dataesr/scientific-paragraphs-categorization huggingface dataset:
Each variant in the disease-related dataset was associate with pathogenicity in at least one peer reviewed publication (Table  S3 ). Out of 108 variants we chose, 65 variants are associated with HCM, eight with RCM, five with myofibrillar myopathies, five with distal myopathies, four with ACM, three with DCM, two with IBM, two with congenital heart disease, two with left ventricular non-compaction, and 12 with other disease phenotypes. For the selection of a negative or neutral mutation set, we gathered single point missense variants found in the Genome Aggregation Database (gnomAD; https://gnomad. broadinstitute. org). This yielded a collection of 1500 missense variants, which were analyzed and selected according to the following criteria: (i) they were found at higher frequency, as recent study suggested that loss of function and disease related mutations tend to be found at extremely low frequencies compared to neutral ones  (33) ; and (ii) the presence of individuals having homozygous alleles for variants was taken as further evidence of a variant being neutral.
We selected variants with high allele frequency (> 10 - 3 ), and high allele counts based on these criteria. By comparison, variants found in the gnomAD associated with pathogenicity were found at frequencies around 10 -6 , a difference of up to three orders of magnitude. We discarded variants that showed less than 10 allele counts as these variants might be rare but harmless. In total, these stringent criteria selections led to a reduction from 1500 to 65 variants and constituted the neutral dataset. The average allele count in the neutral variant dataset was 504, and some selected variants also showed homozygous alleles. In summary, our two datasets, consisting of 108 disease-related variants (Figure  2 , Table  S3 ) and 65 presumably neutral variants (Table  S4 ), were merged to 173 instances and used as training sets for our algorithm.
Defining the attributes for AMIVA-F analysis AMIVA-F tool is based on the hypothesis that pathogenic variants modify protein function in a complex way by impacting the fold stability and integrity as well as by influencing interaction interfaces or phosphorylation sites. To characterize the impact of variants at a biophysical and structural level, we first prepared a collection of 3D structures for all of wild-type FlnC domains that included 6 experimental structures covering 8 Ig domains, and 15 3D computational models covering the ABD and 16 Ig domains (Figure  S1 , Table  S5 ). In order to fully characterize a variant, we defined 11 different attributes (see Materials and Methods for details). These attributes were derived through analysis of 3D structures by using scripts included in the AMIVA-F package.
Benchmarking and validating AMIVA-F Predictions were made with a multilayer perceptron neural network, by using WEKA  (38) , an opensource workbench that allows the use of several machine learning techniques. In order to benchmark the performance of AMIVA-F, we used ZeroR algorithm (38) that predicts the most common instance in a dataset and therefore defines a baseline accuracy. In general, baseline accuracy is solely dependent on the underlying dataset, and it is algorithm independent. For our dataset, ZeroR determined 62. 4% as baseline accuracy. Given the relatively low number of instances (173 variants), particular precautions had to be taken to train the network because, upon regular cross-validation, there is a probability to bin outliers together and artificially increase bias. For larger datasets, this concern becomes less relevant given that binning large datasets results in a lower contribution of potential outliers to the binned averages.
In comparison, in smaller datasets, a few outliers could considerably skew the average. To counteract that, we sampled by using 10-fold stratified cross-validation to ensure equal incorporation of all data while also preventing disproportionate bias through random sampling, which could happen in regular unstratified cross-validation. Table  1. Overall, sensitivity and accuracy are close to or slightly over 0. 8 as well as the area under the ROC curve (AUC) and the F-statistics; specificity, though slightly lower, is still in a comfortable range close to 0. 7 (see Materials and Methods for details).
Their exclusion from the training sets was justified by the absence of concrete information about the specific disease underlying those variants, even though the probability of the variant to be disease causing is sufficient to warrant clinical actions  (41). As shown in Table  2 , six predictions were correct (75%), in line with the cross-validation of AMIVA-F and this reinforces the cross-validation estimations, despite the small dimension of the external test set (8 variants). Additionally, we examined whether predictions are biased by the frequency of occurrence of the variants in the GnomAD database. This was an important point to check because our non-pathogenic variant learning set was assembled based on the premise that non-pathogenic variants exhibit high occurrence frequency  (33, 42).
