dataesr/scientific-paragraphs-categorization huggingface dataset:
Les vecteurs non viraux organiques sont couramment utilisés pour les modifications cellulaires ex vivo. Deux stratégies de thérapie génique sont en cours de développement préclinique et clinique dans l'arthrose. La première (NCT03383471) est une thérapie génique ex vivo qui consiste en la modification et l'amplification des cellules in vitro, puis leur injection IA. Cette stratégie ex vivo a été choisie par Invossa TM pour surexprimer un facteur de croissance, le TGF-␤1, dans des chondrocytes allogéniques irradiés puis mélangés à des chondrocytes non modifiés avant injection IA  [45]. La seconde approche est celle plus classique de thérapie génique in vivo via l'injection locale ou systémique de vecteurs viraux contenant le transgène d'intérêt. En général, les transgènes utilisés pour la thérapie génique de l'arthrose ciblent la réduction de l'inflammation (e.
Remerciements Les auteurs remercient l' Agence nationale de la recherche (ANR) pour le financement des projets JCJC KLOTHOA  ( ANR-18-CE14-0024-01  ; CV) et  PRC PPAROA  ( ANR-18-CE18-0010-01  ; JG). Ils remercient également la «  Foundation for Research in Rheumatology » pour le financement du projet FOREUM-EULAR SEN-OA (CV) , l'Inserm pour le financement du programme transversal AGEMED 2. 0 (JG),  la région pays de la Loire RFI BIOREGATE pour le financement des projets GENOA (CV)  et  MONOMER (CLV) , l'Agence de la biomédecine (18GREFFE012 ; JG), la fondation de l'Avenir pour la recherche médicale appliquée (AP-RM-18-005 ; CLV) et la Société francaise de rhumatologie (SFR 4015 ; CV et SFR 3637 ; JG). Enfin, les auteurs tiennent à remercier particulièrement la fondation pour la Recherche médicale (ARF201809007012 ; VD) et le programme  « Nantes Excellence Trajectory » (NExT Junior Talent 2018  ; VD) pour leurs soutiens financiers.
5), and the potential difference value applied to the WE (-1. 5 V) to study the effect of the deposition time on the resulting material dimensionality and morphology. The width of the produced nylon stripes can be evaluated from OM, SEM and AFM. We found that longer deposition times allow for the formation of the wider polyamide deposits having µm dimensionality (e. g. , average stripe widths of 18 µm and 290 µm for deposition times equal to 30 s and 1800 s were found, respectively). Fig. 3A  and C  show the AFM topology images recorded for 30 s and 600 s deposition time. The data for other deposits can be found in supporting information (see Fig. S3  for AFM and Fig. S4  for SEM micrographs). A few interesting conclusions can be drawn from these results: (i) the roughness of the polymeric deposits increases for the longer deposition times (R q for 1 s = 42.
It must vary spatially or be displaced by faulting over a scale smaller than the distance between the two drill holes because of differences in the depth where it occurs. For example, Section 385-U1551B-4H-1 (23. 6-25. 3 mbsf ) shows parallel laminae and no folding, whereas Sections 385-U1551A-4H-1 through 4H-4 (21. 6-27. 3 mbsf ) are strongly folded and contorted. At deeper levels, folding is not prominent, although a fold is directly imaged as a sigmoidal shape in interval 12H-4, 110-130 cm (~85 mbsf ). Soft-sediment deformation was also caused by the coring process, producing shear adjacent to the core liner. In an extreme case, the shear may affect the entire diameter of the core, resulting in folds coaxial with the core (e. g. , interval 385-U1551A-8H-2, 20-70 cm). Because these are inferred to be caused by drilling, they are not included as structures in the database.
