dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyzes. The sample size was estimated in order to compare the metabolic and physical profile of individuals characterized as high spenders (H-Spenders) versus low spenders (L-Spenders) based on EE during a cycle-desk low intensity exercise. To highlight significant differences greater than 1 point effect-size, 14 participants by group (H-Spenders vs. L-Spenders) were needed for 80% satisfactory statistical power and a two-sided type I error at 5%. Statistical analysis was performed using Stata software (version 15, StataCorp, College Station, Texas, USA). Data were presented as mean and standard deviation. The Shapiro-Wilk test was used to test the assumption of distribution normality for quantitative parameters. Energy profile was determined by categorizing difference between EE at rest and 27 min of exercise (3-27 min) (Delta Exo-Rest) according to statistical distribution, i.
GC-MS analysis A GC coupled to MS (GCT 1er Waters), piloted by the software MassLynx was used to identify the volatile compounds. One microliter (1 mL) of the diluted sample was injected in the split mode (split ratio 1:10). Inlet pressure was 1 kPa and the carrier gas was helium at an on-column flow of 1 mL/min. The gas chromatograph was programmed as follows: isothermal at 60 °C for 5 min, temperature gradient 60À270 °C at 15 °C/min, isothermal at 270 °C for 6 min, then a second gradient was applied to 300 °C at 50 °C/min and finally isothermal at 300 °C for 4. 5 min. Total analysis time was 30 min. Highresolution mass spectrometer was adjusted for an emission current of 663 mA and electron multiplier voltage 70 eV. Trap temperature was 250 °C and that of the transfer line was 300 °C. Mass scanning was performed from 40 to 650 amu.
Statistical analysis All measurements were carried out in quadruplicate. Two-way ANOVA was used for the significance calculation using SPSS 20. 1. Statistical differences between the solvents used in the study were estimated using Tukey's test. The determination of the relationship between TPC and biological activities was assessed by the coefficient of determination (R 2 ). Principal component analysis (PCA) was done using XLSTAT  (version 2014. 5. 03)  for visualization of discrimination between the different parameters.
Significance statement During the COVID pandemic, new SARS-CoV-2 variants emerge and spread, some being of major concern due to their increased infectivity or their capacity to reduce vaccine efficiency. Anticipating new mutations, which might give rise to new variants, would be of great interest. Here we construct sequence models predicting how mutable SARS-CoV-2 positions are, using a single SARS-CoV-2 sequence and databases of other coronaviruses. Predictions are tested against available mutagenesis data and the observed variability of SARS-CoV-2 proteins. Interestingly, our predictions agree increasingly with observations, as more SARS-CoV-2 sequences become available. Combining predictions with immunological data, we find a clear overrepresentation of mutations in current variants of concern.
Introduction The emergence of variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major global health concern. Mutations observed in circulating Variants of Interest (VOI) or Variants of Concern (VOC) have been associated with increased transmissibility  (1, 2) , reduced efficacy of antibody treatments  (3, 4) , and lower antibody neutralization  (5). It is currently under investigation in how far circulating or future mutants can escape the human immune response induced by vaccination or previous infection  (6). Since the beginning of the COVID-19 pandemic, genomic surveillance of SARS-CoV-2 strains has played a pivotal role in tracking new mutations as they appear and expand. Viral sequences sampled from infected individuals from various parts of the world have been continuously deposited in the GISAID database  (7) , and -as of May 2021 -more than 1,500,000 genomes are available.
In this work, we construct unsupervised probabilistic models to predict SARS-CoV-2 mutable and constrained positions. We base our approach on the Direct Coupling Analysis (DCA)  (22)  that overcomes the before-mentioned limitations by explicitly including pairwise epistatic terms into our modeling. The DCA models are trained using families of homologous sequences, broadly collected from all known coronavirus genomes, allowing us to model the general selective pressures acting on the family of coronaviruses. While the use of other coronaviruses enlarges substantially the datasets making data-driven modeling more robust, we may, however, partially lose information about hostspecific constraints like the interaction with host-cell receptors (e. g. ACE2 for SARS-CoV-2) or with the host's immune system.
