dataesr/scientific-paragraphs-categorization huggingface dataset:
SugarBindDB and UniLectin as Key Connectors As visible in Figure  3 , SugarBindDB and UniLectin are suitable connectors between virology, protein science, and chemical biology. This of course reflects the biological role of lectins as key players in cell-cell communication. Since SugarBindDB is dedicated to the characterization of pathogen lectins, it is naturally connected to ViralZone. Unilectin is by definition universal and collects data that cover all kingdoms. Since the intricate network of interactions at the host cell surface also involves host lectins recognizing pathogen glycans, a full description of the situation may require further data from UniLectin. UniLectin includes UniLectin3D, which unsurprisingly compiles three-dimensional information regarding lectins and glycan ligands.
In particular, some structures available in this database provide details of viruses binding to glycans of animal cell surfaces (rotavirus, influenza, etc. ) and to bacterial cell walls (bacteriophage). In Norovirus, the capsid protein sequences differ among the several genotypes, resulting in a fine specificity for different human histo-blood groups. Figure  5  illustrates the tree-search for noroviruses together with selected structures of GI-7 genotype in complex with Lewis Y and blood group A tetrasaccharides as described in  [52]. These screenshots also highlight the links with external databases, as well as the details of the cited publication that are available through the green box expansion. As a final comment in this section, note that new virus-related entries in SugarBindDB are very slowly finding their way into the database due to the poor funding of biocuration.
Discussion Bioinformatics tools have become indispensable for processing and analyzing the now popular -omics data. Raw data associated with published articles are routinely submitted in repositories, while dedicated databases are developed to shape the implicit knowledge revealed in analyzed -omics data. Such a setup is now operational to support the enlightened interpretation of new results in genomics, transcriptomics, and proteomics, as well as to maximize data sharing, reuse, and integration. Based on this interoperability, a broad range of automated procedures are implemented to collect and piece together information from databases and support the formulation of new and testable hypotheses. At this stage, glycomics has not quite yet reached that level despite the essential roles of glycans in the mediation of molecular interactions.
In the particular case of virology, this lack is visible. The panoramic view of the different information resources covering interactions between virus and glycans as shown in Figure  2 , should help to improve both the breadth and depth of knowledge. Obviously, the current connectivity between resources is far from being complete and new cross-links are needed for users to make the most of all data disseminated in several resources. We strive to fill that gap. As partially mentioned earlier, near future plans encompass new content in MatrixDB that will enable the creation of cross-links between SugarBindDB and MatrixDB, with a focus on GAG-binding pathogens. Furthermore, in order to make the substrates and products of enzymatic reactions collected in CAZy more explicit, substantial work on harmonizing knowledge representation is underway.
This requires the inclusion of information from the Rhea database to generate the association of each known glycan structure of GlyConnect with its required enzyme toolbox. The main obstacle to this apparently straightforward application lies in the limitations in format sharing between chemical biology, carbohydrate chemistry, and glycobiology. Figure  6  illustrates the situation by highlighting format (in)compatibility between databases. The diversity of information sources is yet again creating barriers of communication. Standards such as SMILES (Simplified Molecular Input Line Entry Specification) and InChi (International Chemical Identifier) have been adopted in chemical biology and cheminformatics, while IUPAC (International Union of Pure and Applied Chemistry) remains the gold reference in biochemistry.
Glycobiology has developed a series of encoding formats that were attempts in a variety of ways to account for the branched structures. In the end, the multiplicity is such that toolboxes of converters have been developed over the years  [53, 54]. Nonetheless, the (glyco-)bioinformatics community has opted for GlycoCT as a structure encoding format  [55]  shared by the majority of databases. Furthermore, it seems that SNFG  [41]  is spreading as a unique code for representing glycans. Despite growing consensus in each community for relying on shared standards, cross-talk is not established between disciplines, and requires the bridging tasks we have undertaken. Moreover, there are missing data that would be very useful for virology. For example, tissue localization of various glycans in vertebrates would be invaluable to correlate with virus tissue infections.
