dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantification and Statistical Analyses of Proteomics Data All MS/MS data were processed using MaxQuant (version 1. 6. 17; Max-Planck-Institute of Biochemistry, Martinsried, Germany). The precursor ions mass tolerances were set to 20 and 10 ppm for the first and main searches, respectively. A fragment tolerance of 20 ppm was applied. Oxidation (M) and acetylation (protein N-term) were allowed for variable modifications and carbamidomethylation (C) for the fixed modification. The maximum number of missed cleavages by trypsin was limited to two. MS/MS data were analyzed using the extracted database (2021) from UniProt with the Mus musculus taxonomy (55,315 entries). Peptide-spectrum matches (PSMs) and protein identifications were validated using a false discovery rate (FDR) threshold of 1%. Differences in peptide and protein abundance were measured using the fast label free quantitation (LFQ) algorithm with a minimum ratio count of 2, classic data normalization, and the match between run option.
Unmodified unique and razor peptides were used for each protein quantification. Statistical analyses of proteomic experiments were performed using Perseus software (version 1. 6. 15; Max-Planck-Institute of Biochemistry, Martinsried, Germany). Missing values were imputed from a normal distribution with a width of 0. 3 and a down-shift parameter of 1. 8. A two-way ANOVA test, with supplementation and stress as factors, was applied. The three generated lists of differential proteins for stress, supplementation and interaction Stress × Supplementation (p < 0. 05) were independently classified using unsupervised Bayesian clustering  [18]. Proteins were classified according to their expression among the four groups, and each generated cluster was used separately for the analysis of gene ontology (GO) term enrichment.
For several types of physical activities, the obtained experimental data are provided in terms of contact area, pressure and/or force, expressed as body weight percentage (%BW). For example, for daily-life movements, the contact force acting on the hip joint and the tibial plateau measured with implantable sensors ranges between 107% BW and 346% BW. 18, 20, 21  Furthermore, it is important to note that this contact force is not evenly distributed over the cartilage surface. Indeed, the sensor surface is subject to a pressure that varies locally between À0. 13 MPa (the minus sign indicates an attractive or cohesive force) to +7. 14 MPa (the positive value reveals a compressive force) during standing. 20 Few studies evaluated AC deformation, and most of them used magnetic resonance imaging (MRI).
81, 82 Moo and colleagues' study suggest that rather than the intrinsic quality of the collagen fibers (which remained constant), early stages of OA disorganize collagen fibrous network. To be valuable, these complex models need to be initialized by setting several non-trivial parameters, such as the fixed charge density, the fluid fraction and the fibril orientation. Moreover, huge datasets that take into account different mechanical loads are required for the validation of these models. In their review, Klika et al. describe such complex models to precisely reproduce AC behavior by taking into account its composition and structure. 126.
ACKNOWLEDGMENTS We gratefully acknowledge funding support from the  University of Montpellier ,  Inserm ,  Cartigen plateform and Occitanie region. This work benefitted from a  French State grant  managed by the  French National Research Agency  under the Investments for the Future programme (reference n  ANR-10-LABX-20 ). This work has received financial support from  CNRS  through the  MITI interdisciplinary programs (AAP "Osez l'interdesciplinarité 2018 ",  MoTiV  Project).
sgn and min are sign and minimum functions respectively. Lastly, terrain is handled by looking up a pre-processed k-dimensional tree indexed database of heights and the model is able to discriminate and resolve areas of wetting and drying. The scheme has been tested against numerous benchmark cases, including standing waves, sinusoidal wave propagation over a bar, the Tohoku tsunami of 2011 and its dispersive features, viscous hydraulic jumps, and breaking Stokes waves (http://basilisk. fr/src/layered/nh. h#usage). Able to provide accurate solutions for surface gravity waves across a wide range of scales, the multilayer scheme is ideal for resolving the evolution of an explosive source initial condition, resulting wave propagation and shore run-up. (Popinet, 2020).
