dataesr/scientific-paragraphs-categorization huggingface dataset:
The background was assessed by calculating the average fluorescence in a visually selected area devoid of neurons or any other stained structure. From this region, a threshold was then derived, equal to the average immunofluorescence plus three times the standard deviation. All data were then subtracted from this threshold and only positive values were conserved for further analysis. A region of interest was drawn around the neuronal plasma membrane of each cell body. The program calculated the average fluorescence within the region of interest over data that were 20% above the maximum values. This thresholding insured that all pixels taken for calculating the average were part of the plasma membrane and that the same criterion was used for all slices in all conditions.
Cellular data statistical analysis To avoid any bias, the cellular analysis was performed under blind conditions. For each cellular marker, the results are expressed as mean ± standard error mean (SEM). All analyses were performed with GraphPad Prism 9 (GraphPad Software, San Diego, CA). Normality of the data was controlled using the D' Agostino and Person normality test and by visualising quintile-quintile (Q-Q) plots. Since the normal distribution of the data was not rejected, we used parametric tests for statistical analyses and performed a two-way ANOVA to test the impact of the post-operative time and the impact of the group on the expression of the cellular markers, followed by post hoc Tukey's multiple comparisons analysis. Results were considered significant at p < 0. 05.
The animals were placed in an open field and a picture was taken each time the animal landed after the TST test. The support surface was calculated in cm 2 , using an image analysis system developed on MATLAB ®. For each animal, 10 repeated measurements were taken and averaged before the operation (preop) and at each post-operative timepoint starting from d3. All the measurements were normalized so that each animal acted as its own control. To do so, for each rat, we used the preop value as the baseline (referenced as 1 for each rat) and we compared each value measured during the post-operative time to the baseline to visualize the changes of the support surface.
This apparatus enabled us to quantify a typical behavior of a unilateral vestibular loss, ipsilesional circling, defined as fast rotations of the animal toward the lesioned side  [59, 70]. The circling behavior was assessed by counting the number of fast laps performed during an acquisition. We also extracted posturographic parameters at the pre-operative time point and two post-operative time points: d3, corresponding to the acute phase of the vestibular syndrome, and d30, corresponding to the compensated phase of the vestibular syndrome. For the following parameters, data were normalized according to pre-operative values so that each rat acted as its own control: -The average time spent by the animals with their abdomen on the ground sensors, during static and dynamic periods, which has been described and validated as a postural strategy during the acute phase after UVN  [59].
Quantitative assessment of the posturo-locomotor activity We assessed the posturo-locomotor activity of the animals under ecological conditions by measuring different parameters known to be affected by UVN in rats (see  [76]  for details). At the beginning of the session, rats were placed individually in the center of an open field (80 × 80 × 40 cm) for 10 min and tracked with Ethovision ™ XT 14 software (Noldus). We measured the total distance moved (cm), the mean velocity (cm/s), and the mean acceleration (cm/s 2 ) to assess the locomotor activity. These locomotor parameters were normalized by dividing each value measured during the post-operative time by the baseline (i. e. , the value in the pre-operative time) to visualize the progression of the parameters. We also measured a postural variable, the mean body torsion defined as the angle between the nose and the tail of the rat during the acquisitions (expressed in degrees, positive values corresponding to torsion toward the left side and negative values to torsion toward the right side).
Behavioral statistical analysis The results are expressed as mean + SEM. All analyses were performed with GraphPad Prism 9 (GraphPad Software, San Diego, CA). Normality of the data was controlled using the D' Agostino and Person normality test and by visualizing quintile-quintile (Q-Q) plots. Since the normal distribution of the data was not rejected, we used parametric tests for statistical analyses, and performed a repeated measures ANOVA on 1-between ("group"), 1-within ("time") design to test the impact of the postoperative time and the impact of the group on the behavioral markers, followed by post hoc analysis with Tukey's multiple comparisons test. Results were considered significant at p < 0. 05.
C Results representing changes of the mean lateral position of the barycenter, in centimeters, normalized by subtracting the preoperative value for each rat. D Results representing changes of the barycenter inertia, an indicator of the barycenter stability. For each rat, measurements were normalized as a ratio of the preoperative value. E Results representing changes in the barycenter's lateral maximum deviation, an indicator of barycenter stability along the lateral axis. For each rat, measurements were normalized as a ratio of the preoperative value. Each data point represents the mean for each group with error bars representing SEM. *p < 0. 05; **p < 0. 01; ***p < 0. 001, 2 two-way ANOVA, post hoc Tukey: the sham and UVN + placebo comparison is indicated by a blue*; the sham and UVN + met comparison is indicated by a green*; and the UVN + placebo and UVN + met comparison is indicated by a red* Total distance travelled We investigated the total distance travelled in the open field.
