dataesr/scientific-paragraphs-categorization huggingface dataset:
However, unlike in the ecosystem extent account, this information can then be used in a very targeted manner, depending on the respective land use, the status parameter under investigation and the ecosystem service that is assessed. We have shown that the application to the German context, with practical realities considered, has transpired into emerging results. Although we start from the CLC classes, we use the German LBM-DE model, including some more characteristics than just land cover. Additionally, in the geodata system developed here, linear ATKIS elements (e. g. roads, watercourses or even small structures, such as rows of trees and hedges) are converted into polygons by means of buffering and integrating into the polygonal mapped ET and CLC classes. Care has been taken to ensure that no overlapping occurs, thus avoiding potential cases of double recording, thereby confounding the validity of result accuracy (Sect.
3. 2). However, a balance must be achieved between results that are sufficiently ecologically meaningful rather than being simply pragmatic and on which ET classification level this is relevant. We therefore believe that our proposed national system be processed in four levels (Main-ET, Sub-ET, CLC-classes, further subdivision into habitats to integrate biodiversity relevant information, Sect. 3). This would offer a flexible, but also simultaneously best appropriate, approach in this respect. First evaluations have been realised and discussed (Sect. 4). In addition to the reference year 2018, the CLC classes for the years 2015 and 2012 were also calculated on the basis of the available data (LBM-DE/ATKIS), aggregated to Sub-ET and Main-ET, respectively, giving some results that allow for tentative interpretations.
However, the brevity of the timeframe (three reference years) does not yet permit a reliable identification of trends or shiftings. Nevertheless, the results confirm the feasibility of conducting a national spatial monitoring of Germany's ecosystems (Ecosystem Extent Account) in the future and of identifying possible changes on the basis of LBM-DE/ATKIS data. The CLC classes defined in this way, when combined with other available information, are already sufficient for the evaluation of many ES (e. g. recreation, erosion control) and their conditions (e. g. hemeroby/natural condition,). For example, in the case of natural soil fertility for the CLC class "arable land", there is a combined analysis possible by overlapping the ETs with a number of other nationally-available datasets (including soil water balance, soil type, climate, slope inclination), which together enable an assessment of natural soil fertility, according to the Müncheberger Soil Quality Rating (SQR)  (Mueller et al.
2007 , BGR 2013a , BGR 2013b). The system of ET/CLC classes (Table  1 ) is further underpinned by biotopes/FFH habitat types, for which extensive classification tables have been devised and developed (for an overview see Tab. B in Suppl. material 2). The recording and assessment of the condition (here especially biodiversity) of ecosystems can be further supplemented by the spatially explicit and representative data collected for each respective period. To provide a spatial structuring of ecosystems, 'condition indicators of landscape ecosystems' can be used to measure the ratio of structural elements in the open landscape. This includes, for example, the IOER Monitor indicator "woodland-dominated ecotone density", which reports on the density of linear elements, such as hedges, tree rows, woody plants and forest margins.
Future work Besides the classification system for ecosystem types (ETs), the table of the area of various ETs (Suppl. material 3, Table  C ) is the main result of our work. These datasets can form the basis for the regular reporting on the extent, condition and services provided by Germany's ecosystems at national level. The approach for such an ecosystem accounting is in line with SEEA-EEA requirements (UN 2020). The Federal Statistical Office (Destatis) intends to further develop and implement the national ecosystem accounting system in the future. The recently-published IUCN Global Ecosystem Typology (Keith et al. 2020) will likely be a SEEA Ecosystems "reference classification" and it would be useful to bring our mapping and classification approach in correspondence with this IUCN typology.
provides a table that links ecosystem types at level 2 with the CORINE Land Cover classification. Some ecosystem types fully correspond to an entire category of CORINE classes. For instance, urban ecosystems correspond to category 1. Artificial surfaces and include all classes from level categories 2. 3. 1 Pastures and 3. 2. 1 Natural grasslands, which are in different groups at level two of the CORINE classification (Nedkov et al. 2016). The European Environmental Agency provides a dataset aimed at contributing to a better biological characterisation of terrestrial and marine ecosystems across Europe (EEA-39). It represents probabilities of the presence of EUNIS (European Nature Information System) habitats in terrestrial, freshwater and marine ecosystems. The work supports the Mapping and Assessment of Ecosystems and their Services (MAES), namely Action 5 in Target 2 of the EU Biodiversity Strategy to 2020, established to achieve the Aichi targets of the Convention on Biological Diversity (CBD).