For this specific domain, additional deep mutational scanning (DMS) data are available measuring how amino acid mutations of RBD affect protein expression (a proxy of protein stability) and binding to the human ACE2 receptor  (25) , allowing us to investigate more deeply their relationship with the DCA mutability score, and with the observed variability across SARS-CoV-2 variants. Most observed mutations are neutral and do not affect the virus phenotype  (26) , however mutations occurring in SARS-CoV-2 immunogenic regions, i. e. targets of human B and T cells, may allow the virus to evade the immune response induced by vaccination or previous infection. By combining our DCA-mutability scores with data from the Immune Epitope Database (IEDB, (  27 )), we identify a restricted set of positions in the RBD that are expected to be both mutable and highly immunogenic.
Results According to the Pfam protein-domain family database  (28) , the SARS-CoV-2 proteome (isolate Wuhan-Hu-1) contains 39 protein domains (see Table  S1 ) covering 81% (7860 out of 9748 residues) of the entire proteome. For each of these domains, we predict the mutability using both the epistatic DCA and the independent IND models following the general scheme illustrated in Fig. 1  and detailed in Materials and Methods: • For each protein (domain), we extract MSA of homologous sequences from public sequence databases. These sequences, which belong almost exclusively to other coronaviridae, diverged during up to ~10 3 -10 8 years from their common ancestors with SARS-CoV-2  (29). They are used to train IND and DCA models. These models are applied to the protein sequences of the SARS-CoV-2 reference strain Wuhan-Hu-1 to predict the mutability of each site.
Model-based predictions are tested against the observed variability, which is derived from SARS-CoV-2 genomes available in GISAID. The estimate on the years of evolution in MSAs of distant sequences provided here  (29)  is indicative; it can vary strongly between distinct protein domains of SARS-CoV-2. For testing these mutability scores, we use the same 39 protein domains for extracting a second MSA with variants of SARS-CoV-2 from the GISAID database. To minimize frequency biases due to the extremely heterogeneous sequencing efforts in different countries, we decided to remove redundant amino-acid sequences and keep each distinct sequence only once. The positionspecific observed variability is now defined as the number of distinct sequences in the resulting MSA having a mutation in the position under consideration, when compared to the Wuhan-Hu-1 reference amino-acid sequences (see Materials and Methods for more details).
Data-driven sequence landscapes predict mutability and mutational effects in the receptor binding domain To assess our prediction framework, we proceed with the following steps. First, we show that the mutational effect predictions of single-site amino-acid mutations are correlated with the protein expression changes measured in the before-mentioned deep mutational scan. Second, we show that the RBD variants available in the GISAID database are significantly more neutral than a randomized sequence library of the same sequence divergence from the Wuhan-Hu-1 reference, the latter showing an accumulation of deleterious mutations. These two observations allow us to use predicted close-to-neutrality of a position as an indicator of its high mutability, while positions predicted to have mostly deleterious mutations are expected to be of low mutability.
We use extensive comparisons with the sequence variability derived from GISAID to test this hypothesis. As a first test, we compare the agreement of the computationally predicted mutational effects with the experimental protein expression. Taking into consideration the region of the RBD domain defined by the Pfam profile bCoV_S1_RBD (PF09408, aligned length ! = 178), we compare our predictions with the experimentally measured single-site mutations. We focus on the position- In brief, we observe that the protein expression and the epistatic model are well correlated, notably better than for the independent model. We note that the model predicts some mutations to be deleterious, which are neutral in the expression experiments. Two reasons are possible: (a) in limited datasets of functional sequences, undersampled neutral variants may appear deleterious; and (b) mutations without effect on expression may be deleterious for other phenotypes contributing to protein fitness.
