dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We acknowledge  DESY (Hamburg, Germany) , a member of the  Helmholtz Association HGF , for the provision of experimental facilities. Parts of this research were carried out at FLASH. Beamtime was allocated for proposal F-20140072EC. We gratefully acknowledge the tireless efforts and hospitality of the scientific and technical teams at FLASH, who have made these experiments possible. We acknowledge the  Max Planck Society  for funding the development and the initial operation of the CAMP end-station within the  Max Planck Advanced Study Group at CFEL  and for providing this equipment for CAMP@FLASH. The installation of CAMP@FLASH was partially funded by the  BMBF  Grants  05K10KT2 ,  05K13KT2 ,  05K16KT3  and  05K10KTB  from  FSP-302. We thank  Luis Bañares  and  Jesus Gonzalez-Vazquez  for providing the raw data of the calculated trajectories, and  Evgeny Savelyev ,  Jonathan Underwood ,  Rolf Treusch ,  Erland Müller , and  Pascal Lablanquie  for help during the beamtime.
The Kansas team acknowledges support from the  Chemical Sciences, Geosciences, and Biosciences Division, Office of Basic Energy Sciences, Office of Science, U. S. Department of Energy , Grants No. DE-FG02-86ER13491  (AR, FZ, DR) and DE-SC0019451 (EW). TM acknowledges financial support from the  French Agence Nationale de la Recherche (ANR)  through the  ATTOMEMUCHO  Project ( ANR-16-CE30-0001 ). ASM and PKO acknowledge  German-Russian Interdisciplinary Science Center (Grants No. G-RISC , No. C-2015a-6, No. C-2015b-6, and No. C-2016b-7) funded by the  German Federal Foreign Office via the German Academic Exchange Service (DAAD)  and  Saint-Petersburg State University  for financial support. DMPH is grateful to the  Science and Technology Facilities Council (United Kingdom)  for financial support.
Benefiting from the coherent capabilities of radar processing, SAR tomography gave rise to abundant literature in the last decade, mostly based on airborne Land P-band radar image acquisitions with multiple incidence angles  [35]  to image the 3D structure of dense and high forests as well as the underlying topography  [16, 29, 30, [36] [37] [38]. In the framework of the preparatory experiments for the BIOMASS mission, preliminary tests were carried out using data of airborne campaigns in tropical forests, i. e. , in French Guiana  [17]  and Gabon  [39] , in order to improve and validate the algorithms of biomass inversion. Since digital terrain modeling is foreseen as a product of the BIOMASS mission, the data of airborne surveys have also been processed to test the potential of P-band tomography to model the topography, even though these results do not allow straightforward conclusions about the potential of the BIOMASS mission at a very different spatial resolution.
That is why an increasing number of studies aiming to assess the quality of DEMs have been published during the last two decades  [41, 42]. In this paper, we consider two types of quality criteria, namely, external (based on the comparison with external reference data) and internal (based on the detection of artifacts or unrealistic landforms) criteria. Based on these criteria, the following questions will be addressed: What are the main insights of P-band SAR tomography in terms of threedimensional terrain modeling under tropical rainforests? How accurate are the positions and terrain shapes modeled using this technique? How robust and homogeneous are the performances with respect to terrain morphology and acquisition parameters? The study area, the datasets and the quality assessment methods are firstly presented in Section 2; the results are presented and illustrated in Section 3 and discussed in Section 4.
P-Band DTM The SAR data processed in the frame of this study were measured in 2009 by the SETHI sensor by ONERA (Palaiseau, France) and consist of a set of 6 fully polarimetric images acquired in a repeat-pass multi-baseline InSAR mode at the P-band. The emitted radar signals occupy a spectral domain centered around f c = 397. 5 MHz, and the dimensions of the processed 2D spectrum led to spatial resolutions of 1. 2 m and 1. 5 m in the slant range and azimuth directions, respectively. A subset of the original stack, whose slant range and azimuth coordinates, delineated in Figure  3 , coincide with those of the lidar DTM described below, has been selected. This sub-region, located in the near-to-medium range part of the original swath, is observed by the radar under an elevation angle, θ, ranging from 20 to 50 degrees, and is characterized by significant topographic features, with hills and valleys.
