dataesr/scientific-paragraphs-categorization huggingface dataset:
The gels were stained using Coomassie (Thermo Scientific) and scanned with the Amersham Gel doc system. For the mass spectrometry analysis, the samples ingel were digested with trypsin and treated with different solutions to extract the peptides from the gel matrix. Raw mass spectrometry data were processed using the MaxQuant version 2. 0. 1. 0. [130]. The identification of peptides and proteins was done using the UniProt database. Perseus software version 1. 6. 15. 0  [131]  allowed the analysis of the LFQ intensities obtained. Data were log transformed and missing values were replaced with values from a normal distribution.
The reverse transcription of RNA samples was performed using the iScript cDNA synthesis kit (Bio-Rad, Segrate, Italy), according to the manufacturer's instructions. To quantify the expression of CLDN3, CLDN5, OCLN, TJP1, IL-6, IL-10, IL-22 and TNF-α, qRT-PCR was carried out using as gene reference the ribosomal protein unit S14 coding gene. Briefly, 5 µl of iTaq Universal SYBR Green Supermix (Bio-Rad Laboratories), 2 µl of 5 µM primers mix (Table  5 ) and 3 µl of cDNA (5 ng/µl) were used for each sample. Samples were run using a CFX96 Real-Time System (Bio-Rad Laboratories) for 30 s at 95 °C, 5 s at 95 °C and 30 s at 60 °C for 42 cycles. The analysis was performed using Bio-Rad CFX Maestro software (Bio-Rad Laboratories).
Piezometric and contamination data The behaviour of groundwater and dissolved contamination is computed using TRACES (Transport of Radioactive Elements in Subsurface) software  (Hoteit & Ackerer, 2004). The three-dimensional flow model is the combination of the conservation of mass and Darcy's laws, generalized to also apply to the unsaturated zone (Darcy-Buckingham law), resulting in the Jacob-Richards equation (Eq. 1): 𝜕𝜃 𝜕𝑡 + 𝑠 𝜃 𝜙 𝜕ℎ 𝜕𝑡 -∇ • (𝐊∇ℎ) = 𝑓 (1) where  and  are the water content  [-]  and porosity  [-] , respectively, necessary to deal with the unsaturated zone. and  are the specific storage coefficient [m -1 ] and hydraulic conductivity tensor [m. s -1 ], respectively. ℎ is the water head [m], and  is the sink-source term [s -1 ]. To limit inconsistencies with the 2D inversion (where unsaturated flow is not addressed via a physical model), the 3D model is reduced to a fully saturated approach.
Sampling strategies Structural (hydrofacies) and piezometric data are sampled following two subsequent strategies in order to validate the method (Fig. 3 ). First, the methodology is conducted with a very dense data set to assess its potential under ideal conditions and verify the numerical approaches' compatibility. The 400 control points of this sampling form a regular grid with a 500 m step length. The piezometric chronicles used for the inversion cover the nine years of simulation (see Sect. 3). Second, a sparser dataset is extracted to evaluate the method in more realistic conditions. The control points are reduced to 40, and the piezometric chronicles are shortened randomly (down to 2 years). Meanwhile, the hydrofacies logs extracted at the location of the control points are kept in their integrity.
GEMPY (geostatistical interpolation) GemPy (de la Varga et al. ,  2019 ) is an open-source 3D geomodelling package written in Python. It specializes in the reconstruction of stratigraphic series, with the possibility of modelling complex environments by adding faults, folds, plutonic intrusions, and other anomalies. The mathematical background GemPy is a development of the work of  Lajaunie et al. (1997; Lajaunie et al. , 1997; Calcagno et al. , 2008) , using universal cokriging methods to interpolate potential fields (scalar fields). In the software, the interpolation concerns two types of data: isosurfaces (including the interface between stacked lithologies and the boundaries of fault planes or unconformities) on the one hand and surface orientation (the gradient of the said isosurfaces) on the other.
Eventually, the shape of the surfaces mainly depends on the orientations provided and on an arbitrary spherical covariance function that only balances the relative weight of the surfaces and their orientation in the cokriging. A priori very time and memory consuming, the calculation of both the scalar fields and their derivatives is handled by the Theano Python library, which also allows developments toward stochastic modelling. A more precise description of the software is available in De la Varga et al. (2019).
