dataesr/scientific-paragraphs-categorization huggingface dataset:
Calibration accuracy and use Linear regression was performed on the mean water content and count ratio values using the Data Analysis package in Excel (Table  3 ). Separate calibrations were developed for the 10-cm depth, the 30-through 130-cm depth range, and the 150-through 230-cm depth range (Figure  6 ). Graphing of the data and regression lines indicated no outliers for the 10cm data and no outliers for the 150-through 230-cm data. The 150-through 230-cm horizon is the caliche (Btk1) horizon in which neutron scattering is influenced by the larger content of CaCO 3  (Evett et al. , 2007). The different calibration slope in the caliche is similar to results of calibration efforts in four soils of Uzbekistan  (Evett et al. , 2007) , in the Amarillo soil series (fine-loamy, mixed, superactive, thermic Aridic Paleustalfs) at Big Spring, TX (some 320 km south of Bushland)  (Evett & Steiner, 1995) , and in other soils in arid and semi-arid zones with horizons rich in CaCO 3 and CaSO 4  (Van Bavel et al.
An important question in any soil water sensor calibration is: What volumetric water content error can be expected when calibration equations are used at locations some distance away from the calibration site? The study reported here was not designed to explore this question, but there is some evidence that calibration equations produced using the protocol described herein are relevant for accurate determination of profile water content in the Pullman soil at other locations. Tolk and Evett (2009)  reported the use of neutron probe calibrations determined in 2007 using our methods. The calibrations were used periodically to compute profile water content in 12 weighing lysimeters of undisturbed Pullman soil monoliths as irrigations and crop growth proceeded. Changes in soil profile water storage (ΔS) determined from neutron probe data over these periods accurately matched the soil profile water storage changes (also ΔS) determined by mass balance using the lysimeter scales calibrated with masses traceable to the National Institute of Standards and Technology.
The comparison of the Vtg sequences of these three closely related species shows great similarity (74. 62% between H. niloticus and A. gigas (unpublished results), 70. 2% between H. niloticus and O. bicirrhosum, and 71. 17% between A. gigas and O. bicirrhosum, GenBank: AVI01406. 1 [27], and is in accordance with phylogenetic relationship of the family Osteoglossidae  [45]. As A. gigas and H. niloticus are the only two members of the Arapaiminae subfamily, they display the highest percentage of Vtg similarity. As a consequence, we hypothesized that Vtg molecules from these two closely related species would cross-react with their heterologous antibodies. The aim of the present work is (i) develop a reliable and minimally invasive sexing method for H. niloticus based on a specific H. niloticus Vtg antibody, and (ii) assess a commercial A.
2 M; pH 5. 0) + 10 mg of o-phenylenediamine + 30 µL of 30% H 2 O 2 (Sigma) to each well. The reaction was stopped after 30 min by adding 50 µL of 4 M H 2 SO 4 to each well. 6. The absorbance of each well was measured using a microplate reader at 490 nm. A blank well was added to aggregate the plate + reagents background; it contained all reagents except those of the coating step. The blank values were deduced from male plasma ODs to determine non-specific binding for a given antibody dilution. Statistical analyses of OD values were performed with R  [49] , and the rstatix package (https://rpkgs. datanovia. com/rstatix, accessed on 15 November 2021).
Peptide Sequencing and Protein Identification Two local H. niloticus databases were used: an ovarian protein database (437,476 entries) and a liver protein database (244,234 entries), both constructed from the corresponding transcriptomes  [27]. The fragmentation spectrum was used to determine the peptide sequences. The database search was performed using the Mascot 2. 6. 1 program (Matrix Science). The Mascot score for a protein is the total score of individual peptides, i. e. , the peptide masses and ion masses of the peptide fragments for each of the peptides corresponding to a given protein. The Mascot score (http://www. matrixscience. com/, accessed on 17 March 2021) must be above the 95% confidence level for positive protein identification, and the 95% confidence level corresponds to a score of ~90 for a search in the NCBI non-redundant protein database  [50] [51] [52].
Acknowledgments: We wish to thank  Sabbat Affif  and  Silué Drissa , respectively, owner of the Yamoussoukro fish farm and the chairman of the board of directors of the NGO APDRA-CI managing the Daloa fish farm and the owners of local fish farms where samples were collected for this study. Special thanks to all the staff of the  PROVETSUR laboratory at UAGRM in Santa Cruz (Bolivia)  for their collaboration in the development of the H. niloticus Vtg antibody and the ELISA tests.