Although it leads to local variations in range resolution, this choice allows a more intrinsic evaluation of the performance of P-band tomography since it excludes the influence of the projection from slant to ground range or any other resampling step. In other words, the evaluation is based on a raw DTM, resulting from the two basic steps of DTM production, namely, the P-band radar data acquisition and the tomographic processing, without any subsequent post-processing. Despite these limitations, this study is a basis for further work over larger areas and focuses on more diverse configurations, especially in terms of terrain topography, vegetation cover or acquisition parameters. In the case of the BIOMASS mission, those parameters are different and should give different results. Indeed, the radar bandwidth (6 MHz giving a 25 m range resolution) will not give access to the same topographic features, although the computational algorithm or the overall trends related to physical interactions at P-band (central frequency close to 400 MHz) remain very similar.
Specific tomographic techniques will probably have to be implemented in order to cope with the low resolution and low dimensionality of the data  [91]. In addition, it is also worth reminding that the exploitation of BIOMASS data will enable the combination of ascending and descending passes, which would significantly improve the mitigation of slant range geometrical effects and motivate further studies based on such configurations. The multicriteria analysis proposed in our study will then have to be conducted with a different interpretation.
• Internal validation shows that the hydrography is well preserved, with few (2%) and shallow sinks (0. 7 m RMS) and good compliance with Horton's law. These results are based on the analysis of a particular dataset over a small area with a specific landscape. They will be consolidated by further studies over larger areas with variable morphologies. This multicriteria accuracy assessment shows the importance of using ad hoc quality criteria, as proposed in this study, and the high potential of the P-Band SAR data in depicting the topography under forests in comparison to state-of-the-art lidar reference, although limited by the slant range geometry for which the combination of ascending/descending passes could solve the problem, and this could be very meaningful in spaceborne acquisitions such as in BIOMASS mission.
Data analyses Descriptive statistics and measures of dispersion were used to describe the agroforestry systems. We analyzed the variability of the management variables and the relationships between them. Multidimensional analyses (PCA) and a typology of vineyard management (HCA) were carried out in R (R Core Team 2020) with the FactoMineR package  (Lê et al. 2008). This analysis enabled us to test if technical management impacted agroeconomic performance (disease and pest pressure, yield, costs, and income). Moreover, we examined if management correlated with farmers' expectations regarding the usefulness of the vineyard-tree association.
The obtained NR curves were analyzed by the Motofit package (Andrew Nelson, Australian Nuclear Science and Technology Organization) running within the IGOR Pro software (WaveMetrics, Inc. ). [55] SLDs of Si, SiO 2 , D 2 O, and PNIPAM were fixed at 2. 07 × 10 -6 , 3. 47 × 10 -6 , 6. 36 × 10 -6 , 0. 84 × 10 -6 , and 0. 68 × 10 -6 Å -2 , in accordance with the literature. [37, 44] For PSBP, the SLD was calculated from the mass density and monomer structure, assuming a density of 1. 0 g cm  -3  , which was used to characterize the monomer with similar structure in our previous work. [56] By varying the number of layers in the profile of the PSBP 80 -b-PNIPAM 400 thin film and adjusting their thickness, roughness, and SLD value, the evolution of the film structure and kinetics of water transfer was successfully resolved during the hydration and after the thermal stimuli.
Acknowledgements N. H. and L. L. contributed equally to this work. This work was supported by the  National Natural Science Foundation of China  (Grant Nos. 52173087 ,  51403186 , and  51611130312 ),  Zhejiang Provincial Natural Science Foundation of China  ( LY21E030022 ),  National Key R&D Program of China  ( 2017YFB0309600 ), the  Fundamental Research Funds of Zhejiang Sci-Tech University  (Grant No. 2021Y003 ), and the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  ( PA 771/14-1 ,  MU 1487/17-1 , and  LA 611/11-1 ). This work was based upon experiments performed at the D17 at the  ILL, Grenoble, France. The authors acknowledge beamtime allocation and excellent equipment. Correction added on 12 December 2022, after first online publication: Projekt Deal funding statement has been added.
MD Simulation Conditions The MD simulations were carried out using the GROMACS 2018. 2 simulation package  (Abraham et al. , 2015) , along with CHARMM36  (Klauda et al. , 2010)  as force field for the lipids and CHARMM36m  (Huang et al. , 2017)  for the IAPP peptides. Each system was first energy minimized using the steepest descent algorithm to remove initial atom clashes that may have resulted during the setup. This was followed by an equilibration using MD simulations under NVT conditions, where the reference temperature T of 302 K (which was chosen to be close to the temperatures used in the experiments) was regulated with a velocity-rescale thermostat  (Bussi et al. , 2007). Then, the system was equilibrated under NpT conditions to obtain a pressure p of 1. 0 bar, which was realized by regulating the pressure using a semi-isotropic Parrinello-Rahman pressure coupling scheme  (Berendsen et al.
