dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, when combined with the other landslide detection and susceptibility parameters used in this study, dualpol Sentinel-1 data may offer an additional benefit. It is also worth considering which SAR satellites could provide data to be incorporated into empirical models. Here we used two tracks of Sentinel-1 data and one of ALOS-2. ALOS-2 data are acquired at a longer wavelength than Sentinel-1 (L-band rather than C-band), so the data retain a higher coherence in vegetated areas, a significant advantage in landslide mapping. It is therefore unsurprising that the addition of the first ALOS-2 ICFs to the models offered the greatest improvement to prediction of LAD. We have recommended that when the first ALOS-2 image becomes available after an earthquake, the ICFs derived from this image should be incorporated into a global model.
The ALOS-2 satellite system has a 14 d repeat time, and one of the aims of the mission is to enable emergency response, so the first ALOS-2 image is very likely to be available within 2 weeks of an earthquake. In the future, we expect that it will also be possible to derive useful ICFs from L-band SAR data acquired by the planned NASA-ISRO SAR (NISAR) mission, which is due to launch in 2022. The NISAR mission will acquire data continually with a 12 d repeat time  (Sharma, 2019). Therefore, after the launch of this satellite system, it should be possible to use both ascending and descending tracks of Lband SAR data rather than just one track as we have used here. NISAR will also acquire SAR data at a wider swath width (240 km) than the ALOS-2 data we have used here (50-70 km)  (Sharma, 2019) , which is an advantage for earthquakes that trigger landslides across a large area.
For example, in the Nepal earthquake, the ALOS-2 scene we used in this study covers less than half of the landslides that were triggered by the earthquake (Fig. 1a ). Therefore, in order to obtain a complete model of a landslide event of this spatial extent, it would be necessary to combine several adjacent scenes, which are likely to be acquired on different days. ALOS-2 PALSAR-2 data are also acquired at a wider swath width (up to 490 km), but this is at the expense of spatial resolution, which coarsens to 100 m per pixel. Since coherence is calculated over a 3 × 3 window, the resolution at which landslides could be detected would therefore decrease to 300 m. Therefore, we expect that data acquired by the NISAR mission could be extremely useful in generating ICFs.
We also expect that similar models could be developed to combine InSAR coherence and empirical models to predict rainfall-triggered landslides, or to predict earthquake-triggered landslides in more arid environments, although we did not test these cases here. For the same-event models, we observed that the empirical models without SAR performed considerably worse when the area available for training data was restricted as it could be by cloud cover following an earthquake, consistent with  Robinson et al. (2017). However, we also found that the improvement in terms of ROC AUC offered by the inclusion of SAR data was particularly marked in this case. Both Sentinel-1 and ALOS-2 InSAR coherence features were observed to improve these models, with the best overall performance observed when both were used together.
Before the acquisition of the first ALOS-2 image, when only Sentinel-1 InSAR coherence features were available, our same-event models outperformed our global models, but after this our global models performed best. Our global models were only trained on two events, but the addition of the first ALOS-2 SAR data acquired after each earthquake resulted in improved and more consistent performance compared to the model  of Nowicki Jessee et al. (2018)  in terms of ROC AUC. We therefore recommend that in the future, InSAR coherence features from L-band SAR should be routinely incorporated into empirical models of earthquake-triggered landsliding in vegetated regions.
2. 4), the training dataset comprised 10 420 cells. The model shown in Fig. 7e used only the data west of the training area (6106 pixels after balancing across an area 21 km 2 ). From here, the longitudinal extent of the training data used was halved each time, resulting in https://doi. org/10. 5194/nhess-21-2993-2021 Nat. Hazards Earth Syst. Sci. , 21, 2993-3014, 2021.
Acknowledgements. Sentinel-1 interferograms and coherence maps are a derived work of Copernicus data, subject to ESA use and distribution conditions. The authors are grateful to  JAXA  for providing ALOS-2 datasets under the research contract of ). Some figures were made using the public domain Generic Mapping Tools (Wessel and Smith, 1998). We thank  Alex Densmore ,  Nick Rosser  and  Sang-Ho Yun  for useful discussions and feedback on this work. Financial support. The work presented here was carried out as part of a PhD project funded by the  Institute of Hazard, Risk and Resilience, Durham University , through an  Action on Natural Disasters scholarship. This work was partly supported by the  UK Natural Environmental Research Council (NERC)  through the  Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET).