Dans ce cas, la (ARCPJA22020060002267, Cédric Delevoye), Si l'identité des organites dans les-troubles des conduites alimentaires sont médicaments contre la COVID-19. Les sélection de l'action est régie par son quels résident MHV ou SARS-CoV-2 fréquemment associés à un manque de auteurs de cette étude sur le MHV pro-résultat. Les comportements dirigés par reste imprécise, le mécanisme permet-flexibilité cognitive, à une perturba- posent également que la présentation un but se caractérisent par une prise ANR « MYOACTIONS » (ANR-17-CE11-0029-03, Anne Houdusse et Cédric Delevoye), GENESPOIR (Cédric Delevoye), LabEx Cell(n)Scale (ANR-11-LABX-0038), Inserm, Institut Curie, et CNRS.
Summary We extracted data from the French Language Peritoneal Dialysis Register (RDPLF) and retrospectively studied peritonitis and the outcome of 30 patients with polycystic kidney disease initially treated with peritoneal dialysis within our dialysis unit since 1997. There were 15 men and 15 women with a mean age of 54 years. Eighty-five percent of the patients had hepatic involvement. Ten patients did not suffer from comorbidities. The Charlson Comorbidity Iindex was greater than or equal to four in five patients. Most of the patients were treated with automated peritoneal dialysis during the night. Only one patient was not self sufficient with peritoneal dialysis. The entire medical monitoring lasted 836 months, representing an average of 28 months per patient. Eleven patients had one or more peritonitis.
Second, timing of initiation of Cytosorb® was heterogeneous. We cannot rule out that the cytokine profile/response with the Cytosorb® could have been different if we had selected patients with higher level of inflammation before ECMO or if we had protocolized the timing of the Cytosorb® initiation. Third, the number of patients included is small, which limits the interpretation and generalization of our results. The ongoing randomized, multicenter controlled trial evaluating cytokine adsorption in COVID-19 patients on ECMO (NCT04385771) may help to clarify whether this strategy improves the outcomes of these severe patients. In conclusion, ECMO does not exacerbate cytokine release in COVID-19 patients whereas IL-6, IL-8, and IL-10 decrease after 48 hours on ECMO with ultra-protective mechanical ventilation.
Clairement, le "club géomatique" ne couvre pas, loin s'en faut, l'entièreté des enjeux portés par le numérique territorial, mais c'est celui qui aujourd'hui incarne le mieux cette question dans ce qui s'apparente à un "club de l'information et de la donnée urbaine". L'ensemble de ces enjeux ne peut être traité dans le seul "club géomatique" et toute l'ambiguïté est ainsi révélée : faute de club dédié au "numérique territorial", le "club géomatique" apparait le mieux placé pour traiter de la question numérique, mais laisse de fait de côté toutes les problématiques qui ne situent pas au centre la gestion de la donnée territoriale. Ce sont ces enjeux qui dépassent le strict cadre de la connaissance territoriale et la façon dont ils sont  19 Au final, ce sont 2353 actions qui ont été consignées dans une base de données (ACCESS).
Quatre niveaux d'intensité pour six types d'engagement Le large spectre du numérique territorial des agences d'urbanisme 23 L'ensemble des 2353 actions répertoriées dans les 559 rapports annuels que nous avons pu récupérer se distribue en 5 axes et 25 thématiques d'intervention concomitamment identifiées, codées et consignées dans notre base de données après étude des bilans annuels d'activités publiés par les agences entre 1999 et 2018. Ces thématiques dimensionnent in fine le large spectre du numérique territorial. Ce rassemblement d'actions éparses, pour ne pas dire éparpillées çà-et-là, montre que l'addition de l'ensemble des mesures engagées sur le numérique territorial par les agences d'urbanisme permet de saisir la pluralité des enjeux territoriaux liés au numérique. Cela justifie, dans une certaine mesure, notre interrogation sur l'action des agences puisqu'envisagée dans un cadre d'un "inconscient collectif", c'est-à-dire a priori sans grande concertation inter-agences ni feuille de route clairement tracée par la FNAU, se dégage une image assez claire et cohérente des enjeux territoriaux liés au numérique.