These mutants were more stable than the wild-type, and yet were associated with severe disease phenotypes. This suggests that stabilizing changes in FlnC structure can also have a negative impact on its ability to engage relevant binding partners, thus interfering with its function. Overall, our results indicate that the relationship between variant pathogenicity and thermal stability of the human FlnC domains is rather complex and can be unexpected. However, two conclusions are clearly supported by our data: (i) pathogenicity is associated with perturbations of fold stability; and (ii) pathogenicity can be associated with interdomain interactions, suggesting that biophysical pathogenicity-stability studies need to be conducted not with single but multi-domain constructs. Furthermore, when we overlaid structures (solved or calculated) of all Ig domains in FlnC and mapped distribution of pathogenic mutations in our dataset, we observed that these cluster within specific structural elements, most notably between the loop hosting the PXSP motif and the N-terminal side of the Ig domain (Figure  5A ).
Patient data The patients described in the paper were recruited to a multicenter, observational, longitudinal cohort study of patients with FlnC recruited from 19 European cardiomyopathy units between 1990 and 2018. The study conforms to the principles of the Helsinki declaration. The authors from each participating center guarantee the integrity of data from their institution and have approval for anonymised patient data collation and analysis from local ethics committee/internal review board. All participating patients consented to genetic testing. Detailed methods on the genetic testing and clinical assessment were published recently  (45).
Cell biophysics Neonatal rat cardiomyocytes were prepared by standard methods from 3-day old pubs, plated on collagen-coated dishes and transfected one day after plating with pEGFP constructs containing wildtype or mutant filamin-C, tagged at the N-terminus with GFP, as described (gift of D. Fürst, Bonn. on the underlying datasets and is algorithm independent. For our dataset, ZeroR determined a 62. 4% baseline accuracy. Initially, we tried several algorithms, ranging from random forest type algorithms, over decision tables, bayesian logic, linear regressions and many more. Ultimately, the final optimized algorithm was found to be a multilayer perceptron neural network with a weighted average true positive (TP) rate of ~80%. The exact metrics are shown in (Table  1 ). Pertinent details are shown in Table  S8.
Cross-validation and prevention of overfitting Due to the relatively low amount of data (173 variants), particular precautions needed to be taken. This arose due to the fact that upon normal cross validation, probability would be higher to bin outliers together and therefore artificially increase bias. For bigger datasets, this concern becomes less relevant given that binning large datasets results in lower contribution of potential outliers to the binned averages while in smaller datasets, a few outliers could skew considerably the average. In order to counteract that, we utilized 10-fold stratified cross validation to ensure incorporation of all data, while also preventing disproportionate bias through random sampling which could happen in normal unstratified cross validation.
Data collection and structure refinement Datasets of Ig14-15 were collected at beamline i24 of the Diamond Light Source (UK) at 100 K using a Pilatus3 6M. Crystals of Ig14-15 S1624L on i04 of the Diamond Light Source at 100 K using a Pilatus 6M-F and crystals of Ig14-15 G1676R at beamline MASSIF-1 at ESRF using a Pilatus3 2M (http://dx. doi. org/10. 1107/S1399004715011918). The dataset was processed with XDS and symmetry equivalent reflections merged with XDSCONV (59). Intensities were not converted to amplitudes. Initially, we used a conservative high-resolution cutoff 1. 7 Å (CC1/2 = 67. 5; I/σ(I)=1. 57) for Ig14-15, 1. 92 Å (CC1/2 = 86; I/σ(I)=2. 1) for Ig14-15 S1624L and 1. 83 Å (CC1/2 = 93. 9; I/σ(I)=3. 81) for Ig14-15 G1676R  (60). The phase problem of Ig14-15 was solved by molecular replacement using MORDA from ccp4 online  (61, 62).
The models were validated with MolProbity (69) and the PDB_REDO webserver. The statistics on data-collection and refinement are reported in Table  S2. Figures were prepared with PyMOL Molecular Graphics System (Version 2. 4. 0, Schrödinger, LLC). Atomic coordinates have been deposited in the Protein Data Bank under the accession code Ig14-15 (7OUU), Ig14-15 S1624L (7OUV) and Ig14-15 G1676R (7P0E).
