dataesr/scientific-paragraphs-categorization huggingface dataset:
62 to 0. 76 for serious infection, from 0. 65 to 0. 75 for MI, and from 0. 63 to 0. 79 for stroke. The corresponding calibration plots from the data-driven approach are presented in Fig. E. 1-E. 16 in Appendix E. These plots showed adequate calibration for all three outcomes. As expected, the models tend to underestimate or overestimate risk in databases with a higher or lower incidence, respectively  [29]. For example, the model for serious infection underestimated the risk in the IQVIA US Hospital database, where the outcome incidence was more than tenfold higher than the outcome incidence in the development database. To account for this, the models can be recalibrated for use in these databases. The final data-driven models for serious infection, MI, and stroke, including intercept, coefficients, and OMOP CDM concept IDs, can be found in Tables F.
Discussion In this study, we developed and validated prediction models for a variety of adverse health outcomes in RA patients initiating first-line MTX monotherapy. For serious infection, MI, and stroke, the models demonstrated good performance across multiple databases. Internal validation of these models resulted in AUCs of greater than 0. 70 and adequate calibration. External validation of these models resulted in good discrimination, where the data-driven approach of predictor selection (age groups, sex, conditions, drugs) consistently outperformed the benchmark (age groups, sex). This shows that conditions and drugs extracted from routinely recorded data have added value in identifying patients at high risk of serious infection, MI, and stroke. The models showed adequate calibration as well, although for some databases, the models may benefit from recalibration.
For uterine cancer, we were not able to develop models using our data. For this outcome, more data are required. For leukopenia, pancytopenia, opportunistic infection, all infection, colorectal cancer, and breast cancer, we did not externally validate the developed models since they did not discriminate well (AUC < 0. 65) on internal validation. We developed our models using large-scale claims and EHR databases that contain routinely recorded patient information. We chose for a data-driven approach and considered all conditions and drugs in a patient's history as candidate predictors. It is interesting to note that this large set of conditions and drugs in a patient's history also included comedication with nonsteroidal anti-inflammatory drugs and  glucocorticoids, which may capture aspects of RA-specific variables such as disease activity indicators that were not explicitly considered as candidate predictors.
It is important to note that our study was focused on prediction and not on evaluating individual predictor associations; it may be misleading to highlight individual predictors as having predictive value by themselves, since this can lead to causal interpretation  [30]. It could be interesting to investigate whether explicitly considering certain RA-specific variables as candidate predictors would improve prediction performance. A potential limitation of our study is that we were not able to investigate this using our data. A potential limitation of our study is that routinely recorded data can be misclassified. If a candidate predictor or an adverse health outcome was not recorded in a patient's history, we assumed but could not be certain that there had not been such an event. Variation in prediction performance across databases may reflect differences in how data are captured.
The 7 days after index offset was chosen to avoid any other DMARDs that occurred at the index date but were entered late in the record. Although less likely, it is possible that a second DMARD that occurred at the index date was entered in the record with a delay longer than 7 days after index. Our study also has several strengths. To the best of our knowledge, this study is the largest cohort study to date on predicting a variety of adverse health outcomes in RA patients. By developing models using data mapped to the OMOP CDM, we were able to include 14 databases for external validation of the models. The scale of the data allowed us to investigate a variety of adverse health outcomes and validate the models across multiple international databases. Even though more data are still needed for some of the outcomes, our study shows the feasibility of this approach.
Additionally, we have provided software packages that contain the analytical source code used to develop the models and to externally validate our developed models on databases with data mapped to the OMOP CDM. The models developed and internally validated for serious infection, MI, and stroke using a large-scale USA claims database (Optum Claims) showed good performance across multiple claims and EHR databases. We do not believe we have sufficient evidence to recommend the use of the models in clinical practice, but research could be conducted to prove their value as implemented in administrative or EHR software to generate automatic reminders for individuals at high risk of these outcomes. The models are based entirely on routinely recorded data, minimizing the burden to the clinician. However, regulatory approvals would be required before this can be considered, which is beyond the scope of our current work.