Laboratory-scale validation To determine suitability of the numerical method for use in modelling a submarine disturbance, validation against a suitable case study is required. Prins (1958)  conducted a flume experiment investigating surface waves produced from an instantaneously raised or depressed column of water. This case is replicated using the multilayer scheme and, additionally, the N-S/VOF, Saint-Venant and SGN schemes for full numerical comparison. Unless otherwise specified, all length units in this section only are in imperial standard for consistency and ease of comparison with the Prins (1958) dataset. The experimental set up involved a column of water at rest of extent L, height from equilibrium Q and in a one-foot wide flume of water depth h; these parameters were varied by steps: L = 2, 1, 1/3 ft; Q = ±0.
Wavefield classification All simulated cases are plotted in Figure  5  by the size of initial disturbance relative to water depth using a nonlinearity parameter |Q| h against a dispersivity parameter k*h where k*= 2π 2L. Six additional simulations were run outside of the original experimental parameter space to expand the model dataset reach. As done by  Prins (1958) , the +Q runs are categorised into groups with similar wavefield characteristics starting with strong oscillatory properties (blue) where k*h > 10 |Q| h , tending through increasingly solitary wave properties (purple and green) once k*h ≤ 10 |Q| h until a succession of diminishing amplitude solitary waves result where k*h < |Q| h (orange). Beyond this region, the initially generated bore survives far enough down the numerical flume before it would likely separate (black).
Field-scale validation The next stage is to assess use of the underwater explosion models of Sect. 2. 1 within the numerical scheme. To do this, we utilise datasets from the Mono Lake test series in 1965, conducted by the Waterways Experiment Station, and documented by  Walter (1966) ;  Wallace and Baird (1968) ;  Whalin et al. (1970) ;  Pinkston et al. (1970). This was one of the largest chemical explosive test series designed to investigate subsequent water wave generation and shore effects. A series of ten approximately 9,250 lb (∼4,196 kg) spherical TNT charges were detonated off the south shore of Mono Lake, California. The test area is illustrated in Figure  6a , and this also shows the location of wave gauges arranged in four radials directed away from ground zero (GZ), along with contoured terrain.
The sole outflow is the Waikato River, controlled by gates at the largest settlement on the lake, Taupō, which leads to numerous further hydroelectric dams downstream. Surrounded by abundant geothermal resources, strong trout fishing and agricultural industries, the area also boasts plentiful tourism opportunities, hosting over one million tourists each year, and is of great cultural significance accompanied by the surrounding land. In building a model representing an example tsunamigenic explosive eruption in Lake Taupō, it was required to build a terrain dataset by combining a bathymetric model of the lake  (Rowe et al. , 2002)  with an elevation model generated using LiDAR datasets from the Waikato Regional Council which cover the entire foreshore. The limiting resolution of the resultant digital terrain model is that of the bathymetric model (10 m), therefore the simulations were performed at a grid refinement level of 11 resulting in a horizontal resolution of 16 m.
Acknowledgements. This research was funded by the Marsden Fund Council, Royal Society Te Apārangi grant number 17-NIW-017 awarded to NIWA. We wish to thank Sanjay Wadhwa for usage of the bathymetric model of Lake Taupō courtesy of MBIE/NIWA contract C01X1005 (Cumulative Effects of Stressors on Aquatic Ecosystems), and Waikato Regional Council for LiDAR datasets of the Lake Taupō foreshore and surrounding topography. We acknowledge the use of New Zealand eScience Infrastructure (NeSI) high performance computing facilities as part of this research.