Acknowledgements This work was supported by the  Slovenian Research Agency  young researcher grant (No. 35337 ) and research program  P1-0140. KDC research was supported by a  Marie Curie Initial Training Network :  MUZIC  ( N°238423 ),  Austrian Science Fund (FWF)  Projects  I525 ,  I1593 ,  P22276 ,  P19060  and  W1221 ,  Federal Ministry of Economy, Family and Youth  through the initiative " Laura Bassi Centres of Expertise " funding the  Centre of Optimized Structural Studies ,  N°253275 , by the  Wellcome Trust   Collaborative Award  ( 201543/Z/16 ),  Austrian-Slovak Interreg  Project  B301 StruBioMol ,  COST  action  BM1405  -Non-globular proteins -from sequence to structure, function and application  in molecular physiopathology (NGP-NET ),  WWTF (Vienna Science and Technology Fund)   Chemical Biology  project  LS17-008 , and by the  University of Vienna.
JK was supported by the  Wellcome Trust   Collaborative Award  ( 201543/Z/16 ) and  Austrian-Slovak Interreg  Project  B301 StruBioMol , LS was supported by the  Wellcome Trust   Collaborative Award  ( 201543/Z/16 ). MG holds the  British Heart Foundation Chair of Molecular Cardiology. We thank  Jürgen Hoffmann  ( UNIVIE ) aiding in testing and setting up AMIVA-F for MacOS and several Linux partitions. OC acknowledges support from the  Ministero dell'Università e della Ricerca (MUR)  and the  University of Pavia  through the program " Dipartimenti di Eccellenza   2023-2027 ". OC also thanks  G. Frescobaldi  for constant support. LRL is funded by an  MRC   UK Clinical Academic Research Partnership  ( MR/T005181/1 ). PC is funded by  AVIESAN-ITMO Genetique-Genomique-Bioinformatique [ResDiCard project ,  Rare diseases call ].
Shellfish are a source of nutrients but are also a matter of concern in terms of food safety due to natural contaminants such as phycotoxins or anthropogenic contaminants including microbial agents and heavy metals. However, data related to consumption for each mollusk species are scarce and missing for appropriate exposure calculation. The objective of the study was to generate shellfish consumption data in the adult coastal population in France to assess exposure to health risks, the effects of determinants on the frequency of consumption and usual intake, and shellfish food risk perception. Our study, named the CONSOMER study, was carried out using an online survey in 2016 and 2017 and included a food frequency questionnaire. After validation, 2,479 individual questionnaires were available for statistical analysis.
, 2017). With global climate and environmental changes, contamination levels in the sea (and in mollusks) are also evolving, like for pathogenic Vibrio species  (Vezzulli et al. , 2016; FAO & WHO, 2021)  or marine toxins  (Gobler, 2020; Karlson et al. , 2021) , making the risk more difficult to predict at the local scale. The food safety risk in shellfish is also acute, particularly due to paralytic, amnesic, or diarrheic phycotoxins produced by harmful algae  (Andjelkovic et al. , 2012; Liu et al. , 2017). The acute microbiological risk involves bacteria but also viruses (e. g. , norovirus)  (Maalouf et al. , 2010 , Potasman et al. , 2002; Pouillot et al. , 2021). Assessing the acute daily risk requires appropriate data. For example, a combination of three consumption datasets was needed to assess the acute daily risk of hepatitis A in oysters over one year  (Thebault et al.
Large representative national consumption surveys, like the INCA3 study performed in France in 2014-2015  Dubuisson et al. , 2019) , consider all food items and are representative of the general population. In this kind of survey, detailed data on food intake are collected for a limited number of days (3 days in INCA3), leading to a low rate of shellfish consumers and to a low number of eating occasions to estimate the portion size. Similarly, local studies are not designed to be representative of the national or coastal level population. They have focused either on particular groups of (high quantity) consumers, like recreational shellfish harvesters in Western Brittany  (Picot et al. , 2011) , or high-quantity seafood consumers in four coastal areas in the CALIPSO study carried out in  [2004] [2005]   (Sirot, Guérin, et al.