Remerciements Les auteurs remercient chaleureusement le plasticien Xavier Théffo d'avoir autorisé la reproduction d'une de ses oeuvres à titre gracieux. Ils remercient également  Chantal Gascuel ,  Gilles Pinay ,  Alain Ménesguen  et le CNRS pour le soutien matériel et intellectuel apporté à cette recherche interdisciplinaire.
décembre 2014. 2 Recherche soutenue par le Labex G-Eau-Thermie Profonde (Unistra/CNRS) et réalisée dans le cadre du projet H2020 Destress (projet n°16/309/E05, WP 3. 3). S44P. Chavot et al. : Nat. Sci. Soc. 29, S43-S54 (2021).
Acknowledgements We thank the  EPSRC (UK)  and  Winton Program for Physics of Sustainability  for financial support. R. P. thanks  O. Zadvorna ,  T. H. Thomas ,  A. Tanoh ,  A. Cheminal  and  W. M. Deacon  ( Cambridge ) for assistance with experiments and useful advice. The authors also thank  D. Beljonne  (Mons) and  S. Pannir-Sivajothi  ( UCSD ) for fruitful discussions. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreements No  670405  and No  758826 ). The work of G. J, O. D. O. and S. V. was supported by the  European Research Council  ( ERC-2014-STG H2020 639088 ). O. S. O acknowledges the support of a  Rubicon fellowship  from the  Netherlands Organisation for Scientific Research.
R. C. acknowledges support from  Trinity College, University of Cambridge. T. W. acknowledges a grant from the  Swedish research council  (VR,  2014-06948 ) as well as financial support from the  Knut and Alice Wallenberg Foundation  through the project grant  3DEM-NATUR (no. 2012. 0112 ). SMC acknowledges the  Henslow Research Fellowship at Girton College, Cambridge. P. A. M thanks the  EPSRC  for financial support under grant number  EP/R025517/1. C. S. acknowledges financial support by the  Royal Commission for the Exhibition of 1851. We acknowledge access and support in the use of the electron  Physical Science Imaging Centre  ( EM20527 ) at the  Diamond Light Source. A. W. C. , T. B. , L. L. , R. S. and F. M. acknowledge the  CNRS (France)  for financial support. A. J. M. , R. J. and D.
Remerciements 56 Je tiens à remercier les évaluateurs pour leur indispensable regard extérieur sur mon travail et leurs remarques, qui je l'espère, ont permis de bonifier mon propos. Les résultats de cet article sont issus d'un travail doctoral soutenu en novembre 2018. Celui-ci a bénéficié du soutien financier de la Région Auvergne-Rhône-Alpes par le dispositif Coopération et Mobilités Rhône-Alpes et le financement Explo'RA Doc.
The set U n enforces that u 0 = 0 and u N +1 = u d (t n+1 ). The set A n is related to  (36)  and the set D n enforces the fact that d i must be above its previous time-step value and below 1. For all examples treated, the softening elasticity function is chosen as h 2. The optimization problems above are solved by alternate minimization as explained in Section 3. The minimization with respect to the damage variable is performed using the scipy python library. More precisely, the scipy. optimize. minimize function is used. It is described in  [49]. It offers an interface to the sequential least square quadratic programming (SLSQP) routine created by Dieter Kraft in  [50]. Because of its homogeneity, the optimization problem at stake has no unique global minimizer and many local minimizers.
Des solutions sont également proposées pour la gestion et l'archivage des données en quantités croissantes  [35]. Le but est de fournir des solutions à des chercheurs moins experts pour déterminer plus rapidement, et en nombre plus important, des structures de protéines, et leur permettre de se focaliser sur l'interprétation de ces structures. Des efforts ont été réalisés par la communauté de chercheurs impliqués dans la cryo-EM, afin d'établir des standards de qualité et favoriser l'accès des autres chercheurs aux données brutes ou après analyse, avec les banques de données EMDB et EMPIAR  3. Ces données peuvent ainsi être utilisées pour développer de nouveaux programmes d'analyse et de modélisation moléculaire ou cellulaire  [29]. Tous ces développements sont intégrés à des plateformes internationales, comme celles du réseau d'infrastructures européennes INEXT  4.
après leur purification, ce qui permet l'analyse de protéines instables. Elles sont « complètes », c'està-dire qu'elles contiennent les domaines flexibles, les éventuels cofacteurs associés, et, pour les protéines eucaryotes, leurs modifications post-traductionnelles. L'image contient en outre les informations de phase et d'intensité, ce qui permet de reconstruire des modèles 3D quelle que soit la résolution. Les processus de classification 2D et 3D permettent enfin de déterminer les différentes conformations des protéines présentes en solution, donnant ainsi une vision pseudo-dynamique de ces molécules biologiques. Ces modèles 3D, de même que les caractéristiques de leur obtention, sont référencés et identifiés par un numéro de type EMD-XXXX, et accessibles dans une base de données2. La qualité de la reconstruction dépend de la taille et de la flexibilité des protéines : plus l'objet est grand et rigide, meilleurs sont les alignements et la résolution finale.
