dataesr/scientific-paragraphs-categorization huggingface dataset:
), GdL2 ( ), GdL3 (•) and GdL4 ( ) at 37 • C. The lines represent the best fit to the SBM equations. In order to better characterize the coordination sphere of the complexes and to determine the number of water molecules directly coordinated to Ln 3+ , luminescence lifetime measurements have been performed on the EuL1-4 analogues in H 2 O and D 2 O. All the data could be fitted by monoexponential decay functions indicating that one species is present in solution. When the deactivation of Ln 3+ is occurring mainly through the X-H oscillator vibrations (X = O, C, N), the following empirical formulae ((1), (  2 )) can be used to estimate the number of water molecules directly coordinated to the Eu 3+  [31] : q Eu = 1. 11 τ -1 H2O -τ -1 D2O -k XH (1) k XH = 0. 30 + 0. 45n OH + 0. 99n NH + 0. 075n O=CNH  (2)  where τ represents the luminescence lifetimes, and n OH , n NH , and n O=CNH are respectively the number of alcoolic, amine and amide oscillators in the first coordination sphere of Eu 3+ (in the case of the amide, the coordination is through the oxygen).
Patients This retrospective study was performed in 13 French ICUs belonging to a research network (GRRR-OH). All the patients admitted to one of these ICUs between January 2007 and July 2019 (the period of defibrotide access in France) were screened. Inclusion criteria were (1) ICU patient age >18 years, (2) hematologic disease treated by HCT or chemotherapy, (3) SOS diagnosis based on European Society for Blood and Marrow Transplantation (EBMT) criteria  [8] , and (4) defibrotide treatment during ICU hospitalization. Exclusion criteria were missing data on the diagnosis of SOS or outcome. Clinical data were extracted from medical records including age, sex, comorbidities (Charlson index  [21] ), Eastern Cooperative Oncology Group (ECOG) performance status, underlying hematologic diagnosis, previous history of autologous (auto-) HCT or allo-HCT, and specific chemotherapy regimens (ie, oxaliplatin, gemtuzumab ozogamicin, or inotuzumab ozogamicin).
If applicable, date of allo-HCT, donor-recipient HLA match, stem cell source, type of conditioning regimen, graft-versus-host disease (GVHD) prophylaxis, and acute GVHD status and grading  [22]  were registered. The intensity of the conditioning regimen was based on the Bacigalupo classification  [23]. The Disease Risk Index was used to predict outcomes after allo-HCT  [24]. This study was conducted in compliance with the Declaration of Helsinki. The Soci et e de R eanimation de Langue Française (SRLF) Ethics Committee approved this study in France (CE SRLF 19-09). Data were collected and processed anonymously in a dedicated study after authorization of the National Commission for Data Protection and Liberties (CNIL 2211196).
SOS Diagnosis and ICU Hospitalization The diagnosis of SOS was based on the EBMT classification  [8]  after ruling out the differential diagnoses (especially GVHD and infectious complications). Clinical, biological, Doppler ultrasound, and liver biopsy features were extracted to confirm the diagnosis. The prognostic score of the EBMT classification scheme comprises 4 grades of severity: mild, moderate, severe, and very severe  [8]. Defibrotide use was recorded according to its prophylactic use (before any sign of SOS) or therapeutic use (after diagnosis of SOS). Bleeding events during defibrotide treatment were classified with Common Terminology Criteria for Adverse Events v5. 0  [25]. During the ICU stay, data on the reason for admission to the ICU, the presence of documented sepsis, Sepsis-Related Organ Failure Assessment (SOFA) score  [26]  (overall and for each organ), liver and renal function  [27] , vital signs at admission, organ support, and finally the outcome in the ICU and the hospital were collected.
Nevertheless, although this finding warrants further exploration, it probably should be viewed as resulting from unadjusted selection bias. These data are consistent with the results of a recently prematurely stopped prospective randomized trial assessing the efficacy of defibrotide prophylaxis in a high-risk population of children and adults (ClinicalTrials. gov identifier NCT02851407)  [33]. Our study has some limitations. First, data were collected retrospectively, because this disease remains infrequent in ICU. Second, given the high historical mortality rate  [12] , some patients might not have been admitted to the ICU according to the different admission policies of the different centers  [20]. Nevertheless, the 13 ICUs are part of the same research network, minimizing potential heterogeneity.