Cases and controls Phenotype and genotype data of 58,181 entries of the data repository of ILCCO were extracted. Details of the repository is described previously  [4, 28]. QC control samples, individuals without information on smoking status or age, and samples of poor genotyping quality or sex discrepancies, were excluded. To avoid population stratification, this analysis is focused on European-ancestry population (defined as more than 95% probability of being of European descent). Fourteen thousand sixty-eight incident LC-cases and 12,390 cancer-free controls of European descent remained for analysis. Those genotyped with other genome-wide array in addition to OncoArray were separated to form an independent validation set (2nd validation set) of size (n = 4359, including 2360 LC-cases and 1999 controls).
Association analysis We first performed association analysis for each marker separately using the program PLINK  [30, 31]. Crude (model 1) and adjusted odds ratios (ORs) were estimated along with 95%-confidence intervals within logadditive models. Sex, age and smoking status and the first 3 principal components (PCs) to adjust for population stratification (model 2); and in addition the 6 most significantly associated LC-markers (rs55781567, 15q25. 1 CHRNA5; rs11780471, 8p21. 2 CHRNA2; rs7705526, 5p15. 33 TERT; rs56113850, 19q13. 2 CYP2A6; rs71658797, 1p31. 1 AK5; rs11571833, 13q13. 1 BRCA2) (model 3) were included in adjusted models. ORs were estimated for overall LC, small cell LC (SCLC), squamous cell LC (SqCLC), adenocarcinoma LC (adenoLC), ever smokers, never smokers and individuals aged ≤ 55 years (early onset LC) as subgroups.
This was particularly noticeable for SCLC, since AhR/Wnt-markers contribute more than twice as much to the prediction score as LC-markers. It is known, that within current smokers, tobacco consumption is strongest associated to SCLC  [44]. Moreover, within never smokers, a stringed defined score is made up from only two AhR/Wnt-markers, assigned to Axin2 and SFRP4. However, the discriminative ability of PRSs for LC, contributing markers with significance for main effect at different levels, is in general poor. The AUC of the BIC LC score for overall LC (0. 58 in the test set and 0. 55 in the 2nd validation set) corresponds to the AUC = 0. 54 based on four top LC-genes in a simulated population, as given by the GWAS-ROCS Database (https:// gwasr ocs. ca/). This may be due to other overpowering risk factors, since models including, e.
| Congenitalanomalyregistry-baseddesigns In Europe, population-based CA registries (Figure  1 ) cover nearly one third of the European birth population, and many of these CA registries contribute to the EUROmediCAT Central Database 60 and national data linkage resources (Supplementary Appendix 2). 60 The strengths of congenital anomaly registries are that they provide good diagnostic data on specific congenital anomalies; provide more complete ascertainment of CA; cover a very large population to investigate rare exposures, rare CA or moderate risks of more common CA; and include TOPFA and CA diagnosed after the neonatal period. Congenital anomaly registries increasingly use electronic healthcare data for case ascertainment, but the work of the registry is to make sure that the diagnosis in each case is validated, using multiple sources of information and expertise in medical genetics, paediatrics and foetal medicine.
Diagnostic accuracy is important so that CA can be analysed as specific predefined subgroups,  61  since an increase in risk in specific subgroups can be missed if all CA are considered together,  5  or major and minor anomalies are Congenital anomaly registries obtain exposure data mainly from medical records (eg obstetric records) prospectively recorded during pregnancy, supplemented by other sources including interview for some registries. 63, 64 While information on COVID-19 test results, medications and vaccines can be obtained from maternity records (and specific fields have been added in EUROCAT data collection), this information may not be recorded when not directly relevant to the care episode. In eight European countries (Supplementary Appendix 2), it is now also possible to link congenital anomaly registries to prescription data, which considerably enhances the exposure information available.
64 Prescription data for medications given in hospital settings, however, are generally not centralised in population databases, and hospital prescriptions for inpatient treatment therefore need to be separately ascertained. 63  the COVID-19 context, linkage to databases of COVID-19 test results and COVID-19 vaccination records is also highly relevant where available. A limitation is that not all registries have a unique identification number provided at birth which can be used to link to other electronic data sources and, in particular, linkage may not be possible for TOPFA. The COVID-19 pandemic is encouraging the development of linkages between congenital anomaly registries and databases of maternal exposure, but this is still not possible in many European countries.