REMERCIEMENTS Les auteurs remercient Cynthia Taveneau, Julien Maufront, Eugenio de la Mora, Pierre Fribourg, Johan Busselez pour leur contribution aux recherches en cryo-EM de l'équipe Molecular Microscopy of Membranes de l'UMR 168 à l'Institut Curie, ainsi que l'Institut Curie pour son soutien à la cryo-EM depuis 1995, et le CNRS.
-Il convient ensuite de souligner que ces problèmes numériques s'estompent avec une modélisation des problèmes de programmation mathématique mobilisant un AML (Algebraic Modeling Language ; Fourer 2013) utilisé par un modeleur (Xpress, Gams…) permettant l'usage de prédicats pour limiter considérablement l'espace de définition des variables et réduire le nombre de contraintes (ce qu'illustre bien l'article  de Bouzekri et al. , 2021.
Acknowledgements This project has received funding by the  French Research Agency  (project  ANR-17-CE05-0020-01  named  NFA-15 ) and the  LABEX Laboratoire d'Alliances Nanosciences-Energies du Futur  ( LANEF ,  ANR-10-LABX-51-01 ). A. A. Medjahed and  S. Pouget  are acknowledged for their help in the GIWAXS measurements. Dr. P. Maldivi  is acknowledged for training OB and for her assistance in the modelling study of the compounds. M. Bertrand  is acknowledged for helpful discussions on the results and the fabrication of the devices.
To better understand the electrostatic potential variations at equilibrium in the two different LHJ samples, we modeled the system with the help of the 2D drift-diffusion software Silvaco Atlas. Specifically, we fixed the MHP layer ionization energy (the location of the valence band w. r. t. the vacuum level) and electron affinity (the location of the conduction band w. r. t. the vacuum level). as determined from XPS/UPS (assuming a band gap of 1. 55 eV); then, we adjusted other parameters (Table  S1 ) to replicate the potential drop observed across the junction for the two different devices. As can be seen, we used very similar values to the electron affinity, WF, and ionization potential determined from XPS/UPS (assuming a band gap of 3. 2 eV) for the TiO x side. 7] [48]  We did this because the variability of Ni/NiO x in air prohibited us from making accurate measurements with KPFM, whereas its sensitivity to UV light (Figure  S10  versus Figure  S11 ) prohibited us from making accurate measurements with our UPS setup.
Time-resolved luminescence Wide-field illumination imaging was realized with a TR-FLIM setup. The system is composed by a pulsed laser Talisker (l = 532 nm, f = 200 kHz), an intensified electron-multiplied camera CCD (emICCD, PIMAX4, Princeton Instruments), and a home-built opto-mechanical excitation-collection setup. The camera gating time was varied with a 2 ns step from 0 to 1 ms. The photoluminescence was collected through a Nikon 503 objective. The PL is filtered through short-pass and long-pass filters (DMLP650R and FES750, Thorlabs). Acquisitions are post-treated using MATLAB. All samples were grounded on both the TiO x and NiO x contacts during measurements, short-circuiting the device. X-ray absorption spectroscopy XAS measurements were performed on a Beam Line 8-2 at the Stanford Synchrotron Radiation Lightsource (SSRL) with linearly polarized X-rays at normal and grazing incidence w.
ACKNOWLEDGMENTS We would like to thank  Jeff Christians  and  Zhen Li  for help in fabricating the triple cation (FA 1ÀaÀb Cs a MA b PbI 3-x Br x ) and CsFAPbI 3 films in Figure S21. This material is based upon work supported by the  U. S. Department of Energy's Office (DOE's) of Energy Efficiency and Renewable Energy (EERE)  under the  Solar Energy Technologies Office (SETO)  project '' De-risking halide perovskite solar cells'' program  ( DE-FOA-0000990 ). The views expressed in the article do not necessarily represent the views of the DOE or the  U. S. Government. P. S. acknowledges the  French Agence Nationale de la Recherche  for funding under contract no. ANR-17-MPGA0012  and the  French program  of investment for the future ( Program d'Investissement d'Avenir ,  ANRIEED-002-01 ). S.
