dataesr/scientific-paragraphs-categorization huggingface dataset:
The comparison is demonstrated in Fig. 2C , D using the virtual slice marked by a white dashed line in Fig. 2A. FBP algorithm is the standard solution for 3D tomography reconstruction due to its fast reconstruction time and easy implementation. However, because of the intrinsic nature of the algorithm, FBP has severe limitations in the case of noisy datasets and highly under-determined measurement conditions with a small number of projections  36. This can be clearly seen in the first column in Fig. 2C , where the signal-to-noise ratio of all sparse tomograms obtained from 5, 10, and 20 projections is very low. Indeed, the strong streak artifacts of FBP, caused by the small number of projections, shadow the elemental distributions: the Mn and Fe distributions are hardly visible even in the case of 20 angular projections.
In effect, as shown by the Fe intensity profiles (shown in Fig. 2D  by the blue curves), these strong artifacts, resulting also in negative intensity values, are hiding the information on the Fe variation within the sample. As such, since FBP cannot significantly reduce streak artifacts at low number of projections, it is not adapted to sparse tomography reconstruction. MLEM algorithm (second column of Fig. 2C ) is superior to FBP in handling noisy datasets. Moreover, it includes the non-negativity constraint assumption. In our workflow, we included a simple automatic stopping criterion for the MLEM algorithm with the smallest possible noise as a figure of merit (as detailed in "Methods" section). This choice results in terminating the reconstruction process at small number of iterations (thus with short reconstruction time) introducing only a few, weak artifacts in the reconstructed tomograms.
Spatial resolution of the tomograms reconstructed by MLEM. To estimate the spatial resolution of the tomograms reconstructed by the MLEM algorithm, we extracted two subsets with uniform angular sampling from the 2D high-resolution dataset. Taking the 2D high-resolution sinogram of 360 projections as an example, the full projection dataset was divided into two subsets, both having the same projection numbers. These two subsets were reconstructed independently by MLEM with the automatically determined iteration numbers. The two independent tomograms were then used for FRC analysis. Figure  4  shows the FRC curves between the two tomograms reconstructed using a different number of projections from the same 2D high-resolution sinogram. The spatial resolution was determined at the intersection of the FRC curve and the fixed 0.
Mean XRF voxel-spectrum. Next to the elemental distribution maps, complete local XRF spectral information is necessary to obtain detailed information on the chemical composition of local features. This might reveal rare characteristics appearing only in particular sample locations/voxels ("needle in the haystack prob- lem"). Since we collect the full XRF spectrum in each measured pixel during data collection, next to the tomograms of the predefined elements, it is also possible to reconstruct the full XRF spectrum in each voxel of the tomogram. In other words, we can include a spectral dimension to the reconstructed dataset, which can be used to extract the mean XRF spectrum of any chosen sample area or volume. Such complete hyperspectral tomography reconstruction was realized for each energy channel of the measured XRF spectra.
At 10 keV excitation energy, this results in 1000 sinograms for each virtual slice, and the reconstruction process, even with the MLEM iterative method and multi-core, is becoming quite time-consuming. For example, the reconstruction of the high-resolution 3D hyperspectral XRF tomogram (with the energy as the 3rd dimension) took ~ 1 h by MLEM for the Arabidopsis seed. Figure  6A  shows the virtual slice of the Arabidopsis seed reconstructed from the sum-XRF spectra of the dataset containing 360 projections. The mean XRF spectra extracted from the area marked by a red circle in Fig. 6A  by FBP and MLEM can be seen in Fig. 6B, C. The mean XRF spectra of the chosen region obtained by FBP and MLEM do not show a significant difference; the total intensities of the XRF spectra agree within 5%. As such, in the case of high-resolution tomography, FBP is a good compromise to obtain local spectral information within a limited reconstruction time.
As the next step, we selected equiangularly 20 projections out of the 360 ones to simulate a sparse tomography in identical experimental conditions. This allows comparing the mean XRF spectra obtained by sparse and high-resolution tomographies. In the case of sparse tomography, FBP cannot be used for data reconstruction due to the low number of projections. As such, we reconstructed the sparse tomogram for each energy channel by MLEM. The mean XRF spectrum of the same region as before (red circle in Fig. 6A ) was calculated from this sparse tomography dataset. Figure  6C  shows that the two mean XRF spectra agree well, which illustrates that the MLEM algorithm provides reliable hyperspectral tomograms and mean XRF spectra even for a severely limited number of projections. The mean XRF spectrum within a selected volume of interest of the calcification sphere (shown in Fig.
