dataesr/scientific-paragraphs-categorization huggingface dataset:
8-10 is likely associated with the smoother velocity profiles in the upper 100 m (both within profile as well as lateral spatial smoothing arising from the inversion methodology) leading to a greater degree of spatial correlation among systematic site effects than implied by the modelled velocity profiles. During the recording period of the small Loppersum block, the Zeerijp M 3. 4 event on 8 January 2018 at 14:00:52 (UTC) was recorded by the flexible array. This event allows for a comparison between the measured and modelled spatial variability of ground motions at the surface. The distance between the earthquake and the flexible array corresponds to a rupture distance R rup ranging from 4 to 6 km. There are two rupture distances within the 3600 FAS motion database that correspond to these conditions (R rup = 4.
81 and 5. 64 km). For each magnitude M and R rup combination, there are four motions defined corresponding to the stress drop branches of the GMM logic tree. This results in eight input model motions at the reference rock horizon (1 magnitude × 2 distances × 4 stress drop branches). These motions were propagated to the surface using STRATA and the soil columns and V S profiles from the earlier analysis, that is, the model V S and the field V S profiles. The spatial pattern of spectral accelerations at the surface for T = 0. 2 s is shown in Fig. 13  for the observed accelerations and for the modelled accelerations from site response calculations. The values are displayed as deviations from each dataset's mean (delta) to visually enhance patterns. The observed accelerations show a spatial pattern that reflects both variations due to the varying distances to the earthquake hypocentre and the different paths through the subsurface.
Conclusions A new field dataset (V S-to-100 m ) has been processed to be included in site response calculations. We have compared the AF resulting from model soil profiles  (Kruiver et al. , 2017a, b)  to the profiles constructed using V S-to-800 m from  Chmiel et al. (2019)  and the new V S-to-100 m for two sites (Borgsweer and Loppersum) in the Netherlands. The comparison shows that for individual periods, the AF from the field data exhibits less variation than AF from model data and that there is a variable shift in average AF. Combining all results for the 10 periods which are relevant for the seismic risk, the model overpredicts by 16% for Borgsweer and underpredicts by 2% for Loppersum. Both are within the uncertainty range generally accepted in geo-engineering. For Borgsweer, the model is being conservative relative to the field data.
The simulated spectral accelerations for Loppersum could be compared to the recordings of an earthquake that occurred during the monitoring period (M 3. 4 Zeerijp on 8 January 2018). There is a shift to lower spectral accelerations at the surface for short periods (PGA and T = 0. 1 s) for the simulated data. However, the correspondence in spectra acceleration is very good for T = 0. 2 and 0. 3 s, which is the dominant period for most houses in Groningen. These two analyses indicate that the model V S represents suitable input describing the site response in the Groningen GMM, because the spatial variability in V S , reflected by the spatial variability in AFs, is comparable for both field and model datasets. Supplementary material. To view supplementary material for this article, please visit https://doi.
Introduction The amyloid cascade is considered pivotal in the development of Alzheimer's disease. In the last 20 years, numerous transgenic mouse strains have been produced to model the accumulation of amyloid-β (Aβ) peptides and formation of Aβ plaques in the brain. Most are knock-in animals, obtained by insertion of human genes, which usually include mutations observed in familial cases: presenilins (PSEN1/2), amyloid precursor protein (APP), microtubule associated protein tau (MAPT), triggering receptor expressed on myeloid cells 2 (TREM2), etc. The Alzforum database currently lists 123 mouse models with 1 transgene, and 62 multi-transgene mouse models (https://www. alzforum. org/research-models; last access on  May 25, 2021). Fast methods for whole-brain analysis are thus of great value for characterizing and comparing phenotypes among the variety of strains available.
X-ray based virtual histology is a new field of research mainly aimed at 3D datasets of biological tissue virtually sliced in any direction  [1]. X-ray phase-contrast tomography (XPCT) using synchrotron radiation allows imaging of excised biological tissues or organs with weak X-ray absorption (like the brain) at microscopic level  [2]. XPCT achieves a high signal-to-noise ratio without the need to add staining agents, by probing small changes in refractive indices in the tissue microstructure. It provides micrometric spatial resolution and isotropic reconstruction in a ~ cm 3 field of view (FOV), thus offering ideal prerequisites for imaging protein aggregates of ~ 10µm in the entire and intact (unsliced, unstained) mouse brain  [3]. Several pioneer studies already reported detection of Aβ plaques with various X-ray phase contrast techniques  [4] [5] [6].