The analysis reported on herein is based on data collected in 2004 in Hall B of the Thomas Jefferson National Accelerator Facility (Jefferson Lab) in Virginia, USA, reanalyzed here as part of the Jefferson Lab data-mining initiative  [32]. This data comes from measurements of 5. 01 GeV electrons scattered from deuterium and carbon targets  [33] , detecting the scattered electrons, knockedout protons, and recoil neutrons in the CEBAF Large Acceptance Spectrometer (CLAS)  [34]. CLAS utilized a toroidal magnetic field and six independent sets of drift chambers (DCs)  [35] , timeof-flight (TOF) scintillation counters  [36] , Cherenkov counters (CCs)  [37] , and electromagnetic calorimeters (EC)  [38]  for charged particle detection and identification. Charged particle momenta were inferred from their reconstructed trajectories within the magnetic field.
Data source : the French REIN registry The Renal Epidemiology and Information Network (REIN) is the French national registry of all patients being treated by renal replacement therapy (RRT)  (23). Clinical, demographic, and laboratory data are collected at the start of RRT along with dialysis modalities and are updated annually. Events such as death, transfer, withdrawal from dialysis, placement on a transplant waiting-list and kidney transplantation (from living or deceased donors) are systematically reported in real time. Short-term transfers of less than 2 months are not recorded (i. e. hospital stay, vacation).
Statistical analysis Different mixed effect log-linear regression models were built to explore sources of variations in registration rates (Figure  1  ). Successive analyses were made on complete data due to the low level of missing data and the complexity of our model. The dataset has a hierarchical structure on three levels, each of which might be a source of variability in registration: patient (Level 1), dialysis unit network (Level 2) and renal transplantation center (Level 3). To study the contribution of variability associated with each level on total variability we used a multi-level analysis (Supplementary Figure  1  ). In order to take into account the overdispersion of the registration rate at patient level, a negative binomial regression was used, corresponding to a log-linear model with a supplementary parameter modelling the overdispersion.
Acknowledgments The authors wish to thank all REIN registry participants, especially the nephrologists and professionals in charge of data collection and quality control. Dialysis units participating in the registry are listed in the REIN annual report (https://www. agence-biomedecine. fr/Les-chiffres-du-R-E-I-N). We also wish to thank  Teresa Sawyers , English Medical Writer at the  BESPIM, Nîmes University Hospital  for her expert editorial assistance in writing this paper.
In this article, we provide closed-form approximations of log-likelihood ratio (LLR) values for direct sequence spread spectrum (DS-SS) systems over three particular scenarios, which are commonly found in the Global Navigation Satellite System (GNSS) environment. Those scenarios are the open sky with smooth variation of the signal-to-noise ratio (SNR), the additive Gaussian interference, and pulsed jamming. In most of the current communications systems, block-wise estimators are considered. However, for some applications such as GNSSs, symbol-wise estimators are available due to the low data rate. Usually, the noise variance is considered either perfectly known or available through symbol-wise estimators, leading to possible mismatched demodulation, which could induce errors in the decoding process.
In this contribution, we first derive two closedform expressions for LLRs in additive white Gaussian and Laplacian noise channels, under noise uncertainty, based on conjugate priors. Then, assuming those cases where the statistical knowledge about the estimation error is characterized by a noise variance following an inverse log-normal distribution, we derive the corresponding closed-form LLR approximations. The relevance of the proposed expressions is investigated in the context of the GPS L1C signal where the clock and ephemeris data (CED) are encoded with low-density parity-check (LDPC) codes. Then, the CED is iteratively decoded based on the belief propagation (BP) algorithm. Simulation results show significant frame error rate (FER) improvement compared to classical approaches not accounting for such uncertainty.
Moreover, several interference countermeasures have been already proposed at different stages of the receiver including the antenna design  [13] [14] [15] [16] [17] [18] , radio-frequency front-end  [19, 20] , and signal processing  [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31]. However, a key part of GNSS receivers is the data demodulation stage, which allows recovering essential information. The latter has been long disregarded, with only a few articles  [32] [33] [34] [35]  in the state-of-the-art, but may be a critical point under interference scenarios, being the principal object of this article. In recent years, the organizations in charge of the design of the new generation of GNSS signals have decided to include modern error correcting codes (i. e. , such as lowdensity parity-check (LDPC) or convolutional codes  [36] ) in order to enhance the data demodulation performance.