Therefore, a number of researchers have studied the behavior of tunnels under varying types of loading conditions  [13] [14] [15] [16] [17]. However, there are few studies that depict the performance of underground tunnels constructed in rock when subjected to impact load. The numerical tools for computation and modelling have been used by researchers to study the stability of tunnels under varying impact loading conditions. Gao et al. [18]  studied the behavior of intact rocks under an impact load using a commercial tool, LS-DYNA. They proposed a relationship and a model to study the behavior of intact rocks under impact loads. The model was validated with high accuracy using experimental results. Experimental and numerical simulations have been carried out by Aziznejad et al. [19]  using a distinct element code to study the response of rock mass under an impact load.
Impact Loading Simulation The impact resistance of four different sandstone rock tunnels has been studied in the present paper. A missile has been modelled based on the description given by Vidanović et al. [23]. The missile has a 0. 7637 m length and a mass of 100 kg, modelled as a discrete part. The commercial software Abaqus has been used, and explicit mode has been selected for the simulation. The missile had a 5 Mach velocity before it hit the ground surface above the rock through which a tunnel has been constructed. The geometry of the tunnel has been considered based on the DMRC design specifications and published articles  [24] [25] [26]. A dynamic explicit analysis has been carried out in the finite element tool Abaqus. In Abaqus, a step is time allotted for a particular analysis.
Results and Discussion Commercial software based on the finite element method, i. e. , Abaqus/Explicit, has been used for modelling and analysis. Four different sandstone rocks, Jhingurda, Singrauli, Jamrani, and Kota, were considered. A generally used design specification for metro tunnels has been used based on Delhi Metro Rail Corporation designs. A missile having a 5 Mach velocity and weighing 100 kg has been considered. The simulation has been run for 30 milliseconds, which is the time required by a missile to hit the rock ground from 100 m away. Figure  5  has been plotted to compare the deformation variation with time when a missile hits the ground surface for all the sandstone rocks considered in the present study. It has been observed that the amplitude of deformation for Jhingurda sandstone is the maximum, having a magnitude of 2.
Search for the putative parental sequences Splitting conservatively the genome based on the largest breakpoint windows, we performed similarity searches on the GISAID  19  EpiCoV database to identify (closely related) genomes that may represent the lineages that gave rise to this recombinant virus. While the shorter region inherited from BA. 1 returned a large set of genomes from all over the world, likely due to the shorter genomic portion involved and the relatively low diversity of the recently emerged BA. 1, the Delta-like part of the genome led to a small subset of sequences (n=69) within the large AY. 4 sublineage. The closest relatives were predominantly sampled in Europe (France, Germany and Sweden, Extended Data Fig. 3 ). Together with the closest detected AY. 4 genomes, the recombinant XD lineage (with the Omicron part of the genome masked) forms a monophyletic cluster (branch support: 0.
Virus sequencing The consensus genome sequences of SARS-CoV-2 lineage XD / clade GKA described here have all been deposited on the GISAID EpiCoV database (EPI_SET_20220325yn), and have been generated by different PCR ampli cation strategies and on either Illumina or Oxford Nanopore technologies platforms, as described in their sequences entries. We also deposited raw reads data (GISAID ID: EPI_ISL_9879437 and EPI_ISL_9879436, with human host reads removed) for two primary samples generated by the National Reference Center for Viruses of respiratory infection hosted at Institut Pasteur and part of the Emergen consortium. These data were generated using a highly multiplexed short PCR amplicon approach (panel ARTIC V4. 1)  30  , followed by library preparation with the Nextera XT kit. Viral stocks were veri ed by untargeted metagenomic sequencing.
Epidemiological investigations Con rmed cases were de ned as patients with a positive SARS-CoV-2 test and a sequencing result demonstrating infection by recombinant XD. Suspected cases were de ned as patients with a positive SARS-CoV-2 test and an epidemiological link with a con rmed case. Cases were interviewed by the Regional o ces of Santé publique France (SpF, the French national Public Health Agency) using a standard questionnaire. Collected data included demographic information (sex, age, place of residence), travel history, contact persons, vaccination status, symptoms and outcome. The earliest XD case was a family contact of a case with recent travel to Niger, and one case reported recent travel to Italy. A similar investigation was performed during the emergence of Omicron (mostly BA.