Analysis of the MD Simulations All analysis programs mentioned are available via the GROMACS 2018. 2 program package  (Abraham et al. , 2015). Only the MD snapshots were IAPP is within 0. 5 nm of the bilayer were included in the analysis of the system in question. The peptide-lipid interactions were then determined by calculating the interaction energy between each IAPP residue and the DOPC and DOPS lipids, respectively, using "gmx energy". The "gmx mindist" program was employed to determine the number of contacts between each IAPP residue and DOPC/DOPS. A contact was recorded when the distance between any two non-hydrogen atoms from a residue and a lipid was within 0. 5 nm. The hydrogen bond propensity was determined as the ratio of the number of MD snapshots where one or more hydrogen bonds were formed between peptide and lipid and the total number of MD snapshots per system.
The simulations revealed that for all peptide variants this helix is located between residues Q10 and S19, but it generally does not involve all of these ten residues as this would amount to a helical content of more than 27%. The βsheet formation is thus expected to take place in the C-terminal peptide region from residue S20 onwards. For several fragments of that region it has been shown that amyloid-fibril formation is possible. This especially applies to the region S20-S29, which is also considered the amyloid-core region of hIAPP. Solid-state NMR spectroscopy  (Griffiths et al. , 1995)  and X-ray crystallography of microcrystals formed by hexa-or heptapeptides from that region (PDB entries 3DG1, 3DGJ, 5E61, and 5E5V)  (Wiltzius et al. , 2008 (Wiltzius et al. , , 2009;; Soriaga et al. , 2016)  showed that these segments can form both antiparallel and parallel β-sheets, while for the fibrils formed in solution by full-length IAPP (Figure  8 ), only parallel β-sheets have been (Bottom) On the membrane, IAPP adopts initially a helix (green), which is of amphipathic nature and hence tethers the peptide to the membrane.
This orientation is stabilized by the long and flexible side chains of K1, R11, and K18 or R18. The ATR-FTIR results reflect that the mutated peptides H18R-, H18K-, H18E-, and H18A-IAPP do not alter the membrane properties during the initial peptide-membrane interactions, while hIAPP was able to slightly change the membrane properties. Since this is the peptide with the largest amount of β-sheets being formed, this suggests that β-sheets are needed for membrane disturbances. This conjecture is further supported by our MD data which revealed that the initial insertion of IAPP as a helix is only just below the headgroup region, which, apart from small effects on the membrane thickness around the peptide, does not change the lipid tail order. This agrees to the findings from the ATR-FTIR spectra.
(2021)  (ref. 11) obtained two sets of possible crustal models beneath the InSight landing site: a two-layer model with a Moho depth of 20 ± 5 km and a three-layer model with a crustal thickness of 39 ± 8 km (though with a weaker wave-speed contrast across this possible deeper Moho). Subsequent studies from Kim et al. (2021)  (ref. 12) and Durán et al. (2022)  (ref. 13)  based on more comprehensive datasets and different methods confirmed the existence of two seismic discontinuities within the crust (i. e. , at ~8 km and ~20 km, respectively) and favored the existence of the third discontinuity at its base (i. e. , at ~40 km) thus indicating a three-layer crust. Results from ambient noise auto-correlation  14, 15  have also revealed a discontinuity at about 21 km depth, consistent with the base of the second layer of the receiver function studies.
In addition, by combining receiver function results and SH-wave reflection observations, Li et al. (2022) (ref. 16) found the presence of seismic anisotropy in the shallowest 8 km of the crust at the landing site. Although the methods employed above were effective at providing the first seismic model of the Martian crust, all the seismic bodywave studies reflect the structure beneath the InSight landing site. Constraining the crustal structure at other locations is, however, crucial to fully understand the formation and evolution of the crust, and to improve global models of crustal thickness based on gravity data  17. Although surface waves have been recently detected for the first time from two meteorite impacts  18, 19  and the largest marsquake ever recorded  20  , surface waves constrain the average structures along the event-lander path  19, 21, 22  and are less sensitive to subsurface discontinuities.
