dataesr/scientific-paragraphs-categorization huggingface dataset:
Two variants, c. 470 T > G (p. Ile157Ser) and c. 433C > T (p. Arg145Trp), were listed as (likely) pathogenic on ClinVar; both variants have high Helix scores but the number of carriers in our population-based sample was too small to evaluate their association with risk (see Additional File 1: Table  S7 ). One rare variant, c. 349A > G (p. Arg117Gly), was previously identified as risk associated in BCAC samples, as part of the OncoArray genome-wide association study (GWAS) project  [31]. In the current dataset, this variant, which is in the Helix-high category, had an OR 2. 69  (1. 46-4. 94 ). After excluding the BCAC GWAS samples from the current dataset, the OR was 3. 40  (1. 52-7. 61 ). Excluding c. 349A > G from the LR model did not change the overall relative risk associated with the Helix-high category (OR 1.
Model validation We evaluated the calibration of the best fitting models from the training set, for each gene, in the validation set: these included the LR models, the mixture model using the estimated proportions (α) from the training set, and the mixture model using the posterior probabilities derived from the training set. For each gene and each model, carriers of variants in the predicted risk groups were associated with an increased risk, and there were no differences between the observed and predicted ORs (see Additional File 1: Table  S13  and Figs. S2-S6 ). In silico scores, likelihood ratios and posterior probabilities for every variant included in the population training dataset are given in Additional File 2: Tables  S14-18. Using a composite five gene model, we estimated ORs for eleven risk categories (Fig.
3 ). In total, 184 samples carried a missense variant in more than one of the five genes and were excluded from this analysis. Four categories were significantly associated with an increased risk relative to non-carriers, consistent with the estimates derived from the training set: ATM FAT/PIK + CADD5 (OR 1. 76 (1. 16-2. 68), p = 0. 0078), CHEK2 Helix-low (OR 1. 40 (1. 04-1. 88), p = 0. 025), CHEK2 Helix-high (OR = 1. 89 (1. 27-2. 81), p = 0. 0017), and BRCA1 within domain and Helix-high (OR 4. 44 (1. 45-13. 59), p = 0. 0089) risk groups. The OR estimate for BRCA2 Helix-high variant carriers was higher than that in the training dataset, but the confidence interval was considerably wider (OR 1. 54 (0. 88-2. 68)). As predicted, variants in the remaining categories were not associated with risk.
We identified groups of missense variants conferring increased risks of breast cancer in ATM, BRCA1, BRCA2, and CHEK2, but not in PALB2. The ORs for BRCA1 and CHEK2 decreased with age at diagnosis, consistent with previous observations for PTVs  [7]. Previous analysis of the full BRIDGES dataset showed that protein domains in ATM and BRCA1 were predictive of risk  [7] ; the analysis presented here showed that in silico scores improved these predictions, in a formal model evaluation that allowed the models to be tested in an independent validation set. Under the best fitting mixture models, for ATM, BRCA1, and BRCA2, a small proportion of rare missense variants were associated with risks comparable to those for PTVs. In contrast, for CHEK2, a high proportion of CHEK2 missense variants were risk associated and the estimated risk was markedly lower than that associated with PTVs.
In PALB2, the evidence for association was weak; the mixture model analysis indicated that the proportion of missense variants associated with a high risk is likely to be very small. However, we cannot rule out the possibility that some variants are risk-associated since the power for detecting an association with risk for PALB2 is lower than, for example, BRCA1 and BRCA2. One variant in BRCA1 (p. Arg841Trp) was individually associated with a reduced risk of breast cancer (0. 67 (0. 52-0. 87)). Given that this finding is inconsistent with all the other associations and that the variant is not in any of the key functional domains, it seems quite likely that this is a chance association; further replication in other datasets will be required to confirm or refute the association. We used five in silico scores to predict the pathogenicity of individual variants.
