result of a multi-level translation process, from structuring data into datasets to visually transposing a specific viewpoint on the data. As we have tried to show, such a semiotic exploration, attentive to the ways in which data are put into discourse, could work as a deeper and more structural investigation of the social, cultural and at the same time discursive construction implicit in the big data phenomenon. As for media visualization examples, in Visualizing Vertov we have remarked the crucial role of metadata and different combinations of visual characteristics in informing both media visualizations and patterns, as well as in On Broadway we have noticed how the quite "immersive" effect of a bottom-up perspective in the so-called Streetview Top layer depends on the capillary organization of data collection, a real "device of visualization.
H. Summary and conclusions Social media platforms generate large amounts of data that reflect the complexity of human and machine activities. Many researchers have reported new concerns about the viability of these social media channels regarding machine-generated messages. As those data sets continue to grow in quantities, researchers have also used various theoretical and algorithmic frameworks to analyze social media feeds. Semiotics is the discipline that studies the foundation of sign associated with text and images and their meaning found in language and communication. One of the founders of semiotics is the American philosopher Charles Sanders Peirce who offered a triadic foundation for the term sign. This study raises the question: what insights do Peirce's visual sign theory contribute when we try to interpret Twitter data analysis? To address this question, first, we present the results using Twitter Analytics, and Peirce's sign theory R package to generate results visually.
Second, we investigated whether we could use digital image processes and algorithms to evaluate the results considering Jappy's (2013) and Peirtarinen's (2012) discussion of Peirce's visual interpretation. The study's visual result showed common data visualization-type displays of Twitter data analysis using the sign theory of Peirce's R package technology with Twitter Analytics. Those visual results present interactions among the three Peirce visual sign categories and provide deeper insight into key concepts using Twitter Analytics terminology. We then explored the feasibility of developing algorithms for analyzing these visualizations based on criteria set out by  Jappy (2013)  and  Peirtarinen (2012) , who outlined Peirce's search for meaning found in visualizations. However, such algorithms were hard to assemble due to the complexities of those criteria and the complexity of social media data feeds: especially with human vs machine messages.
1 As we can read in their website: "Jake Elwes is a media artist living and working in  London. They  studied at The Slade School of  Fine Art ,  UCL. Recent works explore their research into machine learning and artificial intelligence. Their practice looks for poetry and narrative in the success and failures of these systems, while also investigating and questioning the code and ethics behind them. Their current works in the Zizi Project explore AI bias by queering datasets with drag performers which simultaneously demystify and subvert AI systems. Jake's work has been exhibited in museums and galleries internationally, including the  ZKM, Karlsruhe, Germany; TANK Museum, Shanghai; Today Art Museum, Beijing; CyFest, Venice; Edinburgh Futures Institute, UK ; Zabludowicz Collection, London;  Frankfurter Kunstverein , Germany; New Contemporaries 2017, UK; Ars Electronica 2017, Austria; Victoria and  Albert Museum , London; LABoral Centro, Spain;  Nature Morte, Delhi, India ;  RMIT Gallery, Australia; Centre for the Future of Intelligence, UK  and they have been featured on TV: ZDF aspekte (Germany) and the  BBC Arts (UK) " (https://www.
at 1024×1024 resolution and contains considerable variation in terms of age, ethnicity and image background. It also has good coverage of accessories such as eyeglasses, sunglasses, hats, etc. The images were crawled from Flickr, thus inheriting all the biases of that website, and automatically aligned and cropped using dlib. Only images under permissive licenses were collected. Various automatic filters were used to prune the set, and finally Amazon Mechanical Turk was used to remove the occasional statues, paintings, or photos of photos" (https://github. com/NVlabs/ffhq-dataset).
As expected, the receptor is captured in the inactive conformation (Fig. 1a, b ) based on its overall alignment with the inactive state S1P 1 (PDB ID 3V2Y, Cα RMSD = 0. 84/0. 78 Å on 90% of residues for chains A/B of our S1P 5 structure) and the active state S1P 5 (PDB ID 7EW1, Cα RMSD = 1. 40/1. 40 Å on 90% of residues for chains A/ B of our S1P 5 structure) as well as on the conformation of conserved activation-related motifs described below. A dual toggle switch L(F)  3. 36  -W  6. 48  (superscripts refer to the Ballesteros-Weinstein 29 residue numbering scheme in class A GPCRs) together with P 5. 50 -I 3. 40 -F  6. 44  motif have been characterized as the common microswitches in class A GPCRs that transmit activationrelated conformational changes from the ligand-binding pocket towards an outward movement of TMs 5 and 6 and inward displacement of TM7 on the intracellular side  30, 31.
