dataesr/scientific-paragraphs-categorization huggingface dataset:
We have used iBAQ values to calculate the differential affinity of DD/AA-versus wt-CFTR, as described above (Materials and Methods). The differential affinity of each immunoprecipitated protein between DD/AA-CFTR and wt-CFTR was calculated as the base 2-logarithm of the ratio of the median for each protein for the DD/AA-CFTR IP with the median expression values. We assumed all the proteins outside the threshold of log 2 ratio ±5 were differential interactors between DD/AA-CFTR and wt-CFTR. Among the 1150 proteins identified, we obtained 161 proteins with higher affinity for DD/AA-CFTR (above the threshold log 2 ratio of five) and 77 proteins with higher affinity for wt-CFTR (below the threshold log 2 ratio of -5) (Datasets S2A,B, respectively, and Figure  1A ). Using DAVID (GO Version 6. 8), we analyzed the proteins with higher affinity to DD/AA-CFTR and those with higher affinity to wt-CFTR (based on the threshold previously established) to find the respective GO terms.
Recognition of CFTR AFT Retention Motifs To further characterize the ERQC, we identified proteins involved in recognizing the AFTs (i. e. , at the third ERQC checkpoint). Previously, we used differential interactomics to identify such regulators  [18]. Now, we used a pull-down approach in non-CFTR expressing CFBE cells with peptides mimicking such CFTR regions: 10 amino acid-long peptides corresponding to the CFTR sequence around each of the AFTs (see Materials and Methods). The pull-down was performed using whole cell lysates in order to obtain a larger number of proteins and avoid any "filtering" through this step. Cells 2019, 8, 353 9 of 22 LC-MS/MS results of pulled-down extracts identified a total of 3393 proteins (Dataset S3, Figure  2A ). Using a similar cutoff as above, 742 proteins were identified with an increased association with non-mutated AFTs and 114 with an increased association with mutated AFTs (Datasets S4A,B, respectively).
The number of proteins strongly interacting with non-mutated AFTs was higher, as well as the interaction fold range. The mutated peptides correspond to non-physiological sequences and, thus, are used mainly to discriminate which interactions are AFT-motif-specific. whole cell lysates in order to obtain a larger number of proteins and avoid any "filtering" through this step. LC-MS/MS results of pulled-down extracts identified a total of 3393 proteins (Dataset S3, Figure  2A ). Using a similar cutoff as above, 742 proteins were identified with an increased association with non-mutated AFTs and 114 with an increased association with mutated AFTs (Datasets S4A,B, respectively). The number of proteins strongly interacting with non-mutated AFTs was higher, as well as the interaction fold range. The mutated peptides correspond to non-physiological sequences and, thus, are used mainly to discriminate which interactions are AFT-motif-specific.
Comparative Analysis of Interactors of CFTR Variants To extract more information from the large datasets of interacting proteins identified, we then performed an extended comparison of the four different datasets obtained here (Datasets S1, S2, S3, S4) with the interactomes of F508del-CFTR and F508del-4RK-CFTR that we previously published  [18]. We started by a global comparative analysis to identify common proteins among all datasets (WT: wt-CFTR interactome, DF: F508del-CFTR interactome, 4RK: F508del-4RK-CFTR interactome, DD/AA: DD/AA-CFTR interactome, PeptR, pull-down of non-mutated AFT motifs, PeptK, pull-down of mutated AFT motifs) and calculated for each pair the Jaccard similarity index (Figure  3 , Dataset S5). Interestingly, greater similarity seemed to occur within the four full-length CFTR variants (range 30-47%) and within the two peptide pull-down datasets (32%); this suggests that proteins interacting with CFTR/peptide are very much dependent on the presence of full-length protein and that the presence of the isolated AFT motifs modifies greatly the interactome.
A second relevant observation is that within the four datasets for full-length CFTR variants, greater similarity occurred for the pairs WT/DD/AA (42%) and F508del/F508del-4RK (47%), suggesting that the folding status is a stronger determinant for the interactome when compared to the trafficking status. Cells 2019, 8, x 10 of 21 The list of AFT interactors was analyzed using DAVID to evaluate the most represented protein families (Figure  2 ). For GO term BP, mRNA metabolic process, RNA processing, and organelle organization were the most significantly-enriched categories in the interactors of PeptR (non-mutated AFTs) (Figure  2B ). For GO terms CC, intracellular organelle part, organelle part, and macromolecular complex were the most enriched (Figure  2C ). The number of proteins with increased affinity to the PeptK (mutated AFTs) interactors was low, showing no significant enrichment, which was expected considering that the sequences are "non-physiological".