Petrophysics The physical properties of core recovered from Site U1551 were measured in the core laboratory on whole-round cores as well as working-and (subordinate) archive-half sections. Two holes were cored, one to ~120 (Hole U1551A) and one to ~49 mbsf (Hole U1551B). Measured properties and measurement procedures are listed in Tables  T17  and T18  in the Expedition 385 methods chapter  (Teske et al. , 2021a). The data acquired from Hole U1551A and U1551B cores were compared for lithostratigraphic characterization and correlation of core description information with core- show a positive correlation between their peaks that can in turn be correlated to the relative abundance of diatom ooze, clay, and sand beds.
Magnetic susceptibility Both MS data sets (Whole-Round Multisensor Logger [WRMSL] and SHMSL) are in very good agreement along the entire depth profile (Figure  F20C ) and correlate with peaks observed in NGR, density, and P-wave velocity. A large fluctuation in MS intensity is observed from the seafloor to 6 mbsf. From 6 to 9 mbsf, MS intensity rapidly increases to 36 SI; it then remains constant to ~12 mbsf. After a shift of two orders of magnitude to ~300-822 SI, MS intensity values remain relatively constant over the next 50 m to 65 mbsf. Below 65 mbsf, MS intensity drops by two to three orders of magnitude and remains at low values to 93 mbsf (minimum of 2 SI at ~76 mbsf ). Over the remaining 24 m of Hole U1551A, MS intensity reaches 750 SI and averages ~500 SI. This latter trend matches that observed in the NGR downhole variation.
Methods In this section, a description of the data preprocessing and brief introductions to the methods used in this work are given. The whole procedure until the final cluster assignments, which employs all these methods is then described in Section 2. 4. For several steps in the procedure, we use functions provided by the Python Programming Language's scikitlearn library  (Pedregosa et al. , 2011). Below, we give a description of the particular processing and scikit-learn algorithms we employed:.
K-Means Clustering K-means clustering is an iterative method in which a fixed number of clusters are found in the data  (Jain, 2010). The iteration is usually initiated with a random selection of cluster centroids. During the iterations, the cluster centroids are moved in order to minimize the sum of squared distances of cluster members to their cluster centroid. Among the iterations, the cluster assignments can change as they are made in such a way that the variance is minimized within each cluster. Based on this principle, several variations exist; for this study, we used the k-means clustering algorithm from python's scikitlearn library with random initialization and Euclidean distances.
Silhouette Score We employ the silhouette score to evaluate the cluster quality as it provides for each data point an estimation of how well the point fits in its own cluster or if it might belong to the nearest neighboring cluster. The mathematical formulation is based on the intra-cluster and nearest-neighbor Euclidean distance of each point. The silhouette score can have values in the interval (-1,1) where silhouette scores close to 1 indicate a good match to the own cluster while values close to -1 mean that the point is closer to the nearest cluster. Silhouette scores close to 0 can be interpreted as an overlap between different clusters. A detailed and illustrative description of the silhouette score can be found in  Rousseeuw (1987). For this study, the silhouette scores are derived by the corresponding function of python's scikitlearn library based on Euclidean distances.
Procedure The whole procedure to obtain clusters of similar characteristic from the large ChemCam data set consists of multiple steps that we chose to repeat 100 times. We discuss below (Section 2. 4. 2) how we chose several parameters in this study, including the repetition value of 100 and the six NMF dimensions. Below, we provide a brief overview of these steps and we provide more detail in later sections. One run of the clustering procedure is as follows: 1. A sub-data set is randomly selected using the train_test_split function of python's scikitlearn library. The sub-data set contains 4,866 observations, which is 26% of the whole data set in this study. 2. A fixed six-dimensional NMF model is applied to the sub-data set resulting in six NMF scores for each spectrum. 3. K-means clustering with the number of clusters fixed at six is applied to the NMF scores of the sub-data set.