F+G) RBD 3D structure (PDB code: 6M0J (  33 )) colored according to 3 levels of mutational scores from the DCA model (panel F) and the protein expression (panel G). Lower mutational scores are shown in yellow, medium in blue and higher in green. The width (wider for higher variability) in both panels corresponds to 3 levels of the observed variability (same cutoffs as in panel C). In all ROC curves, the area under the curve (AUC) of the ROC is shown in the legend. Cutoffs to define positions with low and high variability in the ROC analyses were chosen to split into balanced subsets with the most similar number of observations possible in each subset  (Fig S2A). i. e. the median is used as the cutoff. ROC curves for the other combinations of predictors and dates are shown in Fig S3. Our DCA and IND models are built from MSAs of diverged species, where we explicitly remove sequences similar to the Wuhan-Hu-1 reference (Materials and Methods) to avoid overlaps with the GISAID sequences used to estimate the local SARS-CoV-2 observed variability.
More importantly, it shows that our computational model is able to anticipate future variability; it suggests that the performance could be even higher with more data leading to an even better estimation of the variability. We have complemented this analysis also for mutability predictions done using the IND score or protein expression (Fig. S3 ), all show the tendency to improve with the increase of SARS-CoV-2 data available in the GISAID database. Only the IND-score based analysis shows no clear trend between December 2020 and May 2021. Recently deep learning has been used to improve mutational predictions in the case of large training MSA, but the accuracy for viral proteins remains rather limited  (35). To explore this issue, we have used DeepSequence  (35). Its predictions correlate well with the DCA model predictions (ρ=0.
Discussion In this work, we propose to use statistical models to predict the mutability of individual positions in SARS-CoV-2 proteins. The models are based on MSAs coming from various coronaviruses. The inclusion of epistasis into the DCA-based modeling framework allows us to capture local evolutionary constraints specific to the SARS-CoV-2 sequence background. Using several tests for the RBD of the spike protein, for which the most extensive experimental datasets are available, we were able to establish that our computational predictions are able to anticipate position mutated in variants of SARS-CoV-2 from sequence alignments not containing SARS-CoV-2 sequences. This fact is particularly evident in Fig. 2E , which shows that more recent and thus richer releases of the GISAID database of SARS-CoV-2 genomes follow more accurately our model predictions.
Sequence data Sequence data in FASTA format were downloaded from the following databases: GISAID ((7), release 16 May 2021), Uniref90 ((45), release December 2020), ViPR ((46), downloaded in September 2020), NCBI viral genomes ((47), downloaded in September 2020) and MERS coronavirus database ((48), downloaded in September 2020). The amino acid sequence of isolate Wuhan-Hu-1 was used as the reference proteome (genbank identifier: MN908947). Protein domains were detected using the HMMER suite ((49), version 3. 1b2) and the HMM profiles from Pfam. A global database including distant species was built by combining Uniref90, ViPR, NCBI viral genomes and MERS coronavirus database, and used to train the DCA and IND models. We built MSAs by running jackhmmer with 5 iterations and starting both with the full-length reference protein sequence (except for the ORF1ab) and with the trimmed domain sequence (SI text for more details).
54) as well as in the case of the observed variability (ρ=0. 61). For the GISAID database, a MSA for each domain sequence was built with only 1 iteration in jackhmmer as the GISAID sequences are very similar to the reference sequences. We applied the same quality controls as before but keeping sequences closer than 90% sequence identity and removing sequences corresponding to a non-human host. The July and December 2020 subsets of sequences were collected until the 16th of the corresponding month. The alignments of GISAID sequences were used exclusively for testing our predictions. The random sequences in Fig. 2B  are generated by randomly selecting a position and variant following an uniform distribution. For each GISAID sequence, a random sequence is produced with the same number of mutations to the reference.