Scenarios Type of CSI Used Open Sky Scenario Gaussian Jamming Scenario Pulsed Jamming Scenario Perfect CSI Equation (  4 ) Equation (  9 ) Equation (  9 ) Approx (  14  Note from Table  1  that under the open sky scenario, to compute the state-of-the-art LLR values, we need to have access to the symbol variance σ2 n. A first method to infer this variance is by using the narrowband-wideband power ratio (NWPR) method, which is used to infer the signal-to-noise ratio (SNR). Note that the SNR is usually inferred with a lower rate than the data symbols. Therefore, this method cannot infer properly σ2 n under fast SNR variations. On the other hand, we underline that this method is required in the GNSS architecture, and therefore, no extra computation complexity is required. A second method to infer σ2 n is the ML method.
Conclusions In this paper, we address the issue of computing a closed-form LLR value under three particular GNSS scenarios: (i) open sky scenarios with smooth variations of the SNR, (ii) additive Gaussian noise interference, and (iii) pulsed jamming. Moreover, we assume a more realistic signal model, which considers SNR uncertainty. Since the GNSS system is a low data rate DS-SS system, it allows for symbol-wise estimation of the noise variance, thus providing information of the statistical distribution of the noise power. We reformulate the problem of computing the LLR values by modeling the variance as a random variable, which can be characterized by an inverse log-normal pdf whose mean and variance are considered known or well estimated. A Bayesian approach is taken in order to compute the joint posterior distribution of the variance and the transmitted symbol.
ACKNOWLEDGMENTS SS acknowledges  Ravi Sharma  and  Anshul Srivastava  ( IIT Indore, B. Tech. batch 2020 ) for helping him in some initial simulations at the preliminary stage of this work. BU acknowledges funding from  European Union  ( MARIE SKODOWSKA-CURIE ACTIONS  project  Acronym/Full Title: METAQUAKENG-Metamaterials in Earthquake Engineering-MSCA IF-H2020 ).
by DFG-Cluster of Excellence Munich Advanced Photonics. KMP is supported by the EPSRC A-SAIL Program (Ep/K022415/1). KTB is supported by the Medical Research Council (MR/V009605/1). PMG is supported by the Swiss National Science Foundation, Early Postdoc. Mobility No. P2LAP3_187706. VF is supported by the Institut Curie, the Alliance Nationale pour les Sciences de la Vie et de la Santé (ITMO-Cancer 19 CP1223-00) and the Nanotherad IDEX (Paris-Saclay University, France). The authors wish to thank Dr. C. Fouillade for his help in account of emerging transcriptomic techniques. 10 -DATA AVAILABILITY STATEMENTData sharing is not applicable to this article as no new data were created or analyzed in this study.
Clinical Trials Due to historical use and banking facilities, to date, hAM is mainly exploited under its scaffold format instead of its derived cells. Consequently, the assessment of the overall number of clinical trials evaluating hAM as a scaffold for TE purposes revealed that ophthalmology has the biggest share, with 14 clinical trials (Table  2 ). In this indication, various autologous or allogenic cells have been seeded on hAM: limbal (epithelial) stem cells, cornea stem cells, oral mucosal epithelial cells, conjunctival epithelial cells, fibroblasts, and BM-MSC. hAM-based scaffolds have been studied sporadically in Asherman's Syndrome, endometrium infertile patients, and anterior cruciate ligament ruptures. Interestingly, in gynecology, hAM was combined with isolated hAECs. In this indication, these cells have also been explored alone in other clinical trials not included in Table  2  (NCT03207412, NCT02912104, NCT03381807, NCT03223454).
Acknowledgments: The coauthors are grateful to Franck Daval (Bibliothèque Universitaire Santé, Université Bourgogne Franche-Comté, F-25000 Besançon, France) for bibliographic support. This publication is based upon work from COST Action 17116 "SPRINT", supported by COST (European Cooperation in Science and Technology)-http://www. cost. eu (accessed on 7 October 2020).
Acknowledgements. This paper was inspired by discussions during a workshop, 'Synchrony and rhythm interaction: from neurons to ecology', held at the  Lorentz Center in Leiden, Netherlands  in 2019. We are thankful to the  Lorentz Center  for their assistance in funding and organizing that event. Two anonymous referees provided helpful recommendations for the final version of the manuscript. G. A. acknowledges support from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy-EXC  2117-422037984 , and thanks  I. D. Couzin  for telling him about the workshop and funding his trip. M. A. acknowledges the financial support of a  Junior Fellowship  awarded by the  Hanse-Wissenschaftskolleg (Institute for Advanced Study), Delmenhorst, Germany.