This kind of resource exists for proteins  [56] , but not yet for glycans. Finally, a paradigm shift is also needed to advance the field of glycovirology. For example, a comprehensive survey of known glycosylation in Mononegavirales was recently published  [57]. Had the authors known about ViralZone, the valuable though PDF-static information printed in this article could have been structured in a way to be stored straightaway in the database and enhance virus annotation. It takes bioinformaticians days of work to search the relevant information often hidden in supplementary material of published articles when simple awareness of reference online resources could suffice to motivate authors to disseminate their work through interacting upstream with database developers. For as long as automated data submission procedures are not imposed as they are now for DNA sequence or protein mass spectrometry data, glyco-viro-database improvement relies on researchers' good will to actively share their data.
Conclusions Glycans are prevalent molecules that offer a broad range of interplay with viruses crossing the extracellular space to infect new cells. Bioinformatics resources are critical for the analysis of the diversity of available data. We have provided a broad overview of the bioinformatics landscape of glycan-virus interactions. This has fed a reflection and discussion on the need and the means of increasing interoperability between existing databases as a key to support interaction data interpretation. The next step is surely to focus on bridging more tightly immunology and glyco-virology resources.
Funding: ViralZone is developed by the  Swiss-Prot group  and is supported by the  Swiss Federal Government  through the  State Secretariat for Education, Research and Innovation (SERI) , the  National Institutes of Health (NIH), National Human Genome Research Institute (NHGRI) , and  National Institute of General Medical Sciences (NIGMS)  (grant  U41HG007822 ), and the  Swiss National Science Foundation (SNSF)  (grant  IZLSZ3_148802 ). The development of resources of the glycomics@ExPASy initiative has been supported by the  European Union   FP7 Innovative Training Network  (grant  316929 ) and the  Swiss Federal Government  through the  State Secretariat for Education, Research and Innovation (SERI)  and are currently supported by the  Swiss National Science Foundation (SNSF)  (grant  31003A_179249 ).
ExPASy is maintained by the web team of the  Swiss Institute of Bioinformatics  and hosted at the  Vital-IT Competency Center. MatrixDB is supported by the  Fondation pour la Recherche Médicale  (grant  DBI20141231336 ), and the  Institut Français de Bioinformatique  (call 2015). UniLectin is supported by  ANR PIA Glyco@Alps  ( ANR-15-IDEX-02 ). Overall cooperation between groups is supported by the  Alliance Campus Rhodanien Co-Funds  (http://campusrhodanien. unige-cofunds. ch).
The academic and clinical importance of biomarker development in MND is universally recognized by various scientific consortia and is regarded as a strategic funding priority by MND charities and funding agencies around the world. MNDA, ALSA, ARSLA, NISALS, NEALS, CALSNIC, RMN, JPND, IMNDA, SPF etc. are just some of the many organizations actively engaging in the development of multicenter data repositories, and establishing biobanking infrastructures for MND. Due to the limitations of single markers, it is generally accepted that a panel of biomarkers will most likely aid clinical management, guide care planning, and serve as monitoring markers in clinical trials. It is also increasingly clear that precision individualized therapies will be needed for specific phenotypes and genotypes instead of a "one-drug-for-all" approach.
ACKNOWLEDGMENTS We acknowledge all patients with motor neuron disease, their caregivers, family members, neurologists, basic scientists, and allied health care professionals around the globe who participate in motor neuron disease research. We also acknowledge the contribution of authors from the following universities and research centers;  University of Sussex, University of Athens, Aix Marseille University, Harvard Medical School, Macquarie University Sidney, University of Oxford, Hebei Medical University Shijiazhuang, Lille University, Trinity College Dublin, Cleveland Clinic, University of Sydney, University of Nottingham, Peking University, Northern Ireland Center for Stratified Medicine, University of Alberta, Chinese Academy of Medical Sciences, Icahn School of Medicine at Mount Sinai, University of Strasbourg, Sorbonne University, University of Ulm, The University of Queensland, University of Sydney, University of East Anglia, Vita-Salute San Raffaele University, University of Lisbon, University of Toronto, University Hospitals Leuven, University of Tours, National Institutes of Health US, Mayo Clinic, University of Jena, Brighton  and  Sussex Medical School, University of Regensburg.