60 A/C, R111 3. 28 A/Q, and E112 3. 29 A/Q either fully abolish or significantly (by an order of magnitude or more) decrease the response for both ONO-5430608 and S1P (Fig. 2c  and Supplementary Table  2 ). Notably, some mutations have different effects on S1P and ONO-5430608. While mutations of N92 2. 60 , R111  3. 28  , and E112  3. 29  completely eliminate response to the inverse agonist, they only decrease the potency for S1P. A similar effect of mutations of homologous amino acids on S1P potency was previously observed for S1P 3 21. In this case, each of the three amino acids independently interacts with the amine group of S1P (see PDB ID 7C4S). On the other hand, in our S1P 5 -ONO-5430608 structure, these three amino acids are interconnected and form a stable cluster that further interacts with the tertiary amine and the carboxyl group of ONO-5430608.
Naturally occurring mutations in S1P 5 In order to characterize additional functionally important residues in S1P 5 , we performed mapping of known point mutations from genomic databases onto the crystal structure (Fig. 2d ). Multiple databases carry information about S1P 5 point mutations including gnomAD (229 SNVs)  43  , which contains genomic information from unrelated individuals, and COSMIC (124 point mutations)  44  , which accumulates somatic mutations in cancer. The most frequent gnomAD mutation L318  8. 55  Q in helix 8 (3% of the population) was shown to impair G 12 signaling  45  ; however, according to our functional data it only slightly decreases the potency of S1P in G i -mediated signaling (Fig. 2c ). It was previously proposed  45  that a possible cause of this mutation on the signaling impairment is the prevention of palmitoylation of the downstream C322  8.
3 ± 0. 2 Å and the active state structure (PDB ID 7EW1, S1P 5siponimod) is 3. 0 ± 0. 2 Å. The conformational heterogeneity of Y(F) 2. 57 observed in experimental S1PR structures and metaMD simulations were also well captured by AlphaFold predictions (Fig. 5a-f ). In all S1P 1 , S1P 3 , and S1P 4 models, Y 2. 57 has an upward conformation, except for a single S1P 1 model, in which this residue adopts a downward orientation similar to that previously observed in all-atom MD simulations  52. Furthermore, 19 out of 50 S1P 5 models display a downward Y 2. 57 orientation, while all the others have an upward Y 2. 57 orientation. Notably, S1P 2 is the only receptor, in which Y 2. 57 is replaced with F 2. 57 which adopts a downward conformation in all generated models. The downward orientation of F 2.
Discussion Here, we present the 2. 2 Å crystal structure of the human S1P 5 receptor in complex with its selective inverse agonist. The structure was obtained by room temperature SFX data collection at PAL-XFEL using sub-10 μm crystals. In combination with site-directed mutagenesis, functional assays, metaMD simulations, and docking studies, this structure revealed molecular determinants of ligand binding and selectivity as well as shed light on the mechanism of inverse agonism in the S1PR family. The obtained structure also allowed us to map locations of known missense SNVs from gnomAD and COSMIC genome databases and annotate their potential functional roles providing future insights into personalized medicine approaches. We found that the inverse agonist ONO-5430608 binds to the receptor's orthosteric site, suppressing S1P 5 basal activity.
LCP crystallization Purified and concentrated S1P 5 was reconstituted in LCP, made of monoolein (Nu-Chek Prep) supplemented with 10%w/w cholesterol (Affymetrix), in 2:3 (v/v) protein:lipid ratio using a syringe lipid mixer  27. The obtained transparent LCP mixture was dispensed onto 96-wells glass sandwich plates (Marienfeld) in 40 nl drops and covered with 900 nl precipitant using an NT8-LCP robot (Formulatrix) to grow crystals for synchrotron data collection. To prepare crystals for XFEL data collection, the protein-laden LCP mixture was injected into 100 μl Hamilton gas-tight syringes filled with precipitant as previously described  27. All LCP manipulations were performed at room temperature (20-23 °C), while plates and syringes were incubated at 22 °C. Crystals of S1P 5 grew to their full size of <30 μm (in plates) or <10 μm (in syringes) within 3 days in precipitant conditions containing 100-300 mM KH 2 PO 4 monobasic, 28-32%v/v PEG400, and 100 mM HEPES pH 7.
Diffraction data collection and structure determination XFEL data for S1P 5 -ONO-5430608 crystals were collected at the NCI (Nanocrystallography and Coherent Imaging) beamline of the Pohang Accelerator Laboratory X-ray Free Electron Laser (PAL-XFEL), Pohang, South Korea. The PAL-XFEL was operated in SASE mode at the wavelength of 1. 278 Å (9. 7 keV) and 0. 2% bandwidth, delivering individual X-ray pulses of 25-fs duration focused into a spot size of 2 × 3 μm using a pair of Kirkpatrick-Baez mirrors. LCP laden with dense suspension of protein microcrystals was injected at room temperature inside a sample chamber filled with helium (23 °C, 1 atm) into the beam focus region using an LCP injector  56  with a 50-μm-diameter capillary at a flow rate of 0. 15 μl/min. Microcrystals ranged in size from 5 to 10 μm.