We used the random forest classifier class provided by python's scikitlearn library and set up a model with a maximum depth of 10 and with 50 estimators, meaning 50 de-correlated decision trees. With the train_ test_split function, the 14,715 observations with a final cluster assignment were split into training and test data in an 80:20 ratio. Thus, the NMF scores and the corresponding cluster assignments of 11,772 spectra were used to train the random forest. Next, predictions with the classifier were made on the NMF scores of 2,943 spectra in the test data set. This process was repeated 50 times with randomly changing the selection of training and test data but keeping their sizes constant. The lowest accuracy for the predictions on the test data was 0. 998 among the 50 repetitions and in 14 runs an accuracy of 1 was obtained.
Conclusion This study highlights the broad compositional variations seen by ChemCam between the stratigraphic units traversed by the Curiosity rover as summarized in the pie diagrams. This work can therefore support the effort to map the chemostratigraphy of the Gale crater. But it is also striking to see the variability when the data are displayed by groups of 10 sols in the bar plots, which might be an expression of the various degrees of mixing of the sedimentary sources. Even though the six clusters are able to display distinct changes in composition, the approach is not sensitive to varying abundances of minor and trace elements, which can be crucial for understanding environmental changes. In addition, we cannot discount the potential impact of bias in target selection since most ChemCam targets were selected according to scientific and operational priorities of the time.
Cluster 3 correlates to the sediments of the Murray formation hypothesized to be from a distinct silica-rich basaltic source that likely underwent some chemical weathering, and cluster 5 relates to the isochemically altered or relatively unaltered basaltic sediments of the Bradbury group, Stimson formation, and soils  (Bedford et al. , 2019 (Bedford et al. , , 2020)). The random forest classifier trained with ChemCam data measured until sol 2756 of the mission gives promising results for predicting cluster memberships of new data. The validation process returned overall high accuracies, which might be due to the exclusion of ambiguous points identified by inconsistency among the 100 clustering repetitions or bad silhouette scores. For the predictions on the data, which was not used in the clustering and the training of the random forest, the probabilities for belonging to the assigned cluster served as a useful criterion for removing uncertain predictions.
REMERCIEMENTS Cette recherche est réalisée dans le cadre du projet Offensive Sciences numéro 3. 11 « Virtual Innovative Real Time Factory » (VIRTFac) qui bénéficie du soutien financier de l'Offensive Sciences de la Région Métropolitaine Trinationale du Rhin Supérieur, du programme INTERREG V Rhin Supérieur et du Fonds européen de développement régional (FEDER) de l'union européenne.
Comment fonctionne ICELLNET ? Nous avons développé ICELLNET, une méthode bioinformatique (package R) pour étudier la communication intercellulaire de manière globale à partir de profils transcriptomiques de populations cellulaires (Figure  1B )  [7]. ICELLNET intègre notamment une base de données originale qui rassemble les gènes codant les différentes sous-unités des ligands et récepteurs impliqués dans de nombreuses interactions décrites dans la littérature scientifique. Cette base de données rassemble actuellement plus de 700 interactions classées par familles de molécules, et contient toutes les interactions connues pour les cytokines (247 interactions), chimiokines (112 interactions) et les molécules de contrôle immunitaire (immune checkpoints) (59 interactions) avec leurs récepteurs respectifs.
Pour étudier la communication entre deux types de cellules, ICELLNET calcule un score de communication à partir des profils transcriptomiques et donc de l'expression des gènes codant les ligands et récepteurs inclus dans la base de données. Chaque profil transcriptomique correspond aux gènes exprimés par un type de cellule dans un contexte biologique défini. Ainsi, le score de communication peut être calculé entre deux types différents de cellules provenant d'un même tissu, ou bien entre types cellulaires provenant de deux jeux de données différents, comme, par exemple, un même type cellulaire dans différents états d'activation. ICELLNET met également à disposition 31 profils transcriptomiques de référence, permettant de reconstruire les voies de communication d'une cellule d'intérêt avec ces cellules « types », à partir de son seul profil transcriptomique.