Funding This activity under the  European Health Data & Evidence Network (EHDEN)  has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No. 806968. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA. The sponsor of the study did not have any involvement in the writing of the manuscript or the decision to submit it for publication. The research was supported by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). DPA is funded by a  National Institute for Health Research Senior Research Fellowship. TDS is funded by the  Department of Health of the Generalitat de Catalunya  under the  Strategic Plan for Research and Innovation in Health  (PERIS;  SLT002/16/00308 ).
6m 1996- 2020 IQVIA Australia EMR IQVIA Australia Australia EHR Outpatient / General population 6m 2006- 2020 IQVIA Disease Analyser Germany IQVIA Germany Germany EHR Outpatient / General population, public and private 37m 1992- EMR insurance 2020 IQVIA Hospital US Charge Master IQVIA US USA EHR Inpatient and outpatient hospital encounters, including 86m 2007- Hospital Emergency Room visits / General population 2020 IQVIA LPD France IQVIA LPD France EHR Outpatient / General population 7. 8m 1994- France 2020 IQVIA UK THIN IMRD EMR IQVIA THIN UK EHR General population / Primary care records with 15m 1989- hospitalization / referral information 2020 IQVIA US Ambulatory EMR IQVIA US USA EHR Outpatient / General population 49m 2006- Ambulatory 2020 Japan Medical Data Center JMDC Japan Claims Society-Managed Health Insurance 12m 2005- 2020 Optum® De-Identified Optum Claims USA Claims Privately insured 87m 2001- Clinformatics® Data Mart Database 2020 Optum® De-identified Electronic Optum EHR USA EHR Privately insured 100m 2006- Health Record Dataset 2020 The Information System for Research SIDIAP-H Spain EHR Primary care linked (partially) to inpatient data 5.
Introduction In addition to ultra-fine particles produced naturally (as by-products of wildfires, volcanic eruptions, and other natural processes) or unintentionally released as a consequence of human activities (present in polluting emissions, such as welding fumes, cigarette smoke, aircraft waste gas, or diesel exhaust), engineered nanomaterials are produced purposefully to take advantage of their unique physical, chemical, and/or biological characteristics associated with their nanostructure. The remarkable development and applications of nanomaterials lead to an increasing release of these materials in the environment, and, consequently, ecosystems and humans are increasingly exposed to them, while the adverse effects they may elicit are not always fully characterized. By January 2022, the Nanodatabase  [1]  counted 5224 products containing nanoparticles available on the European market.
An example is the contamination by endotoxins or lipopolysaccharides (LPSs) that cause acute inflammatory responses in humans  [55]. Another important factor inducing variation in nanotoxicity data is the medium in which the nanomaterials are dispersed. This can alter the agglomeration/aggregation state of the nanomaterial, which will subsequently have an impact on the cell uptake and toxicity of the nanomaterial  [15]. This is why it is recommended to characterize the physicochemical properties of nanomaterials after dispersion in a cell culture medium. Consequently, possible nanomaterial-assay interactions should be carefully assessed. Furthermore, each biological endpoint should be evaluated with multiple assays which ideally supply complementary information and have different assay and detection principles, to validate the obtained results  [15].
They are usually used for a series of often related compounds: datasets. This approach assumes that nanomaterials with similar molecular structures will have similar biological effects  [153]. Consequently, such models are able to predict a nanomaterial's behavior and effects in biological environments based on its physicochemical properties and molecular descriptors without experimental testing, which allows saving time and money  [56, 148, 154]. Such predictions could also permit researchers to streamline and prioritize toxicological assays and focus on the most promising (i. e. , safer) nanomaterials  [114, [154] [155] [156] [157]. The concept of QSAR was proposed a century ago for chemical compounds [158,159] and has been extensively used in the areas of drug discovery and chemical toxicity modeling, but its application to nanomaterials is not without challenges due to their specificities.
Indeed, it requires the development of nano-specific descriptors and curated experimental datasets  [147, 150, 153, 154, 160]. As an example, a molecular descriptor can be experimentally determined or calculated  [148]. In the latter case, we should ensure that the calculated descriptors are correlated with experimental values; otherwise, they could have a strong impact on the validity of the model. Therefore, descriptors should be developed and tailored using the material's specific information provided by the physicochemical characterization (experimentally determined), rather than using idealized structures  [154, 160]. Regarding the datasets necessary to develop QSAR models, they should contain a sufficient number of diverse nanomaterials. These two conditions (diversity and a sufficiently high number of samples) are currently difficult to meet, except when HTS methods are used  [154].