Data processing The data were originally recorded by the broadband SEIS seismometer on the U, V, and W components. We first pre-filtered the raw data from 0. 01 to 8 Hz and removed the instrument response using ObsPy  47  to get the displacement records. We subsequently rotated the coordinates from UVW to NEZ and then from NE (i. e. , North and East) to RT (i. e. , Radial and Tangential directions) using the back azimuth determined by Horleston et al. (2022) (ref. 7). Finally, a two-pass, secondorder Butterworth band-pass filter was applied to the waveforms on the Z, R, and T components (e. g. , in Fig. 2 , and Supplementary Fig. 1 ). Since the SEIS seismometer is deployed under an extremely harsh environment, glitches (i. e. , transient one-sided pulses) are generated due to internal thermal stresses resulting from the diurnal temperature changes and might interfere with the seismic signals  48.
(2021) (ref. 24). For each model, we also varied the focal depth from 0 km to 50 km and the epicentral distance from 146. 3 -6. 9 o to 146. 3 + 6. 9 o , and then calculated the ray parameters using software Taup  56. The average ray parameters for the S-and P-waves are 10. 13 s deg -1 and 4. 84 s deg -1 , respectively (Supplementary Fig. 6 ). Finally, we chose three typical ray parameters to calculate the synthetic arrival time and amplitude ratio, i. e. , the mean value (10. 13 s deg -1 for S-wave and 4. 84 s deg -1 for Pwave), the mean value plus one standard deviation (10. 13 + 1. 10 s deg -1 for S-wave and 4. 84 + 0. 71 s deg -1 for P-wave), and the mean value minus one standard deviation (10. 13-1. 10 s deg -1 for S-wave and 4. 84-0. 71 s deg -1 for P-wave). For the synthetic waveform shown in Fig.
3 , we extracted the source time function from the data after polarization filtering (i. e. , from -3. 2 to 5. 5 s for PP and from -5. 9 to 9. 7 s for SS). To simulate the precursor generated at each interface, we first multiplied the amplitude of the source time function with the calculated synthetic amplitude ratio. Then, we shifted the source time function according to the differential travel time derived from Eqs. (  1 ) and (2). We also compared this modeling approach with another software, QSEIS  57  , which calculates the full wave field, and found a consistent amplitude ratio when there is no significant interference from other phases. If there are interferences (e. g. , depth phases), since they show up after both the main SS phase and its precursor, they do not significantly affect the phase identification (Supplementary Fig.
Data availability The datasets generated during the analysis are available on the Zenodo repository: 10. 5281/zenodo. 7252035 and in the Supplementary Data 1 folder. The raw dataset used in this study is achieved and released by InSight Mars SEIS Data Service and is available to the science community (InSight Mars SEIS Data Service 71 ) and is publicly available through the Planetary Data System (PDS) Geosciences node (InSight SEIS Data Bundle  72  ), the Incorporated Research Institutions for Seismology (IRIS) Data Management Center under network code XB and the Datacenter of Institut de Physique du Globe, Paris.
Acknowledgements We appreciate  M. A. Wieczorek ,  A. -C. Plesa , and  R. Maguire  for their discussions. We acknowledge  NASA ,  CNES , their partner agencies and Institutions (UKSA,  SSO ,  DLR ,  JPL ,  IPGP-CNRS, ETHZ, IC, MPS-MPG ), and the flight operations team at  JPL , SISMOC, MSDS, IRIS-DMC, and  PDS  for providing SEED SEIS data. J. L. and C. B. are supported by  NASA   InSight PSP  grant # 80NSSC18K1679. S. M. M. is supported by  NASA   InSight PSP  grant # 80NSSC18K1622. P. L. is supported by  Agence Nationale de la Recherche  ( ANR-19-CE31-0008-08   MAGIS ,  ANR-18-IDEX-0001 IdEx Université Paris Cité ) and by  CNES  for SEIS science support. W. B. B. is supported by the  InSight  Project at the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with the  National Aeronautics and Space Administration  (# 80NM0018D0004 ).