Identi cation of putative parental sequences To identify putative parental genome sequences, we retrieved all SARS-CoV-2 sequences available on the GISAID EpiCoV database as of March 9 th , 2022. We did not restrict our search to a window of time corresponding to the earliest detection of BA. 1 to the days prior to the earliest available sequence of lineage XD, to be able to capture all possible related genomes of the parental lineages. We reduced this dataset to sequences longer than 28000 nt and including less than 5% of Ns, on which we performed a similarity search using gofasta (https://github. com/cov-ert/gofasta). As a query, we used XD genomes after masking either the region corresponding to the AY. 4-like or to the BA. 1-like part of the genome. Speci cally, we set the boundary coordinates to the ends of sequential tracts of mutations speci c to the putative parental sequences.
This is a conservative approach to assigning parental lineages and consequently no parental lineage is assigned to those genome regions that do not contain unambiguous lineage-de ning mutations or deletions. We conservatively masked the entirety of the breakpoint windows in each case, and the genome extremities (keeping nt 101-22034 and 25584-29802 for the AY. 4 search, and 22193-25469 for the BA. 1 search). All numberings are based on the Wuhan-Hu-1 reference genome (GenBank ID: NC_045512). The AY. 4 search resulted in 69 sequences post quality ltering. For BA. 1, we restricted our selection to sequences 100% identical to our recombinant query, resulting in 39557 sequences. We also selected all sequences belonging to AY. 4 lineage or one of its sub-lineages (995977 sequences), after annotating the sequences with Pangolin (v3.
1. 20, pangolearn 2022-02-28, scorpio v0. 3. 16, constellation, v0. 1. 3, pangodesign v1. 2. 32) with Usher placement. We compiled statistics for each position of the sequences sets. We also inferred maximum likelihood phylogenies for each putative parental sequences dataset (randomly subsampled for the related BA. 1 genomes) (Extended Data Fig. 3 ). Single nucleotide differences between the XD recombinant sequences and closely related putative parental sequences were visualized using snipit (https://github. com/aineniamh/snipit), Extended Data Fig. 3. Analysis of the variations within the putative parental AY. 4 sublineage suggests that the breakpoint window at the beginning of the spike could be further reduced to nt 22035 to 22096, as the closest AY. 4 sequences all carry an A at position 22097 (C in the recombinant sequences).
Phylogenetic analysis To analyze the recombinant genomes, we generated a contextual dataset representative of the global circulation of SARS-CoV-2 since its emergence using the subsampling strategy implemented in Nextstrain (v. 3. 0. 5)  34. We added high quality XD genomes (n = 16) reported here, as well as subsets of the AY. 4 (n = 29) and BA. 1 (n= 30) genomes identi ed by similarity search (EPISET EPI_SET_20220324xe). As in the similarity search, we also used versions of the recombinant genomes where either one of the regions with distinct ancestry was masked. To characterize the genetic diversity of lineage XD, the complete genome was used, and the reference genome added to root the phylogeny. Maximum-likelihood phylogenies were inferred using IQ-TREE v2. 0. 6 and branch support was calculated using ultrafast bootstrap approximation with 1000 replicates  35, 36.
Prior to the tree reconstruction, the ModelFinder application  37  , as implemented in IQ-TREE, was used to select the best-tting nucleotide substitution model. The resulting phylogenies were visualized using Auspice v. 2. 29. 1 and Figtree v. 1. 4. 4 (http://tree. bio. ed. ac. uk/software/ gtree/). We separately downsampled this dataset to perform recombination analyses using methods implemented in RDP5  38  , including 3SEQ 39 , con rming the clear signal for the two recombination breakpoints.
Viral Isolates We isolated the XD virus and the putative parental AY. 4 virus from nasopharyngeal swabs received in the context of our random genomic surveillance. The sequences of the swabs and the outgrown viruses (XD: Virus isolation was performed on VeroE6 cells in the presence of 1 µg/mL TPCK trypsin as previously described  40  , followed by 2 passages on VeroE6 cells at a MOI of 10-4 to obtain a viral stock. We performed metagenomic sequencing on the sample to check the presence of potential contaminants and verify if the virus had acquired genomic changes during isolation, and each isolate sequence was identical to the primary isolate sequence. All work with infectious virus was performed in biosafety level 3 containment laboratories. SARS-CoV-2 isolate B. 1 (BavPat1 EPI_ISL_406862) was obtained from Pr.
C. Drosten through EVA GLOBAL (https://www. european-virus-archive. com/). The Omicron BA. 1 virus used in the monoclonal antibodies assay was isolated from a nasopharyngeal swab of the 1 st of December in Marseille, France. The full genome sequence has been deposited on GISAID: EPI_ISL_7899754. The strain, called 2021/FR/1514, is available through EVA GLOBAL (www. european-virus-archive. com, ref: 001V-04436).