04 μm colloidal silica particles. SEM for microstructure observation was undertaken using Inspect F50, FEI in backscattered electron (BSE) mode with EDX for elemental detection on the specimen surface. The magnetic properties were measured using a MPMS-3 SQUID-VSM magnetometer. For the samples containing Cr, the magnetic hysteresis loops were measured at 300 K and 10 K, to determine the magnetic behaviour, along with the saturation magnetisation, if they were ferromagnetic. Zero-field cooled (ZFC) and field cooled (FC) temperature measurements were taken to determine the Curie temperature between 350 K and 10 K. These were taken at 24 kA/m and 795 kA/m. AC susceptibility measurements were taken between 5 K and 350 K, at an applied field of 798 A/m and frequencies, 10, 100 and 1000 Hz. While first quadrant magnetisation vs magnetic field data were taken every 10 K between 350 K and 10 K for the magneto-caloric data.
A c c e p t e d M a n u s c r i p t The XRD and neutron data are both presented as function of 1/d, to allow comparison between the two (Figure  1 ). The XRD data were taken for the CFN-Cr1. 0Cux samples (Figure  1a ) and CFN-CryCu1. 0 samples (Figure  1b ). For all the samples studied, the only phase was FCC. It was observed that with the increase in Cu addition, there is a broadening of the (  220 ) and (311) peaks, which suggests that a second FCC phase occurs (Figure  1a ) with x > 1. 0. As the XRD data did not conclusively show this, neutron data for three samples (CFN-Cr1. 0Cu0. 0, CFN-Cr1. 0Cu1. 0 and CFN-Cr0. 0Cu1. 0) were studied. From Figure  1c. it is observed that for the CFN-Cr1. 0Cu1. 0 sample, there exist two FCC phases. While for the CFN-Cr0. 0Cu1. 0 sample, only one FCC phase exists, for the CFN-Cr1.
Acknowledgements This work was supported by the  Royal Society Mid-Career Leverhulme Trust Fellowship scheme  ( SRF\R1\180020 ) and the  Leverhulme Trust  ( RPG-2018-324 ). The samples have been prepared within the  FP7 European  project  AccMet   NMP4-LA-2011-263206. We wish to acknowledge the  Henry Royce Institute for Advanced Materials , funded through  EPSRC  grants  EP/R00661X/1 ,  EP/S019367/1 ,  EP/P02470X/1  and  EP/P025285/1  for access to the MPMS-3 SQUID within the  Royce at the University of Sheffield. ZYL would like to thank  Dr. Pratik Desai  for his useful discussions on Cu transition states and magnetisation. CRediT author statement JH: Methodology, Investigation, Validation, Formal Analysis, Writing -Original, Writing -Review ZYL: Conceptualisation, Methodology, Validation, Formal Analysis, Writing -Original, Writing -Review PG: Investigation, Writing -Review JC: Investigation, Writing -Review CP: Investigation, Writing -Review.
Acknowledgments The authors would like to thank  Christophe Lecouvey  for his contribution. This work was supported by the  French National Research Agency (ANR)  under the '' Investissements d'avenir'' programs :  ANR 10-AIRT-0005  ( IRT NANO-ELEC ),  ANR   10-EQPX-0030  ( EQUIPEX FDSOI 11 ) and by the  Nano2022  project.
Inspection Inspection of clinical practice by a regulatory body is unusual in medicine but the BSSD requires this for medical exposures. In practice the focus is on processes rather than individual actions and this applies to the justification process. Inspectors will assess compliance with local procedures and regulatory requirements for justification. Specific elements of the process will include assessing whether an institution's policies and procedures are for purpose, identification of the referrer and practitioner, the competence of individuals to act in these capacities (i. e. , education and training records), verification that clinical data was provided to support the justification of a specific imaging procedure and that the procedure was justified prior to the exposure taking place.
vallet@bordeaux-inp. fr X. Mestre is with Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Av. Carl Friedrich Gauss 08860 Castelldefels, Barcelona (Spain), xavier. mestre@cttc. cat This work was partially supported by project ANR-17-CE40-0003. The material of this paper was partly presented in the conference paper  [1]  see e. g. [6] ,  [7] ,  [8] ,  [9] ,  [10] ,  [11]. Also relevant are the approaches in  [2]  and  [3] , where the possible useful signal is supposed to be the output of a filter driven by a low-dimensional white noise sequence. Our focus here is on another type of formulation, referred to as frequency domain approach, which consists in rewriting problem  (2)  as H 0 : S y (ν) = dg (S y (ν)) for all ν ∈ [0, 1] H 1 : S y (ν) = dg (S y (ν)) for some ν ∈ [0, 1] ( where S y (ν) is the M × M spectral density matrix of (y n ) n∈Z at frequency ν, defined by S y (ν) = k∈Z R y (k)e -2iπνk.
