dataesr/scientific-paragraphs-categorization huggingface dataset:
154 nm) and a Ni filter. XRD patterns were recorded in the 2θ range of 8 • -80 • , with a 0. 0165 • step size and a 2-s dwell time per step. The XRD patterns were assigned to known crystalline phases using the Inorganic Crystal Structure Database (ICSD, Karlsruhe, Germany) and Joint Committee of Powder Diffraction Standards (JCPDS, PA, USA) databases. Raman spectroscopy was performed on a Renishaw in Via Reflex Spectrometer (Watton-under-Edge, UK) equipped with a He-Ne laser operating at 633 nm with 17 mW. The measurements were performed as area scans covering at least 156 × 234 µm 2 with an effective spatial resolution of 1. 3 µm in the range of 80-1300 cm -1 , with 5% laser intensity, 60-s acquisition time, and the laser shape adjusted to a line. TEM imaging was performed with a 2010F field emission TEM (Jeol, Akishima, Tokyo, Japan) at 200 kV.
X-ray Nanotomography X-ray nanotomography studies were performed at beamline ID16B of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) operated at a fixed energy of 29. 6 keV  [56]. Samples were fixed on stainless-steel tomography pins using thermal resistant glue (Resbond 940HT, Cotronics, Brooklyn, NY, USA), then aligned in the centre of rotation axis using an optical microscope integrated into the beamline and the X-ray imaging camera. Full-field holotomography data was recorded using a pco. edge 5. 5-s CMOS camera (2560 × 2160 pixels, 6. 50-µm pixel size) coupled to an LSO: Tb scintillator with 10× magnifying optics. The sample was imaged at four distances from the detector (710, 720, 724, and 725 mm), and the detector remained at a fixed distance of 754 mm from the beam focus.
This gave an effective pixel size of 25 nm at the longest sample-to-detector distance. Three thousand projections were collected with a 1-s exposure time at each position, with references collected after each 500 projections. A total of four subtomograms (collected in 1 h each) were taken into account for holotomographic phase retrieval with a Paganin-like approach, resulting in one set of phase projections of an entire catalyst particle (~50 to 60 µm). The entire dataset was collected in 4 h. XRF-CT and STXM-CT data were recorded simultaneously in scanning mode with a nanofocus beam size of 51 × 55 nm 2. A translate-rotate measurement scheme was used across a horizontal range of 55 µm (70 µm for sample FSP-1R) with a 100-nm step size to generate one slice. The sample was then translated vertically in 500-nm steps and the measurement repeated to collect 8-10 slices.
Two three-element Si drift fluorescence detectors were placed perpendicular to the beam path on either side of the sample, and XRF-CT data was recorded with an integration time of 100 ms per point. STXM-CT data was collected with photodiodes placed up-and downstream of the sample. The following fluorescence lines were defined as regions of interest (ROI): Mo K α (17. 48 keV), Bi L α (10. 84 keV), Co K α (6. 93 keV), and Fe K α (6. 41 keV). Sinograms for the elements of interest were obtained by fitting the measured spectra with the main fluorescence lines (Mo, Co, and Fe K-lines and Bi L-lines), in addition to some minor peaks due to impurities in the sample and known background peaks from beamline components. One tomogram of a sample cross section with a 4-5-µm height was collected after 8-10 h.
STXM-CT data was collected by measuring the incident and transmitted beam intensity using X-ray photodiodes placed up-and downstream of the sample. Tomography data was reconstructed by the filtered back projection (FBP) method using PyHST2 code (v. 2016, ESRF, Grenoble, France) for holotomography data and custom Matlab scripts for XRF-CT and STXM-CT. Data was visualised using the Avizo 9. 3 (ThermoFisher Scientific, Waltham, MA, USA, 2017) software package (SM,  Section 7). Different stages of the catalyst lifetime were studied. In this case, tomographic images were obtained for a single particle (~50 µm diameter) of freshly prepared catalyst (FSP-1F). The same particle of FSP-1 was then treated under reaction conditions (70/14/8/8 vol. % = N 2 /O 2 /C 3 H 6 /H 2 O, 400 • C for 6 h) at ESRF in a quasi in situ approach, and a congruent sample volume was measured (FSP-1QI).