Detector filter  [20] , as suggested by Astolfo et al. [7]. A step-by-step guide to perform all image processing, "Amyloid-β XPCT Workflow", is publicly available (DOI: 10. 5281/zenodo. 4584752) [21]. 3D parameters. Sphericity, elongation, flatness, sparseness and distance to nearest object were outputs from 3D ImageJ Suite. Surface area, volume, mean breadth and mean signal intensity (here normalized by background intensity) were outputs from MorpholibJ. Definitions of these parameters are reported in Table  2.
Mean Breadth Average of the Feret diameter of convex objects measured over a selected number of orientations (µm) Distance Distance between two closest segmented objects (µm) T1892) (0. 05% in ethanol 70%) was performed for 10 min at RT after dewaxing, and slides were mounted with DAPI-containing medium, after two 1 min washes in ethanol 70%. Fluorescence images were captured on a microscope (AxioScope A1, Zeiss) equipped with a digital camera interfaced with image-analysis software (ZEN 2 lite, Zeiss). Data availability statement. The raw data required to reproduce these findings cannot be shared at this time due to the large size of the XPCT datafile, but are available on request. The image processing workflow is available on Zenodo (DOI: 10. 5281/zenodo. 4584752) [21].
Acknowledgments This study was performed within the framework of  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the  "Investissements d'Avenir" program  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). The research was in part funded by the  French ANR  project  NanoBrain  ( ANR15-CE18-0026 ). The study was supported by the  European Synchrotron Research Facility (ESRF)  by allocation of beam time ( LS2292 ,  MD1018 ,  IN1041 ), and the authors would like to thank  ESRF local contact Lukas Helfen. Carlie Boisvert  was supported by  Mitacs Globalink Canada  (travel grant). 3xTg and WT brains were kindly provided by  Dr Catherine Lawrence  of the  University of Manchester. Paraffin embedding and microtome slicing were performed at the  CIQLE Imaging Platform, University of Lyon , with the help of  Annabelle Bouchardon  and  Batoule Smatti.
K E Y W O R D S adjunct to procedures, anti-aging product, observational study, peptides, topical formulation, vitamin C a Wilcoxon test for matched samples if the normality hypothesis of distributions was rejected by the Shapiro-Wilk test at the threshold of 1%, was used to analyze changes in wrinkle grade, changes in skin quality; a separate analysis was performed for wrinkles in Russian women as a 7-point scale was used. All statistical analyses were performed at a 5% significance level using 2-sided tests, except a threshold of 1% was used for the normality Shapiro-Wilk test. All summaries and statistical analyses were generated using SAS version 9. 4.
Smoothing method for the phenotyping of adaptive trajectories Because the MY and plasma NEFA concentrations were significantly affected by FR, a smoothing method considering the time patterns of these two biological variables was used. Individual profiles of the MY and plasma NEFA concentrations in response to FR were obtained from daily data using FDA, which is a smoothing approach previously described by  Ramsay et al. (2020). This approach was implemented using the statistical software RÓ version 3. 5. 2 (R Development Core Team, 2020). Detailed algorithms for differential smoothing of time series from animals exposed to controlled FR were described in  Barreto-Mendes et al. , 2022. The quality of the fit was assessed visually by adjusting the roughness coefficient (k) value. A higher k value indicates that finer details of the curve are captured.
Statistical analyses The statistical analyses were performed using SAS software (version 9. 4; SAS Institute INC, Cary, NC, USA). The daily DMI, MY, BCS, BW and plasma metabolite data were averaged within animals and periods to perform comparisons among the periods (pre-experimental period, FR4, RECOV4, FR10 and RECOV10). The models included the fixed effect of the period and the random effect of cows. Time differences were tested using posthoc Tukey's test. The daily MY and plasma NEFA concentrations were analysed as repeated measures by mixed models that included day as a fixed effect, cow as a random effect, and Kenward-Roger adjustments for the calculation of denominator degrees of freedom. The choice of variance-covariance structures (compound symmetry, autoregressive, heterogeneous autoregressive, and spatial power) was based on Akaike's criterion obtained from the analyses.