A second relevant observation is that within the four datasets for full-length CFTR variants, greater similarity occurred for the pairs WT/DD/AA (42%) and F508del/F508del-4RK (47%), suggesting that the folding status is a stronger determinant for the interactome when compared to the trafficking status. We then performed pairwise comparisons within the four variants (Figure  4  showing comparisons of WT/F508del, F508del/F508del-4RK, DD/AA/F508del, F508del-4RK/WT, DD/AA/F508del-4RK, plus the comparison WT/DD/AA shown in Figure  1 , Datasets S6) and bioinformatics analysis for the proteins with statistically-significant stronger affinity in each pairwise comparison. For these comparisons, we used a holistic approach and higher stringency to reduce the number of proteins detected (see Materials and Methods).
Significant proteins were identified using  We then performed pairwise comparisons within the four variants (Figure  4  showing comparisons of WT/F508del, F508del/F508del-4RK, DD/AA/F508del, F508del-4RK/WT, DD/AA/F508del-4RK, plus the comparison WT/DD/AA shown in Figure  1 , Dataset S6) and bioinformatics analysis for the proteins with statistically-significant stronger affinity in each pairwise comparison. For these comparisons, we used a holistic approach and higher stringency to reduce the number of proteins detected (see Materials and Methods). Significant proteins were identified using the thresholds: -log 10 (p-value) ≥ 1. 6 (corresponding to p < 0. 025) (log 2 (Condition 1)log 2 (Condition 2)|) ≥ 6. Comparison of the wt-and F508del-CFTR interactomes (Figure  4A  and Table S1A-D ) recapitulated previous observations reporting an enrichment in GO terms related to membrane localization, ER targeting (BP), and for cytoskeleton-related categories (CC) for wt-CFTR  [15].
Overall, the pairwise analyses shown in Figure  4  and Table  S1  confirmed what was suggested by the Jaccard similarity index (Figure  3 ), that the interactomes (and the categories enriched) were closer for variants with higher folding affinities (wt and DD/AA that were properly folded versus F508del and F508del-4RK that had deficient folding) than for those with trafficking similarities (DD/AA and F508del that were retained versus wt and F508del-4RK membrane-located variants). A qualitative analysis covering only proteins present in at least two replicates (Figure  S2 ) evidenced more striking differences at the level of protein folding machinery-related terms. For this analysis, the protein lists for F508del-, DD/AA-, F508del-4RK-, wt-CFTR (Dataset S1 and  [18] ), and their pairwise combinations were assessed for enriched GO terms related to ER retention and folding processes.
From a total of 808 proteins identified in the F508del-CFTR interactome, 243 proteins were not present in neither the DD/AA-, nor the wt-CFTR interactomes, suggesting that these are proteins specifically involved in F508del-CFTR folding and ER retention-related processes (Figure  S3A , Dataset S7). Comparison of these data with a proteome map study for ER membrane proteins  [33]  revealed that F508del-CFTR, contrary to wt-and DD/AA-CFTR, interacted exclusively with 24 ER membrane proteins (Figure  S3B , Table  S2 ). Finally, the proteins exclusively interacting with either F508del-, wt-, or DD/AA-CFTR (Figure  S3A ) were analyzed for enriched GO terms (Figure  S4 ). RNA processing and protein folding were the BP more enriched for F508del-CFTR. Among these proteins, we found membrane-enclosed as a CC term enriched in F508del-CFTR and DD/AA-CFTR.
Comparative Analysis of Interactors Involved in AFT Recognition We applied a similar analysis to the one described in the previous section to the proteins interacting with the AFT and control peptide sets. Besides the comparison between interactors of the two sets of peptides (Figure  2 ), we performed comparisons of these interactomes with those of wt-, F508del-, and F508del-4RK-CFTR. Comparison of the peptide interactomes with that of wt-CFTR (Figure  5A ,C and Table  S3A -H, Dataset S5) showed an enrichment in protein targeting categories for wt-CFTR and an enrichment in mRNA and protein biosynthesis/translation categories for the peptides interactomes (and some fewer specific categories for PeptK, which correspond to sequences that are "non-physiological"). Cells 2019, 8, x 13 of 21 in mRNA and protein biosynthesis/translation categories for the peptides interactomes (and some fewer specific categories for PeptK, which correspond to sequences that are "non-physiological").
