dataesr/scientific-paragraphs-categorization huggingface dataset:
Improved prediction of ascospore release, using the rABCD thermal time scale Based on regression analysis, the best fit to the 11 ascospore release datasets (R² = 0. 98) was obtained with Richard's law with parameters α=100, β=13. 5930, γ=0. 1343 and δ=0. 5629. Based on Student's T test, the residuals of this rABCD-based model were significantly smaller than those of the initial ABCD-based model (P < 0. 05). Using the datasets from 2009 to 2013 for a validation step, the rABCD-based model improved the accuracy of ascospore release predictions compared to both the reference NH model and the ABCD-based model (Figure  6 ). The improvement was most substantial for 2009 with predicted values very close to observed values. For all years, the overall amplitude of the deviation and the average of absolute values of the residuals were always smallest for the rABCD-based model (Table  3 ).
The most illustrative year was 2000, for which no rain event occurred within the 40 days that followed the observation of the first mature ascospores. On that year, the total amount of captured ascospores was the smallest in the 15 datasets used in this study (total of 610 spores observed, compared to a maximum of 48443 in 2004), but the first rain triggered the release of 41% of the total stock (Figure  4 ). Halting the thermal time during that long dry period would have led to provide farmers with an erroneous "low risk" advice. This absence of improvement when using halting rules in our region was quite surprising because their introduction provided important progress in scab prediction in various countries  (Rossi et al. , 2000; Stendsvand et al. , 2006a). Furthermore, using halting rules for the pear scab model described by  Eikemo et al.
Quantifying the uncertainty related to these BRDF-adjustment techniques is a critical part of producing consistent SR data set  [15]. Several approaches to perform such assessment have been explored. The most traditional validation approach relies on albedo validation using in situ measurement  [3, 11, 12, 16]. Albedo is derived from the angular and spectral integration of the BRDF among the full sun-view angle range (i. e. , white sky albedo) and the solar spectral domain. The albedo uncertainty includes these uncertainties related to various integrations: (i) Angular integration of BRDF; (ii) spectral integration of the spectral albedo  [17] ; and (iii) spatial integration of the measurement footprint  [16]. These integrations make the BRDF uncertainty retrieval more difficult to estimate.
F1 is the volume scattering kernel, based on the Ross-thick function derived by  [26]  and F2 is the geometric kernel, based on the Li-sparse model  [27]  but considering the reciprocal form given by Lucht et al. [28]. Vermote et al. [6]  introduced two proxy variables, V (as Volumetric) and R (as Roughness), to describe the BRDF "shape". V and R are defined as k1/k0 and k2/k0, respectively. Equation (1) can thus be written as: 𝜌(Ɵ, 𝜆) = 𝑘 0 (1 + 𝑉(𝜆)𝐹 1 (Ɵ) + 𝑅(𝜆)𝐹 2 (Ɵ)) (2) Using the MODIS CMG SR dataset, Vermote et al. [6]  found that the V and R variations with time were well correlated to the NDVI variations over vegetated areas. Thus, they generated two linear regressions (Equations (  4 ) and (  5 ), including four coefficients: V0, V1, R0 and R1) to estimate V and R as a function of the NDVI.
Data This study is based on the SPOT4 (Take5) experiment accurately described in  [20]. Among the 42 SPOT4 (Take5) sites, we focused on three sites that include multi-angular acquisitions. SPOT4 data has a spatial resolution of 20 m in 4 wavelengths (555 nm, 660 nm, 850 nm, and 1600 nm). We limited this study to the Red (660 nm) and NIR (850 nm) bands (Figure  1 ). SPOT4 data acquired over the Maricopa (Arizona, USA) site follow a requested multi-angular acquisition plan. Data of the same footprint were indeed acquired from two successive satellite tracks, inducing two distinct view angles (Table  1  and Figure  2 ). The two acquisitions are delayed by one day and follow the five-day repeat cycle of the experiment. Figure  1C ,D shows the study area footprint with the SPOT4 satellite tracks.
The study area, defined by a 120 × 60 km 2 footprint SPOT4, is mostly covered by semi-desert area. About 20% of the area is covered by irrigated crops. SPOT4 data acquired over the ProvLanguedoc and Sudmipy sites (both in France) are covered by two parallel footprints of 180 km and 280 km long, respectively, and acquired from two successive satellite tracks (Figure  1B, E, F ). The two footprints overlap over an area 10-20 km wide, which was used to analyze the SR differences due to the view angle variation. Since one of the five BRDF techniques (LC-dis, see Section 4. 3. 2) is based on surrounding land cover and MODIS BRDF a-priori information, we defined an area covering more than 50 times the overlap area to account for a sufficient amount of MODIS data and land-cover types. MODIS data, used for this study, are derived from the daily M[O/Y]D09GA products.