Acknowledgments We are grateful to  Erik Aurell ,  Matteo Bisardi  and  David Gfeller  for interesting discussions, to  David Gfeller  in particular for his help with the immunologic data, and to  Richard Neher  for his valuable feedback on our manuscript. Our work was partially funded by the  Faculty of Science and Engineering of Sorbonne University  in the context of the call SU-COVID19-FSI, by the  EU   H2020 Research and Innovation Programme MSCA-RISE-2016  under Grant Agreement No. 734439  InferNet, and by the  H2020 Marie Sklodowska Curie Individual Fellowship  ( H2020-MSCA-IF-2020 ), No. 101027973  to  Giancarlo Croce.
Availability To ensure reproducibility and access to our results we provide at https://giancarlocroce. github. io/DCA_SARS-CoV-2/ the data generated in the course of this research and a jupyter-notebook to reproduce key figures and guide data analysis. This notebook will also contain data updated as compared to the datasets used in this article. The code to generate the predictions for the IND and DCA models is available at https://github. com/juan-rodriguezrivas/covmut.
Zirconium alloys are used in the nuclear industry due to their low neutron capture crosssection and resistance to corrosion, irradiation and creep. The microstructure of the nuclear fuel components evolves during the manufacturing route and can impact the subsequent processes or the final properties. Thus, numerical modeling of thermo-mechanical manufacturing processes is of interest to understand and master these microstructure evolutions. Numerical modeling of thermo-mechanical manufacturing processes with FORGE ® NxT 1 software is applied. These models provide the thermo-mechanical history of the material at each integration point of the finite element (FE) mesh, which can be used to assess locally the continuous dynamic and post-dynamic recrystallization during hot extrusion. Mean-field models were developed in Python and integrated into FORGE ® NxT software, to quantify the microstructure evolution at the macro-scale of the component.
Full-field models (DIGIMU ® software 1 ) were also developed for considering microstructural heterogeneities and the influence of initial microstructure at the mesoscopic scale while improving the mean-field equations by homogenization. After validation based on experimental results, these two recrystallization models provide complementary information to optimize the process parameters at the macro-scale and to better understand mesoscopic scale phenomena, such as: • At the macro-scale: influence of hot extrusion parameters on the continuous dynamic and post-dynamic recrystallization of Zircaloy-4 • At the meso-scale: influence of the initial microstructure on the recrystallization phenomena with improved precision. Indeed, the topology of the microstructure is predicted and not only the mean values/distributions of the state variables.
Recrystallization Characterization Methods. Recrystallization was characterized on zircaloy-4 industrial samples after hot extrusion and laboratory compression tests with additional heat treatments. Both type of samples were prepared with a final electro-polishing, and an EBSD analysis was performed and post-processed using Matlab and the MTEX toolbox. Extrusion samples were also analyzed with optical microscopy to assess the recrystallized fraction based on the fraction of equiaxed grains. Industrial samples. Tubes were extruded with the industrial standard extrusion parameters which are an extrusion ratio of about 10 and an initial temperature of 600°C. Mode details about this hot extrusion process and associated parameters can be found in  [9]. Microstructure was characterized at mid-thickness, at 4 different positions from the front end of an extruded tube (0, 60, 150 and 300 mm) with optical microscopy, and at 2 positions (60 and 300 mm) with EBSD.
The decrease of recrystallization fraction observed between 150mm and 300mm is not considered to be significant because of the scatter in microstructure that was observed in the samples but not precisely quantified. The GAKAM criteria is consistent with the fraction of recrystallized grains assumed as equiaxed ones on the optical micrographs. The GAKAM + GOS criteria gives a lower recrystallized fraction at 150 mm. This is due to the presence of sub-grains with a misorientation greater than 3° in some big grains, which fulfill the GAKAM criteria but not the GOS one. Defining a recrystallization criterion from EBSD data is then not obvious for CDRX and PDRX due to the presence of the substructure.