Code availability. SAR data were processed using the commercial software package GAMMA and the LiCSAR software of Lazeckỳ  et al. (2020). The open-source GDAL software was used to reproject and resample the different datasets used here (https://gdal. org/, last access: October 2021) (GDAL, 2021). The random forest method was implemented in Python using the scikit-learn package  (Pedregosa et al. , 2011). Data availability. Three landslide datasets were used in this study: first, the inventory of Ferrario (2019), which is available as a Supplement to that publication; second, the inventory of  Zhang et al. (2019)  available at https://doi. org/10. 5281/zenodo. 2577300; and, third, the inventory of  Roback et al. (2017)  available at https://doi. org/10. 5066/F7DZ06F9. Sentinel-1 data are open access and available to download from https://scihub.
copernicus. eu/ (last access: November 2020) (Copernicus, 2020). The ALOS-2 data used in this study were obtained from JAXA but are not open access. Empirical models of landsliding based on Nowicki Jessee et al. (  2018 ) and estimates of peak ground velocity were downloaded from https://www. usgs. gov/ natural-hazards/earthquake-hazards/data-tools (last access: January 2021) (USGS, 2021). Land cover data used in this study are available at https://www. esa-landcover-cci. org/ (last access: June 2020) (ESA, 2020). Lithology data from  Hartmann and Moosdorf (2012)  are available at https://www. geo. uni-hamburg. de/en/ geologie/forschung/aquatische-geochemie/glim. html (last access: June 2020) (Universität Hamburg, 2020). Supplement. The supplement related to this article is available online at: https://doi.
The approach to comparing axonal distributions as semi-quantitatively recorded data points in 3D was adopted from well-established methods used in earlier studies of cerebro-cerebellar organization in rats  (Leergaard et al. , 2000a; Leergaard and Bjaalie, 1995; Leergaard et al. , 1995; Leergaard et al. , 2000b; Lillehaug et al. , 2002). By representing signal expression and axonal labeling as 3D point clouds, it became possible to more directly explore and compare location and distribution patterns in 3D in different combinations of data sets. For the additional benchmark data extracted from the Allen Mouse Brain Connectivity Atlas, we used the same sagittal image orientation as in our microscopic data, to facilitate comparison of microscopic images in addition to the 3D comparisons. The relevance and accuracy of the approach was confirmed by demonstrating that similarly located cortical tracer injections in control animals gave rise to similarly distributed labelling patterns in the pontine nuclei.
Topographical map of corticopontine projections from somatosensory and motor areas To establish a 3D benchmark map of corticopontine projections from somatosensory and motor areas in in adult wild type mice, we utilized a selection of public experimental tracttracing data available from the Allen Institute mouse brain connectivity atlas (http://connectivity. brain-map. org/). We selected 11 experiments in which the anterograde tracer EGFP was injected in the right primary/secondary motor cortex (n =6) or primary somatosensory cortex (n = 5) of wild type C57BL/6J mice (Supplementary Table  1 ). Serial two photon fluorescence images were interactively inspected online using the Projection High Resolution Image viewer of the Allen Institute, and from each case, 5 sagittal oriented images of the right pontine nuclei (matching the orientation of the histological material generated from our knock-out mice), spaced at ~100 µm were captured by screen shot from the largest 3D multiplane thumbnail viewer.
Microscopic imaging Mosaic microscopic images were acquired using an Axio Imager M2 epifluorescence microscope (Carl Zeiss Microscopy GmbH, Jena, Germany) equipped with a halogen lamp, a MCU 2008 motorized stage, and an EC Plan-Neofluar 10x/0. 30 and an AxioCam MRm camera. ZEN blue software was used for imaging and automatic stitching. Images were exported in TIFF format and serially ordered from lateral to medial, rotated and if needed, mirrored to consistent anatomical orientation using Adobe Photoshop CS6 (RRID: SCR_014199), before being converted to PNG format and resized to 60% of original size using ImageJ (RRID:SCR_003070) with bilinear interpolation. The resized serial images were loaded into Adobe Photoshop as a stack, spatially aligned using the ventral surfaces of the pons and cerebral peduncle as landmarks, cropped and exported as individual PNG files.