The residuals were checked for normality. The daily plasma NEFA concentrations were log-transformed, and statistical P-values were obtained based on the transformed dataset, whereas the least square mean (LSM) and SEM values are reported for non-transformed data. Individual variables resulting from the FDA analyses and describing the deviation and recovery profiles obtained for the MY and NEFA data were calculated within each FR period. Two cows had missing MY data during FR4, and one cow had missing MY data during FR10. The means and SDs per FR were compared by paired Wilcoxon's test. The individual variability in each calculated variable was quantified by the CV. The homogeneity of variances observed during FR4 and FR10 was tested with Levene's test. The relationships between FR4 and FR10 for each variable describing the deviation and recovery profiles for the MY and plasma NEFA concentrations were explored by Spearman rank correlations using R software (https://www.
2 The experimental period included two periods of feed restriction (FR4: short-term feed restriction for 4 days; and FR10: short-term feed restriction for 10 days) in which the feed allowance was reduced to meet 50% of the net energy requirements for lactation calculated from BW, BCS and MY data collected during the last week of the preexperimental period (FR4) and two recovery periods (RECOV4 and RECOV10) with ad libitum intake. 1 During the pre-experimental period, suckling cows were ad libitum fed hay supplemented with a concentrate to meet their predicted and metabolisable protein requirements  (INRA, 2007). respectively, than those measured in cows fed ad libitum (Fig. 3A , P < 0. 05). An increase in the plasma NEFA concentrations was observed on the first day of FR4 and thereafter, and a 2-fold increase was observed on the second day of FR10.
Acknowledgements The authors thank the staff of the  INRAE  experimental unit (https://doi. org/10. 15454/1. 5572318050509348E12 ),particularly  Bernard Sepchat , for their expertise in beef cow nutrition and  Marc Barbet  and  Sebastien Gatignol  for the help with the animal management and sample collection. We also thank  Isabelle Constant ,  INRAE, France  for the technical assistance provided and  Amir Aliakbari  for his careful review.
Pre-Processing To obtain a georeferenced image in spectral radiance (W•m -2 •sr -1 •µm -1 ), the hyperspectral image was processed from raw data (level 0) to a radiometrically and geometrically calibrated image (level 1c) using the HYPIP (HYPperspectral Image Preprocessing) chain of Hytech-imaging that includes ATCOR/PARGE software applications (ReSe Applications, Wil, Switzerland). To calculate the surface reflectance, atmospheric corrections were performed in a two-step process: first, using the ATCOR-4 software, and then empirically adjusting each spectrum. To adjust each spectrum, coefficients of gain and bias were calculated per spectral band, by linear regression between surface reflectance data and the reflectance signature. This reflectance signature was obtained by positioning pre-calibrated targets (tarps) near the area of interest overflown during the survey.
Data Classification For this study, supervised classifications were performed, where categories (classes) correspond to spectral signatures defined by the user. A class contains a characteristic spectral signature for each dominating fucoid species, macroalgal group or an abiotic component and corresponds to homogeneous regions delineated on the UAV image. The software then assigns each pixel of the image into a cover type to which its signature is most comparable  [60]. The supervised classifications were performed after defining regions of interests (ROIs) which are training data. ROIs were created for each class using 'ROI tool' in ENVI version 5. 6. 1 (Exelis Visual Information Solutions, Boulder, CO, USA) by manually circling pixel areas on the image. More than one training ROI were usually used to represent a particular class (ROIs = multiple polygons) (Table  3 ).
1993 [67] : α = ∑ (∑ ) (∑ ) ( 2 ) where t is the spectra for a pixel, r is for the reference spectrum pixel, α is the spectral angle between t an r (measured in radians or degrees) and n is the number of bands. The average spectral reflectance curves from the ROIs were extracted since SAM requires endmember spectra. If two ROIs were identical, they were averaged in order to obtain one curve with the maximum possible data. The use of spectra derived directly from the image is usually better than using ground or library spectra due to better Two supervised classification methods were performed to test the representativeness of the spectral classes running the software ENVI version 5. 6. 1 (Exelis Visual Information Solutions, Boulder, CO, USA), i. e. , the algorithms maximum likelihood classification (MLC) and spectral angle mapper (SAM).