FUNDING PB was supported by the  Health Research Board (HRB-Ireland ;  HRB EIA-2017-019 ), the  Andrew Lydon scholarship , the  Irish Institute of Clinical Neuroscience IICN-Novartis   Ireland Research Grant , the  Iris O'Brien Foundation , and the  Research Motor Neuron (RMN-Ireland) Foundation. P-FP was supported by the  French Association for Myopathies (AFM-Telephon) , the  Institute for Research in Brain and Spinal Cord (IRME ) the  French Association for Research in ALS (ARSLA)  and the  Target ALS Foundation.
36 m telescope at Kumeu Observatory (Kumeu) in Auckland, the 0. 36 m telescope at Turitea Observatory (Turitea) in the R-band, and the 0. 36 m telescope at Possum Observatory (Pos) without a filter. Pos data were excluded from the analysis because they are flat over their two days of observations, giving no useful constraint on the model. The MiNDSTEp team monitored the events using the Danish 1. 54 m telescope located at ESO's La Silla observatory in Chile, with a simultaneous two-colour instrument (wide visible and red; See Figure  1  of  Evans et al. 2016). This event was also observed in the Bessell I-band by the 50-inch H127 telescope at the University of Tasmania (TAS) Photometry of the OGLE, MOA, KMTNet, LCO, 1 The Spitzer observation is a part of a large program measuring the Galactic distribution of planets in different stellar environments  (Calchi Novati et al.
2001 (MOA) ,  Albrow et al. 2009 (KMTNet, LCO, Auckland, TAS and Kumeu) and Bramich 2008 (Danish). The CT13, Pos and Turitea images were reduced using DoPHOT  (Schechter et al. 1993). The Spitzer data were reduced using specialized software for crowded fields  (Calchi Novati et al. 2015b ).
ρ * = θ * /θ E is the scaled source radius associated with the finite-source effects, where θ * is the source angular radius. Finally, (π E,N , π E,E ) are the north and east components of the microlens parallax vector, respectively. Furthermore, we also introduce two flux parameters ( f s,n , f b,n ) on account of the possible blending effect for each observatory. Specifically, the observed flux for each dataset is modelled as F lens,n (t) = A n (t) f s,n + f b,n , where A n (t) is the magnification at the n-th observatory as a function of time, which is characterized by the six microlensing parameters mentioned before. Due to the small separations between different ground-based sites compared with the projected Einstein radius, we approximate one magnification parameter A ⊕ (t) for all the ground-based sites.
ACKNOWLEDGEMENTS S. -S. L. , W. Z. and S. M. acknowledge support by the  National Science Foundation of China  (Grant No. 11821303  and  11761131004 ). The OGLE has received funding from the  National Science Centre, Poland , grant  MAESTRO 2014/14/A/ST9/00121  to A. U. Work by Y. S. was supported by an appointment to the  NASA   Postdoctoral Program  at the  Jet Propulsion Laboratory, California Institute of Technology , administered by  Universities Space Research Association  through a contract with  NASA. D. H. acknowledges support by the  National Science Foundation  ( AST-1717000 ). This research has made use of the KMTNet system operated by the  Korea Astronomy and Space Science Institute (KASI)  and the data were obtained at three host sites of CTIO in Chile, SAAO in South Africa, and  SSO in Australia.
The  MOA  project is supported by  JSPS KAKENHI  Grant Number  JSPS24253004 ,  JSPS26247023 ,  JSPS23340064 ,  JSPS15H00781 ,  JP16H06287 , and  JP17H02871. Work by A. G. was supported by  AST-1516842  from the  US NSF  and  JPL  grant  1500811. Work by P. F. and W. Z. was supported by  Canada-France-Hawaii Telescope (CFHT). S. D. acknowledges Projects  11573003  supported by the  National Science Foundation of China (NSFC). Work by C. H. was supported by the grant ( 2017R1A4A1015178 ) of  National Research Foundation of Korea. The research has made use of data obtained at the Danish 1. 54 m telescope at  ESOs La Silla Observatory. Y. T. acknowledges the support of  DFG priority program SPP 1992 'Exploring the Diversity of Extrasolar Planets'  ( WA 1047/11-1 ). CITEUC is funded by  National Funds through FCT -Foundation for Science and Technology  (project:  UID/Multi/00611/2013 ) and  FEDER -European Regional Development Fund  through  COMPETE 2020 -Operational Programme Competitiveness and Internationalization  (project:  POCI-01-0145-FEDER-006922 ).