(a), agrees well with the theoretical one for cubic Al 1. 23 Ca 3 Fe 0. 99 Hf 2 Si 0. 99 O 12 garnet (Crystallography Open Database (COD) card #96-901-3967). No other phases are found. The structure of Tm:CLTGG was refined using the Rietveld method. The Match3! software was used and a total of 196 reflections were analyzed. The obtained reliability factors are R p = 4. 94%, R wp = 6. 38%, R exp = 2. 21% and the reduced chi squared χ 2 = (R wp /R exp ) 2 = 8. 37. Tm:CLTGG crystallizes in the cubic class (sp. gr. 3 Ia d , No. 230) with a lattice constant a = 12. 5158(0) Å, the volume of the unit-cell is V = 1960. 54 Å 3 , the calculated density is ρ calc = 5. 893 g/cm 3 and the number of formula units in the unit-cell is Z = 8. The fractional atomic coordinates, the site occupancy factors (O.
Remerciements Ce travail a bénéficié du soutien financier du CNRS, programme Gazhouille (2013Gazhouille ( -2015) ) coordonné par  Y. Gunzburger. L'article a bénéficié d'échanges dans différents séminaires pour lesquels l'auteur remercie les personnes suivantes :  université du Québec à Rimouski  ( M. -J. Fortin ,  Y. Fournis ),  « Journée  sous-sols » (X. -A. de Sartre,  P. -O. Garcia ), AFSP 2017 session « Politiques énergétiques : modèles alternatifs et dynamiques locales » ( S. Topçu ,  S. Chailleux ).
Numerical Results We consider the test case Model 2, case 1 from the benchmark of Takeda and Ikeda  [19]. The geometry of the core is three-dimensional and the domain is {(x, y, z) ∈ R 3 , 0 ≤ x ≤ 140 cm; 0 ≤ y ≤ 140 cm; 0 ≤ z ≤ 150 cm}. This test is defined with 4 energy groups, isotropic scattering and vacuum boundary conditions. Figure  1  represents the cross-sectional geometry on the plane z = 75 cm. Since the scattering is isotropic, the SP 3 formulation can easily be reformulated as a multigroup diffusion problem with 8 energy groups and an isotropic albedo boundary condition  [3]. We then made the computations with the PRIAM solver from the code CRONOS2  [14]  for the conforming case and with the MINARET solver  [13]  from the APOLLO3 R code  [18]  for the SIPG discretization. In Figure  2 , we consider the convergence of the fundamental mode where we used the SP 3 formulation with Q 1 finite elements and a regular cartesian mesh of size h.
Through a model-data comparison of both [CO 2 ] and meteorological variables, we aim to shed light on the ability of global models to simulate carbon weather over the central and eastern U. S. We describe the OCO-2 MIP simulations, ACT-America campaigns, and observational metrics in Section 2. In Section 3, we present process-based analyses of observed and simulated [CO 2 ] variability in two frontal cases (one in summer and one in winter). We then conduct seasonal statistical analyses of modeled [CO 2 ] in the cold and warm sectors of frontal systems (Section 4) and examine the model performance of ACT observational metrics in four different seasons (Section 5). Finally, we present our discussion and conclusions in Section 6.
Note that CT is run at 1° × 1° over N. A. and 2° × 3° globally (referred to as CT_Global). Five suites of experiments are performed with the OCO-2 MIP model systems to simulate global [CO 2 ]: (a) PRIOR, in which models are driven with prior fluxes and no inversion is performed; (b) IS, the inversion experiment assimilating in situ [CO 2 ] measurements from the Cooperative Global Atmospheric Data Integration Project (https://www. esrl. noaa. gov/gmd/ccgg/obspack/); (c) LNLG, inversions that assimilate 10s averaged Land Nadir and Land Glint retrievals (version 9) observed by  OCO-2 (Eldering et al. , 2017) ; (d) OG, in which models assimilate 10s averaged OCO-2 Ocean Glint retrievals; and (e) LNLGOGIS, where inversions are constrained with in situ measurements and all OCO-2 𝐴𝐴 𝐴𝐴co 2 retrievals over both land and ocean surfaces.