In the best fitting mixture model, the proportion of associated variants was high, and the estimated risk was clearly lower than for PTVs. A model in which there were two levels of risk, with the higher level equal to the PTV risk, fitted slightly better in the full training dataset but not in the population-based training studies. In addition, however, three individual CHEK2 variants were associated with differing levels of risk: c. 470 T > C (p. Ile157Thr) OR 1. 24 (1. 09-1. 42); c. 538C > T (p. Arg180Cys) OR 1. 44 (1. 12-1. 84); and c. 349A > G (p. Arg117Gly) OR 2. 69 (1. 46-4. 94). The c. 470 T > C variant was too common to be included in the main analyses, possibly explaining why the heterogeneity in risk was not readily detectable by the mixture models; however, the confidence interval for c.
For CHEK2, approximately 60% of rare missense variants were risk associated and the estimated carrier frequency of pathogenic missense variants in CHEK2 was comparable to the frequency of PTVs. The predicted proportion of breast cancer cases possessing pathogenic germline missense variants in these genes is approximately 0. 6%, 0. 3%, 0. 2%, and 1. 3% for ATM, BRCA1, BRCA2, and CHEK2, respectively. The estimated additional contribution to the familial relative risk of breast cancer made by pathogenic missense variants in these five genes is approximately 2. 7%. The task of identifying which specific individual missense variants are risk associated is a complex one and is difficult to resolve fully even with a large dataset, since most variants are rare and there are many possible models to consider.
Despite the size of our study, it was difficult to distinguish, for any gene, between the LR models (in which all variants in a given category confer a given risk) and the mixture models (in which all risk-associated variants confer the same risk, but the proportion that are associated varies by category). This difficulty arises because the number of carriers for individual variants is small, and as a result, the estimated risk of pathogenic missense variants and probability of pathogenicity (α) are strongly confounded. Further, selecting the best models and estimating the risks based on these models is likely to result in overfitting and biased risk estimates. In order to strengthen the validity of our findings, we used a training-validation study design. We were able to replicate the predicted OR estimates in the validation dataset, suggesting that any bias due to overfitting was small.
Nevertheless, the validation dataset was relatively small, so further validation of the best models reported here in large independent datasets is critical. Ultimately, high-throughput functional assays that can evaluate all possible missense substitutions may provide more precise definitions of risk categories. The analyses of the BRCA1 SGE scores and the BRCA2 HDR assay scores suggest that this approach should be useful, although the scores for BRCA1 were highly concordant with the best in silico score in this case. The available PALB2 functional assays did not predict risk, but this may just reflect the low power of these analyses when the proportion of risk-associated variants is very low. As further prediction algorithms based on in silico and/or in vitro data are developed, large population-based epidemiological datasets such as BRIDGES can be used to validate their predictions.
Breast cancer risk association results from logistic regression and mixture models of population training samples a Number of unique missense substitutions in population dataset b Logistic regression odds ratio estimate for missense variant carriers c 95% confidence interval for logistic regression OR estimate for missense variant carriers d Mixture model odds ratio and 95% confidence interval for missense variant carriers e Alpha: estimated proportion of risk associated missense variants f 95% confidence interval for alpha g CADD quintiles 1-4 includes all CADD score values ≤ 3. 736542; CADD quintile 5 includes all CADD score values > 3. 736542.
4. PRELIMINARY RESULTS -PERFORMANCE COMPARISONS 4. 1. Implementation and experimentation testbed DCA and DSDCA algorithms were implemented in the NodeJS framework and interfaced with a tweaked version of IPFS, compatible with Linux x64 and OS X platforms. Source code is freely available on GitHub  [26]. The following performance measures were carried out using Grid'5000 testbed  [27] , a large-scale testbed for experiment-driven research supported by a scientific interest group (GIS) hosted by Inria, including CNRS, RENATER and several French Universities as well as other organizations. Only IPFS bandwidth was measured during preliminary experiments, but inconsistencies between IPFS and network bandwidth, including TCP/IP overhead, were observed. It was then decided that total network bandwidth usage, including TCP/IP overhead, was the most relevant resource to monitor.