Indeed, there are only small databases on experimentally measured basic endpoints  [161]. This is partly due to the fact that, although many nanotoxicological studies are available, they are not standardized, resulting in heterogeneous data, and the few nanomaterial databases available are often not suitable for building models  [161]. In addition, nanomaterials represent very structurally diverse groups of chemicals, making it difficult to build a significant dataset of structurally related nanomaterials  [152]. To sum up, a large quantity of experimental data of a high quality should be available to develop a reliable nanoQSAR model  [150, 152, 156, [162] [163] [164] [165] [166]. Stronger collaborations between experimentalists and modelers should result in more useful datasets and allow QSAR predictive models to improve their potential [154,  161, 167] , as shown in Figure  3.
The need for appropriate measures of goodness of fit, robustness, and predictivity indicates that the internal performance (as represented by the goodness of fit and robustness) and predictivity of a model (as determined by external validation) should be carefully assessed  [154]. QSAR models have been developed in particular for metal and metal oxide nanomaterials  [69, 157, 169]. Using a dataset of 17 metallic oxide particles differing by their chemical composition, it was found that their toxicity to Escherichia coli bacteria could be predicted on the basis of their chemical nature only thanks to calculated or experimental descriptors such as metal electronegativity and cation charge  [155, 170]. Surprisingly, dimensional or shape descriptors did not play any role. These results were confirmed in a model of mammalian cells, the HaCaT cell line, regarding the toxicity of 18 metal oxide nanoparticles  [152, 171].
More recently, QSAR models for the prediction of the inflammatory potential of metal oxide nanoparticles were developed based on a dataset of 30 samples  [20]. Similarly, a QSAR model able to predict the cellular uptake of 109 functionalized metal oxide nanoparticles to pancreatic cancer cells (PaCa2) has been recently developed  [172]. A predictive QSAR model for the cellular association of gold nanoparticles based on their physicochemical properties and protein corona fingerprints was developed and validated using a dataset of 105 nanoparticles  [173]. Regarding the statistical methods used for QSAR models, different types of machine learning algorithms can be used (Figure  3 ), including principal component analysis (PCA), multiple linear regression (MLR), partial least squares (PLS) methods, random forests (RFs), support vector machines (SVMs), k-nearest neighbors, Bayesian networks, and artificial neural networks (ANNs)  [20, 149, 166, 172, [174] [175] [176] [177] [178] [179] [180] [181] [182] [183] [184].
Data currently available from these cratons suggest the magnitude of D 33 S observed in sedimentary pyrites between 2. 8 and 2. 5 Ga may be related to Archean paleogeography. The relatively well-studied Kaapvaal  (Hu et al. , 2003; Kamber and Whitehouse, 2007; Papineau et al. , 2007; Guo et al. , 2009; Ono et al. , 2009a Ono et al. , , 2009b;; Zerkle et al. , 2012; Farquhar et al. , 2013; Fischer et al. , 2014; Guy et al. , 2014) , Pilbara  (Ono et al. , 2003; Ohmoto et al. , 2006; Farquhar et al. , 2007; Kaufman et al. , 2007; Philippot et al. , 2007; Partridge et al. , 2008; Thomazo et al. , 2009a; Gregory et al. , 2015; Muller et al. , 2016)  and Yilgarn  (Xue et al. , 2013; Chen et al. , 2015; Gregory et al. , 2016; Caruso et al. , 2018)  cratons exhibit wide ranges in D 33 S, from À5‰ to 11.