Acknowledgments: We gratefully acknowledge  ESRF  for providing beamtime at ID16B. We thank  Matthias Stehle  ( ITCP, KIT ) for contributing to the experiments;  Kalin Valev ,  Alexander Jaks , and the mechanical workshop at  ITCP (KIT)  for design and construction of the tomography sample holder. We thank  Stéphanie Belin  and  Valérie Briois  for their support during experiments at ROCK beamline (SOLEIL). We thank  Angela Beilmann  ( ITCP, KIT ) for BET analysis and  Hermann Köhler  ( IKFT, KIT ) for ICP-OES analysis. Evonik Industries AG  are acknowledged for TEM-EDX and XPS measurements, along with  Wu Wang (INT, KIT)  and the  Karlsruhe Nano Micro Facility (KNMF). Finally, we thank  Wolfgang Kleist  ( ITCP/IKFT, KIT ) for scientific input. We acknowledge support by  Deutsche Forschungsgemeinschaft  and the open access publishing agreement of  Karlsruhe Institute of Technology.
Acknowledgements This work was partly supported with grants from the  Norwegian Research Council  ( ES508288 ) and L. W. K. M. and J. S. are grateful for the support. J. S. was further supported by a VINNMER M. C. incoming grant from  VINNOVA  (grant  2014-01435 ). H. P. and G. C. were supported by the  Centre for Marine Chemical Ecology  (http://www. cemace. science. gu. se) at the  University of Gothenburg. C. H. and R. T. were supported by  Biogenouest  (http://www. biogenouest. org) at the  University of Western Brittany. Authors wish to thank  CA COST Action BCA15216 European Network of Bioadhesion Expertise: Fundamental Knowledge to Inspire Advanced Bonding Technologiesf  or support. J. Lehmuskallio  (http://www. luontoportti. com)  is acknowledged for providing the  Empetrum nigrum image  and  R.
In this case, to attain high dense compacts, a maximum temperature of 1530 °C, for 3 h , was used. Polished and thermally etched fracture surfaces (at 1480 ºC for 60 s) of the sintered samples were first inspected by scanning electron microscopy (SEM, Hitachi S-4100) to have the general knowledge on the morphological details and to check phase purity with energy dispersive X-ray spectroscopy (EDS). SEM images were analysed using Image J software. For transmission electron microscopy (TEM) studies both CS and HP ceramics were thinned down to 20 m by polishing using tungsten grid paper followed by diamond pastes and made electron transparent using precision the ion polishing system (PIPS-Model 691; Gatan, Pleasanton, CA); TEM studies were done using Hitachi 9000; High resolution TEM (HRTEM) studies were performed with SACTEM TECNAI-F20 microscope in CEMES, Toulouse.
Acknowledgments: The authors acknowledge  Fundação para a Ciência e a Tecnologia (FCT) ,  Fundo Europeu de Desenvolvimento Regional Portugal (FEDER) ,  QREN-COMPETE Portugal , the  Associate Laboratory CICECO  ( PEst-C/CTM/LA0011/2013 ) for funding support. NBS acknowledges FCT  for financial support ( SFRH/BPD/82158/2011 ). The authors acknowledge the support of  European Union  seventh framework programme under a contract for an integrated infrastructure initiative ( 312483-ESTEEM2 ).
GENERATION OF SYNTHETIC DATA All synthetic data were generated with Matlab (The MathWorks Inc. , Natick, MA). Images must have met the conditions of a signal-dependent noise model, as defined in Eq. 4, to achieve a controlled scenario. The PSF of unitary power is defined by three parameters, ,  0 and   , where   is the size of the window support. When it is requested,  or   were set. Otherwise specified, the synthetic data were generated based on the following values:  0 = 1. 25 pixels,  0 = 300,  = 1, a pixel size of 107 nm and   = 25.
UNLOC ALGORITHM Description -UNLOC provides a list of coordinates and associated parameters for each detected particle for a posteriori quantification and image reconstruction. The algorithm is based on the decision theory, which only requires the PFA value to be set without the initialization of any parameters relative to the data (SNR, particle density, or background level). UNLOC is an iterative algorithm that alternates an overestimation of the number of particles to obtain a minimal fitting residue with a general likelihood ratio test (GLRT) to suppress useless particles. UNLOC calculates the position errors based on the CRB for particles at high density per frame and without any prior on their intensity. The UNLOC principle is illustrated in Figs. 4  and S9 and the mathematical details are provided in Note S6.