5D ) of the renal papilla is demonstrated in Fig. 7. The reconstruction process for the 4D hyperspectral tomography dataset (with the energy as the 4th dimension) took around 1 h by the MLEM algorithm. To obtain the mean XRF spectra within a volume of interest, we selected the 3D volumes of interest by ImageJ, a freeware frequently used by our users. We applied this mask to each energy channel in the reconstructed 4D dataset. The extracted mean XRF spectra shown in Fig. 7B  reveal that Ca is the dominant element in the core of the calcification microsphere (red XRF spectrum), which also contains Zn. Within the surface rim (indicated by green in Fig. 7A  and green XRF spectrum in Fig. 7B ), the increased Zn to Ca ratio, due to the threefold larger Zn and ~ 20% smaller Ca content, provides evidence of the association of Zn to the pathological process.
Discussion The proposed holistic multi-scale and multimodal scanning X-ray tomography workflow, implemented at Nanoscopium, was tested on mesoscale samples. To tackle the scientific problems presented in the recent paper, multi-scale XRF-and scanning absorption contrast tomography were the best-adapted imaging modalities. The best compromise for semi-automatic on-site tomography reconstruction for sparse-, local-and highresolution scanning X-ray tomography is proved to be the MLEM algorithm if it is benefited from the apt early stopping strategy. This algorithm also efficiently handles the missing wedge sampling conditions. The FBP algorithm is, in general, not adapted for the reconstruction of sparse and missing wedge tomographies since the measured dataset does not fulfill the optimal Nyquist sampling criterion.
In the case of high-resolution hyperspectral tomography, which results in large 3D/4D datasets, the figure of merit is a compromise between the reconstructed data quality and the reconstruction time. Here, FBP is the best compromise to obtain, within a limited reconstruction time, reliable mean XRF-spectra from regions/volumes chosen from the reconstructed XRF hyper-spectral tomograms. The semi-automatic use of these two algorithms according to the above-detailed conditions permits obtaining a flexible semi-automatic workflow providing good quality on-site reconstruction for various samples in diverse experimental conditions. Due to its flexibility, our approach is especially well suited for on-the-fly tuning of the experiments to study features of interest progressively at different length scales.
The medium resolution 3D elemental distribution of the Arabidopsis seed has been obtained by sparse XRF tomography at 11 keV excitation X-ray energy. We collected 20 projections at 18 degrees angular intervals over 360°. These scanning parameters are proposed by default for sparse tomography by our workflow since, according to our experience with multiple samples and imaging modalities, they are proved to be the best-balanced parameters for almost all experimental conditions. At each projection angle, a total area of 581 × 445 μm 2 has been scanned with 2 μm pixel size and 20 ms dwell time in continuous scanning mode. The collection of the full XRF sparse tomography dataset of this mesoscale sample took 8 h 40 min. For high-resolution single slice tomography, we measured 360 angular projections over 360° with a lateral step size of 2 μm and a dwell time of 20 ms/pixel, with a total measurement time of 35 min.
Data processing. The elements present in the investigated samples were identified from the sum-spectra of all angular projections. The sum XRF spectra were fitted by the PyMCA software  49  for the identification of the elements present in the sample. The elemental distribution maps and sinograms were then extracted from the raw dataset by integrating pre-selected spectral regions of interest corresponding to the detected elements. The transmitted and incident beam intensity ratio at each pixel gives access to the sample absorption maps and sinograms. The sinograms were then reconstructed either by the MLEM or FBP algorithms. The overall reduction process is performed automatically with a robust in-house MATLAB code. The reconstructed volumetric data were exported from MATLAB as 16-bit z-stacks and imported either to ImageJ 50 for analysis or to Chimera 51 for 3D visualization.