Conclusions A method has been developed to assess 3D aquifer parametrization by combining the strength of 2D hydrodynamic parameters estimated by model calibration and 3D facies interpolation. While this kind of modelling is generally based on a heavy geophysical survey, the proposed methodology is based solely on piezometric series and geological logs (commonly available at the same locations). Both 2D flow model calibration and 3D interpolation parts of the algorithm are independent. Therefore, the approach is not restricted to the tools described in the article (i. e. , other interpolation methods than GemPy and B-splines can be used). The synthetic test carried with a relatively sparse dataset yields a consistent hydrodynamic parametrization (highest discrepancy: 61 % of the initial value for permeability, 16 % for effective porosity) and quite low errors in facies distribution (10.
[ 13 C]-labelled Metagenome-Assembled Genome closely related to the family Beijerinckiaceae may represent a new methylotroph family of Alphaproteobacteria, which is found in metagenome databases of forest soils samples worldwide. Gene markers of the only known pathway for aerobic CH 3 Cl degradation, via the methyltransferase system encoded by the CH 3 Cl utilisation genes (cmu), were undetected in the DNA-SIP metagenome data, suggesting that biological CH 3 Cl sink in this deciduous forest soil operates by a cmu-independent metabolism.
Gas chromatography CH 3 Cl was measured in headspace gas volume by injecting 100 μL of a headspace gas sample into a ISQTM Quadrupole GC-MS System using a TRACETM Ultra gas chromatograph (Thermo Fisher Scientific, USA) fitted with a 60 m, 0. 32 mm GS-GasPro capillary column (Agilent Technologies, California, USA) with helium as the carrier gas (constant column flow rate, 1. 5 mL min -1 ). Headspace gas samples (100 μL) were injected into the column with a temperature ramp from 40 to 200 °C (15 °C increase min -1 ). High-throughput measurements were carried out by the MultiPurpose autosampler MPS (Gerstel Inc. , USA). Chromatograms were analyzed with the software Openchrome ® (Lablicate GmbH, Germany). CH 3 Cl concentrations were calculated by regression analysis based on a six-point calibration with CH 3 Cl standard ranging from 10 to 150 mM.
Metagenomics and bioinformatics Metagenome sequencing was carried out using DNA from [ 13 C]-CH 3 Cl-amended microcosms in three biological replicates, together with three replicates of [ 12 C]-CH 3 Cl-amended microcosms. Library preparation and sequencing were performed at LGC Genomics GmbH, Berlin, Germany. Reads of Raw Fastq files were trimmed for the presence of Illumina adapter sequences using Cutadapt version 1. 2. 1  [32]. The reads were further trimmed by Sickle version 1. 200  [33]  applying a minimum window quality score of 20. Quality trimmed metagenome reads were then assembled using metaSPAdes v3. 11. 1  [34]  and binned with MyCC version MyCC_2017  [35]  using default settings. Estimation of genome completeness and contamination was carried out using the CheckM program  [36].
Furthermore, ShortBRED (version 0. 9. 3)  [44]  and GraftM (version 0. 13. 0) [45]  were used to identify cmuA in unassembled metagenomic reads. To estimate the distribution of MAGs in public available datasets, metagenomics reads from various regions (Additional file 1: Table  S2 ) were downloaded from sequence read archive (SRA) using sra-tools 2. 8. 0. Metagenomic short reads were mapped to Mineral Soil Bin2 using BBmap (v38. 79) [46]  with an identity threshold of 97% (-idfilter = 0. 97). Bedtools intersect (v2. 29. 2)  [47]  was used to exclude regions that also displayed high identity alignments of length > 50 bp to the RefSeq database. These ambiguous regions were previously identified by aligning the bin against ncbi's RefSeq database (downloaded 26. 08. 2021) using blast +  [48]  and filtering for pident ≥ 97 (detailed code: https:// github.
Two taxa of 13 C-labelled bacterial genome bins did not occur in the 12 C control treatments and occurred exclusively in the [ 13 C]-CH 3 Cl treatment: i. e. 'Mineral Soil Bin 2' and 'Degraded leaves bin 11' (Fig. 1 ), which indicates that these are involved in the cycling of CH 3 Cl. We reconstructed genome fragments (so called metagenome assembled genomes, MAGs) of the [ 13 C]-CH 3 Cl 'heavy' samples, and chose 7 MAGs with the highest quality (> 70% completeness and < 10% contamination) for further analyses (Additional file 2: Table  S1 ). Among those, 'Mineral Soil Bin 2' and 'Degraded leaves bin 11' harboured no 16S RNA-encoding genes. Taxa assignment was proposed according to the closest representative found in the RAST database (Additional file 2: Table  S1 ). The first represented a member of the Alphaproteobacteria (retrieved from mineral soil) and the second represented a member of Acidobacteria (retrieved from the organic layer and degraded leaves).