UNLOC -UNsupervised particle LOCalization UNLOC Principle -Our theoretical study allowed us to conceive UNLOC, an algorithm based on an unsupervised heuristic, while minimizing the computational cost and improving the analytical reliability of complex data (Note S6). Additionally, UNLOC is designed to achieve the best performances, as delineated here by the CRB, for any particles separated by a distance greater than    ⁄ (SNR) and without any prior on their intensity. Although UNLOC accounts for spatiotemporal variations in the background, SNR and local particle density, it only requires the PSF size ( 0 ) of the microscope to build a list of particles. The algorithm is divided into three modules (Fig. S9a ): (i) a detection/estimation module performed in an iterative process (Fig. 4 ) together with optimization of the number, position and intensity of the particles, and also the PSF size if requested (Fig.
S9b ); (ii) a reconnection module to minimize an overrepresentation of particles in the reconstructed image using statistical tracking of the particle history over the frames; (iii) an optional module for drift correction and classical image rendering methods. Detection/estimation -Two modes of detection/estimation are provided. A standard mode appropriate under effective LD conditions is similar to MTT  (40). It is based on a GLRT at unknown background which is an efficient unsupervised detector that only depends on the PFA  (41). Alternatively, the HD mode of UNLOC should be applied for any data with high local density/frame and complex background, a condition which occurs very frequently with experimental data. Preserving the GLRT robustness under HD conditions requires an estimation of the background mean and variance.
5b ). Compared to the standard ThunderSTORM HD algorithm  (25) , UNLOC provides more reliable estimates on such synthetic data concerning the localization precision as well as achievable resolution (47) (Figs. S12, S13 ). Moreover, UNLOC differs from super-resolution algorithms based on local density distribution reconstruction such as SRRF  (7). As illustrated by the image reconstruction of experimental data for actin filaments in fixed cells, UNLOC efficiently challenges the background and density variability throughout the stack of images in a range of SNR of 18 to 40 dB with a localization precision ranging from 60 to 10 nm (Figs. 6, S12). Our theoretical study is intended for a homogeneous distribution for randomly distributed particles. However, experimental data deviate strongly from these ideal conditions.
Then, we develop UNLOC, a flexible and efficient unsupervised algorithm for high-density data. UNLOC is based on a heuristic insuring an optimal localization precision and counting of particles by considering both the SNR and. Finally, we also create synthetic data charts allowing blind benchmarking of any SMLM algorithms aiming specifically at providing a list of localizations of single molecules and not only a reconstructed image. Altogether, this work provides an understanding of the key features usually underestimated or incorrectly taken into account in SMLM analyses.
ACKNOWLEDGMENTS We acknowledge  Marc Allain ,  Sophie Brasselet ,  Hai-Tao He ,  Hervé Rigneault  and  Muriel Roche  for their critical reading of the manuscript. We thank  Christophe Leterrier  for valuable discussions and  Florian Levet  for his advices on the SR-Tesseler analysis. This work was supported by institutional funding from the  CNRS, Inserm ,  Aix-Marseille University  and  Centrale Marseille  and program grants from the  Research National Agency  ( ANR-10-BLAN-1214  to N. B. and D. M. ), the  French "Investissements d'Avenir"  ( ANR-11-IDEX-0001-02  A*MIDEX to N. B. and D. M. ,  ANR-10-INBS-04   France BioImaging ,  ANR-11-LABX-0054   Labex INFORM  and  ANR-14-CE09-0008-02  to D. M. ), the  Fondation pour la Recherche Médicale  ( FRM-DEQ-20090515412  to D. M. ) and the  Institut National du Cancer  ( C15005AS  to D.
INTRODUCTION Optogenetics uses light to control cells genetically modified to express light-sensitive membrane proteins, generically known as opsins. A major breakthrough was the discovery that the introduction of these opsins into neurons made them responsive to light allowing their modulation within a precise timeframe  (Boyden et al. , 2005). Optogenetics has opened new ways of experimentation in neuroscience, that enables optical modulation of selected cells within variety of complex tissues, but also holds promising therapeutic potential in retinal degenerative diseases  (Deisseroth et al. , 2006; Herlitze and Landmesser, 2007; Häusser, 2014). Today, there are two ongoing clinical trials using microbial opsins for vision restoration in patients affected with  RP (NCT02556736 and NCT03326336).
Super-Resolution Imaging Super-resolution imaging was performed with a commercial CoDiM  (Caron et al. , 2014; Fallet et al. , 2014). The BioAxial super-resolution module (CODIM100, BioAxial) is an add-on integrated to the Nikon confocal system previously described. The CODIM module acts as a powerful beam shaper generating local structured illumination. A sCMOS camera plugged at the back port of the microscope (Orca Flash 4. 0, Hamamatsu Photonics) is used for the detection generating individual micro-images for each scanning point containing independent information. The set of all micro-images obtained from the scan procedure are processed and reconstructed by CODIM algorithm  (Nesterov, 2005)  to generate a super-resolved image. In both confocal and super-resolution modalities, a 60x 1.