MLEM belongs to the class of majorization-minimization algorithms  57. By construction, it decreases the negative log-likelihood of the estimated image of the given data monotonically. Moreover, it involves only simple multiplicative updates in such a way that the estimated image has positive pixel values, if all the pixels of the starting assumption image are positive. The MLEM algorithm provides good performance with sparse datasets, and the streak artifacts can be reduced as long as it is combined with an appropriate early stopping strategy, as is described hereafter. Determination of the number of iterations: automatic early stopping strategy for MLEM. Although the loss of the MLEM algorithm decreases along with iterations, there is also an increase in the noise amplitude, which significantly impacts the final reconstruction results.
According to our experience, the algorithm generally reaches the R ≥ threshold condition around 20 iterations, well before 200 iteration steps. The process of the optimization of the iteration number can be summarized as follows: (1) For all the slices, set the threshold to -0. 15% and the initial number of iterations to a relatively large value (~ 200); (2) Start the reconstruction for the first slice using the MLEM algorithm while calculating the ratio R for each iteration; (3) Stop the iteration when the condition R ≥ threshold is reached; (4) Repeat (  2 )-(  3 ) for all slices. This robust automatic reconstruction process provides exploitable results even for 5 angular projections, and it assures that even inexperienced users in synchrotron-based imaging/tomography can reconstruct their tomographic dataset on-site.
The most common method to estimate spatial resolution is the knife-edge technique. However, in the case of sparse tomography of highly heterogeneous samples, it can be challenging to find a sharp edge for the knife-edge method. To overcome this difficulty, the Fourier ring correlation (FRC)  [60] [61] [62]  method can be used as a general approach. For the iterative reconstruction methods, the estimation of the achievable spatial resolution in the function of the number of projections can be described by the Fourier ring correlation approach. FRC estimates this by measuring the normalized cross-correlation of two independent datasets of the sample. The spatial frequency elements at different radii can be integrated circularly in the frequency domain: where r i is the ith frequency element at radius r , F 1 and F 2 denote the Fourier transform of the two reconstructed tomograms.
At a specified cut-off threshold, the FRC curve drops below the threshold, indicating an indistinguishable signal-to-noise ratio. This defines the spatial resolution. Theoretically, the two data sets used in FRC calculation should be formed by independent measurement  63  , but this can be impractical, especially in the high-resolution scanning experiments, where the acquisition time is generally several hours at 3rd generation synchrotrons. To overcome this problem, the two subsets used for FRC calculation were extracted from the high-resolution tomography sinogram, from which independent tomographic images of the same slice were reconstructed. This resolution estimation method is mostly applied to 2D high-resolution datasets.
Acknowledgements The authors wish to thank  E. Chouzenoux  ( Centre de Vision Numerique, CentraleSupélec, 91190 Gif-sur-Yvette, France ) for her help in the validation process of the reconstruction algorithm and  G. Correc  ( Synchrotron Soleil ) for his help during the X-ray experiments. The measurements have been performed at the Nanoscopium beamline of Synchrotron Soleil during consecutive in-house research projects and user experiments (20201481 and 20191993).
1D ). Strains belonging to Bacteroidetes phylum that were mainly Gram (-) were significantly distant from the strains from Firmicutes phylum that are mainly Gram (+) on PC2 (Student's t-test P = 0. 0017) that explained the second-highest variation within the dataset. Similar to the results observed by Gram staining properties, carbamazepine had a greater inhibitory impact on Bacteroidetes strains than on Firmicutes and Actinobacteria strains (Student's t-test P = 0. 024 and P = 0. 011). Interestingly, lamotrigine inhibited Actinobacteria more than strains of Bacteroidetes (Student's t-test P = 0. 026). Besides the cellular membrane and wall properties, several known and unknown mechanisms may contribute to the influence of APIs on microbial growth. To decipher the potential role of microbial genetic makeup on growth under physicochemical constraints including compounds with antimicrobial properties, we extracted the reactions, pathways, and metabolism deduced from full sequences present at the MetaCyc database (Suppl.