Note that the PRIOR experiment with CT, UT, and CAMS and the LNLGOGIS experiment with CSU are not conducted (Table  1 ). LoFI uses a bias-correction method to adjust a diagnostic collection of surface fluxes to match inventory data and in situ observations, which is different from other OCO-2 MIP inversion systems  (Weir et al. , 2021). The simulation with LoFI is considered as an IS experiment and other experiments with it are not available. Modeled [CO 2 ] and meteorological variables are sampled from hourly or 3-hourly 4D gridded simulations at the measurement times and locations using linear interpolations in time (from hourly/3-hourly to 5-s/8-s) and bilinear (in horizontal direction) and log-linear (in vertical direction) interpolations in space (from model grids to specific locations).
In addition to [CO 2 ] observations, airborne in-situ measurements of wind speed and lidar measurements of PBLH made during the campaigns are used in our model evaluation. In-situ wind speeds were measured on both airborne platforms  (Wei et al. , 2021). PBLHs were determined by the aerosol backscatter profiles measured with the Goddard Cloud Physics Laboratory lidar aboard on C130 using a wavelet method  (Davis et al. , 2000; Pal et al. , 2010). A detailed description of the PBLH datasets for all four deployments can be found in Pal,  Davis, Pauly, et al. (2020). In this work, we use 5-s averaged measurements of [CO 2 ] and wind speed and 8-s averaged PBLH measurements. Measurements of [CO 2 ] and wind speed are classified into three vertical layers: planetary boundary layer (PBL), lower free troposphere (LFT) which defined as the layer between the top of PBL and 4 km a.
Given the relatively coarse resolutions of global models, here we use both level leg and profile measurements for computing this ACT metric to conduct a synoptic-scale model-data comparison. This is slightly different from the method by  Pal, Davis, Lauvaux, et al. (2020)  that only uses profile data. We calculate observed and simulated PBL-to-FT [CO 2 ] differences in both warm and cold sectors for each frontal research flight as well as for pre-and post-frontal flights. Pre-frontal flights are included in the warm sectors and post-frontal flights the cold sectors, as discussed in Pal,  Davis, Lauvaux, et al. (2020). Seasonal statistics of the PBL-to-FT differences are calculated using all frontal, pre-, and post-frontal flights in each season.
In the FT, the ensemble persistently underestimates [CO 2 ]. We find similar results as for the LNLGOGIS experiment. In comparison, the IS and LNLG ensembles are closer to the observations than other ensembles in both the warm and cold sectors, which is different from the results when averaging the simulations over different sectors in weather systems. This implies that the two experiments may have more realistic spatial distributions of surface fluxes as compared to the other experiments. The PRIOR simulations overestimate observed [CO 2 ] in both warm and cold sectors in all seasons. Large model-data discrepancies exist among different inversion systems, and discrepancies are overall smaller in warm sectors compared to cold sectors and smaller in FT compared to PBL (Figure  5  and Figures S9-S16 in Supporting Information S1).
In the UFT, however, all models simulate stronger negative frontal differences than observations and have small model discrepancies. The models also often simulate weaker variability in frontal [CO 2 ] difference across different frontal cases due to coarse model resolutions (Table  2 ). Within a single inverse modeling system, varying the data source makes a relatively small difference in the simulated frontal [CO 2 ] difference (Figure  6 ). Comparisons among PRIOR, IS, LNLG, and LNLGOGIS experiments (OG is excluded since it tends to degrade model performance) show that the models have a limited spread (5%-25%, represented as the normalized standard deviation among the four experiments with the same model) among the simulated frontal differences in the PBL. In the FT, the models have even smaller discrepancies among different data source experiments.