Bré on et al. [30]  concluded that the optimal aggregated spatial resolution for MODIS time series analysis is 2000 m. By simply extrapolating this finding to the 250 m nominal resolution, 1000 m resolution may be optimal. We defined a 1250 m resolution ensuring a relatively stable footprint of the measurement even in case of off-nadir observations. The MODIS preprocessing and BRDF derivation is described in Section 4. 1. For the implementation of the LUM technique (Section 4. 3. 5), we used the 2013 Cropland Data Layer (CDL) available from the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)  [32]. The CDL is an annual crop-type mask derived from the Landsat data. It accounts for major crops growth in US, including those cropped in the Maricopa area: alfalfa, cotton, barley, and corn.
Coarse Resolution Processing and BRDF Derivation The BRDF of the coarse resolution was derived using the VJB method and the same inversion approach as Vermote et al. [6]  used to adjust the BRDF of MODIS CMG data. Instead of aggregating MODIS full resolution to a 0. 05 degree grid (~5 km), we defined a finer grid at 1250 m. M[O/Y]D09GA products were averaged over the 1250 m grid, considering only the best quality pixels according to M[O/Y]D09GA quality assessment field. To be consistent with the SPOT4 (Take5) dataset, we used the same SPOT4 metric projections (UTM zone 13N for Maricopa site and Lambert 93 for both French sites). The V and R BRDF coefficients inversion approach, presented by  [6]  and based on MODIS CMG data, was applied with the 1250 m-aggregated MODIS data. Although the SPOT4 (Take5) experiment lasted from February to June 2013, we considered the data acquired over the full 2013 year to run the inversion approach in order to cover the full phenology.
On this day, bright surfaces were flagged as saturated on SPOT4 data and not used in the processing. This saturation did not concern class #7 pixels exclusively, but it affects most of the coarse resolution pixels (encompassing these class #7 pixels) that were not used for the BRDF inversion (see Section 4. 3. 2). It yielded to a lower confidence of the retrieved BRDF because it relies on a lower number of used coarse resolution pixels to do the inversion. One case (class #5-NIR) displays lower noise of non-BRDF-adjusted SR TS than noise of normalized SR TS due to over adjustment of all techniques during vegetation peak (March-April). Compared to all other classes, class #5 (equivalent to barley) during vegetation peak displays an opposite anisotropic behavior: NIR measurement from west track (dayi) is higher than the one from east track (dayi + 1).
ProvLanguedoc and SudMipy Sites The analyses were reproduced over the overlap area of ProvLanguedoc (Figure  8 ) and SudMipy (Figure  9 ) sites. LUM technique was not applied since it relies on CDL data available only over the US. Similarly to Maricopa, VI-dis and LC-dis techniques display the lowest level of Noise, similarly to Maricopa site. The Cst technique, calibrated with Maricopa data, performed correctly over these two other sites. One can notice that Cst distribution is the highest Noise values among the five techniques. The Av techniques display narrow distribution, signifying a very good stability of the technique. Considering the two French sites, only 5% of the NoiseRatios are higher than 1, while other techniques display percentage from 6% to 13%. The good performances of these two simple techniques (Cst and Av), which do not include spatial variability of the BRDF shape, suggest that BRDF variability within the range of the view geometry (see Table  1 ) is limited.
Applied only over the Maricopa site, the LUM techniques performed very well over grassland and agriculture land cover. The performances over shrubland were worse because this technique is based on the crop data layer, which is poorly documented outside the crop area. Finally, the VI-dis technique shows one of the best performances with a very good stability across the three sites, the five selected LC and the two spectral bands. This technique, which relies on BRDF a-priori information derived from MODIS, is easy to implement since it is not scene-dependent. Forestland cover, related to important anisotropy effects, was weakly analyzed in this paper because forest does not cover a large part of the study areas. For this reason, we requested multi-angular acquisitions of a forest site in Finland (Sodankylä ) during the SPOT5 (Take5) experiment occurring from April to August 2015.