Proposal for a Systematic Approach to Ecosystem Types (ETs) in Germany Basic structure of the classification system account land cover features (according to CLC). The BAU is simultaneously the basic spatial operational unit for detailed analyses, including the assessment of ecosystem services based on thematic data. The resolution of the database corresponds to scale 1:100,000. Minimal mapping units are: • The following criteria were determined in developing the typology (classification) of natural and semi-natural non-forested areas (meadows, pastures, wetlands, dunes and sands, sparsely vegetated areas) of size 10 ha; ecosystems: • • • • remaining areas (e. g. high-density buildings, industrial areas and transport clear and consistent classification principle (systematic organisation, cf.
The • current and future possibilities for incorporating nature conservation data available advantage of CLES is the detailed resolution of ecosystems down to the local level. throughout Germany (Natura 2000 data, biotope mapping etc. ); However, in its current form, CLES cannot be used to track changes in the value of ecosystem services (Vačkář et al. 2018). • open for further development. The European CLC classification system was used to define those ecosystem classes that, The Italian ecosystem types were identified and mapped by integrating the land cover on the basis of the Digital Land Cover Model for Germany (Digitales database with additional spatial datasets that focus on biophysical features of the Landbedeckungsmodell für Deutschland or LBM-DE), can be delimited with sufficient environment, such as bioclimate and vegetation (Blasi et al.
2016). The mapping of ecosystems in Bulgaria was based on CLC data and on MAES typology. Drawing on the CORINE dataset, nine main ecosystem types (according to MAES typology) can be delineated in Bulgaria. The scale of the output product was fixed at 1:100,000, giving a spatial precision of the CLC database of 100 m (N). The work in Poland was divided into the following stages (Anonymous 2015): 1. Analysis of land cover forms defined according to CORINE Land Cover (CLC level 3) classification with respect to their occurrence on Polish territory; 2. A definition of types of ecosystems occurring in Poland, based on EUNIS ecosystem classification at levels 2 and 3; 3. Defining and delimitation of the basic assessment unit (BAU): basic typological unit representing a given ecosystem type (based on EUNIS classification) taking into.
The remaining two SPWs were used to obtain a continuum image of the field. The on-source time was 2. 8 hours. Data were reduced with the ALMA pipeline, which is part of the Common Astronomy Software Application (CASA)  28. The flux scale was calibrated by observing J2148+0657, whereas the phase stability was checked by observing J2101+0341. Absolute flux uncertainties are expected to be less than 10%. The final data cubes and continuum images were obtained using natural weighting in order to maximize sensitivity. This resulted in a beam size of 1. 2 ′′ ×1. 0 ′′ and a 1-σ background standard deviation of 12. 6 µJy beam -1 for the continuum image. A pseudo-narrow band image was also generated centered on the redshifted [C II] emission line. For the pseudo-narrow band image, the emission line channels were collapsed resulting in a beam size of 1.
2 ′′ ×0. 9 ′′ and a 1-σ sensitivity of 1. 764 × 10 -2 Jy beam -1 km s -1. For each source, 2-σ contour areas are regarded as emission regions. We use these emission regions as the photometric aperture for resolved sources, while for unresolved sources, the peak surface brightness represents the total flux. The photometry results are shown in Table  1. HST Broadband Imaging. For the field surrounding QSO J2054-0005, the HST archival database has two optical observations F606W(V) and F814W(I)  21  and three near-infrared broadband images F105W(Y), F125W(J) and F160W(H) (PID: 15064, PI: Caitlin Casey). These broad filter images are used to estimate the photometric redshifts of targets in this field through the use of the Lyman break drop-out technique  29. Galaxies at z ≈ 6 are expected to drop out of the F606W filter.