For comparative analyses of topographical organization (see below), variations in YFP signal expression intensity within and between groups were normalized by adjusting the brightness and contrast of images to equal levels using a custom-made histogram matching script available for ImageJ (National Institutes of Health; https://imagej. nih. gov/). One selected, representative case (Experiment 5, Cre-negative, nr: 14250, Supplementary Table  2 ) was used as reference.
Spatial alignment to common 3D reference atlas Serial sectional images were spatially registered to the Allen Mouse Common Coordinate Framework, version 3, 2017 edition of the delineations (CCFv3,  (Wang et al. , 2020)  using the QuickNII software tool (RRID:SCR_016854;  (Puchades et al. , 2019). Multiple anatomical landmarks (hippocampus, caudoputamen, inferior and superior colliculus, and external surface of the neocortex) were used to determine the mediolateral position and orientation of the sagittal section images. For each section image, custom atlas diagrams were aligned to anatomical landmarks in the experimental images using affine transformations, with emphasis on matching the ventral surface of the pons and white matter tracts close to the pontine nuclei and exported as PNG images.
As a secondary step, to further optimize registration, the custom atlas images were non-linearly transformed using the software tool VisuAlign v0. 8 (RRID:SCR_017978), with particular focus on fitting the template to the outer brain surface, subcortical white matter, and the outer boundaries of the pontine nuclei. To co-display images and the spatially registered custom atlas images, we used the software tool LocaliZoom, which is embedded in the Navigator3 image management system (bit. ly/navigator3), developed and hosted by the Neural Systems Laboratory at the University of Oslo, Norway.
Cortical distribution analysis in Emx1-cKO and Nex-cKO mutants Serial section images from Nex-cKO and Emx1-cKO mutants co-registered to the Allen Mouse Brain Connectivity Atlas were analyzed using an ImageJ macro allowing automatic counting of spots by area of interest in the brain. The spots are considered as specific staining using a threshold based on intensity and shape of the elements and the composite RGB segmentation atlas as a mask for the region of interest. The macro will create 3 separate folders, one will contain the atlas masks, a second will contain the raw TIFF data and finally a third folder that will collect all the output images and excel files. MORE DETAILS ARE ILLUSTRATED AS AN ANNEX IN THE RESPONSE OF THE REVIEWERS' DOCUMENT.
Corticospinal tract morphometric analysis in Emx1-cKO and Nex-cKO mutants Serial section images from Nex-cKO and Emx1-cKO mutants were analyzed by using the Fiji-ImageJ Software tool  (Schindelin et al. , 2015)  used to determine the total dorsoventral width of the bundle expressing fluorescent signal in the descending fiber tract in different positions: rostrally and caudally to the pontine nuclei, and 250 µm and 500 µm caudal to the nuclei. The width of separate fiber fascicles was also measured 250 µm and 500µm from the terminal edge of the pontine nuclei (Figure  4A' ).
Semi-quantitative recording and 3D visualization of spatial distribution patterns To investigate and compare the 3D distributions of YFP signal expression or anterograde axonal labelling within the pontine nuclei, we used the annotation functionality in the LocaliZoom tool to semi-quantitatively record YFP signal expression or labelled axons in all sections through the pontine nuclei as point coordinates (specified in the coordinate system of the reference atlas, CCFv3), reflecting the overall density of signal/labelling observed in the images (Figure  2C-D ). To compensate for the spacing between sections and allow inspection of point distributions perpendicularly to the section angle, the z-coordinate of each point was randomly displaced within the thickness of the gap between sections using a custom Python script.
ACKNOWLEDGEMENTS We thank  Isabelle Dusart in Paris  for providing us with AAV9-CAG-tdTomato viral particles produced by  Alexis Bemelmans  ( CEA, France ),  Nicolaas Groeneboom  and  Gergely Csucs  for expert technical assistance, the iBV PRISM platform for imaging and automatic quantification support, and  Sharon Yates , and  Maja A. Puchades  for useful discussions. This work was funded by the  "Fondation Recherche Médicale ; Equipe FRM 2015" # DEQ20150331750  to M. S. ; by an '' Investments for the Future'' LabEx SIGNALIFE  (grant  ANR-11-LABX-0028-01 ) to M. S. , by a PhD contract from  Région PACA/Inserm  and FRM 4 th year PhD for C. T, with additional funding from the  European Union 's  Horizon 2020 Framework Program for Research and Innovation  under the  Specific Grant  Agreement No.