Each grid structure was replaced using 'Advanced Digitizing toolbar' ('Move Feature' and 'Rotate Feature' options) on Qgis. Corresponding polygons were accurately positioned using pictures taken during the field sampling, in order to decrease the potential GPS error and to compare the exact same position. To compare in situ data and classification data, vectors of the grid structure were replaced on the Porspoder image using the 'Vector to ROI' tool, and the percentage of pixels for each class in ROIs was extracted with the ROI statistics tool on ENVI. Statistical analyses were conducted using the R environment  [70]. Normality and homoscedasticity were first tested on each biological and classification variable, corresponding to seaweed species and substratum covers, with Shapiro-Wilk and F Test/Levene tests, respectively.
These tests then determined what analyses were the most suitable (parametric or not). In order to represent the distribution of the replicates described by the three approaches, a distance-based redundancy analysis (db-RDA) was constructed, based on the method described by Escobar-Briones et al. (2008)   [71]. Values for each class (apart from 'Water') were first converted into a distance matrix by calculating the Hellinger distance for each class in the whole dataset. Then, a principal coordinates analysis (PCoA) was performed on this matrix. The PCoA allows to convert the distance between items (distance matrix) into a map-based visualization (each item is assigned a location in a low-dimensional space, materialized by its eigenvector) in order to better understand the relation between each object.
Acknowledgments: Authors thank  Sara Terrin  and  Alain Guenneguez  for helping with the field sampling, and  Marion Jaud  for her valuable advice in hyperspectral analysis and the use of ENVI. Acknowledgments: Authors thank  Sara Terrin  and  Alain Guenneguez  for helping with the field sampling, and  Marion Jaud  for her valuable advice in hyperspectral analysis and the use of ENVI.
Surface Elevation Changes Elevation changes of the glacier and landslide areas were measured using time-series of digital elevation models (DEMs) derived from aerial photographs and ASTER images (Table  S1  in Supporting Information S1). Aerial photographs were processed using the MicMac software  (Rupnik et al. , 2017)  following the guidelines presented in  Belart et al. (2019). The bulk of the archives of aerial photographs in the area of study were processed in  Belart et al. (2020) , resulting in a series of DEMs co-registered to a lidar DEM acquired in 2010  (Johannesson et al. , 2013). We also added into this time-series the aerial photographs from  1945 , 1989 and 1994 (National Land  Survey of Iceland) and 2004 (acquired by the private company Loftmyndir efh. ). Uncertainties in individual elevation measurements from the aerial photographs range from 1 m (the oldest photographs) to 0.
3 m (the newest photographs), based on similar datasets analyzed in previous studies  (Belart et al. , 2019 (Belart et al. , , 2020)). To densify the time-series over the last 20 years, 160 satellite DEMs were generated from ASTER stereo imagery acquired between May 2000 and July 2020 with different initial coverage and data voids due to clouds or poor image correlation. Typically 30-60 ASTER elevation values are available to describe the temporal evolution of the landslide or the glacier tongue. ASTER DEMs were processed using the Ames Stereo Pipeline (ASP  (Shean et al. , 2016) ), using the same setup as in  Brun et al. (2017). Each DEM was then horizontally and vertically co-registered to the 2010 lidar DEM by minimizing the standard deviation of the elevation difference on stable terrain, that is, after excluding the glacier and landslide areas  (Berthier et al.
Seismicity The catalog of seismic activity analyzed in this study comes from the database produced from the South Iceland Lowland (SIL) seismic network, that has been operating with sufficient seismometers to locate events in Mýrdalsjökull since January 1995. The data contains earthquakes with a magnitude completeness of 1. 7  (Jónsdóttir et al. , 2007). Events are located with a horizontal and depth uncertainty of 3 and 5 km respectively. This database and its specificities are presented in details by  Jónsdóttir et al. (2007) ;  Jónsdóttir et al. (2009). Previous studies indicated a clear cluster of shallow seismic events of Magnitudes below 2. 8, nearby Myrdalsjökull, with a strong seasonality pattern, and a strong increase in activity in the period 2001-2004. This unusual seismic activity questioned its origin, which was either attributed to volcanic intrusion activity  (Soosalu et al.