2 M ⊙ yr -1 27. [C II]2054 also was not detected in any of the HST broadband imaging (Extended Data Fig. 1 ). From the HST imaging, we determine a 3-σ upper limit of the UV continuum at the position of [C II]2054 of 3. 413 × 10 44 erg s -1 (F105W band). This corresponding to an upper limit of the UV-based SFR of SFR UV < 15 M ⊙ yr -1. Continuum sources. There are five HST broad-band observations in this field (F606W(V), F814W(I), F105W(Y), F125W(J), F160W(H)), the photometric results for each source are tabulated in Table 1. To constrain the photometric redshifts of these continuum sources, we use color-color diagrams as shown in Extended Data Fig. 2 , and compare the colors of the galaxies with those colors of simulated galaxies. In the HST image (Extended Data Fig. 1 ), C1, C3, C5 are resolved as multiple targets.
Acknowledgements Z. C. and Y. W. are supported by the  National Key R&D Program of China  (grant No. 2018YFA0404503 ). M. N. acknowledges support from  ERC Advanced  grant  740246  (Cosmic Gas). F. W. thanks the supports provided by  NASA  through the  NASA Hubble Fellowship  grant # HST-HF2-51448. 001-Aawarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555.
Simulated Data The influence of the water column optical properties (attenuation due to scattering and absorption by hydrosols) is accounted for using OSOAA model simulations, using the selected water turbidity and depth as inputs of OSOAA. Please note that the approximations outlined in Section 2 to establish the model given in Equation  (10)  were not applied on simulated data, since the radiative terms L dp u , E tot , E 0-, T dir , T di f , δ ↑ were calculated using the OSOAA radiative transfer model, including all multiple reflections on the seabed and the water column. Some stationary white noise was also added to the sub-surface reflectance R to simulate environment and sensor noise, with an SNR value of 40 dB. The values of K 1 and K 2 determine the relative importance of the direct and scattered light, respectively.
6 is the only regularization process applied in the simulations, in order to avoid any interference with the evaluation of the influence of the adjacency effects on the unmixing algorithm. The unmixing algorithm was initialized using biased endmembers, obtained using a random mixing of the true endmembers with other spectra from the spectral library shown in Figure  5f. Each initialization spectrum was composed of 80% of the true endmember plus 20% of a uniform random mixture of the true endmember and another one from the library. The abundances were then initialized using the FCLS (Fully Constrained Least-Square) algorithm  [52]  applied to the seabed. The algorithms were developed in the MATLAB language. Minidisco (N MF, no water) converged in about 4 s on a standard laptop computer (Intel(R) Core(TM) i7-461, CPU 3.
Application to Real Data An airborne data campaign has been conducted by the Hytech Imaging company [53] on 13 September 2017, using the HySpex sensor  [54] , over the Porquerolles Island coast (France). The hyperspectral images were corrected to obtain georeferenced reflectance images. The pixel size of the observed images is 0. 5 m × 0. 5 m, the wavelengths have been resampled to achieve a 10 nm resolution, and the images were analyzed in the spectral range from 410 nm to 700 nm. Sun glint  [47]  and the air/water interface  [22]  were corrected to finally obtain the sub-surface remote-sensing reflectance image R (in sr -1 ) by subtracting the water upward reflectance. In situ measurements were performed with the help of the PNPC (Parc National de Port Cros) [55] at six stations in the Alicaster Bay, where the seabed depth ranges from 2 m to 20 m.
In situ data consisted of measuring chlorophyll-a (Chl), suspended matter (SM) and colored dissolved organic matter (CDOM) concentrations. A diver brought back samples of the benthic species from the seabed to measure their spectral signatures (Figure  5d, f ). A Remotely Operated underwater Vehicle (ROV) data campaign has been carried out by the French Institute for Marine Exploration (Ifremer) [56] over a transect of 1500 × 2 m surface, with a high spatial resolution (2. 3 cm), to acquire RGB images at the seabed level  [57]. The images have been classified in three classes, namely sand, Posidonia, and Caulerpa taxifolia. The supervised classification method is based on color information, using a weighted combination of Euclidian and spectral angle distance, and reference spectra representative of each class.