L. M. acknowledges support from the  Italian Minister of Instruction, University and Research (MIUR)  through  FFABR 2017  fund. J. -P. B. was supported by the  University of Tasmania  through the  UTAS Foundation  and the endowed Warren Chair in Astronomy. J. -P. B. acknowledges the financial support of the  CNES  and the  ANR COLD-WORLDS ,  ANR-18-CE31-0002. Development of the Greenhill Observatory was supported under the  Australian Research Council's LIEF funding scheme  (project  LE110100055 ). We thank  C. Harlingten  for the use of the H127 Telescope. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium).
Data analysis A two-way analysis of variance (ANOVA) was used to elucidate the effects of hypoxia and suspension and their possible interactions (hypoxiaxsuspension) on the outcomes. When interactions in the two-way ANOVA were statistically significant, pairwise comparisons were investigated using Newman Keuls post-hoc test. Whenever interaction was not significant, main effect of hypoxia or suspension was thus reported. Results of statistical tests with p≤0. 05 were considered significant (Statview software, SAS Institute Inc. , Cary, NC, USA). All data are expressed as mean±standard deviation (SD).
Acknowledgments The authors thank  Catherine André ,  Laurent Begot ,  Dominique Bonneau ,  Xavier Butigieg ,  Bernadette Guerton  and  Sylvie Renault  for their valuable technical assistance in our laboratory. The authors are also grateful to  Dr. Veronique Chastres  for her statistical expertise. This work was supported by a grant from  DGA  ( Délégation Générale pour l'Armement ; Grant  PDH-1-SMO-0205  to.
Despite California being a highly seismic prone region, most homeowners are not covered against this risk. This study analyses the reasons for homeowners to purchase insurance to cover earthquake losses, with application in California. A dedicated database is built from 18 different data sources about earthquake insurance, gathering data since 1921. A new model is developed to assess the takeup rate based on the homeowners' risk awareness and the average annual insurance premium amount. Results suggest that only two extreme situations would lead all owners to cover their home with insurance: (1) a widespread belief that a devastating earthquake is imminent, or alternatively (2) a massive decrease in the average annual premium amount by a factor exceeding 6 (from USD 980 to 160, 2015 US dollars).
Last, this study is also innovative by separately modelling the contribution of the risk perception and the premium amount to the level of earthquake insurance consumption. In the first section, data are collected from several sources, processed and summarized in a new database. A take-up rate model is then developed by introducing the two following explanatory variables: the average premium amount and the subjective annual occurrence probability, defined as the risk perceived by homeowners of being affected by a destructive earthquake in 1 year. The perceived annual probability of occurrence is then studied from 1926 to present, in the next section. Finally, the last section is dedicated to analysing the current low earthquake insurance take-up rate for California homeowners.
Data collection and processing Developing such a model, even at the state level, faces a first challenge in data collection. Despite the California earthquake insurance market having been widely analysed, data volume before the 1990s remains low and extracted from scientific and mass media publications which refer to original data sources that are no longer available. As a consequence, data description is often sparse and incomplete and values can be subject to errors or bias. Consequently, data quality of each dataset has to be assessed. Here, it is done on the basis of the support (scientific publication or mass media), the data description (quality of information on the data type and the collecting process) and the number of records. The datasets with the highest quality are used and presented in Table  1  (values are available in the Supplement).
In order to expand the historical database, they are also estimated in an indirect way for the following periods  : 1926-1930, 1956, 1971-1972, 1976-1978 and 1989-1995, leading  us to consider additional data (Table  1 ) and to use linear regressions (Fig. 1 ). By definition of the average premium rate (p N ), P N can be approximated by the product of p N and the average sum insured (ASI N ). The latter is estimated from the real building cost index (RBCI N ), which is an economic index (base: 31 December 1979 = 100) capturing the evolution of the cost of building construction works  (Shiller, 2018). The following linear regression has been calibrated on data from Table  1  between 2006 and 2015: log 10 (ASI N ) =0. 0233 × RBCI N + 3. 7554 R 2 = 0. 84 Fig. 1a , (1) where ASI N is in 2015 US dollars and R 2 is the coefficient of determination.