Analysis of the competition assays For each sample, we trimmed the reads using trimgalore (https://github. com/FelixKrueger/TrimGalore/tree/0. 6. 7). We mapped the reads on the reference sequence Wuhan-Hu-1/2019 using bwa mem (v0. 7. 17)  42. We then counted the coverage of each nucleotide at all positions of the genome using bam-readcount (v1. 0. 0) as performed in ViralFlow  43. We selected the SARS-CoV-2 genomic positions that differ between the sequence of the recombinant isolate (IPP08027i) and the BA. 1 isolate (IPP46934i) using goalign (v0. 3. 5)  44 , and counted the relative amounts of each nucleotide corresponding either to the recombinant XD or to the BA. 1 at each of these positions.
EPI_ISL_10819657 and AY. 4: EPI_ISL_11373018) were identical. All SARS-CoV-2 isolates were supplied by the National Reference Centre for Respiratory Viruses hosted at Institut Pasteur (Paris, France). The human sample from which XD strain hCoV-19/France/HDF-IPP08027i/2022 was isolated has been provided by Dr. Jean-Marc Corcy Laboratoire Corcy et Associés, Soissons, France (hCoV-19/France/HDF-IPP08027/2022 GISAID ID: EPI_ISL_9879437). The human sample from which the AY. 4 virus hCoV-19/France/IDF-IPP52092i/2021 was isolated has been provided by Dr. Stéphanie Marque-Juillet, CH de Versailles, France (hCoV-19/France/IDF-IPP52092/2021 GISAID ID: EPI_ISL_8546521). The human sample from which the BA. 1 virus hCoV-19/France/PDL-IPP46934/2021 was isolated has been provided by Dr. Axelle Paquin, CH de Laval, France (GISAID ID: EPI_ISL_8588473).
Acknowledgments We would like to thank all the healthcare workers, public health employees, and scientists involved in the COVID-19 response. We acknowledge the authors, originating and submitting laboratories of the sequences from GISAID and GenBank (Supplementary Table 1). We thank the  Institut Pasteur C2RA Animal Facility  staff for their dedication to breeding and care of mice, the team of the  Mutualized Platform for Microbiology at Institut Pasteur ,  Dr Justine Schaeffer  for scienti c discussions, and  Dr Cécile Baronti  and  Dr Boris Pastorino  for virus production and neutralizing activity experiments. We acknowledge our colleagues of the  Danish Covid-19 Genomics Consortium  (www. covid19genomics. dk/about),and our colleagues from the  Covid19seq group of the Wisplinghoff Laboratories.
, 2011). Herrmann et al. (2021)  implemented the  Toyota et al. (2011)  mechanism in WRF-Chem and found that snowpack Br 2 production was capable of replicating ozone depletion events observed in multiple datasets. Marelle et al. (2021)  implemented a surface snowpack mechanism based on  Toyota et al. (2011)  and a blowing snow SSA mechanism based on  Yang et al. (2008)  and  Huang and Jaeglé (2017)  and found improved prediction of ozone depletion events, the majority of which were triggered by the snowpack mechanism. The  Toyota et al. (2011)  mechanism was also implemented in the EMAC model and replicated many of the features of reactive bromine events observed by the satellite-based GOME sensor  (Falk and Sinnhuber, 2018). Field campaigns have directly observed the production of SSA from blowing snow  (Frey et al.
on Icelander platforms (deployed on top of sea ice instead of within), and measurements from the various MAX-DOAS systems were found to be comparable  (Simpson et al. , 2017). The reactive bromine season ends when the BrO slant column densities fall below the instrument detection limit and do not recover, which we call the seasonal end date  (Burd et al. , 2017). All O-Buoy and BARC (Utqiaġvik) data are available at arcticdata. io  (Simpson et al. , 2009; Simpson, 2018). More information on the time periods of spring BrO observations can be found in  Swanson et al. (2020)  and  Burd et al. (2017). For comparison to the MAX-DOAS BrO observa- tions, GEOS-Chem model simulations are sampled along the GPS-derived paths of O-Buoys 10, 11, and 12 as well as at BARC.