68  In Europe, the majority of congenital anomaly registries do not collect data on non-malformed controls. Instead, case-control studies can be performed with malformed controls, where the controls either have genetic anomalies (which cannot be due to medication exposure or infection during pregnancy) or anomalies not associated with the primary hypothesis of interest. 71 The advantage of this design is that exposure data are collected in the same way for cases and controls, including any data that may be affected by the possibility of maternal recall bias. 71 The disadvantages are the possibility of 'teratogen non-specificity bias', where the controls may include CA associated with the exposure in question, thus diluting risk estimates,  72  and the inability to produce an overall estimate of CA risk related to the exposure.
Size of the birth population covered Large population size is one of the main advantages of using existing data sources, which allows risks relating to rare exposures or specific CA to be addressed. 6] [77] [78]  A better option is linkage of congenital anomaly registries to healthcare databases with exposure data for all pregnancies. This also allows the inclusion of TOPFA and stillbirths, although TOPFA cannot be linked to prescription data in some countries. Some studies have developed algorithms to improve the predictive value of healthcare data for CA where CA registries are not available. 36, 75, 76 ality of maternal exposure data The quality of data on COVID-19 disease symptoms, tests, treatment, or vaccination which are available in electronic healthcare databases.
Quality of data on pregnancy timing and exposure timing Gestational age at exposure is critical to establish exact weeks of exposure in relation to critical development windows for specific CA. Gestational age at exposure (or pregestational exposure) can be estimated from the date of the prescription or procedure, combined with the expected date of delivery or gestational age and birth date. Primary care databases are particularly prone to very incomplete data on pregnancy timing. 77 ality of establishment of study population of mother-baby dyads Several countries, for example the Nordic countries and Scotland, have a mother-baby linkage spine which identifies the healthcare number of the mother for each baby. The most successful linkage uses unique identification (ID) number provided at birth for every individual in the population, but biases may still be present (e.
g. , preterm babies dying before being allocated an ID number)  79  and TOPFA may remain unlinked. Studies which conduct linkage based on non-unique matching variables may have a large proportion of unlinked or incorrectly linked pairs. Mother-baby dyads need to be present in the study population from at least estimated conception date (or before to allow for periconceptional exposures or preconceptional comparators) to age of baby at confirmation of outcome. When healthcare databases are not population-based, there may be considerable attrition due to movement during or after pregnancy out of the selected healthcare units, or health insurance schemes, and some such movements of families may be associated with specialist services for children with CA, introducing bias. High proportions of unlinked motherbaby dyads are a "red flag" for interpretation.
Useful for rare events such as CA. Since data are already standardised in a common format, data quality improvement processes are performed on an ongoing basis, and collaboration between data providers is established with mutual understanding of data quality, the study can be conducted quickly with appropriate data interpretation. However, the agreement and establishment of an ongoing central database for a network (rather than specific study) is an infrastructure development which takes time and needs a long-term vision. When a central database is used, the availability of individual patient data (IPD) from all contributing centres enables complete exploration of the data. Iterative procedures to obtain the best fit for models can be employed. Multi-level models can be fitted to characterise and adjust for any centre differences both in terms of outcomes, but also in terms of risk factor associations varying between centres.
| Populationhealthcaredataorbirthcohorts The fourth type of data relates to all births in the population (unselected by exposure or outcome, Figure  1 ), whether this is obtained from secondary use of existing data sources, particularly electronic healthcare databases (which can be linked to congenital anomaly registers), or by primary data collection involving recruitment of pregnant women into birth cohorts. Primary data birth cohorts are usually time-limited for research purposes, and few in Europe were recruiting when the COVID-19 pandemic began. 74 pulation cohort studies based on existing data sources can have the advantages of being large, population-based, comprised of exposed and unexposed pregnancies, and both normal and adverse pregnancy outcomes. The quality of such data for studies of COVID-19 and CA can be judged by several criteria (Table  2 ).