Experimentations with DCA NP and NS versions were carried out on various clusters from 5 different sites (Lyon, Lille, Nancy, Nantes, Grenoble). Each experiment was launched for 100 rounds on five machines in parallel, and network bandwidth was measured with vnstat. Various settings were used, such as the number of nodes (from 10 to 226), FloodSub and GossipSub router protocols, and NP / NS DCA versions. At the end of each experiment, the total network bandwidth used for IPFS nodes bootstrapping and CA processing was measured, and the average network bandwidth per node was processed. Each DCA ran on individual machines, and network bandwidth was measured through vnstat software in interactive mode, monitoring each machine's loopback (lo). Experimentations for Decentralized Swarms of DCA were carried out across five sites with machines from various clusters with 10GBps connectivity (Nova-Lyon, Chiclet-Lille, Gros-Nancy, Ecotype-Nantes, Dahu-Grenoble).
Each Swarm was running on a specific site, and network bandwidth was measured with vnstat software. Nova was used as the IPFS private network and Swarm bootstrapping node, Chiclet, Gros, Ecotype and Dahu as Swarms of DCA nodes. Various settings were tried, such as the number of swarms (3 and 4), nodes (from 13 to 404), and FloodSub and GossipSub router protocols. Each experiment was run five times for 1000 rounds. At the end of each experiment, the total network bandwidth used between sites was measured, and the average network bandwidth per node was processed. CONCLUSION AND FURTHER WORKS This article introduced an experimental implementation of simple asynchronous distributed cellular automata and decentralized swarms of asynchronous distributed cellular automata driven by IPFS and Libp2p PubSub.
ACKNOWLEDGEMENT Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www. grid5000. fr). I want to express my gratitude and thanks to Pierre Neyron from CNRS, which granted me Grid'5000 open access to run DCA and DSDCA experiments across 5 French datacenters. I want to thank especially George Polyzos from AUEB, Nuno Santos from INESC-ID and Jorge Soares from Protocol Labs, who reviewed this paper at a very early stage and whose feedbacks motivated me to elaborate and dig further. I would also like to thank the French Government for awarding social grants, providing me with the financial means to complete this self-funded, non-profit and independent research.
Simulation Details The parameter values used for these results are given in Table  A1. The initial concentrations of susceptible and viral particles are 10 7 cells/ml and 10 5 phage/ml, respectively, (Multiplicity of infection, MOI = 0. 01) and the rest are set to 0. The initial resource concentration is 40  g/ml with no influx, except when the effect of resource level on switching point is studied (where the values used are explicitly stated in text). Simulation code is written in MATLAB R2020a and is available for download at https://github. com/Weitz-Group/ArbitriumLysisLysogeny.
Limits are calculated based on the method introduced in  Pozzetti et al. (2010)  in a manner consistent with COSMOS2015  (Davidzon et al. 2017, yellow dashed). For clarity, the total sample limit has been raised by 0. 02 dex so that both it and the star-forming limit are visible. distant universe, each of which includes photometric redshifts and other physical parameters computed from two independent codes. COSMOS2020 builds on more than a decade of panchromatic observations on the COSMOS field. Compared to previous releases, COSMOS2020 features significantly deeper optical, infrared, and near-infrared data all tied to a highly precise astrometric reference frame, Gaia. Starting from a very deep multiband detection image and using two different photometric extraction codes, one based on aperture photometry and one based on a profile-fitting technique, two photometric catalogs have been extracted.
A detailed comparison in Section 5 shows that at bright magnitudes, the classic aperture catalog is marginally superior whereas at faint magnitudes the trend is reversed with the profile-fitting technique providing a better result. This close agreement provides a unique validation of our measurement and photometric redshift techniques. Superseding our previous catalogs, COSMOS2020 represents an unparalleled deep and wide picture of the distant universe. It will be of invaluable assistance in preparing for the next generation of large telescopes and surveys. One can already start to imagine what COSMOS2025 might contain. After 15 years of observations, the UltraVISTA survey will have been completed, providing an unparalleled near-infrared view of COSMOS. These data, combined with the Spitzer data presented here, will lay the foundation for a next-generation catalog combining deep high-resolution optical and infrared imaging data from Euclid and the James Webb Space Telescope with ultradeep optical data from Rubin.