The minor isotopes 33 S and 36 S are reported in capital delta notation D 33 S, as described above, and D 36 S = d 36 S -1000 Â [(d 34 S/1000 + 1) 1. 89 -1]. The D 33 S and D 36 S data were reported regarding the Canyon Diablo Troilite (CDT) international standard, with analytical errors of ± 0. 01‰ and ± 0. 2‰ (2r), respectively, based on the reproducibility of analyses of the international standard IAEA S-1. The D 33 S values determined by fluorination were compared with results from the SO-SO 2 method and D 36 S values were used to confirm S-MIF or S-MDF relationships.
g. ,  Zahnle et al. , 2006; Domagal-Goldman et al. , 2008; Zerkle et al. , 2012)  or intensification of volcanic SO 2 inputs  (Philippot et al. , 2012; Thomazo et al. , 2013) , increasing UV photolysis. Another possibility to explain higher positive D 33 S would be a consequence of progressive seawater oxygenation and oxidative sulfur cycling during early diagenesis (e. g. ,  Fakhraee et al. , 2018). Therefore, our dataset might be non-specific about the processes that resulted in the larger range of S-MIF. Considering that redox sulfur cycling between S 0 and sulfate pools was limited in the late Archean ocean  (Ono et al. , 2009a) , and moderate amounts of CH 4 form more S 0 aerosols  (DeWitt et al. , 2010) , it is reasonable to argue for a decrease of organic haze. Furthermore, as mentioned before, changes in D 33 S could also be explained by mixing sulfur derived from biologic and photolytic reactions  (Farquhar et al.
Acknowledgments We thank  São Paulo Research Foundation  ( FAPESP grants   2016/02933-2  to ABS,  12/15824-6  to EPO and  2015/16235-2  to PP) and the  Agouron Institute  (to WPG) for financial support. We also thank  Jaguar Mining Co  for technical and logistic support. Special thanks to  Armando José Massucatto  and  Romulo Thiago Cruz  for their assistance at  Roça Grande  mine. Thank you to  Brooke Vander Pas  (IUPUI) and  Eric Haitt  ( University of Wisconsin-Oshkosh ) for preparing the thin sections. We appreciate helpful comments and discussions with  Wil Leavitt  and  Mariana Brando Soares  that helped improve our interpretations. Comments and detailed revisions from  Nivea Magalhães  and an anonymous reviewer significantly improved the manuscript.
b) 23 National Rare Disease Health Networks (FSMRs) The FSMRs were set up in 2014-2015, as part of PNMR2. They each cover a broad but coherent field of rare diseases. Each FSMR groups the stakeholders involved in a given area, including caregivers, researchers, patient representatives and manufacturers. Each d) The BaMaRa national register, developed by the BNDMR (National Data Bank for Rare Diseases), has now reached the deployment stage. This process involves staff training, data entry checks, data exhaustiveness checks, administrative follow-up and harmonisation of coding. Connected to the National Health Data System (SNDS), the BaMaRa register aims to develop medical-economic knowledge on rare diseases and to conduct epidemiological studies in order to better assess the prevalence and incidence of rare diseases.
e) Orphanet Created in France in 1997, Orphanet was initially developed to gather the scarce information concerning rare diseases, to improve the diagnosis and the treatment of the patients. Orphanet has now become a network of 41 countries throughout Europe and the world and represents the reference source of information on rare diseases. The objectives of Orphanet are to provide a common language in rare diseases with the Codes ORPHA to have a better comprehension between the actors; provide high quality data and expertise on rare diseases to allow an equal access to knowledge, and guide users in the field of rare diseases; contribute to a better understanding of rare diseases. f) The French national programme on Rare Disease Cohorts, RaDiCo. This programme aims to establish national and international cohorts of patients with rare diseases.
In line with these priorities, AMED has put in place the following approach: a) The Initiative on Rare and Undiagnosed Diseases (IRUD) implemented by AMED in 2015, is part Japan's Rare Disease Plan  (37). IRUD is a national consortium that brings together patients, physicians and researchers, as part of the process below: b) Launched by AMED in February 2017 and funded by AMED and the Ministry of Health, Labour and Welfare (MHLW), the National Platform for the Rare Diseases Data Registry of Japan (RADDAR-J) collects and structures data from research projects into rare diseases  (38). The experts involved in the IRUD consortium are connected to this registry, which includes data relating to over 2,000 undiagnosed patients  11. The database is designed to be linked to international platforms in compliance with the standards set out by the IRDIRC consortium as part of the Automatable Discovery and Access-Matrix  (39).