In the GMCS, Alice encodes information on coherent states of light, by realizing a Gaussian modulation on both quadratures {X A , P A } with the same variance V A. This signal travels through the public quantum channel, that can be characterized, in the context of the asymptotic security against collective attack (which we will adopt in this article), by two parameters: the transmission T (in power) and the excess noise ξ (equivalent excess noise at the input). At reception, Bob randomly chooses to measure one of the two quadratures, X or P , with a homodyne detector and publicly announce which quadrature he has measured. Alice and Bob will then reveal part of their data to estimate the channel parameters T and ξ. Provided the estimated parameters are compatible with secure key generation, which can be learned from the security proof, then key distillation, i.
e. error correction followed by privacy amplification, will be performed on the non-revealed data to generate key. By symmetry we can assume without loss of generality that Bob has measured the quadrature X, and thus that Alice and Bob share respectively the correlated continuous variables (X 1 A • • • X N A ) and (X 1 B • • • X N B ) in R N. To perform parameter estimation, i. e. estimate T and ξ and then secret key rate, Alice and Bob reveal part of their data and can make use of Eq. 1-4  [33] : var(X A ) ≡ V A (1) X A X B = η B T V A (2) var(X B ) = η B T V A + N 0 + η B T ξ + v ele (3) var(X B 0 ) = N 0 + v ele (4) Here η B -the transmission efficiency of Bob and v ele -the variance of the electronic noise are calibrated values, evaluated offline and stable during an experiment. Eq. 2 and 3 are therefore sufficient to evaluate T and ξ, provided shot noise variance N 0 is calibrated, which needs to be done by an auxiliary measurement, corresponding to Eq.
The quadrature information is retrieved by using a balanced homodyne detector of electronic noise -25dB below the shot noise. The intensity of the local oscillator is set in order to have a mean number of 10 8 photons per pulse at Bob. The input voltage range of the data acquisition card is set sufficiently low (±1 Volts) to obtain a good resolution for the homodyne output measurement, which reduces the electronic noise down to 0. 3% of shot noise. Such setting however could open a door for recently proposed saturation attack on CV-QKD system  [36] , but we will not be considering this issue, or other issue related to side-channel attacks here. To perform shot noise measurement (Eq. 4) Alice blocks the signal pulses at emission with her amplitude modulator while a second amplitude modulator, placed on the classical channel (in green on Fig.
Moreover, this approach allows to directly take into account possible variations of the insertion loss of the ADMs, due to wavelength drift of the CV-QKD laser around the transmission window of the ADMs, in the estimation of channel transmittance T (Eq. 2). Data acquisition is divided in relatively short periods (N =10 5 pulses, corresponding to 100 ms). Shot noise and total noise measurements are performed alternatively on each consecutive period, in order to limit the relative drift.
4b. The relative drift at the 100ms timescale is around 10 -5 N 0 (measured at Bob), can be neglected since it is significantly smaller than the CV-QKD system excess noise (10 -3 N 0 in our case, at Bob). As proposed in  [37] , it is possible to use another scheme in order to perform timeresolved shot noise measurements: it consists in using an amplitude modulator located at the entrance of Bob to modulate η B , and to use Eq. 3 (for different value of η B ) to evaluate both shot noise and excess noise variance. This approach is feasible but would interfere with some routines already implemented in our CV-QKD implementation, related to Alice-Bob data synchronization and phase tracking. We have therefore not opted for this design in our DWDM test-bed but plan to do so it in future field trials.
VIII. Acknowledgement We thank Gérard Mouret for his very helpful support for the design of the electronics of the experiment. We also thank Paul Jouguet and Sébastien Kunz-Jacques for useful discussions on CV-QKD noise evaluation and Zhiliang Yuan for suggesting corrections in Fig. 6. This research was supported by the ANR, through the project QUANTUM-WDM (ANR-12-EMMA-0034), by the DIRECCTE Ile-de-France through the QVPN (FEDER-41402) project and by the European Union through the Q-CERT (FP7-PEOPLE-2009-IAPP) project. IX. Appendix A. Negligible source of noise in our DWDM experiment. The excess noise from different sources is calculated based on the assumptions given in sectionIII. While spontaneous Raman scattering constitutes the main source of noise, we assess here the impact of other potential sources of noise and show that they can be neglected in our experiment, being significantly below the limit of the smallest observable system excess noise of 10 -3 N 0.