Interestingly, when we compared the F508del-CFTR interactome with those of the peptides (Figure  5B ,D and Table  S3  I-N), the number of differential interactors was very small, suggesting some similarity in these datasets, which was expected to include proteins scrutinizing CFTR (or other secretory pathway proteins) in the ER. Examples of these common proteins detected include calnexin, components of the COPII machinery or even the translocon subunit Sec62. This analysis providing comparison of interactomes from peptides and full-length CFTR, together with the results presented above (Figure  2 ), evidences the specificity of the AFT recognition in terms of function (protein retention) and localization of activity (the ER). Interestingly, when we compared the F508del-CFTR interactome with those of the peptides (Figure  5B ,D and Table  S3I -N), the number of differential interactors was very small, suggesting some similarity in these datasets, which was expected to include proteins scrutinizing CFTR (or other secretory pathway proteins) in the ER.
Clustering was performed with HAC using hclust heuristics. The total number of proteins clustered into eight different groups (see Materials and Methods), and the red arrows represent the four CFTR variants under consideration. Results showed that F508del, F508del-4RK, and DD/AA aligned in the same axis with the same direction. DD/AA and WT aligned through PC1 axis although with a different PC2 direction. Figure  6. Component analysis for full-length CFTR variants. PCA was performed using the package "pca3d" from R programming  [31]. Data corresponding to the combinations of the median of the amount of each protein immunoprecipitated with each CFTR variant calculated as described above were used for PCA analysis. The PCA scores corresponding to the three main components are plotted in a scatter plot in which the position along the axes show the PCA score of the protein.
The cyan cluster (58 proteins) is enriched in proteins annotated in terms similar to the ones in the black cluster (RNA processing, regulation of RNA stability for BP, nuclear and intracellular organelle for CC). The purple cluster (122 proteins) groups proteins with enrichment for terms related to vesicle-mediated transport or membrane invagination (BP) and cytoskeleton or filaments (CC). Finally, a small group (yellow cluster with only six proteins) was detected for which Figure  6. Component analysis for full-length CFTR variants. PCA was performed using the package "pca3d" from R programming  [31]. Data corresponding to the combinations of the median of the amount of each protein immunoprecipitated with each CFTR variant calculated as described above were used for PCA analysis. The PCA scores corresponding to the three main components are plotted in a scatter plot in which the position along the axes show the PCA score of the protein.
Compared to the PCA described above, in this case, protein folding was evidenced as a distinctive category, which was absent when the full-length variant interactomes were compared. Component analysis for the protein sets corresponding to the arginine-framed tripeptide-related checkpoint. PCA was performed using the package "pca3d" from R programming  [31]. Data corresponding to the combinations of the median of the amount of each protein immunoprecipitated with each CFTR variant (or pulled-down with each set of peptides) calculated before were used for PCA analysis. The PCA scores corresponding to the three main components were plotted in a scatter plot in which the position along the axes shows the PCA score Component analysis for the protein sets corresponding to the arginine-framed tripeptide-related checkpoint.
PCA was performed using the package "pca3d" from R programming  [31]. Data corresponding to the combinations of the median of the amount of each protein immunoprecipitated with each CFTR variant (or pulled-down with each set of peptides) calculated before were used for PCA analysis. The PCA scores corresponding to the three main components were plotted in a scatter plot in which the position along the axes shows the PCA score of the protein. Clustering was performed using hierarchical agglomerative clustering through the standard R function "hclust" and using average linkage as a clustering method.