The targeted sample size for each stratum was defined considering the most recent national annual census data available, corresponding to the year 2014, for the selected coastal departments  (Guggemos & Vidalenc, 2015). Once enough people were obtained in all strata (at least 50 individuals in each stratum), the sampling period was closed. The survey was carried out in two periods to take into account seasonal variability in seafood consumption: the first one from October 10, 2016 to December 7, 2016, and the second from May 17, 2017 to June 14, 2017. As the questionnaire covered consumption habits over the previous 6 months, the first period covered summer and autumn and the second covered winter (including Christmas) and spring. The participants selected during the two periods were different in order to avoid respondent follow-up loss, and to ease the statistical analysis with independent respondents.
There were 2,523 participants in the CON-SOMER study, before further corrections. The database was anonymous; an identification number identified participants. No personal data were made available to ANSES nor CREDOC. Questionnaire. The CONSOMER study used an online selfreported questionnaire. The questionnaire included a food frequency questionnaire (FFQ), questions on socio-demographic characteristics (sex, age, occupational category, living resources, household composition, highest educational level, type of housing, and type of residence area), and questions on recreational shellfish harvesting. Body weight was also collected to evaluate dietary exposure to chemical substances per kg body weight. FFQs are known to be a suitable tool to assess usual dietary intake  (EFSA, 2009). In our survey, the FFQ focused on the frequency of seafood consumption and considered four main categories of seafood: fish, oysters and mussels, crustaceans, and other seafood products (including other shellfish species).
), razor clams (Solenidae), Venus clams (Venus verrucosa), tellin shells (Tellina spp. ), and abalone (Haliotis tuberculata). Common names in French are provided in Table  S1. All calculations, data management and statistical analyses were performed using R software, version R 3. 6. 3 (R Core Team, 2017) and the Rstudio environment, version 1. 2. 5033 with packages dplyr (ver 2. 0. 0), icarus (ver 0. 3. 0), survey (ver 4. 0)  (Lumley, 2010) , mice (ver 3. 6. 0), fitdistrplus (ver 1. 1-1), and MuMIn (ver 1. 43. 17). Maps were built with QGIS 3. 8, and spatial data were obtained from GEO-FLA (Open License IGN). All corrections related to comments made on the interviews were stored in the R script, in order to ensure traceability. Participant exclusion. To ensure data independence and reliability, the following exclusion criteria were used: • Participants who declared having answered both the first and second phase of the survey (343 exclusions); • Participants who declared frequency of consumption strictly greater than 912.
Declared portion sizes were expected to fol- low a log-normal distribution. After graphically checking the quality of the fitting with log-normal distribution, parameters of the parametric distribution were estimated. The probability P i to exceed each individual value X i in the sample was calculated from the cumulative distribution function of the log-normal distribution for each sampled quantity (P i = 1plognorm(X i , mu, sd)). If this probability P i associated with the given value X i was lower than 5%, the corresponding value was excluded. All canceled out values of this item were replaced by a missing value flag. The canceled values for each species of shellfish are listed in Table  S1. Data processing. Edible weight was calculated as raw product even if eaten cooked, because the contamination of shellfish is normally calculated with raw shellfish meat.
For each shellfish species and preservation form, the individual weekly intake was calculated by multiplying the usual portion size by the consumption frequency during the year and dividing by 52 (number of weeks in a year) and the individual reported body weight. When grouping different shellfish species, the intake of each shellfish  was summed over the individual, and statistical estimates were made at the population level. Statistical analysis. In order to ensure the representativeness of the final sample regarding the target coastal population, individual weighting factors were calculated by margin calibration using the R package icarus and taking into account sex, age, socio-professional category, type of household, size of urban area, and region as margins, using a raking-ratio calibration method  (Deville & Särndal, 1992).