Bromide content is tracked separately on accumulation-mode SSA and on coarse-mode SSA. Freshly emitted SSA is alkaline and can be titrated to a pH of 5 by uptake of acid gases SO 2 , H 2 SO 4 , and HNO 3  (Alexander et al. , 2005). Heterogeneous chemical reactions can convert SSA-transported bromide into gaseous reactive bromine species in the atmosphere. We run our open-ocean SSA calculations at 0. 5 • latitude × 0. 625 • longitude spatial resolution using the harmonized emissions component (HEMCO) for the highest possible detail  (Keller et al. , 2014; Lin et al. , 2021) , including cold water corrections used in  Jaeglé et al. (2011). Production of SSA from open oceans followed by advection can lead to reactive bromine recycling over Arctic Ocean sea ice. Each of our model runs reads the dataset generated offline by HEMCO rather than spending computational time replicating open-ocean SSA emissions.
The humidity threshold is based on relative humidity with respect to ice. Sublimation from snow crystals cannot occur if the air is saturated, and no SSA is produced if RH ice is greater than 100 %. The wind speed threshold requires 10 m wind speed to be greater than a threshold value defined in Eq. (  1 ) for any production of SSA  (Déry and Yau, 1999, 2001). U t = 6. 975 + 0. 0033(T s + 27. 27) 2 (1) The wind speed threshold (U t ) is dependent on surface temperature (T s ) in Celsius with a minimum threshold of 6. 975 m s -1 at -27. 27 • C and a maximum threshold at 0 • C of 9. 429 m s -1. The 10 m wind speed threshold is the most stringent and often controls the production of SSA from blowing snow. Production of blowing snow SSA is highly sensitive to surface wind speed. We use the highest-resolution surface wind speed dataset to ensure the most accurate modeling of SSA and reactive bromine.
GEOS-Chem chemistry and transport model The GEOS-Chem global atmospheric chemistry and transport model  (Bey et al. , 2001)  simulates emissions, transport, and chemistry of atmospheric trace gases and aerosols, including halogens. The chemical mechanism in GEOS-Chem 12. 9. 3 (http://www. geos-chem. org, last access: 29 October 2019, https://doi. org/10. 5281/zenodo. 3974569, The International GEOS-Chem User Community, 2020) includes HO x -NO x -VOC-O 3 -halogen-aerosol tropospheric chemistry  (Mao et al. , 2013; Fischer et al. , 2014 Fischer et al. , , 2016;; Travis et al. , 2016; Wang et al. , 2021). The model has been regularly and consistently updated to reflect the current understanding of heterogeneous and gas-phase halogen chemistry. Halogens in the troposphere may be sourced from photooxidation of halocarbons, emissions of iodine from the ocean surface, downward transport of halogens from the stratosphere, and release of halogens through heterogeneous chemistry on SSA.
Acknowledgements. We thank the global  GEOS-Chem community  for their tireless work in improving the model. We recognize the work of  Jiayue Huang  in adding the blowing snow SSA mechanism to  GEOS-Chem. We owe a debt of gratitude to all members of the  Atmospheric Chemistry and Global Change group at Florida State University  for their support in working with  GEOS-Chem and Python. We also thank all involved in the O- Buoy  project for data collection and analysis. Financial support. This research has been supported by the  National Science Foundation  (grant nos. ARC-1602716 ,  AGS-1702266 ,  AGS-2109323 , and  ARC-1602883 ) and by the  CNRS INSU LEFE-CHAT program  (grant no. Brom-Arc ) and  NASA  (grant no. 80NSSC19K1273 ). This research has received funding from the  European Union 's  Horizon 2020 Research And Innovation Program  (grant no.
Code availability. Community authored code is available through GitHub at https://github. com/geoschem/geos-chem (last access: 29 October 2019) and https://doi. org/10. 5281/zenodo. 3974569 (The International GEOS-Chem User Community, 2020). First described in  Bey et al. (2001). Documentation and guides may be found at http://wiki. seas. harvard. edu/geos-chem/index. php/Main_ Page (last access: 15 January 2022).
Experimental Setup Simulations were carried out in Matlab and Simulink environments to validate the suggested identification approach, utilizing a 1 DOF system simulating the real system depicted in Fig. 7. Simulations are chosen here since they provide the ground truth for the confirmation of results. The actual system is a cable-driven robot with a reduction ratio of 15. It is made up of a handle and a mass linked by a cable to a maxon motor (EC-max 40mm, brushless 120W, model 283871) with a HEDL 5540 en- coder that measures the angular position of the motor shaft. This encoder has a resolution of 500 counts per turn, which corresponds to a quantization step Q quant = 2π/(500 × 4) = 0. 0031416 rad. A PID controller implemented in an EPOS3 driver connected to the maxon motor directly controls the system.