Therefore, the AFT-interacting retention proteins are likely part Comparison of our datasets with one of the most comprehensive CFTR interactomes  [15]  showed major overlap, that is about 34%, 42%, and 76% for the pairs F508del/F508del-4RK, WT/DD/AA, and PeptR/PeptK, respectively, a relevant figure considering the methodological differences. On extending our recent characterization of the interactome of F508del-4RK-CFTR, to further dissect the ERQC, we observed that misfolded F508del-CFTR and F508del-4RK-CFTR established less interactions when compared to wt-CFTR and DD/AA-CFTR interactomes (Figure  3 ). Interestingly, we observed that F508del-CFTR (n = 808) established more interactions when compared to F508del-4RK-CFTR (n = 722) and that DD/AA-CFTR (n = 878) also presented more interactions than wt-CFTR (n = 828).
Interestingly, such enrichment was observed also for F508del-4RK-CFTR, which traffics to the PM despite its folding defect. That is probably the reason for which we detected ubiquitin ligases such as RFN14 or proteasomal subunits such as PSMA7 with increased association with F508del-4RK-CFTR; such observations are in agreement with the peripheral quality control that assesses CFTR folding status after exiting the ER  [48, 49]  and also with refined characterization of other ubiquitin ligases as potential targets to rescue F508del-CFTR  [43, 50]. The detection of such components of the peripheral quality control may also account for the reduced amount of F508del-CFTR that may reach the cell surface, being thus the substrate for this machinery. By comparing the wt-CFTR, F508del-CFTR, and DD/AA-CFTR interactomes, we identified 243 proteins that are only present in the F508del-CFTR interactome, but neither in the DD/AA-CFTR, nor in the wt-CFTR interactome (Figure  S3A , Dataset S7).
Funding: Work supported by the center grant  UID/MULTI/04046/2019  to  BioISI  and the  Romain Pauwels Research Award  to C. M. F. , S. C. , J. D. S. , and H. M. B. are recipients of fellowships from the BioSys PhD program  PD/BD/114393/2016  ( SFRH/BD/52491/2014 ),  PD/BD/106084/2015  ( SFRH/BD/106084/2015 ) and  SFRH/BPD/93017/2013  from  FCT , Portugal, respectively. Proteomics Core Facility-SGIKER is part of the  ProteoRed-ISCIII  network and is partially funded by  ERDF  and  ESF.
The close aggregate packing method: Kanadasan and Razak proposed to use the particles packing concept to secure the fresh and the hardened properties of SCC  [9]. This method helped to promote sustainability by incorporating palm oil clinker aggregate. Sebaibi et al. proposed a method based on optimizing the SCC concrete using RENE LCPC software  [10]. The mixture design method based on the statistical factorial method: Bouziani developed a simplex-lattice mixture design with different factors and levels  [11]. This study confirms that this approach is valid for a wide range of mixture proportions. Ozbay et al. used  Taguchi's experiment design by using different factors  [12]. This study analyzed the mixture proportion parameters of a high-strength SCC and it shows that the laboratory tests can be reproduced in a full-scale production.
Packing density and water demand Water demand consists in determining the mass of water M w required to make the cementitious paste go from the state of pellets to a state of homogeneous paste. Tests were carried out on 350 g of cement using a standard kneader (described in standard NF EN 196-1  [40] ) and the Vicat apparatus. The optimisation of water demand was performed by using the Réne-LCPC, a developed software by Sedran and Larrard  [41] , which is based on a mathematical model in order to secure a maximum packing density of the skeleton. This model makes it possible to calculate the theoretical compactness of any granular mixture, and the viscosity of any suspension at a given concentration of this mixture, based on various material characteristics. Physically speaking, a higher compactness refers to a minimum percentage of voids between solid grains.
Penetration measurements were performed for different water contents. The mass of water M w required to obtain a height h = 6 mm can be determined by successive tests. In order to limit the number of tests, it is possible to interpolate M w from two points (A & B) framing the normal consistency. However, in order to minimize the error, points A and B must fulfill the following conditions (Eq. 2): Eq. 2 ≥ 2 0. 25 ≤ / ≤ 0. 4 - ≤ 5 The compactness ∅ of the powder is then determined by Eq. 3: Eq. 3 ∅ = ! " ! where ρ C is the density of the cement, M w and M C are the total mass of water and cement, respectively. The theoretical compactness (∅) was calculated using the commercial software Réne-LCPC as it shown in Fig. 8. It represents that the amount of sediments up to 70% improve the density of the mixture which can be explained by the finesse of the marine sediments and their ability to fill into voids between the cement grains.