Acknowledgments P. Lacroix  deeply thank  Mathieu Causse  and  François Renard  for all the interesting discussions on the analysis of the seismic data set, and  Andreas Kääb  and  François Renard  for the invitation at the  University of Oslo. We thank  Bryndís Brandsdóttir  for providing the seismic data used in this study. E. Berthier  and  P. Lacroix  acknowledge the support from the  French Space Agency (CNES)  through the  TOSCA, PNTS, SPOT World Heritage  and ISIS programs.
Data Availability Statement All satellite data used in this study are publicly accessible. The Landsat-7/8 images are available on the USGS Earthexplorer website (https://earthexplorer. usgs. gov/). The SPOT1 image is available on the catalog Regards (https://regards. cnes. fr/user/swh/modules/8). The ASTER images are available through NASA EarthData search (https://search. earthdata. nasa. gov/). The aerial photographs from 1945 to 1994 are openly available at www. lmi. is. The SIL catalog and climatological data are openly available at www. vedur. is. The softwares used to produce the DEMs and the time-series of horizontal displacement are available for download online. The mic-mac software is available at https://micmac. ensg. eu/index. php/Accueil. The cosi-corr module is available at www.
tectonics. caltech. edu/slip_history/spot_coseis/download_software. html. The TIO module is available at https:// sourcesup. renater. fr/www/tio/. The AMES stereo pipeline is available at https://ti. arc. nasa. gov/tech/asr/groups/ intelligent-robotics/ngt/stereo/. The time-series of DEMs derived from ASTER, velocity maps, fitted dh/dt map of 2000-2020, fitted velocities of 1999-2019 and filtered seismic data from the SIL network are openly available in the Zenodo repository associated to this work  (Lacroix et al. , 2022) : https://zenodo. org/record/6397629.
Population Scale Analysis The effect of the founder aphid morph and the clover treatment on different variables was analyzed at the population/arena scale (i. e. , 1 data per arena). The variables were the distance founder tiller-deposit tiller, aphids per arena, aphids per founder tiller, the occurrences of aphids and virus per tiller, and virus occurrence per non-infested tiller and per infested tiller. Generalized Linear Mixed Effects Models (GLMM) were used with a session random effect on the intercept to account for potential heterogeneity in aphid population growth and virus transmission between experiments. The model included the fixed effect of the morph (2-level factor: wingless and alate morphs) in interaction with the fixed effect of the treatment (3-level factor: b, bc1 and bc2).
Tiller Scale Analysis The effect of the clover treatment and the distance from the founder tiller on different variables was analyzed at the tiller scale (i. e. , 1 data per tiller). The data were analyzed separately considering the two founder aphid morphs. The variables were aphids per tiller or per infested tiller, the occurrences of aphids and virus per tiller, and the occurrences of virus per infested and non-infested tiller. To analyze the spatial spreading pattern of variables around the founder tiller, the distance from the founder tiller (numerical) was used as an explanatory variable. GLMM with a binomial link were used for occurrence variables and GAMM (Generalized Additive Mixed Effects Models) with a negative binomial link for count data that displayed non-linear responses regarding to the distance variable.
The model included the fixed effect of the treatment (3-level factor: b, bc1 and bc2) in interaction with the distance from the founder tiller (number of tillers from the founder tiller). The random effect of the arena nested in the session was used to account for nonindependent observations due to experimental variations of aphid population growth and virus transmission. Variable tested ~treatment + distance + treatment × distance + (1|session/arena) Statistical analyses were performed using R version 4. 1. 1 with the lme4 package for GLMM and gamm4 package for GAMM. The drop1 function was used to perform LRT.
Clover and Distance Effect at Tiller Scale To complete the description of data associated to aphid density and virus occurrence in arenas, spatial parameters have been used to evaluate the impact of clover on the overall movement of the founder aphid and its progeny, and on the viral spread. Analyses were carried out at the tiller scale (36 data per arena). For each variable tested, GLMM (for occurrence variables) or GAMM (for count data that displayed non-linear responses regarding to the distance variable) were used including in the models the fixed effects of the treatment, the distance, the interaction between the two, and the arena nested in the session as a random effect (see Section 2. 4. 2 for further details). GLMM analysis: R 2 m: marginal R-squared value; R 2 c: conditional R-squared value.