The Sentinel CDM has been specifically adapted for pregnancy pharmacovigilance using US databases. Protocol-driven CDMs such as used by EUROlinkCAT choose a subset of the variables that are needed for the set of studies to be conducted, perform mapping and validation, and subsequently facilitate rapid centralised programming. They can be expanded to new protocols for new studies by adding variables and data sources. Intermediate solutions, such as the CDM of ConcePTION performs syntactic but not semantic harmonisation for pregnancy studies, requiring semantic harmonisation on a study-specific basis so that in practice building a library of semantic harmonisation algorithms will be required. Using a CDM with automated data access means that a syntax script from the analysis centre can be sent to all centres and within each centre a model analysing individual patient data will be automatically fitted and different results concerning the relative fits of the model will be sent back to the analysis centre.
These results will be automatically collated and a second model for fitting automatically generated and resupplied to all centres. This process will continue until the optimum fit across all centres is obtained. Full automation enables complete exploration of the data to occur. An example of this structure being available is the Sentinel System. 80 sing a CDM without automated data access means that iterative model fitting cannot be used as each syntax script from the analysis centre needs to be downloaded and run individually. Often the capacity for running many models is limited (for example if all output files need to be checked independently for small number suppression -disclosure control before being released) and therefore potentially important covariates need to be identified in advance and included in models run by all centres.
Risk factor associations can be investigated by performing meta-analysis of coefficients in fitted models. This is equivalent to two stage IPD analysis and does not result in dramatic loss of power compared to the one stage method of analysis  81. sharing, there are a number of infrastructure options (Table  3 ). For example, the European network EUROmediCAT uses a number of different approaches-a central database for case-malformed control and signal detection studies, a distributed data approach using a common data model for registries able to link to prescription data for case-malformed control studies, and a distributed data approach for linkage to population healthcare databases for population cohort studies.
| CHALLENG E SANDOPP ORTUNITIE S In addressing the CA risks associated with the COVID-19 pandemic, it is important to maximise use of the data resources we have, while also addressing the major challenges for this area of pregnancy research. CA research should not be an 'afterthought' and requires special attention in protocols. where healthcare providers are involved in recruitment and in provision of medical records for CA diagnoses. 43  relation to secondary use of existing data, European countries need to invest in making data more rapidly available, providing safe havens for data access with appropriate data protection, creating ongoing mother-baby linkage systems (such as in France and Finland; Supplementary Appendix 2), providing for linkage of pregnancies and congenital anomaly registers with population databases of COVID-19 test results and vaccinations, and providing for linkage of TOPFA to prescription and other healthcare data.
Centralising inpatient hospital medication data, which is currently unavailable in most countries,  85  will be particularly important for COVID-19, with the use of biologics and injectables which are only delivered in the hospital context. The data flow for secondary use studies can be seen as a pyramid (Figure  2 ). Balanced investment of resources is needed at every level of this pyramid. Hybrid approaches to data collection, linking primary and secondary data, are not being used to their full potential in relation to COVID-19, nor more generally in pharmacovigilance. The most efficient use of research resources would be to concentrate primary data collection on pertinent information not available in existing data sources, and link these data to congenital anomaly registries  86  and electronic healthcare databases.
EUROmediSAFE and EUROlinkCAT networks for methodological discussions. F I G U R E 2 The Data Pyramid for research based on secondary use of data, from healthcare practitioners (HCPs) to study teams Healthcare data recording for clinical care by HCPs NaƟonal electronic health care data safe havens, experƟse Research-ready databases eg Congenital Anomaly Registries, Mother Baby PV cohortsmain modes of action of medications used or proposed in SARS-CoV-2 infections 31,35.