Parametric study The optimal experimental matrix was defined to achieve reliable and effective results. The dedicated software gives the mix proportions of a total of 20 points for modelling. Table  4  presents the determined arrangements of the 20 runs and their corresponding responses. Six mixtures were removed from the model design because they did not meet the criteria of self-compactibility (having a low workability and fluidity). The water demand of the sediments was not considered in this arrangement. It was added separately for each mixture depending on the mass of sediments. The significance of the model (linear, quadratic, cubic, two-factor interaction, etc. ), was investigated using robust software (Design-Expert and SPSS) and using the statistical model ANOVA (analysis of variances).
Rheological properties The paste rheology was characterized using a rheometer and a plane geometry with variation of the shear rate (γ) from 1 to 100 s -1. The following properties were determined: yield stress (τ 0 ), shear stress (τ), and viscosity (η). The temperature was kept constant at 20±0. 1 °C. The results fitted perfectly the Bingham model (Table  5 ) with a coefficient of determination R 2 ≥ 0. 95 as depicted in Fig. 11. This mixture can be characterized by a yield stress of 2. 990 Pa and a viscosity of 1. 163 Pa. s. A correlation analysis was performed using the commercial software SPSS 24. Spearman's correlation coefficient (ρ Spearman ) was used to investigate the correlation between variables. The Spearman correlation is a non-parametric test that does not involve any assumptions about the distribution of the data.
-For procedure B, a temperature cycle was performed beforehand to modify the gas hydrate distribution at the grain scale. For this purpose, the pore pressure and the confining pressure were first decreased from 7 MPa to 4 MPa and from 10 MPa to 7 MPa, respectively. All the drainage valves were then closed and the temperature of the cell was increased to higher than 20°C. That corresponds to a heating of the sample under undrained conditions in order to progressively dissociate the existing gas hydrates. During this phase, the pore pressure (measured by the pressure transducer) increased progressively because of the heating. A Labview program was used to automatically control the confining pressure (via the volume/pressure controller (3)) in order to maintain the mean effective stress (confining pressure minus pore pressure) at 3 MPa.
The results corresponding to the water-saturated state are plotted in Figure  13. In this figure, the hydrate saturation was calculated from the quantity of methane gas dissociated at the end of each test (the gas that dissolved in water was ignored). The data obtained after the gas hydrate reformation, with the temperature cycle (only for procedure B), are plotted with closed symbols, while the data obtained without the temperature cycle (for procedure A tests) and before the temperature cycle (for procedure B tests) are plotted with open symbols. It should be noted that, for procedure B, Vp decreases after the temperature cycle in tests B2, B3 and B4, while it remains constant in test B1. Unlike the case of the gas-saturated state, the effect of hydrate saturation is less significant. Vp increases from 1850 m/s for the reference case (without hydrates) to 2500 m/s for Sh = 50%.
Rock physics models were also compared to the experimental data to assess the grain scale distribution of the gas hydrates at each state. The following conclusions can be drawn: -Pressurizing methane gas into an already chilled moistened packed sandy specimen creates gas hydrates at the grain contacts. This excess gas method allows for a significant increase in Vp. -Subsequent water saturation converts (and/or redistributes) the gas hydrates from grain contacts to pore spaces and, as a result, decreases Vp. This process may take several days, depending on the hydrate saturation, and cannot be completed for high hydrate saturation. -The heating/cooling cycle allows for the completion of the conversion (and/or redistribution) of gas hydrates from grain contacts to pore spaces. -The effects of hydrate saturation on the mechanical properties of MHBS obtained by the two procedures (with and without thermal cycles) are similar at low hydrate saturation.
Every 48 h cocoons were removed and placed in marked vials with the species, female number, dates, and days of exposure marked on the vials. Fresh cocoons, honey and water were presented to female wasps in new Petri dishes. This procedure was repeated throughout the lives of female wasps. Vials were checked daily for adult eclosion and newly emerged wasps were sexed and counted. These data make it possible to determine fecundity schedules of the three wasps, total fecundity, offspring sex ratios over time and longevity. The experiment was repeated with 10 females of each species. In order to measure longevity for female wasps without host access (=control), the procedure was repeated with 10 females of each species in Petri dishes without host access. To measure male longevity, the procedure was repeated in Petri dishes with 10 males each of the sexual species G.