6 ). The PDRX model is based on Avrami approach  [13]  and describes the growth of recrystallized grains during the cooling of the extruded tube or subsequent heat treatment. The mean-field model equations are described in detail in  [12]. The latest version of the model is coded on Python with a graphical user interface. It is also coded on FORGE® NxT software as a user variable. It is used to assess the evolution of the microstructure (recrystallized fraction, grain size, LAGB density, dislocation density) as a function of the local thermo-mechanical history of the material during and after hot forming. Recrystallization full-field model. The recrystallization full-field model is based on a level set description of interfaces (grain boundaries and second phase particles) in a FE framework.
It was first developed by Bernacki et al. [14]  and followed by many improvements. The baseline model (DIGIMU ® software) used in this study includes the Smith-Zener pinning effect (pining of grain boundaries by second phase particles  [15] ) and the influence of stored energy in the form of dislocation density  [16] , as well as the latest computational performance improvements  [17]  that allows 2D and 3D computations including the main physics of dynamic and post-dynamic recrystallization  [18]. The equations of this model are described in detail in these references  [14, 16, 17, 18]. The baseline full-field model includes the discontinuous dynamic recrystallization mechanisms, characterized by the nucleation of new grains based on a critical strain criterion  [18]. The continuous ones, characterized by the progressive nucleation and misorientation of the sub-grains and occurring in zirconium alloys, are under development and will include the orientation dependence of LAGB mobility and energy (Fig.
Identification and validation of models parameters. The mean-field model coded on Python was first applied as a post-processing of thermomechanical loading during compression tests that was calculated using FORGE ® NxT modeling. At that time, only CDRX EBSD results (with only 1 or 2 seconds of PDRX due to the time needed to quench the samples) were available. A sensitivity analysis was performed on the 23 parameters of the model. Since most of the experiments did not exhibit significant PDRX, CDRX model parameters were identified as the more influent, except the activation energy of grain boundary mobility which can have a significant impact for some experiments with short PDRX times. A total of 7 parameters were then selected for an automated optimization using a Monte Carlo approach, aiming to reduce the difference between modeled recrystallization fraction and experimental one.
Key Engineering Materials Vol. 926 Characterizations. CDRX and PDRX of zircaloy-4 alloy were characterized by optical microscopy and EBSD on industrial extruded tubes and compression tests samples. These are a valuable data set for the understanding of recrystallization mechanisms occurring in zirconium alloys during and after hot forming processes. Nevertheless, a finer analysis of EBSD data will be needed to get reliable results for the identification of recrystallization models parameters. Modeling. A physically based mean-field model was developed to describe the CDRX and PDRX mechanisms. The latest Python version of this model allowing fast computations, a Monte-Carlo approach can be used to optimize the parameters, thanks to the compression and heat treatments test results. The full-field model is still under development for CDRX and PDRX mechanisms, but it is already validated for the modeling of pure grain growth with or without SPPs.
Phenolic Compound Identification All extracts were analyzed by LC-MS. The most intense peaks were extracted for each retention time. These results were then processed using MassHunter software version 8. 0. 0 (Agilent, Santa Clara, CA, USA). Data were firstly compared with a homemade database followed by a commercial library (NIST and Metlin databases) if no matches were obtained. Commercial standards of the main compounds were used to certify the first identifications. Spectra and retention times were compared to confirm the correct assignment of peaks.
Phenolic Compound Identification and Quantification Extracts were obtained from cherry branches by conventional solvent extraction in ethanol-water, 70/30, v/v, at a temperature of 70 • C. The main PCs (14 molecules) were identified through databases and their presence was confirmed using commercial standards. The characteristics of the identified compounds obtained by mass spectrometry as well as their content are shown in Table  1. Some compounds, having multiple m/z, were identified by fragmentation in the source. This is the case of prunin, a glucoside of naringenin, whose [M + H] + is 435. 1283 Da, and which, by losing a glucose, has a fragment at 273. 0754 Da for the positive mode ionization. On the other hand, genistein and apigenin, which have the same raw formula and were not differentiated with the spectra of the extracts, could be differentiated thanks to the standards which have different retention times, 15.