The authors thank  Nathaniel Strickley ,  Dustin Lang ,  Clara Giménez Arteaga ,  Istvan Szápudi ,  Andrew Repp , and  Emmanuel Bertin  for helpful discussions. We are also grateful for the many helpful and constructive comments from the anonymous referee. We gratefully acknowledge the contributions of the entire COSMOS collaboration consisting of more than 100 scientists. The HST COSMOS program was supported through  NASA  grant  HST-GO-09822. More information on the COSMOS survey is available at https://cosmos. astro. caltech. edu/. The  Cosmic Dawn Center (DAWN)  is funded by the  Danish National Research Foundation  under grant No. 140. S. T. , G. B. , and J. W. acknowledge support from the  European Research Council (ERC)   Consolidator Grant funding scheme  (project  ConTExt , grant No.
The work of D. S. was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with  NASA. J. D. S. is supported by  KAKENHI  ( 26400221 ) through the  Japan Society for the Promotion of Science (JSPS) , the  World Premier International Research Center Initiative (WPI) ,  MEXT, Japan. G. E. M. acknowledges the  Villum Fonden  research grant  13160  "Gas to stars, stars to dust: tracing star formation across cosmic time. "D. R. acknowledges support from the  National Science Foundation  under grant numbers  AST-1614213  and  AST-1910107. D. R. also acknowledges support from the  Alexander von Humboldt Foundation  through a  Humboldt Research Fellowship for Experienced Researchers. M. S. acknowledges the support of the  Natural Sciences and Engineering Research Council of Canada (NSERC).
Some of the data presented herein were obtained at the  W. M. Keck Observatory , which is operated as a scientific partnership among the  California Institute of Technology , the  University of California  and the  National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the  W. M. Keck Foundation. This research is also partly supported by the  Centre National d'Etudes Spatiales (CNES). These data were obtained and processed as part of the  CFHT Large Area U-band Deep Survey (CLAUDS) , which is a collaboration between astronomers from  Canada, France , and China described in Sawicki et al. (2019, MNRAS 489, 5202). CLAUDS is based on observations obtained with  MegaPrime/ MegaCam , a joint project of CFHT and CEA/DAPNIA, at the CFHT which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
It retrieves sulfate-specific optical depth at 1170 cm -1 (i. e. the peak of the mid-infrared extinction cross section; Sellitto and Legras, 2016), assuming a Gaussian extinction coefficient profile shape peaking at 20 km altitude, with 2 km full-width half-maximum. The bulk of the spectroscopic information on SO 2 and sulfate aerosols, in the IMS scheme, thus comes from the Infrared Atmospheric Sounding Interferometer (IASI)  (Clerbaux et al. , 2009). We refer to the two retrieved products as IMS SO 2 and SA optical depth (SA OD in the figure titles) in this work. The data are provided daily on a regular grid with 0. 25 • resolution in latitude and longitude, with one image collecting the daytime swaths and another collecting the night-time swaths.
A3 Vertical motion from CALIOP and MLS The observed vertical motion is obtained from CALIOP and MLS by applying a second-order Savitzky-Golay filter with a 31 d window to the daily mean vertical location of CALIOP scattering ratio and MLS water vapour, retaining data above 2 and 6 ppmv offsets, respectively. The offset are defined to isolate the aerosol and the water plumes from the background. The 31 d window has been adjusted from several trials with 11, 21, 31 and 41 d as the value beyond which the resulting motion curve was rid of short time fluctuations and did not change any more in shape. This was reached with the 31 d window for MLS and the 21 d window for CALIOP, but for the sake of consistency, we use the 31 d window for both. The diabatic and adiabatic background vertical velocities are calculated from ERA5 zonal means.
, 2021)  or ocean warm water inflow into the continental shelf  (Thompson et al. , 2018) , start being appropriately resolved. Moreover, the regional nature of PARASO maintains its computational cost reasonable: about 2 simulated years per day with approximately 250 cores (see Appendix B). Our tool relies on distinct models for each treated Earth system subcomponent: f. ETISh v1. 7 for the ice sheet  (Pattyn, 2017; Sun et al. , 2020) ; COSMO v5. 0 for the atmospheric circulation, coupled to CLM v4. 5 as a land module  (Rockel et al. , 2008) ; NEMO v3. 6 for the ocean  (Madec et al. , 2017) , coupled to LIM v3. 6 for the sea ice  (Rousset et al. , 2015). A specificity of PARASO lies in the computation of the subshelf melting, which is directly assessed from the explicitly resolved oceanic circulation within ice-shelf cavities.