Total number of reads that are mapped to 'Mineral Soil Bin 2' can be highly variable (Additional file 1: Table  S2 ) but detected at high abundance in forest soil samples across the globe (Fig. 3 ). Moreover, reads mapped to the 'Mineral Soil Bin 2' were also detected in soil with different land uses (Fig. 3 ). Highly related OTUs to the closely related Beijerinckia detected by Chaignaud et al. are present in the same forest soil samples across the globe (Additional file 1: Table  S2 ). Reconstruction of the metabolic pathways revealed that the 'Mineral soil bin 2' MAG contained two known pathways to oxidize aerobically methyl-groups to carbon dioxide (Fig. 4 )-the tetrahydrofolate (H 4 F) and the tetrahydromethaneopterine (H 4 MPT)-dependent formaldehyde oxidation pathways. However, we could not identify any genes that may code for a dehalogenating enzyme, such as genes cmuAB (assessed by BLAST searches and via ShortBRED and GraftM-data not shown).
It is divided into two parts. The first half shows the minimal requirements for running the basic utilities, which include building and training the model and making predictions from it. The second part of Table  1  comprises an exhaustive list of dependencies. These dependencies are required to utilize all functionalities of AI4Water. However, these utilities are optional and do not hamper the basic package functionality. Moreover, the modular structure of AI4Water allows the user to install libraries corresponding to a particular sub-module while ignoring the others which are not required. For example, in order to use the HyperOpt class for hyperparameter optimization, libraries related to post-processing are not required. Table  1  also presents the exact version of the underlying libraries which were used to test the 1.
Acknowledgements. This study was supported by  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Korean Ministry of Education  (no. 2017R1D1A1B04033074 ), as well as the  Korea Environment Industry and Technology Institute (KEITI)  through the  Aquatic Ecosystem Conservation Research Program  funded by  Korean Ministry of Environment (MOE)  (no. 2020003030003 ). The authors also thank  Campus France  ( PHC STAR 41510WH ) for their financial support. Financial support. This study was supported by  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  (no. 2017R1D1A1B04033074 ). It was also supported by the  Korea Environment Industry and Technology Institute (KEITI)  through the  Aquatic Ecosystem Conservation Research Program  funded by  Korean Ministry of Environment (MOE)  (no.
1 pixels  (Leprince et al. , 2007; Dille et al. , 2021). For openly available Sentinel-2 imagery, this means that displacements of less than 1 m cannot be detected from a single image pair. Commercial imagery datasets are available with higher spatial resolution (e. g. , 3 m for Planet Dove CubeSat, 0. 3 m for WorldView), which may resolve smaller displacements (e. g. ,  Stumpf et al. , 2014) , but are expensive to procure and process and do not offer systematic repeat coverage. Similarly, displacements may be mapped from high-resolution UAV imagery  (Peppa et al. , 2017) , but this requires prior knowledge of the hazardous area and targeted acquisition campaigns.
Objectives The objective of this study is to use the 7 February 2021 Chamoli rock-ice avalanche as a detailed case study to assess the potential and limitations of satellite-based landslide monitoring. We first assess the pre-collapse conditions of the 7 February 2021 Chamoli rock-ice avalanche and then inter-pret them in a broader context. Our research question is the following. Can the pre-collapse remotely sensed datasets be used automatically to identify the location or timing of the 7 February 2021 Chamoli rock-ice avalanche, and is it possible for these monitoring techniques to be upscaled for hazard monitoring on a regional or global scale?.
Methods We used a range of datasets and processing workflows to investigate the pre-collapse conditions of the Chamoli rock-ice avalanche: 1. optical satellite imagery (Landsat and Sentinel-2) was used to investigate visible changes in the collapse region over the years to decades prior to the rock-ice avalanche; 2. feature tracking of optical satellite imagery (Sentinel-2, Planet, Cartosat-1, and SPOT 7) was used to derive horizontal displacements; 3. Sentinel-1 C-band radar imagery was used to calculate interferometric synthetic-aperture radar (InSAR) displacement maps; 4. digital elevation models (DEMs) from optical satellite stereo imagery (WorldView-1/2/3, GeoEye-1, Pléiades-HR (high resolution), SPOT 7, and Cartosat-1) were used to derive vertical changes.