0007, pooled data for experiments of both genotypes, Mann-Whitney test). Furthermore, a similar effect was observed in mice chronically infected with a HCV genotype 2a serum (Fig. 2e , Supplementary Table  1 ). Mice infected with primary isolates of HCV genotype 4 (n=4) also showed a marked and time-dependent decrease in HCV RNA levels (Supplementary Fig. 5a ) with no viral rebound and stable function of the engrafted human hepatocytes as shown by serum albumin levels (Supplementary Fig. 5b ). In summary, these data show that a CLDN1-specific mAb can clear chronic HCV infection in vivo and exhibits antiviral activity against different viral genotypes. These findings suggest CLDN1 as a therapeutic target in a clinically relevant animal model. To assess antibody safety, we analyzed the histopathology of chimeric uPA-SCID mouse livers.
Human hepatocyte-specific staining demonstrated similar repopulation and structure of human hepatocytes in HCV Jc1-infected mice treated with control or CLDN1-specific mAb (Supplementary Fig. 6a ). TEM analysis of chimeric infected livers of treated mice showed no detectable alteration in hepatocyte morphology or TJ ultra-structure (data not shown). Human albumin, transaminases (ALT, AST) and total bilirubin levels remained stable following antibody administration and were similar in control and CLDN1-specific mAb-treated mice at all time points tested (Supplementary Fig. 6b-e ). To assess the functional integrity of human hepatocytes in CLDN1 mAb-treated mice we challenged the mice with HCV of a different strain and genotype. CLDN1-specific mAbtreated animals previously protected from HCV infection (Fig.
Imaging of CLDN1 in human and chimeric livers Chimeric mouse and human liver samples were formalin fixed and paraffin embedded. Human liver tissue was obtained from patients undergoing liver transplantation due to HCV or from normal livers taken from surplus donor tissue used for reduced size transplantation. Informed consent from each patient was obtained with regional ethics committee approval (project number 04/Q2708/40). Sections were subjected to low temperature antigen retrieval pre-treatment, followed by fluorescent detection of CLDN1, CD10 or CK18 as previously described  31. Labeled sections were visualized using laser scanning confocal microscopy (Zeiss LSM 780, 100 x Plan Apochromat 1. 4NA oil immersion objective). Background and autofluorescence of tissue samples were corrected throughout and 3D composite images were generated using the Zeiss Zen analysis software.
Analysis of protein expression and phosphorylation Immunoblots of cell lysates using ERK1/2-specific antibodies were performed using Hybond-P membranes and visualized using ECF substrate (GE Healthcare) according to the manufacturer's protocol. Fluorescence emission was detected using Typhoon Trio high performance fluorescence scanner (GE Healthcare) and quantified using Image Quant TL software (GE Healthcare). Phospho-array analysis was performed using Proteome Profiler Human Phospho-kinase Array (R&D Systems) as described by the manufacturer. For imaging, blots were incubated with ECL (GE Healthcare) and exposed to ECL Hyperfilm (GE Healthcare). Phospho-kinase array results were quantified by integrating the dot blot densities using Image J software (NIH). Immunoblots from biopsies were analyzed as described  24.
EGF-induced MAPK activation Huh7. 5. 1 cells were serum starved for 4h prior 1h incubation with 100 μg/mL control or CLDN1-specific mAb and 15 min incubation with increasing concentrations (1, 10 and 100 ng/mL) of EGF. P-EGFR, p-ERK1/2 and total ERK were detected by Western blotting. Quantification was performed using a Typhoon Trio laser scanner and ImageQuant Software (GE Healthcare).
Images were acquired with a LSCM (LSM710, Carl Zeiss Microscopy) and Zen2 software, using same settings for all the tissues analyzed. Five to seven random fields were selected from each section, based only on the presence of human GAPDH signal to ensure the presence of human hepatocytes. Images were then acquired also including the channel for the HCV detection. Image analysis was performed using ImageJ and CellProfiler software, with a customized pipeline. Total number of cells, number of human hepatocytes (HH), number of HCV+ HH and HCV signal average intensity (and s. d) were evaluated.