Suggestions Based on comprehensive datasets from eleven laboratories, our survey shows the evolving nature of imprinting disorder diagnosis. We suggest the following modifications to diagnostic testing for imprinting disorders (Fig. 1 ): Development or adoption of a multi-locus imprinting test, capturing DNA methylation and relevant copy number analysis of imprinted loci on chromosomes  6, 7, 11, 14, 15, 16, 20. This could conveniently be compassed on one two-tube MS-MLPA kit, or in a novel test which should be validated in respect to a uniform and standardised coverage of all relevant DMRs and major molecular subtypes. Multi-locus testing of all individuals referred for growth restricting imprinting disorder testing (i. e. for SRS, TS14). This would efficiently capture known imprinting disorders and MLID, after which SNP array or NGS (next generation sequencing) analysis could be targeted for individuals; (a) with unusual DNA methylation patterns, to seek uniparental diploidy or structural variants, (b) with MLID to investigate trans acting variants in maternal effect genes; (c) without a detected imprinting change to seek variants in genes involved in growth restriction.
Acknowledgements Not applicable 75012 Paris, France. 10  Institute of Human Genetics, University of Hamburg, Hamburg, Germany. 11Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK. 12 Centre de Référence du Syndrome de Prader-Willi et Autres Obésités Avec Troubles du Comportement Alimentaire, Unité d'Endocrinologie, Obésité ,  Maladies Osseuses ,  Génétique et Gynécologie Médicale, Hôpital des Enfants CHU Toulouse, Toulouse, France. 13Laboratoire de  Génétique Médicale ,  Institut Fédératif de Biologie CHU Toulouse, Toulouse, France. 14Kennedy Center, Department of Clinical Genetics, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark. 15Department of  Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
One of the important features of Resistive RAM (RRAM) is its conductance modulation, which makes it suitable for neuromorphic computing systems. In this paper, the conductance modulation of Oxide-based RAM (OxRAM) devices is evaluated based on experimental data to reveal its inherent analog synaptic behavior. A test chip made of a classical 1T-1R elementary memory array is used to demonstrate the conductance modulation. Using an array of cells, as opposed to an isolated cell, allows to catch temporal as well as spatial variabilities. Thus, the multiple resistance levels capability of OxRAMs is assessed in a more realistic context. Two different programming techniques are used to program the OxRAM cells. The first approach leverages on RESET (RST) voltage control. The second approach relies on compliance current control during the SET operation.
Introduction Uses of external controls drawn from real-world data and natural history data (RWD/NHD) are of high interest for drug evaluations in Duchenne muscular dystrophy (DMD), and have informed drug evaluation in other rare conditions  [1] [2] [3] [4]. While randomized, placebo-controlled trials are the gold standard for measuring drug efficacy and safety, a number of factors complicate the methodological, practical, and ethical considerations for placebo controls in DMD trials, especially for longer periods of follow-up beyond 48 weeks. Variability in rates of disease progression across individuals reduces power to detect meaningful treatment effects with readily achievable sample sizes. Safe and effective blinding to treatment assignment is not always feasible with invasive administration procedures or tell-tale reactions to active therapy  [5 , 6].
The NSAA score has been used as a primary or secondary endpoint in clinical trials enrolling ambulatory boys with DMD  [29] [30] [31] [32]  , included in open-label extension studies  [33] [34] [35]  , and studied in several natural history studies and realworld databases  [27 , 36]. In assessing the consistency of NSAA outcomes across data sources, the present study builds on our prior study of the consistency of the six-minute walk distance (6MWD) test across data sources in DMD  [37]. We also identify prognostic factors for 48-week change in NSAA total score and evaluate different approaches to mitigating the risk of bias by adjusting for these prognostic factors when comparing to external controls.
Data sources Patient-level data from three clinical trial placebo arms and six RWD or NHD sources accessed by the collaborative Trajectory Analysis Project (cTAP) were used in this study ( Tables A. 1  and A. 2 ). Clinical trial placebo arm data came from three phase 3 trials in ambulatory DMD: the tadalafil DMD trial (patients primarily enrolled and followed from 2013 to 2015)  [38]  , the ACT-DMD trial of ataluren (2013-2015)  [35]  , and the DEMAND III trial of drisapersen (2011-2013)  [39]  , provided to cTAP by Lilly, PTC Therapeutics and CureDuchenne, respectively. Curated RWD came from Universitaire Ziekenhuizen Leuven (2011-2016), the North Star UK database (2005-2015; http://www. northstardmd. com), and Cincinnati Children's Hospital Medical Center (CCHMC)  (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016).