In addition, one individual answered "Other" without further information when asked about their level of education; the answer was therefore considered a missing value. Variables concerning risk perception were not included in models because more than 50% of the data were missing. The analysis was performed on individuals who had no missing data for all the explanatory variables. Of the 2,479 individuals considered in the descriptive part, 2,258 were included in the statistical analyses of consumer rates. Considering the consumer rate, the determinants were identified by shellfish species using a generalized linear model with a binomial distribution and logistic link  (Zuur, 2009) , with the survey package. For statistical analysis of usual shellfish intake, after graphically checking the log-normal density, the log of intake was used to perform the generalized linear model with the MuMin package.
Discussion Main limitations and interest of FFQ web-based studies. The CONSOMER study used an FFQ known to be suitable for the assessment of usual food consumption. However, FFQs may not be accurate since they rely on memory  (Shatenstein et al. , 1999). The National consumption database cannot provide as many details on a particular food item intake or on a particular subpopulation of concern as specific studies focusing on a particular food group or subpopulation. Their objective is to provide representative estimates of the whole diet for the general population at the national level. Given the growth of internet access (79% of households connected to the internet in France in 2016, 83% in 2019)  (Schmid, 2019)  and the widespread use of personal computers, web-based studies are more widely used even for epidemiological studies  (Link & Mokdad, 2005; Smith et al.
, 2007; Rebouillat et al. , 2021). Web-based studies are less expensive for organizers, are more convenient for participants (place and time of survey completion), and provide ease for converting data to an analyzable format  (Kesse-Guyot et al. , 2016). However, web-based studies are subject to persistent concern about extrapolation and representativeness of their results  (Andreeva et al. , 2016; Kesse-Guyot et al. , 2016) , considering that individual probability of inclusion remains unknown, and due to possible input errors. Regarding input errors, different checks and algorithms were carried out in the CONSOMER study to exclude extreme values, and weighting was applied so that the statistical weight of an individual made the sample analysis representative of the targeted population: coastal adults who represent approximately 30% of the adult population in France.
In our study, it appears that shellfish harvesters, having higher consumption rate, are potentially more exposed to shellfish contaminants than other adults in coastal areas. In another study, it was shown that respondents reporting self-caught fish consumption were more exposed than others  (von Stackelberg et al. , 2017). It is also a matter of concern that they are not particularly aware of safety recommendations regarding the area where they harvest shellfish. This concern is shared with another study showing that recreational shellfish harvesters are not aware of openings and closures of shellfish areas for safety reasons  (Reich et al. , 2015). Our results suggest that communication strategies should be developed to better protect this subpopulation. The CONSOMER survey provides detailed and qualitative consumption data for shellfish that can be used in dietary exposure assessments both for acute scenarios (marine biotoxins, microbiological pathogens) and chronic scenarios (chemical contaminants).
The consumption data collected in this survey are of interest for updating, as other member states, the European Food Safety Authority default portion size in its risk assessment associated with marine biotoxins  (EFSA, 2010). The data used in this paper are available on request. The CON-SOMER survey also includes consumption data for many marine fish species and other seafood products (crustaceans, cephalopods, and urchins) that will be made publicly available in the near future via a public website.
Acknowledgments This study was carried out with the financial support of the  French National Research Agency's (Agence Nationale de la Recherche) ''- ACCUTOX '' project  13-CESA-0019 (2013 )(2014)(2015)(2016)(2017). They also acknowledge contributors for specific data access (photos, edible factors):  Peggy Pinard  and  Nawel Bemrah , and for improvements to the questionnaire:  Véronique Sirot ,  Frederique Audiat-Perrin , and  Marie-Bénédicte Peyrat.
This is done not only in a behavioral sense but modeling the platforms at the level of micro-architectural components. gem5 has been used to emulate attacks against micro-architectures such as RowHammer  [Fra+21]  and Spectre  [AM21]. The main interest for studying these attacks in gem5 is that this software is open source and can be easily customized to model different hardware components. For example, it is possible to emulate custom hardware accelerators along complex processor systems. It is also possible to parameterize the simulation and customize the system to test multiple architectures. Therefore, it is a useful tool to test multiple target architectures and implement and test potential countermeasures. A gem5 simulation creates a large set of statistics associated with the operation of the system.