Une révolution en conception optique Avec les freeforms les instruments de précision peuvent gagner en compacité, en performance … ou bien sur les deux aspects. Mais optimiser avec plus de degrés de liberté devient plus complexe. Le temps est révolu où, afin de calculer les optiques de ses zooms, Pierre Angénieux faisait calculer la propagation de rayons par son équipe de comptables, à la règle à calcul… La puissance des ordinateurs augmentant, on est passé des cartes perforées, à des logiciels faisant tourner des algorithmes très complexes sur des machines ultra-rapides : de quelques minutes pour calculer la propagation d'un rayon en 1944, on est passé à plusieurs millions de rayons par seconde aujourd'hui. Mais tout ne se résume pas à un problème de puissance de calcul. Les méthodes de calcul elles-mêmes évoluent, selon les applications envisagées (médicale, automobile, affichage, imagerie, éclairage), et conduisent à une panoplie de logiciels, « petits » (mais résolvant des cas particuliers) ou « gros » (CodeV, LightTools et LucidShape de Synopsys, OpticStudio de Zemax, SPEOS de Ansys), intégrant des améliorations régulièrement.
In  [27] , the network was generalized to the case of noisy data and to various noise levels. A deep learning based solution for recovery of single-pixel HSI was proposed by Arias et al. in  [28]. The approach is two-step: during the first stage, HSIs are reconstructed from compressive samples in a band-wise manner via ISTA-Net, while the second stage incorporates a merge-and-run deep neural network for image refinement. Recently, DL-based methods also showed to be an effective instrument in tasks of hyperspectral image denoising. Given the high dimensionality of the hyperspectral data, many researchers naturally resorted to 3D neural networks to solve the HSI restoration problem  [29] [30] [31]. However, while the other computational hyperspectral approaches (e. g. , CASSI  [32]  or CS MUSI  [33] ) exploit full 3D convolutional networks to engage the hyperspectral dimension, the existing single-pixel reconstruction networks are still limited to 2D convolutions, with the problem of hyperspectral reconstruction still awaiting to be addressed.
In this paper, we propose deep learning networks for hyperspectral single-pixel imaging. Contrary to black-box approaches, we propose interpretable networks where layers provide solution of classical inverse problems and generalize the reconstruction network proposed in  [27]  to the more complex problem of HSI single-pixel imaging. Our networks combine the classical optimization scheme for denoising in the measurement domain with the deep learning approach for projection from the measurement domain to the image domain, and a final regularization of the solution in the image domain. To the best of our knowledge, this is the first effort to apply generalized Tikhonov regularization and to develop a 3D reconstructor for the single-pixel HSI. In addition, since there is a lack of HSI datasets to train such networks, we propose a method to simulate a HSI dataset from RGB images dataset.
Deep image reconstruction Following the recent widespread success of deep neural networks, the reconstruction of f was proposed to be performed with the non-linear models  [26] f = G θ (m α ), (3) where G is a neural network, the parameters of which are denoted as θ. Given an image database consisting of S measurement-image pairs {m α,(s) , f (s) }, 1 ≤ s ≤ S, the network parameters θ are optimized during a training phase by minimizing the so-called loss function min θ S ∑︂ s=1 ∥f (s) -G θ (m α,(s) )∥ 2 2 + J (θ), (4) where J is typically chosen to penalize large network parameters and reduce the freedom in the neural network. Traditionally, neural networks are a composition of cascaded layers G θ = G L θ •. • G 1 θ , (5) where G ℓ θ , 1 ≤ ℓ ≤ L is the ℓ-th (nonlinear) layer of the network and • is the function composition.
Estimation of the mean and covariance matrices The first steps of the denoised completion network, given by Eq. (  6 ), require the mean µ and covariance Σ of the data, as well as the noise covariance Σ α. As described in  [27] , we compute the mean µ and covariance Σ from the STL-10 database prior to the training phase and estimate the noise covariance from the raw measurements. Here, we have Σ α λ = Diag(σ 2 α λ ) = 1 Kα 2 λ Diag (︁ m α +λ + m α -λ )︁ -ρ 2µ dark α 2 λ K + ρ 2σ 2 dark α 2 λ K 2 , ( 10 ) where Diag(σ 2 α λ ) refers to the diagonal matrix with the diagonal coefficients being the elements of the noise variance σ 2 α λ , which depends on the spectral channel, as different channels of the same measurement contain different numbers of photons. The experimental parameters µ dark , σ dark and K can be estimated as described in  [39].