This classification allows the generation of an abundance map of 0. 5 m spatial resolution, which serves as a ground truth data for the evaluation of the unmixing results. A colored composition of the sub-surface image, with the vehicle's path, is shown in Figure  5a , together with the bathymetry and an example of ROV underwater image containing sand, Posidonia and Caulerpa taxifolia (Figure  5b, c ). The mean measured values of the concentrations of the water constituents in the Alicaster Bay were Chl = 0. 03 mg•m -3 , SM = 0. 5 mg•L -1 , CDOM = 0. 05 m -1. The bathymetry was provided by the LITTO3D database, available in  [58] , supported by the IGN (French National Institute for Geographical and Forestry Information) and the SHOM (Marine Hydrographic and Oceanographic Service). Please note that the entire French coast bathymetry is available with a precision of 95%, and 1 m spatial resolution.
Initialization of the Unmixing Initial matrices S init and A init must be determined for the unmixing procedure. In order to find such matrices, two steps are performed. Step one: a first sea column correction is carried out from a classical LS method (lsqcurvefit function of MATLAB), using a chosen library S 0 , and the model SM wnoadj of Equation  (11) , to obtain the coefficient matrix C est. Although the spectra in S 0 should be representative of the local benthic habitat, they are not supposed to be the exact endmembers. Consequently, the sum-to-unity constraint is ignored in the inversion, in order to permit scale factors for the spectra. A preliminary estimate of the seabed reflectance, given by X inv = S 0 * C est , is then recovered. Step two : the initial matrix S init is obtained by applying the VCA algorithm  [59]  to X inv , and the initial abundance matrix A init is obtained using the FCLS algorithm  [52]  and S init.
Experiments on the Validation Zone The validation data have been divided in two parts, Part 1 and Part 2, as shown in Figure  5a , in order to avoid the use of big matrices for calculation. A spectral library has been acquired in the Porquerolles area in June 2016, September 2016 and September 2017. Different spectra representative of sand, substratum and Posidonia are shown in Figure  6. It can be seen that the Posidonia class can be represented by various spectral signatures, as well as sand or substratum. Moreover, organic surface deposits such as waste stems can be found on dark sand and substratum, so their reflectances are very close to those of brown Posidonia or shell colonized Posidonia. Consequently, in large areas such as the one covered by the robot transect, some confusion can be expected between Posidonia and dark sand or substratum classes.
Experiment on Another Area A small area of 200 × 530 pixels (100 m × 265 m), delimited by a red rectangle in the Figure  5a , has been selected as shown in Figure  9b. This area shows a diversified seabed, and the bathymetry is in the range between 2 m and 6 m (Figure  9d ). The endmembers used for the LS inversion were dark green Posidonia, sand, substratum and photophilic algae, given in Figure  5f. Photophilic algae consist of a rich variety of algae conditioned by light penetration, often located on rocky areas. They constitute an important environmental issue. The environment parameter δ, K 1 , K 2 values and the water reflectance L dp u E 0-were calculated in the same way as in Section 4. 2. Because the size of the matrix P is too large for computation on MATLAB, the image has been split into five contiguous sub-images to run the WADJU M algorithm, and the obtained abundances have been reassembled (Figure  9c ).
The number of vascular branching points was automatically assessed based on the vascular skeletonization using imageJ software. Eyes were staged based on Goldberg's classification of SCD retinopathy (SCDR) using UWF imaging. Forty-six eyes of 24 patients were included in the HbSS (n = 27) and HbSC (n = 19) groups and 16 eyes of 8 unaffected patients in a control group. In the DCP, the FAZ was significantly larger in the HbSC (p = 0. 0001) and HbSS (p = 0. 0004) groups compared to controls. The FAZ area in the SCP, CRT and number of superficial vascular branching points did not significantly differ between both genotypes. There were less branching points in the HbSC (p = 0. 034) and HbSS (p = 0. 0014) groups than in controls. The Goldberg stage was significantly higher in the HbSC group than in the HbSS group (2.
Raw images were extracted. The motion artifact reduction module was used, and even if the software has a projection artifact removal function  17  , all segmentations were verified for presence of projection artifact which was an exclusion criteria. Quantitative analysis. The main outcome was the FAZ area (Fig. 1 ), manually measured using the area measurement tool (in mm 2 ) software, drawing the inner wall of the vessels in the SCP and DCP  8, 18. Secondary outcomes were vascular density in the SCP and DCP, and retinopathy severity stage (Goldberg classification). Two independent examiners (AM and AGA) performed the measures for each eye. In case of disagreement a third observer (FF) adjudicated the result. To quantify the plexus density, two-dimensional (2D) images were analyzed using Fiji/ImageJ 1.