When the average premium rate corresponds to the residential line of business (p Res N ), it can be converted into p N with the following linear regression built on the CDI database between 2006 and 2016 (Table  1 ): p N = 0. 96 × p Res N R 2 = 1 Fig. 1b. (2) Again using the CDI database between 1996 and 2016 (Table 1), the following linear regressions have also been developed between P N , t N , and W N and the corresponding metrics for the residential line of business (P Res N , t Res N and W Res N ). Before 1996, the total premium amount for earthquake insurance, mentioned in CDI (2017b) reports, was about the earthquake line of business, and in terms of total earned premium (E N ). While the total written premium (W N ) corresponds to the total amount of premium paid by policyholders to insurance companies during the year N, E N is the amount of premium used to cover the risk during the year N.
Eq N from E Eq N for the earthquake line of business, the following relationship has been defined based on data from the CDI (2017a, b) database and over the period 1992-2016 (Table  1 ): (7) W Eq N = E Eq N + E Eq N+1 2 R 2 = 0. This strong assumption was used by  Garamendi et al. (1992)  and was verified for the loss after the 1994 Northridge earthquake reported by  Eguchi et al. (1998). Lastly, when variables cannot be inferred from other variables, they are estimated based on the annual variation for P Res N , P Res N = P Res N -1 × P Res N P Res N -1 , (8) and the biennial variation for t Res N , t Res N = t Res N-2 × t Res N t Res N-2 , ( 9 ) where Y X is equal to the ratio Y over X. Furthermore, the variation in the total written premium for the residential line of business ( W Res N W Res N-1 ) is linked to P Res N P Res N -1 , t Res N t Res N -1 and Fi Res N Fi Res N-1 as follows: W Res N W Res N -1 = W Res N W Res N-1 = P Res N × Nb Res N P Res N -1 × Nb Res N -1 = P Res N × Fi Res N × t Res N P Res N -1 × Fi Res N -1 × t Res N-1 = P Res N P Res N -1 × Fi Res N Fi Res N -1 × t Res N t Res N -1.
( 10 ) As we have no data regarding the annual variation in the number of fire insurance policies for the residential line of business ( Fi Res N Fi Res N -1 ) before 1996 (Table  1 ), it is assumed to be equal to the variation in the California population Pop N : Fi Res N Fi Res N -1 ≈ Pop N Pop N -1. ( 11 ) Figures 1 and 3 illustrate the goodness of fit of the regressions developed (Eqs. 1 to 6) and the variations in the population compared to the number of fire insurance policies between 1996 and 2016 (Eq. 11), respectively. Finally, values of P N , t N and W N collected (Table  1 ) and estimated in this study (Eqs. 1 to 11) are aggregated into a new database presented in Table  2. Financial amounts are converted into 2015 US dollars using the Consumer Price Index (US Department of Labor, 2018).
The data quality of each variable is also assessed at the weakest data quality used (Table  1 ), by as many levels as the number of equations used to assess it (listed in the column equation ID). For example, the annual average premium P 1926 has been calculated from the premium rate for the residential line of business p Res 1926 (Table  1 ) and Eqs. (  1 ) and (  4 ). The associated data quality is C (Table  2 ) because the data quality of p Res 1926 is A (Table  1 ), downgraded by two levels for the two equations used. The model developed in the next section uses this new database to estimate the take-up rate for the California homeowners (t N ) from the average premium amount (P N ) and another variable capturing the relative earthquake risk awareness. 3 Model development for the period 1997-2016 In order not to presume a linear trend between the consumers' behaviour and explanatory variables (e.
Category Period  The historical earthquake database used is taken from the UCERF3  (Field et al. , 2013; Felzer, 2013). nitude greater than M5 reached the highest peak since 1855 between 1984 and 1998, as illustrated in Fig. 7. The sequence includes in particular the 1983 Coalinga and the 1987 Whittier Narrows earthquakes. This specific temporal burst in seismic activity may have contributed to homeowners' rising risk awareness (Table  4 ). Some major socio-economic events also had an impact on r N (Table  4 ), such as the 1929 Great Depression and the post World War II economic expansion. Insurance regulation acts also have an impact. During the period 1984-1985, the California senate assembly voted on the Assembly Bill AB2865  (McAlister, 1984) , which required insurance companies to offer earthquake coverage.