0 ± 3. 1 4. 2 ± 2. 3 4. 8 ± 2. 3 4. 4 ± 2. 8 3. 9 ± 2. 4 4. 6 ± 3. 3 6. 3 ± 3. 1 5. 8 ± 2. 1 Autumn 5. 0 ± 5. 0 2. 5 ± 1. 3 (2. 3 ± 1. 2) 3. 8 ± 2. 0 4. 1 ± 2. 1 3. 7 ± 2. 2 2. 7 ± 1. 9 2. 6 ± 2. 5 3. 1 ± 2. 3 4. 3 ± 3. 4 3. 4 ± 1. 9 5. 1 ± 3. 4 Spring 2. 6 ± 2. 6 0. 3 ± 1. 9 (0. 4 ± 1. 9) 1. 5 ± 1. 3 1. 5 ± 1. 7 1. 5 ± 1. 3 1. 3 ± 1. 7 1. 0 ± 2. 6 1. 4 ± 1. 4 1. 8 ± 2. 9 1. 6 ± 1. 8 2. 4 ± 1. 6 Note. Model results are averaged across five suites of experiments constrained with different data sources. Uncertainties represent the ranges of ACT metrics across all frontal cases. LoFI and CAMS reproduce the observed PBLH but they differ in frontal [CO 2 ] differences. TM5 (TM5-4DVAR, CT, and OU) is biased low in both warm and cold sectors in winter. However, TM5-4DVAR outperforms CT and OU in summer frontal differences, which may suggest more reasonable surface fluxes in TM5-4DVAR.
Discussion and Conclusions Our sector-dependent evaluation of simulated [CO 2 ] reveals that averaging the model simulations within an entire weather system may cancel out model biases in different sectors of the weather system (i. e. , warm and cold sectors) and consequently hide possible model errors in CO 2 transport or/and fluxes. In OG and LNLGOGIS experiments, the unbiased PBL-[CO2] is a result of canceling out the model underestimates in warm sectors with the overestimates in cold sectors of weather systems, which hides possible surface flux biases in the two experiments. In comparison, although the IS and LNLG ensembles are biased in [CO 2 ] across weather systems, they better capture observed [CO 2 ] in both warm and cold sectors, suggesting the two experiments may have more reasonable flux estimates than the other data experiments.
Acknowledgments This study was funded by the  National Aeronautics and Space Administration (NASA)  under the following awards:  NNX15AG76G  to  Penn State  (Davis);  NNX15AJ07G  to  Colorado State  (Baker and Schuh);  80NSSC19K0730  to  Texas Tech  and a  Texas Tech  faculty start-up grant (Pal); and  80NM0018D0004  to  Jet Propulsion Laboratory, Caltech  (Liu). Additional support for research was provided by  NASA  Grants  NNX12AP90G  (Davis) and  NNX14AJ17G  (Davis). Co-author Johnson acknowledges the internal funding from  NASA 's  Earth Science Research and Analysis Program  and co-author  Philip  acknowledges financial support of the  NASA Academic Mission Services  by  Universities Space Research Association  at  NASA Ames Research Center. The  ORNL  DAAC is sponsored by the  National Aeronautics and Space Administration  under Interagency Agreement  80GSFC19T0039.
Experimental data We analyzed 11 experimental campaigns conducted at the Geosciences Department of Mines ParisTech on salt specimens from different locations in France and in the US between 2004 and 2012. Each testing program is comprised of conventional uniaxial and triaxial compression tests and creep tests. As volumetric strain measurements are only provided in triaxial compression tests, we focused our analysis on those experiments and the total number of studied tests was about 123. The tested specimens are 13 cm high cylinders with a slenderness ratio of 2. A triaxial compression test consists in maintaining a constant confining pressure on the lateral surface of the specimen by injecting/withdrawing a confining fluid into a triaxial cell, while imposing a constant axial strain rate via loading platens between which the specimen is placed  (Wawersik and Hannum, 1980; Mellegard et al.