Optical feature tracking We used feature tracking with a range of medium-(10 m) to high-resolution (2. 5 m) satellite imagery to evaluate the pre-collapse motion of the north slope of the peak of Ronti. We used two different feature-tracking toolboxes: GIV for rapid processing of Sentinel-2 data (Glacier Image Velocimetry; Van Wyk de Vries and Wickert, 2021) and autoRIFT (Autonomous Repeat Image Feature Tracking;  Lei et al. , 2021)  for processing Planet data as part of a pipeline also including orthorectification of imagery. Both GIV and au-toRIFT are based on three core components: a pre-processing module which applies one or more filters (described below) to images to enhance distinct surface features for tracking, a multipass two-dimensional image correlator, and a postprocessing module to identify and filter erroneous displacement values (Van Wyk de Vries and Wickert, 2021;  Lei et al.
Despite the higher product resolution (3 vs. 10 m Sentinel-2 images) and use of a high-resolution DEM for improved orthorectification, the Planet velocity maps had a high random background noise. We attribute this to spurious correlation over surfaces with varying shadow cover due to steep slopes and changing illumination, as the images were captured by different satellites during different times of the day/year. To compensate for this higher background noise, we chose a higher minimum temporal separation (100 d, compared to 50 d for Sentinel-2) between Planet image pairs when calculating time-averaged velocity maps. We also calculated displacements (using both GIV and autoRIFT) on one pair of high-resolution Cartosat-1 images (October 2017 to November 2018). We used these velocity data to evaluate whether the collapsed block moved prior to collapse -with a null hypothesis that the block moved no more than the surrounding "stable" (non-glacierized) bedrock.
InSAR maps We processed Sentinel-1 data from ascending and descending orbit tracks 56 and 63, respectively, to investigate whether the precursory motion of the collapse block could have been detected in radar interferograms. All radar data were downloaded from the Alaska Satellite Facility Distributed Active Archive Center (ASF DAAC). Because the descending track is heavily affected by layover artifacts, we https://doi. org/10. 5194/nhess-22-3309-2022 Nat. Hazards Earth Syst. Sci. , 22, 3309-3327, 2022 only performed the full processing with data from the ascending orbit. We processed the data with the InSAR Scientific Computing Environment (ISCE;  Rosen et al. , 2012) , removed the topographic phase using the 2015 pre-event WorldView DEM  (Bhushan and Shean, 2021)  composite (resampled to 8 m), and masked out all pixels with an interferometric coherence of less than 0.
Optical feature tracking Feature tracking provides the most complete spatiotemporal assessment of displacement of the methods used in this study -with data coverage from late 2015 until early 2021. We used results from the Cartosat-1 image pair and the Planet archive for validation of the Sentinel-2 displacements. In all three cases, the collapsed block (most notably, the bedrock ridge at the center of this block) exhibited displacements exceeding the background noise level on stable bedrock (< 1 m yr -1 ). The horizontal velocity of the collapsed block ranged from around 5 to 20 m yr -1 , with the most rapid motion occurring in the summers of 2017 and 2018 (see Fig. 4d-g ). We do not observe an increase in velocity of the collapsed block immediately prior to its failure in February 2021. The Sentinel-2 image record includes seven cloud-free images from early 2021, including one image taken 2 d prior to the collapse; therefore this lack of speedup is unlikely the result of a temporal data gap.
5a ), and any motion -assuming it is largely in the direction of the steepest slope -is oriented in the direction in which the radar instrument is least sensitive. Additionally, the non-glacierized area of the collapse wedge is small, making it challenging to identify fringe patterns amongst the noise. Furthermore, with the largest velocities reaching tens of meters per year, the InSAR measurements are prone to phase aliasing and underestimation of the true displacement. Sentinel-1 InSAR would not have provided an adequate tool for monitoring in this case, even with knowledge of the location of the instability. The second key limitation is that none of the datasets produced in this work could predict the timing of collapse. While most methods pick up precursory signs of slope failure, these begin almost 5 years prior to eventual collapse.