SUPPLEMENTARY TABLES Supplementary Table 1 Allen Mouse Brain Connectivity database The custom Python script (spread. py) used to randomly displace data points within the thickness of a section is available upon request. The LocaliZoom and Meshview viewer tools are available from https://search. kg. ebrains. eu/ Experiment Sex Age (±2) Genotype Injection site Shown in number # 100141780 Male P56 C57BL/6J Primary motor cortex Fig. 2, 6A-F 114290938 male P56 C57BL/6J Primary somatosensory Fig. 2A-P, 7A and 7H cortex, mouth region 112229814 male P56 C57BL/6J Primary somatosensory Fig. 2, 7B and 7F cortex, upper limb region 112952510 male P56 C57BL/6J Secondary motor cortex Fig. 2 114292355 male P56 C57BL/6J Primary somatosensory Fig. 2, 7A and 7D cortex, lower limb region 126908007 male P56 C57BL/6J Primary somatosensory Fig.
| Sampling design and data collection The study was conducted around the Moni River where we established forest inventory plots along permanent transects oriented perpendicular to the watercourses and contours. Eight 5-to 8-km-long parallel transects, east-west oriented and separated by ~450 m, were opened in the forest (Figure  2 ). These transects together cover a total length of ~50 km. Along the transects, botani- The target species measured in the plots were the 32 of the most common light-demanding species in YBR  (Kearsley et al. , 2013). Four of these species are short-lived pioneers (SLP), 13 are long-lived pioneers (LLP), and 15 are non-pioneer light demanders (NPLD).
30 m) ≥10 cm were identified, mapped (x-and y-coordinates), and measured for their dbh. For trees with buttresses or any deformations at 1. 30 m, measuring point was taken about 50 cm above the deformation. Trees from non-target species were solely counted. Data on environmental variables were recorded at subplot level. Slope and topography were observed directly in the field. The slope was measured in percentage with a Suunto clinometer  (Vormisto et al. , 2000). For the topography, four categories were considered: flat surface, slope, crest, and shallow. Altitude and the distance to watercourses were derived from different maps of the study area and based on the geographical coordinates of the center of each subplot.
| Statistical analysis Statistical analyses were carried out in R software, version R 3. 6. 1 (R Core Team, 2019). Analyses were performed at species level and at regeneration guild level. For the species-level analysis, only species with a minimum of 50 individuals in the database (i. e. , 15 species) were considered to ensure the robustness of the analysis, and the other species were excluded. For the guild-level analysis, we standardized the diameter data to the mean of each species to avoid blurring the results due to interspecific differences in growth rates and maximum diameter. Several analyses were performed. First, tree spatial pattern was assessed using tree x-and y-coordinates considering a point pattern process based on the pair correlation function (PCF;  Stoyan & Stoyan, 1994).
, 2013). When g(r) > 1 (i. e. , observed distribution function above the upper limit of the confidence envelop), the spatial distribution is considered aggregated. When g(r) = 1 (i. e. , observed distribution function within the confidence envelop), the spatial distribution is considered to be random, whereas g(r) < 1 (i. e. , observed distribution function under the lower limit of the confidence envelop) indicates regular spatial distribution  (Challis et al. , 2016; Law et al. , 2009). This analysis was done in the R package "spatstat"  (Baddeley and Turner, 2005). Zero value suggests the absence of spatial autocorrelation, that is, substantial variation in dbh at a local scale  (de Frutos et al. , 2007).
| Overview of the data A total of 84678 trees were registered in all transects. Our target species represented 8. 88% of this total. The SLP guild had 834 trees (3. 33 trees ha -1 ), LLP 800 trees (3. 19 trees ha -1 ), NPLD 4694 trees (18. 73 trees ha -1 ), and G. dewevrei 1195 trees (4. 77 trees ha -1 ). More details and descriptive statistics of dbh for these species are presented in Table  S2.