Optimized basal sliding coefficients and steady-state modeled geometry for the Antarctic ice sheet were obtained after a forward-in-time integration of 60 000 yr with thermomechanical coupling. The initial temperature field (prior to that forward run) is derived from a steady-state temperature solution based on the present-day surface temperature, surface mass balance and geothermal heat flux. The latter is based on the seismic-based dataset due to  Shapiro and Ritzwoller (2004). For the relaxation, ice shelves are allowed to evolve and the hybrid shallow-shelf-shallow-ice approximation (HySSA;  Bueler and Brown, 2009)  is used to obtain the velocity field. The applied subshelf melt rates are taken as the constant (time-wise) 1984-1993 mean from a NEMO stand-alone run. The PARASO setting of f.
On the NEMO side, the ocean receives turbulent fluxes from the COSMO surface scheme instead of an ocean-calibrated bulk formula (e. g. , CORE  Large and Yeager, 2004). Performing NEMO stand-alone experiments with a COSMO-derived bulk formulation is a considerable challenge, as the COSMO surface scheme requires several near-surface atmosphere properties (e. g. , TKE at bottom levels or laminar transfer coefficients) as input. On the COSMO side, the atmosphere receives tiled fluxes which harbor subgrid-scale heterogeneity in surface properties. Since the COSMO surface scheme has been tuned for non-tiled fluxes (it uses instead a binary approach), the new tiling method, which is a requirement for coupling with NEMO, may lead to undesired systematic biases. Performing COSMO stand-alone experiments with tiled fluxes has not been pursued because this would have required further adapting the COSMO sources and forcing datasets.
Accordingly, we did not impose any relaxation in the inner domain of the atmospheric model towards the large-scale driving model (ERA5). In this way, the CCLM 2 dynamics is preserved. PARASO uses the default snow roughness length of CLM4. 5 of 2. 4 × 10 -3 m, which is constant in time and space. This contrasts with AEROCLOUD, for which the snow roughness length had been set to 10 -5 m. Moreover, PARASO uses the standard snow and ice module of CLM4. 5  (Oleson et al. , 2013)  and does not include the modified scheme implemented in AEROCLOUD. Future sensitivity studies of the modified snow and ice scheme and of the value for the surface roughness length are planned. In COSMO, the turbulence scheme uses a 1-D closure with a prognostic equation for TKE. To better represent very stable conditions over the Antarctic ice sheet, a reduction of the limit on the vertical diffusion coefficients has been considered in both AEROCLOUD and PARASO, based on  Cerenzia et al.
A7 ). While the increase of these sea-ice dynamical parameters is quite significant (+50 %), the uncertainty of their actual value is also quite high  (Tsamados et al. , 2014; Ungermann et al. , 2017). Code and data availability. The full PARASO sources are available to CLM-Community members on their RedC (https://redc. clm-community. eu/ → COSMO-CLM → "Downloads"). In addition to this tarball, all PARASO sources, at the exception of their COSMO parts, are publicly available for didactic purposes at https://doi. org/10. 5281/zenodo. 5576201 (Pelletier et al. , 2021a). All the input data for running a 3-month long PARASO experiment are publicly available at https://doi. org/10. 5281/zenodo. 5588468 and https://doi. org/10. 5281/zenodo. 5590053 (Pelletier et al. , 2021b; Pelletier and Helsen, 2021).
Scripts and data for generating all figures contained herein are publicly available at https://doi. org/10. 5281/zenodo. 5763819 (Pelletier, 2021). The COSMO-CLM model is free of charge for all research applications; however, access is license restricted (see http://www. cosmo-model. org/content/consortium/licencing. htm, last access: 24 August 2021). To download, the user needs to become a member of the CLM-Community or the respective institute needs to hold an institutional license. The documentation of the COSMO model is maintained by the COSMO-Consortium at https://www. dwd. de/EN/ourservices/cosmo_documentation/ cosmo_documentation. html (last access: 24 August 2021). NEMO, LIM and XIOS (a NEMO-compatible I/O library) are developed by the NEMO consortium and distributed under the CeCILL license.