Acknowledgements This work was supported by the  European Union  ( ERC-2008-AdG-233130-HEPCENT, ERC-2010-StG-260767-ncRNAVIR )  INTERREG-IV-Rhin Supérieur-FEDER-Hepato-Regio-Net 2009 and 2012 ),  ANRS  ( ANRS 2009/183 ,  2009/136 ,  2011/132 ,  2012/239 ,  2013/108 ),  ANR  ( Laboratoires d 'excellence  ANR-10-LABX-0028_HEPSYS  and  ANR-10-LABX-36  netRNA),  Fondation ARC pour la recherche (NanoISI and TheraHCC IHUARC   IHU201301187 ),  Institut Hospitalo-Universitaire (IHU) Strasbourg , the  Wilhelm Sander Foundation ,  Région Alsace, Institut National du Cancer , the  Institut National de la Santé et de la Recherche Médicale ,  Centre National de la Recherche Scientifique ,  Université de Strasbourg , the  Ghent University  ( GOA 01G01712 ) and the  Research Foundation -Flanders  (projects  1500910N  and  G052112N ).
been effective at the early stages of the pathology. Our objective is to investigate the efficiency of a new MR-based hippocampal grading score to detect incident dementia in cognitively intact patients. This new score is based on a pattern recognition strategy, providing a grading measure that reflects the similarity of the anatomical patterns of the subject under study with dataset composed of healthy subjects and patients with AD. Hippocampal grading was evaluated on subjects from the Three-City cohort, with a followup period of 12 years. Experiments demonstrate that hippocampal grading yields prediction accuracy up to 72. 5% (P < 0. 0001) 7 years before conversion to AD, better than both hippocampal volume (58. 1%, P 5 0. 04) and MMSE score (56. 9%, P 5 0. 08). The area under the ROC curve (AUC) supports the efficiency of imaging biomarkers with a gain of 8.
With this aim, highly sensitive biomarkers are needed. While structural imaging markers are considered sensitive enough to detect very early stage of disease  [Braskie and Thompson, 2014; Cuingnet et al. , 2011; Frisoni et al. , 2010] , the current model assumes that their modification occurs after the apparition of the first symptoms  [Frisoni et al. , 2010; Jack et al. , 2013] , and MRI-based studies were based on datasets with a relatively short followup period before diagnosis (around 3-5 years). However, it is now well admitted that pathological changes to the brain occur decades before the first symptoms appear in AD. Thus efforts to identify subjects in the prodromal phase of AD have recently shifted to the presymptomatic phase of the disease. In this context, it is highly desirable to study population-based cohorts that include healthy elderly with longer term followup.
In addition, population-based cohorts give us the opportunity to avoid the potentially biased selection associated with recruitment in memory clinics and enable the study of imaging and neuropsychological parameters at the presymptomatic stage (silent phase) of the disease. Herein, in the present study, we propose to evaluate MRI-based imaging biomarkers on a dataset from a population-based cohorts of healthy elderly subjects with long followup (12 years) to enable the study of imaging and neuropsychological parameters over the course of the presymptomatic phase of AD. During the 12 years followup period some subjects will convert to AD 7 years (on average) after enrollment into the study. This long followup enables to track evidence of neurodegeneration at least 7 years before clinical diagnosis using MRI.
Another limitation of previous studies evaluating neurodegeneration biomarkers is the method of estimating the MRI neurodegeneration signature. Among the biomarkers proposed in the literature, hippocampal atrophy is considered a sensitive and powerful marker of AD progression  [Frisoni et al. , 2010]   et al. , 2011; Wolz et al. , 2011]. However, the prediction accuracy obtained using hippocampus volume is relatively low, around 65% in subjects 18 months before conversion to AD  [Wolz et al. , 2011]. Therefore, performing automatic individual prognosis using hippocampus volume as a neurodegeneration surrogate appears limited. Recently, we developed advanced image analysis methods that have been proposed to better detect the AD signature in MRI  [Coupe et al. 2012a,b]. Based on a pathological pattern recognition strategy, such approaches are able to provide a grading measure that reflects the similarity of the anatomical patterns of the subject under study with datasets composed of healthy and pathological subjects.
In this manner, it is possible to accurately reveal the presence of neurodegeneration, even when it is subtle. These methods take advantage of using a large training library composed of cognitively normal (CN) controls and AD patients to learn from example. Using the ADNI dataset  [Jack et al. , 2008] , we have showed that hippocampal grading significantly enables better automatic prediction of AD compared with hippocampal volume  [Coupe et al. 2012b ]. This advanced image analysis method is based on an MRIbased pattern recognition strategy that is able to provide a grading measure that reflects the similarity of the anatomical patterns of the subject under study with datasets composed of healthy subjects and patients with AD. The present study aims to assess the predictive value of hippocampal grade on incident dementia in cognitively intact patients.