51d (open source image analyzer), on extracted En-Face images. Image noise was removed by processing a smooth filter (Plugins > Process > Smooth > Gaussian Method-1 sigma). Then, images were binarized (Image > Adjust > Threshold > Apply) and the skeleton was extracted from binary images using Skeletonize (2D/3D) as previously described  19. Finally, the resulting skeletons (Fig. 2 ) were analyzed on the 2D images using Analyze Skeleton 20 (2D/3D). For each skeleton, the vascular branching point density was used to compare each plexus density in terms of number of branching points/mm 2. Since no automatized vascular density quantification was available at the time of the study, with the Triton device, one engineer (GG) designed a custom-made software using Matlab in order to obtain quantification.
This software provided a binarization of the raw images in order to assess the vascular density, and a skeletonization of the row image to assess the total vessel length. The size of all images was 232 × 232 pixels. For binarization, the algorithm used the local thresholding function "adaptthresh" embedded in MatLab Image Processing Toolbox. Based on a variant Bradley method  21  , the function performs local thresholding (T = 2 × (sizeimage/16) + 1 corresponding to 30 pixels for size image = 232 or a 0. 3 threshold on a 0-1 scale. Qualitative analysis. Qualitative abnormalities (Figs. 1  and 2 ) were identified on the OCTA frame based on the location and distribution of microvasculature abnormalities in the SCP and DCP (temporal, nasal, inferior or superior). A control group included unaffected African patients enrolled on routine examination, without ophthalmological abnormality and without known hemoglobinopathy and they underwent the same examinations as SCD patients.
Statistical analysis. Statistical analyzes were conducted using XLSTAT and PRISM 7, and results were compared using the Mann-Whitney test. In order to assess the reproducibility of the FAZ measurement, we evaluated the intra-class correlation coefficient (ICC) over two measures of each image. ICC was calculated using SPSS statistical package version 20 (SPSS Inc, Chicago, IL) based on a mean-rating assessment (k = 2), consistency-agreement, 2-way random-effects model. Statistical significance was set at p < 0. 05 for all analyses. Data are presented as median (range) or counts (percentage).
84 (min-max: 0. 3-1. 5) mm 2 and 0. 86 (min-max: 0. 5-1. 5) mm 2 , respectively compared to the control group: 0. 57 (min-max: 0. 4-0. 8) mm 2 (p = 0. 0004 and p = 0. 0001, respectively). ICC was > 0. 99 for all groups. Vascular density. The vascular density was assessed using two parameters: the number of vascular branching points, which reflects the number of vascular ramifications, and an estimate of the vascular density based on our custom-made software. Vascular branching points: there was no significant difference in terms of branching point number between all groups in the SCP. There were less branching points in the HbSC (29. 9 ± 6. 8/mm 2 ; p = 0. 034) and HbSS (27. 9 ± 5. 7/ mm 2 ; p = 0. 0014) groups than in the control group (37. 3 ± 9. 4/mm 2 ) (Table  2 ) in the DCP. No difference was found between the two genotypes.
Even if our automatized custom-made software allowed reproducible repeated measurements, it is not as accurate as software provided by OCT companies. In conclusion, Swept-source OCTA provides useful information on macular microvasculature and structural alterations associated with SCDR. However, the clinical significance of these lesions remains to be established. This study highlights the absence of difference between genotypes in term of fovea microvasculature abnormalities in absence of visual loss conversely to the peripheral SCDR, which is more commonly observed in the HbSC genotype. Further studies with correlation of OCTA, UWF imaging, and function testing such as microperimetry, could help to characterize SCD maculopathy.
Flow cytometry-based assay for autophagy Flow cytometry-based assays for autophagy were performed as described previously  25. Briefly, GFP-LC3 expressing cells were washed once with PBS and then with either PBS containing 0. 05% saponin or with PBS alone. Ten thousand events were captured for analysis. Flow cytometry data were collected using a C6 Accuri flow cytometer (Becton Dickinson, Le Pont de Claix, France) with CFlow Plus software. Data analysis was then carried out with CFlow Plus software (Becton Dickinson, Le Pont de Claix, France). Data acquisition and data analysis were conducted at the Cochin Cytometry and Immunobiology Facility.