49NA Oil immersion Nikon Plan Apo TIRF objective was used to focus the laser beam and collect of the emitted fluorescence. The 488 and 561 excitation wavelengths of a multi laser engine (iChrome MLE, Toptica Photonics Inc. ) are used for fluorescence excitation. The confocal image captures were performed using Nikon Nis-Elements software (Nikon Instruments Europe). The laser power was properly chosen to be out of the saturation regime. Confocal and super-resolution montages were subsequently built in ImageJ (NIH).
Whole-cell recordings were obtained using the Axon Multiclamp 700B amplifier (Molecular Device Cellular Neurosciences). Patch electrodes were made from borosilicate glass (BF100-50-10, Sutter Instrument) pulled to 6-9 M and filled with 115 mM K gluconate, 10 mM KCl, 1 mM MgCl 2 , 0. 5 mM CaCl 2 , 1. 5 mM EGTA, 10 mM HEPES, and 4 mM ATP-Na 2 (pH 7. 2). Photocurrents were recorded while voltage-clamping cells at a potential of -60 mV. A monochromatic light source [Polychrome V, TILL photonics (FEI)] was used to stimulate cells with a pair of 450 or 590 nm full-field light pulses during electrophysiological experiments and hence record photocurrents. Stimuli were generated using custom-written software in LabVIEW (National Instruments) and output light intensities, of 1 × 10 16 and 3. 2 × 10 17 photons cm -2 s -1 , were calibrated using a spectrophotometer (USB2000+, Ocean Optics).
DISCUSSION Several optogenetic-based therapeutic strategies have successfully targeted different retinal neurons in mouse models of retinal degeneration. AAV-mediated delivery achieved stable expression of ChR2 variants in the RGCs of rodent models of retinal degeneration  (Bi et al. , 2006; Tomita et al. , 2009 Tomita et al. , , 2014;; Sugano et al. , 2011; Sengupta et al. , 2016). ChR2 has also been used to target bipolar cells in the rd1 mouse model  (Lagali et al. , 2008; Doroudchi et al. , 2011; Macé et al. , 2015)  and resensitization of dormant cone PRs has been achieved in mice and monkeys using eNpHR and Jaws  (Busskamp et al. , 2010; Chuong et al. , 2014; Khabou et al. , 2018). Based on these works, two recent clinical trials were started to test the potential of microbial opsin-based vision restoration in the clinic (NCT02556736 and NCT03326336).
ACKNOWLEDGMENTS We thank  Emilie Keomani  for her help producing viral vectors,  Eleonor Bombard  for help with cloning,  Amelie Slembrouck-Brec  for her help in the maintenance of the iPSC cultures,  Karen Daoud  and  Parisa Khalilian  for their assistance cryosectioning the organoids. We are grateful to  Morgane Belle  for assistance with 3DISCO clearing protocol. This work has benefited from the  Photonic Bio Imaging platform of Imagopole (Institut Pasteur). We thank  Stefan Herlitze  for providing the hLWO and mSWO plasmids and  Serge Picaud  for feedback and discussions.
The EU Copernicus Climate Change Service (C3S) European Climatic Energy Mixes (ECEM) has produced, in close collaboration with prospective users, a proof-of-concept climate service, or Demonstrator, designed to enable the energy industry and policy makers assess how well different energy supply mixes in Europe will meet demand, over different time horizons (from seasonal to long-term decadal planning), focusing on the role climate has on the mixes. The concept of C3S ECEM, its methodology and some results are presented here. The first part focuses on the construction of reference data sets for climate variables based on the ERA-Interim reanalysis. Subsequently, energy variables were created by transforming the bias-adjusted climate variables using a combination of statistical and physically-based models.
A comprehensive set of measured energy supply and demand data was also collected, in order to assess the robustness of the conversion to energy variables. Climate and energy data have been produced both for the historical period  and for future projections (from 1981 to 2100, to also include a past reference period, but focusing on the 30 year period 2035-2065). The skill of current seasonal forecast systems for climate and energy variables has also been assessed. The C3S ECEM project was designed to provide ample opportunities for stakeholders to convey their needs and expectations, and assist in the development of a suitable Demonstrator. This is the tool that collects the output produced by C3S ECEM and presents it in a user-friendly and interactive format, and it therefore constitutes the essence of the C3S ECEM proof-of-concept climate service.