Diffraction data were collected at a pulse repetition range of 30 Hz with a Rayonix MX225-HS detector, operating in a 4 × 4 binning mode (1440 × 1440 pixels, 30 fps readout rate). The beam was not attenuated and delivered full intensity (5 × 10 11 photons per pulse). A total number of 490,000 detector images were collected. Due to a high systematic background, Cheetah 57 v. 2019-1 was initially used only to apply dark current calibration, and all images were used for further processing. The overall time of collection from a sample with a total volume of about 36 μl was approximately 4 h and yielded 6918 indexed frames with 7492 crystal lattices. During the XFEL data collection, a high systematic background scattering from upstream to the interaction point occurred due to a high-intensity X-ray lasing conditions (Supplementary Fig.
None of these strategies yielded better results than the initial processing (column G). The final merging was performed with partialator, iterations = 2, push-res = 5. 0, and model = ggpm (Supplementary Table  1 ). The structure was initially solved by molecular replacement using phenix. phaser  60 with two independent search models of the polyalanine S1P 1 7TM domain (PDB ID 3V2Y) and BRIL from the highresolution A 2A AR structure (PDB ID 4EIY). Model building was performed by cycling between manual inspection and building with Coot 61 v. 0. 9. 6 using both 2mFo-DFc and mFo-DFc maps and automatic refinement with phenix. refine 62 v. 1. 19. 2 using automatic torsion angle NCS restraints and 2 TLS groups. Ligand restraints were generated using the web server GRADE v. 1. 2. 19 (http://grade.
AlphaFold predictions Prediction runs were executed using AlphaFold 50 v. 2. 1. 1 + 110948 with a non-docker setup (https://github. com/kalininalab/alphafold_non_ docker, git commit 7ccdb7) and an updated run_alphafold. sh wrapper with added -random-seed parameter. The use of structural templates was disabled by setting "max_template_date" to 1900-01-01; thus, no S1PR structures were used for prediction, and all AlphaFold models analyzed in this work were constructed based on multiple sequence alignment alone. 50 AF2-models (ranked_…. pdb models) were generated for each of 5 human S1PRs with protein sequences obtained from UniProt. For each receptor, 10 prediction runs with different seeds (-random-seed" = <run_number>) were executed; each run generated five models. Structures were used as provided by the Alphafold's pipeline with Amber relaxation (see Supplementary Methods 1.
0, mostly S1P 1 agonists), inactive molecules from ChEMBL (pChembl <5. 0), and decoys. Decoy molecules were selected from the Enamine REAL library [https://enamine. net/compound-collections/realcompounds/real-database], matching the distribution of active molecules by charge and weight. The benchmarks have the following ratios of active:inactive:decoy molecules: 6:5:60 for "ONO", 12:10:120 for "Selective", and 158:39:1207 for "Non-selective", with the imbalance parameter (ratio of the total library size to the number of active molecules in it) of 11. 8, 11. 8, and 8. 8, respectively. Docking scores and ligand structures are provided in Supplementary Data file 2. For estimation of the virtual screening quality, metrics enrichment at 10% and receiver operating characteristic (ROC)-area under the ROC curve (AUC) were used, as implemented in RDKIT  74  v.
Acknowledgements We thank  S. Ustinova ,  A. Podzorov ,  A. Awawdeh ,  P. Utrobin , and  Yu. Semenov  for technical assistance,  T. Maruyama  for providing ONO-5430608. We acknowledge the  Paul Scherrer Institut, Villigen, Switzerland  for the provision of synchrotron radiation beamtime at beamline PX1 of the  SLS  and would like to thank  Dr. V. Olieric  for assistance. The authors are grateful to the staff of the accelerator and beamline departments at PAL-XFEL for their technical support. The XFEL experiments were performed at the  NCI PAL-XFEL  experimental station under proposal No. 2019-2nd-NCI-012. Protein production and crystallization were supported by the  Ministry of Science and Higher Education of the Russian Federation  agreement  075-00337-20-03 , project  FSMG-2020-0003  (V.
Data availability Coordinates and structure factors for the S1P 5 -ONO-5430608 structure have been deposited in the Protein Data Bank (PDB) under the accession code 7YXA. Raw SFX diffraction data have been deposited to CXIDB database under accession number 196. Publicly available amino acid sequences for S1PRs used in this study were obtained from the UniProt database under accession numbers: P21453, O95136, Q99500, O95977, Q9H228. Publicly available structures used in this study can be found in the Protein Data Bank under accession codes: 3V2W, 3V2Y, 4EIY, 7C4S, 7EVY, 7EW1, 7EW2, 7EW4. SNV data for S1P 5 used in this work are available from public databases gnomAD [https://gnomad. broadinstitute. org/gene/ENSG00000180739?dataset=gnomad_r2_1] and COSMIC [https://cancer. sanger. ac. uk/cosmic/gene/analysis?ln=S1PR5].