(10) The integral I N does not depend on the child bubble size d, so that the bubble size distribution for d < cd h follows N (d, t) ∝ d -3/2 , (11) with the scaling exponent -3/2 independent of the details of the break-up cascade above the Hinze scale. To evaluate the validity of the physical arguments leading to N (d, t) ∝ d -3/2 , we perform DNS of a single initial bubble larger than the Hinze scale in a turbulent flow using the free software Basilisk  [31, 32]. A detailed description of the numerical configuration can be found in Rivière et al. [33]. We first create a homogeneous and isotropic turbulent flow at Taylor Reynolds number Re λ = 38, following the method introduced by Rosales and Meneveau  [34]. We then introduce a spherical bubble of diameter d 0 within the inertial range of the Kolmogorov cascade  [26] , i.
We compute the values of the equivalent diameters ∆ and δ from parent and child bubble volumes. All individual bubbles are tracked from birth to death to determine T (∆, δ) using a reconstruction process of the full event sequence for each simulation. To do so, all individual bubbles are first tracked in space and time using the Python package trackpy  [36]  based on the Crocker-Grier algorithm  [37]. Using volume and momentum conservation during break-up events, we reconstruct the breakage tree event by event. Each criterion has been manually adjusted and tested on simple situations to validate the algorithm robustness. The processing is systematically applied to the entire data set, and leads to the identification of 4329 breaking events for d 0 /d h ranging from 2. 9 to 5. 2, using 78 different 3D DNS realizations of bubble break-up.
Acknowledgments This work was supported by the  NSF   CAREER award   1844932  to L. D. A. R. was supported by an  International Fund  grant from  Princeton University  to L. D. S. P. and A. R. were supported by the  Labex ENS-ICFP. Computations were performed on the Princeton supercomputer Tiger2, as well as on Stampede, through XSEDE allocations to L. D. and W. M. , XSEDE is an  NSF  funded program 1548562. We would like to acknowledge high-performance computing support from  Cheyenne  (doi: 10. 5065/D6RX99HX )provided by  NCAR's Computational and Information Systems Laboratory , sponsored by the  National Science Foundation.
Remote sensing is a practical and cost-efficient tool for such ends, as demonstrated by the growing number of studies on land cover monitoring. However, even when conducted over the same area, the limited set of land cover classes used in generalized regional or national scale approaches  [16] [17] [18] , coarse temporal resolution  [19, 20] , limited time span  [21, 22]  and/or the focus on specific topics (such as impervious surfaces  [23]  or cropland  [24] ) may restrain the possible uses of their derived datasets for finer analysis on specific areas. Still, this flourishing literature provides valuable insights for adapting methods and good practices for conducting annual LULC quantification and change analysis over other regions of interest with different needs. While focusing on the Tucson metropolitan area, this research aims to provide a generic and adaptative tool for producing yearly LULC maps over long time periods.
Achieving a multitemporal analysis over several decades requires overcoming numerous technical challenges, such as rectifying the differences between multiple sensors across different time periods, or addressing heterogeneous data quality and availability due to various factors, notably cloud cover  [25, 26]. Recent cloud computing and big data approaches  [27, 28]  have brought new solutions to such problems, allowing researchers to assemble and process very large datasets composed of collections of remote sensing images and other ancillary data  [29, 30] , thus potentially dramatically improving the performances of image classification and LULC analyses  [31, 32]. Rather than hand-picking a set of images with 5-to 10-year intervals, these new techniques now allow time-effective processing, development and analysis of vast, virtually seamless and high-resolution annual land cover map products  [17, 33]  that can be updated each year.
7%). As our script can be used for automated classifications of oncoming years, this allows for precise monitoring of LULC changes past and future, which will be illustrated with a succinct analysis of urban sprawl dynamics in the region, i. e. , the expansion of city footprints through the creation of new low density urban areas on surrounding undeveloped lands  [41]. The GEE scripts associated with this study, designed to be generic enough for easy adaptation to other areas of interest with different needs, are openly accessible for re-use and modification  [42].
The watershed boundaries used to define our region of interest were derived from the United States Geological Survey (USGS) National Hydrography Dataset Plus High Resolution  [44] , and used to clip all data used during our overall yearly LULC classification process (Figure  2 ). Figure  2. Google Earth Engine workflow scheme for yearly LULC classification (Pre-processed yearly biseasonal Landsat scenes were exported between each step not to overflow the server's computing capacities  [42] ).