In our method, the masked regions are treated as pixels with infinite noise, circumventing the need to treat unobserved areas as being cut from the analysis. The paper is organised as follows. Section 2 describes the data model for the cosmic shear and the likelihood. Section 3 gives an overview of the Bayesian inference framework, BORG, as required for this work. In Section 4, we described the simulated data employed in testing and validating the method. The results are presented in Section 5, showing that the method provides unbiased matter density fields. In Section 7, we discuss the effect of the prior power spectrum in the results. Finally, Section 8 summarises the results.
For these tests, we used a simplified scenario, assuming flat-sky and distant observer approximations. In future work, we will drop these approximations and consider the projection effects. In the flat-sky approximation, we can obtain the shear values from the convergence field. On a flat-sky, the shear and the convergence are related in Fourier space. We, therefore, use a discrete Fourier transform (DFT) to obtain the shear values as 𝛾 𝑏 𝑚𝑛 = DFT -1 (𝑙 𝑥 + 𝑖𝑙 𝑦 ) 2 𝑙 2 𝑥 + 𝑙 2 𝑦 DFT(𝜅 𝑏 𝑚𝑛 ) , (6) where ì  = (  ,   ) is the wave-vector written as a complex quantity. Since the convergence is also computed as part of the hierarchical model, this method has the advantage that it can analyse reduced shear, which, rather than the shear alone, controls the shape distortion. 𝑔 = 𝛾 1 -𝜅 , (7) To analyse cosmic shear observations in our Bayesian framework, we now built a likelihood based on this data model.
This likelihood is then implemented into the large-scale structure sampler of the BORG framework. The corresponding physical forward modelling approach is illustrated in Fig. 1  and proceeds as follows. Using realisations of the three-dimensional field of primordial fluctuations, the dynamical structure formation model evaluates non-linear realisations of the dark matter distribution, accounting for the light-cone effects inherent to deep observations. Using these 3D dark matter field realisations and the data model, BORG predicts shear fields that are compared to the observed data via the likelihood in equation (  8 ).
THE BORG FRAMEWORK This work extends the previously developed BORG algorithm to analyse the spatial matter distribution underlying cosmic shear observations. In this section, we provide a summary of the algorithm. A more detailed description of the BORG framework can be found in  Jasche & Wandelt (2013a) ;  Jasche et al. (2015) ;  Lavaux & Jasche (2016) ;  Jasche & Lavaux (2019) ;  Lavaux et al. (2019). The BORG framework is a Bayesian inference method aiming at inferring the non-linear spatial dark matter distribution and its dynamics from cosmological data sets. The underlying idea is to fit full dynamical gravitational and structure formation models to observations. By using non-linear structure growth models, the BORG algorithm can exploit the full statistical power of high-order statistics of the matter distribution imprinted by gravitational clustering.
To explore this high-dimensional parameter-space efficiently, the BORG framework uses a Hamiltonian Monte Carlo (HMC) method, which exploits the information in the gradients and adapts to the geometry of the problem. We need, therefore, the adjoint gradient of the data model, which transforms the error vector from the likelihood space to the initial conditions. For the case of weak lensing, we derive this gradient in Appendix A. More details about the HMC and its implementation are described in  Jasche & Kitaura (2010b)  and  Jasche & Wandelt (2013b).
THE MOCK DATA To test the inference framework, we generated mock observations of cosmic shear with 30 sources per arcmin 2 as expected for the Euclid survey, with four tomographic bins and a non-trivial survey mask. In this section, we describe the properties of the synthetic data. Mock data are constructed by first generating Gaussian initial conditions on a Cartesian grid of size 1ℎ -1 Gpc ×1ℎ -1 Gpc ×4ℎ -1 Gpc with 128×128×256 voxels. To generate primordial Gaussian density fluctuations we used a cosmological matter power spectrum includ-ing the baryonic wiggles calculated according to the prescription provided by  Eisenstein & Hu (1998 , 1999). We further assumed a standard ΛCDM cosmology with the following set of parameters: Ω  = 0. 31, Ω Λ = 0. 69, Ω  = 0. 049, ℎ = 0. 6711,  8 = 0. 8,   = 0.