Tiwari et al. (2022)  show that seismic signals were detectable from the Chamoli block as early as 2. 5 h prior to collapse. While they do not explore how easily detectable the pre-collapse signals would have been without prior knowledge of the event, they do note that the ∼ 15 min between initial collapse and impact of the Tapovan dam would have enabled evacuation if a local early-warning system had been in place. One final limitation is related to the immense size of hazardous areas relative to the scale of hazards themselves. The Chamoli collapsed block had an area of around 0. 25 km 2 , while the Himalaya cover over 0. 5×10 6 km 2. Any methods aimed at automatically detecting hazards prior to their occurrence must have a low "false-positive" (identified as a hazard in the database but not of real concern) rate or any resulting database will be populated primarily with these incorrectly flagged regions.
Snow and ice loading in the headwall crack would progressively increase the driving force of the collapse block, while meltwater infiltration and permafrost degradation in a bedrock fracture would steadily reduce its resistive forces (basal friction). The combination of these two processes would reduce the factor of safety and pre-condition the block for failure, with the early February positive temperature anomaly and loading from snowfall providing a final driver for mid-winter collapse. Overall, forecasting the 7 February 2021 Chamoli rockice avalanche prior to its occurrence from remotely sensed datasets would have been very challenging and certainly not routine work using well-established methods. Current image resolution, characteristics, and processing algorithms result in noise levels on a similar order to the signal itself.
, 2021). Feature tracking, DEM difference, and InSAR datasets can be processed and analyzed on a regional or even global scale -and in many cases pre-processed datasets are already available online (e. g. ,  Morin et al. , 2016; Gardner et al. , 2018; Crosetto et al. , 2020; Dai et al. , 2020a; Provost et al. , 2022). While these pre-processed datasets are not generally produced for slope stability monitoring, they can be used to improve hazard maps and reduce landslide-related damage. Future advances in Earth observation satellite capabilities and processing algorithms will improve the quality of such products.
Conclusions The deadly 7 February 2021 Chamoli rock-ice avalanche was initiated by the failure of more than 25×10 6 m 3 of rock and ice high in the Uttarakhand Himalaya. We investigated the conditions of the avalanche source zone over the decades preceding collapse through a combination of optical and radar satellite images. We used feature tracking to calculate horizontal slope displacements and differenced photogrammetrically generated DEMs to investigate vertical displacements. We showed that the collapsed block moved 20-30 m prior to its collapse, with the most rapid motion occurring around 3 years prior to failure. Comparison between our datasets and modeled displacement maps shows that the motion occurred primarily via downslope translation, opening up a deep crack at the headwall. A combination of permafrost degradation and snow and ice debris loading within this headwall crack may explain both the lag between initial instability and collapse and the mid-winter timing of the collapse.
Finally, we assessed the potential of these datasets and approaches for monitoring other unstable slopes. While they are effective at identifying precursory signals at a known collapse site, it remains very challenging to predict such collapses with sufficient levels of confidence in high-mountain areas. Appendix A: Pre-event DEM mosaics A1  Cartosat-1 (2017 Cartosat-1 ( -2018) )  We procured four Cartosat-1 stereo pairs from October 2017 and November 2018 (data sheet in the Supplement) to com-pute DEMs for an intermediate period between 2015 and 2021. Initial assessments of the Cartosat-1 products revealed high stereo-ray intersection errors (> 100 m) and offsets from reference elevation models (∼ 400 m), indicative of poor relative and absolute accuracy of the vendor-supplied RPC (rational polynomial coefficient) models.
Acknowledgements. We acknowledge comments from editor  Filippo Catani  and three anonymous reviewers, which helped improve this paper. Financial support. Maximillian Van Wyk de Vries  was funded by a  University of Minnesota College of Science and Engineering fellowship  and a  Doctoral Dissertation Fellowship. Dan H. Shugar  was funded by the  Natural Sciences and Engineering Research Council of Canada (NSERC ;  Discovery Grant  no. 2020-04207 ). Simon Gascoin  and  Etienne Berthier  acknowledge funding from the  French National Centre for Space Studies (CNES). Simon Gascoin  received funding from the  Programme National de Télédétection Spatiale  (PNTS; grant no. PNTS-2018-4 ). Andreas Kääb  acknowledges support from the  ESA Glacier Climate Change Initiative (CCI)  project (grant no. 4000109873/14/I-NB ).