Image Data and Pre-Processing We chose to work with Landsat data, which provides imagery of the Earth's surface for over 40 years at a medium/high spatial resolution of 30 m (60 m for the first satellites and thermal bands), in spectral bands which are generally consistent across the 8 different sensors launched since 1972, and will be in the next decades with the recent launch of Landsat 9. We used orthorectified and atmospherically corrected Landsat Surface Reflectance Tier 1 imagery collections available in Google Earth Engine for Landsat OLI/TIRS and TM sensors (Landsat ETM+ data were not used due to its Scan Line Corrector error). Clouds and saturated pixels were detected and masked using the associated quality band  [49]. We expanded the cloud masks by 240 m to include possibly undetected fuzzy cloud edges and pixels affected by "adjacency effect", i.
3% coverage of our study area for both periods of each year between 1986 and 2021. Due to rare meteorological conditions, this percentage falls, however, to 97. 6% in 1990. Additionally, no data allowing a relevant coverage of the study area for both seasons was available for years previous to 1986, nor for the year 2012 (because of Landsat 7 issues and the delayed launch of Landsat 8 from July 2011 to February 2013), which was thus left out of our time series. In summary, we used Landsat 5 TM imagery from 1986 to 2011 and Landsat 8 OLI imagery from 2013 to 2021.
Image Transformation and Ancillary Data In order to improve the accuracy of the classification, we calculated a number of indices and gathered ancillary data. These derived datasets were subsequently included as new predictor variables in the yearly land cover classifications (Table  1 ). Normalized Difference Vegetation Index (NDVI) NIR -Red NIR + Red  [56]  Normalized Difference Water Index (NDWI) NIR -SWIR NIR + SWIR  [57]  Normalized Difference Built-up Index (NDBI) SWIR -NIR SWIR + NIR  [58]  Built-up Area Extraction Index (BAEI) Red + 0. 3 Green + SWIR  [58]  Normalized Difference Bareness Index (NDBai) SWIR -TIR SWIR + TIR  [58]  Dry Built-up Index (DBI) Blue -TIR Blue + TIR -NDVI  [59]  Dry Bare-Soil Index (DBSI) SWIR -Green SWIR + Green -NDVI [59] Topographic Position Index (TPI) Elevation-Mean (Elevation in 15 pixel radius)  [43, [60] [61] [62]  Gray-Level Co-Occurrence Matrix (GLCM) Textural Correlation ∑ i,j p(i, j) (i-µ i )(j-µ j ) σ 2 i σ 2 j [63] Gray-Level Co-Occurrence Matrix (GLCM) Textural Contrast ∑ i,j | i -j | 2 p(i, j) [63] Multitemporal Kauth-Thomas (MKT) See references  [64] [65] [66]  Multitemporal Kauth-Thomas (MKT) transformations between the two periods of each year were computed using sensor-dependent coefficients found in the literature  [64] [65] [66].
MKT transformation allows for a comparison between multidate datasets by producing a 12 bands matrix which contains 6 standard coefficients (greenness, wetness, brightness, and 3 "un-named" others) and their respective orthogonal change coefficients. We derived Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and several built-up and bareness indices from Landsat bands of both periods, namely the Normalized Difference Built-up Index (NDBI), the Built-up Area Extraction Index (BAEI), the Normalized Difference Bareness Index (NDBai), as well as the Dry Built-up and the Dry Bareness indices which are better performing in arid climates  [56] [57] [58] [59]. Gray-Level Co-occurrence Matrix (GLCM) textural features were then generated for every Landsat band, MKT transformation, and above-mentioned indices.
We also included other ancillary data as input for the classifier. Slope and a 15-pixel radius Topographic Position Index (TPI) for each pixel were derived from the Shuttle Radar Topography Mission (SRTM) 30 m resolution global digital elevation model  [43]. TPI is a method of terrain classification which compares the elevation value of each cell against the mean elevation of its specified neighborhood  [60] [61] [62]. We also used the USGS National Hydrography Dataset Plus High Resolution  [44]  Flowline data to calculate a buffer area around main streams proportional to the total drainage area (in km 2 ) of each section with the formula: buffer = log 10 (Drainage Area) × 30 m We extracted the main roads network (highways and trunk roads) from the Open-StreetMap  [67]  dataset and buffered it by 30 m.