Our results seem to support this hypothesis. All of the LLP and NPLD species in our dataset are either deciduous or semi-deciduous except one (Pycnantus angolensis). In addition, most of the LLP and NPLD species are randomly distributed in the study site. Based on this combination of patterns, H2 could explain the presence and abundance of these species in the study site given that deciduousness is known as a drought-avoidance strategy  (Condit et al. , 2000; Fauset et al. , 2012; Vico et al. , 2017). The forest would have responded to increasingly drier conditions over the last millennia by multiplying the number of individuals of deciduous or semi-deciduous species to the detriment of evergreen species. This scenario is supported   et al. , 2020; Fauset et al. , 2012)  and in the Congo Basin  (Zhou et al.
Our spatial approach therefore does not allow to unambiguously confirm or exclude the first two hypotheses, although our results seem to favor H2 (drought adaptation) because most species in the dataset seem to be randomly distributed in the forest, especially the LLP species. Concerning G. dewevrei, its individuals preferred environments near watercourses and low altitudes, and its mode of dispersal (autochory) favors to aggregation. Limited seed dispersal and habitat heterogeneity therefore would be major factors in its aggregation. Based on the comparison between the affinity degree of light-demanding species and the one of G. dewevrei with the environmental variables, we would exclude the third hypothesis for the light-demanding species. Finally, we think that further insights require a multidisciplinary approach combining data of soil properties, tree dating, anthracology, history, and repeated forest inventories to elucidate the major factors determining the spatial distribution of light-demanding species in our study site.
Funding information This work was supported by the  XIth European Development Fund  through the  FORETS  project ( FOrmation, Recherche, Environnement dans la TShopo ) implemented by the  Center for International Forestry Research (CIFOR)  with help from local and international partners. Further financial support was provided by the  Belgian Science Policy Office (BELSPO)  through the  FED-tWIN2019-prf-075 profile CongoFORCE  and the  Brain-be  project  AFRIFORD  ( BR/132/A1 ); and through the  International Foundation for Science (IFS , grant  No I-1-D-6087-1 ). ) and non-pioneer light demanders (NPLD). activities, which abruptly ended in the 19th century.
Objective: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes. Methods: In this study, we developed machine learning models using selective key pathological categories to identify clinical test attributes that will aid in accurate early diagnosis of CKD. Such an approach will save time and costs for diagnostic screening. We have also evaluated the performance of several classifiers with k-fold cross-validation on optimized datasets derived using these selected clinical test attributes.
Results: Our results suggest that the optimized datasets with important attributes perform well in diagnosis of CKD using our proposed machine learning models. Furthermore, we evaluated clinical test attributes based on urine and blood tests along with clinical parameters that have low costs of acquisition. The predictive models with the optimized and pathologically categorized attributes set yielded high levels of CKD diagnosis accuracy with random forest (RF) classifier being the best performing. Conclusions: Our machine learning approach has yielded effective predictive analytics for CKD screening which can be developed as a resource to facilitate improved CKD screening for enhanced and timely treatment plans.
II. DATA AND METHODS The present work was accomplished in several stages: data collection, pre-processing, model training, important attributes selection, and evaluation of the models and selected attributes, which are summarised in a schematic diagram in Fig. 1. The labeled data was collected from hospital-based sources [for details see subsection below]  [22]. In the preprocessing stage, the database was analysed and various techniques applied to transform the data into the proper structure, with missing value imputations. After completing the preprocessing, we split the data into training and test sets. The training data set was used to train a number of ML classifier models; these included random forest (RF), gradient boosting (GB), XGBoost (XGB), Logistic Regression (LR), and support vector machine (SVM) models.
Subsequently the SHAP technique was employed to interpret the model decisions and identify the important features that contributed most to the classification process. The selected highest importance attributes were then used to form new reduced datasets with which the classifiers were trained and tested to determine whether those attributes were sufficient to build an ML-based computer-aided CKD diagnosis system that shows high classification accuracy. This approach will allow users to predict and diagnose their possible CKD cases using the minimum necessary number of clinical tests, which will reduce cost and resources.