(22)ected system is able to secure data(21). It provides a global link between patients with identical genotypes and phenotypes, as well as clinicians and scientists working on similar cases. PhenomeCentral is funded by the CIHR IG, the Ontario Genomics Institute, and the Natural Sciences and Engineering Research Council (NSERC). It is a partner of the NIH, RD-Connect (an EU-funded integrated platform) and the International Rare Diseases Research Consortium, funded by the US and the EU. f) Ontario Genomics is a not-for-profit organisation supported by the CIHR IG and the government of Ontario. It brings together companies, researchers, policy makers and funders to generate funding and accelerate the development of genomics(22).
Located in London, the CGT Catapult has a process development laboratory with dedicated facilities for clinical trials and reproducibility studies. Further, the CGT Catapult site in Stevenage, which opened in 2018 for a cost of €92 million (of which €4 million funded by the European Union), is an industrial-scale manufacturing centre. This centre is dedicated to manufacturing gene and cell therapy products in collaboration with industry (16). B. NORTH AMERICA5. CANADA: globally renowned expertise in gene identificationAlthough Canada only developed a National Plan for Rare Diseases in 2015, it has developed internationally recognised expertise in identifying the genes involved in rare diseases. a) Set up in 2000, Genome Canada is a not-for-profit organisation supported by the Canadian Institute of Health Research, Institute of Genetics (CIHR IG) (17).
It coordinates federal and provincial studies through six genome centres with the following missions:  develop ties between public and private players.  invest in major scientific and technological projects. In 2017, for example, Genome Canada invested €7 million in the Silent Genome Project led by researchers from the University of British Columbia, whose purpose is to create a database of genetic variations in indigenous populations around the world 6 (18). b) The a national network coordinating research projects to gain a clearer understanding of the biology underlying rare diseases in children. It is funded by Genome Canada (19). Present across Canada, the RDMMs are managed by the Maternal Infant Child & Youth Research Network (MICYRN), which brings together 23 networks of experts and 20 hospitals and academic research centres specialising in maternal and childhood diseases.
for Computational Medicine at the Hospital for Sick Children in Toronto, this (22)ected system is able to secure data(21). It provides a global link between patients with identical genotypes and phenotypes, as well as clinicians and scientists working on similar cases. PhenomeCentral is funded by the CIHR IG, the Ontario Genomics Institute, and the Natural Sciences and Engineering Research Council (NSERC). It is a partner of the NIH, RD-Connect (an EU-funded integrated platform) and the International Rare Diseases Research Consortium, funded by the US and the EU. f) Ontario Genomics is a not-for-profit organisation supported by the CIHR IG and the government of Ontario. It brings together companies, researchers, policy makers and funders to generate funding and accelerate the development of genomics(22).
Methods: Improving constraints on r and Galactic dust The possible improvement from Prime-Cam on the constraint on r beyond the SO-SATs is analyzed as follows. 2  We first simulate realistic foreground sky maps at Prime-Cam and SO frequencies using the PySM simulation package  (Thorne et al. 2017a ). We then add Gaussian simulations of CMB (without the primordial B -mode but with the scalar E -mode and the lensing-induced B -mode) and noise (including white and 1/f components extrapolated from on-sky measurements; see  Choi et al. 2020b ; Simons Observatory Collaboration 2019) modulated by the realistic survey hit-count maps of SO-SAT and FYST, and compute the auto-and cross-frequency power spectra of all the simulated maps using the NaMaster package  (Alonso et al. 2019)   3  with the "purify B" option and the "C2" apodization scheme (10 deg).
(2019) , the WFS should detect 300-600 blazars above 400 mJy in each epoch. This flux density is sufficient to measure (3 σ) excess variance at the roughly 10 % level. This would generate a rich data set to explore AGN science. For instance, neutrino experiments like the IceCube Neutrino Observatory are now routinely detecting new astrophysical neutrinos above 0. 1 TeV and localizing them to a few square degrees (e. g. , the neutrino identified with the blazar TXS 0506+056; IceCube  Collaboration et al. 2018). The FYST variable blazar sample will provide a powerful real-time resource for identifying flaring blazars that might be generating these neutrinos.