This objective has been tackled through a close interaction with stakeholders, in a framework that allows for co-design of the service. The C3S ECEM Demonstrator is constituted of a visual tool to display and investigate climate and energy data, along with a comprehensive set of documentation such as factsheets, methods and assumptions, key messages, event case studies and frequently asked questions. It also has the option to enter feedback for the developers of the Demonstrator to take on board. However, response to users' queries, data updates or fixes are not acted upon within set time frames. Thus, while the Demonstrator has been developed following detailed guidelines (as described in the C3S ECEM data management plan) in preparation for the future C3S operational service for the energy sector, it is a (pre-operational) proof-of-concept climate service, rather than a fully-fledged (operational and/or commercial) climate service.
The C3S ECEM project started in November 2015, and represents a collaboration between teams from the University of East Anglia (UEA, UK), Électricité De France (EDF, France), the Met Office (UK), MINES ParisTech/Armines (France), the University of Reading (UK) and the Agency for new technologies, energy and sustainable development  (ENEA, Italy). The project has a duration of 29 months. The specific underlying challenges motivating C3S ECEM are: -To describe the ways in which energy supply and demand over Europe are affected by the spatial and temporal variations of their climate drivers -To produce scenarios that demonstrate how different energy supply mixes can meet demand at the European scale, particularly given the projected high level of highly climate-sensitive renewable energies. To this end, the C3S ECEM has produced consistent and high-quality datasets for climate and energy variables, selected for their relevance for the electric energy sector.
These datasets can be explored and downloaded through a flexible and user-friendly platform, provided by the Demonstrator. The datasets and Demonstrator, as well as the underlying energy conversion models, have been developed by subject experts in close collaboration with energy sector users. The Demonstrator allows to explore relationships between climate and energy over the last 40 years and the effects of climate change over coming decades, as well as assessment of the skill of seasonal forecasts. As mentioned, a wide range of documentation and guidance is also provided with the Demonstrator. This paper provides an overview of the main aspects of the C3S ECEM based on a presentation given at the 2017.
In  Jones et al. (2017)  the differences between our bias-adjusted ERA-Interim data and for instance the well-used WFDEI dataset (WATCH Forcing Dataset for ERA-Interim, developed by  Weedon et al. , 2014)  are displayed and discussed. A key outcome of this assessment is that since there are a number of observational datasets that represent reality, it is vital to clearly state which is used in any bias-adjustment applications. These bias-adjusted ECV time series, together with the original PSL and SD, have been made available through the C3S ECEM Demonstrator: http://ecem. climate. copernicus. eu/demonstrator/ (last access: 3 July 2018). While the main ECV products displayed on the C3S ECEM Demonstrator are country and cluster averages, the 0. 5 • by 0. 5 • resolution gridded data are also made available through the C3S ECEM ftp site (ftp://ecem.
climate. copernicus. eu/, last access: 3 July 2018), with complete details (including all the data processing of ERA-Interim and the observational datasets with which this was compared in  Jones et al. , 2017). As described in  Jones et al. (2017) , the bias-adjusted ECVs were produced assuming that the variables were independent of one another (univariate bias adjustment). However, in C3S ECEM a bi-variate bias adjustment was considered too. This work concluded that bias adjustment becomes harder for more than two variables and becomes more complex if bias adjustment of climate change projections is contemplated as questions of non-stationarity arise  (Dekens et al. , 2017). The problems are not insurmountable but require further study, beyond this particular project.
, 2016; Clark et al. , 2017). The skill of a forecasting system is assessed using retrospective forecasts, i. e. forecasts of past seasons made using the current forecasting system. The accuracy of the skill assessments is limited by features of those retrospective forecast data sets, such as the number of years they cover and the size of the forecast ensembles produced each season. Forecast skill can be assessed through a wide range of different statistics, each of which probe different aspects of the forecasts with different levels of detail (see e. g. Wilks, 2011; Jolliffe and Stephenson, 2012). For the C3S ECEM project, we have focused on the simple Pearson correlation between the ensemble-mean retrospective forecasts and the bias-adjusted historical data (described above). This is often seen as a measure of deterministic skill, although using the ensemble means (enhancing the detectable forecast signal by reducing the noise from the ensemble) can provide an indication of the skill of probabilistic forecasts  (Palin et al.