dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene expression analyses -Quantification of transcript expression was performed on Trinity assemblies with Kallisto v. 0. 46. 1  (Bray et al. 2016 ) using the align_and_estimate_abundance. pl script provided in the Trinity suite  (Grabherr et al. 2011). Kallisto relies on pseudo-alignments of the reads to search for the original transcript of a read without looking for a perfect alignment (as opposed to classical quantification by counting the reads aligned on the assembled transcriptome; Wolf 2013). Counts (raw number of mapped reads) and the Transcripts Per kilobase Million are reported (result files available from Zenodo). Based on the previously inferred orthogroups, orthogroup-level abundance estimates were imported and summarized using tximport  (Soneson et al. 2016). To minimize sequencing depth variation across samples and gene outlier effect (a few highly and differentially expressed genes may have strong and global influence on the total read count), orthogroup-level raw reads counts were normalized using the median of the ratios of observed counts using DESeq2  (Love et al.
2014)  for orthogroups containing up to 20 gene copies by species. Chitinase expression in salivary glands -The chitinase orthogroup was extracted from the orthogroups inferred by OrthoFinder2 using BLASTX with the reference chitinase database previously created. The 476 amino acid sequences composing this orthogroup were assigned to the nine chitinase orthologs (CHIA1-5, CHIT1, CHI3L1, CHI3L2, OVGP1) using the maximum likelihood Evolutionary Placement Algorithm implemented in RAxML-EPA  (Berger et al. 2011)  with the reference chitinase sequence alignment and reconciled phylogenetic tree previously inferred using GeneRax (result files available from Zenodo). This allowed excluding three additional contaminant sequences and dividing the chitinase orthogroup into nine sub-orthogroups corresponding to each chitinase paralog.
Within the CHIA5 clade of Muroidea (Spalacidae, Cricetidae and Muridae), we found four subclades (named here CHIA5a-d) representing potential duplications specific to the muroid rodent species represented in our dataset. From the CHIA5a paralog, two consecutive duplications gave rise to the three CHIA5b-d paralogs represented by long branches, characterizing rapidly evolving sequences. The duplication giving rise to the CHIA5c and CHIA5d paralogs concerns only the Cricetidae and Muridae, Nannospalax galili (Spalacidae) being present only in the clade of the CHIA5b paralogous gene.
analyses. We are also indebted to Frank Knight, Mark Scherz, Miguel Vences, Andolalao Rakotoarison, Nico Avenant, Pierre-Henri Fabre, Quentin Martinez, NathalieDelsuc, Aude   Caizergues, Roxanne Schaub, Lionel Hautier, Fabien Condamine, Sérgio Ferreira-Cardoso,   and  François Catzeflis for their help with tissue sampling. Computational analyses benefited from the Montpellier Bioinformatics Biodiversity (MBB) platform. The JAGUARS collection is supported through a FEDER/ERDF grant attributed to Kwata NGO, funded by the European Union, the Collectivité Territoriale de Guyane, and the DEAL Guyane. This work has been supported by grants from the European Research Council (ConvergeAnt project: ERC-2015-CoG-683257) and Investissements d'Avenir of the Agence Nationale de la Recherche (CEBA: ANR-10-LABX-25-01; CEMEB: ANR-10-LABX-0004).
Data and Resource Availability Raw RNAseq Illumina reads have been submitted to the Short Read Archive (SRA) of the National Center for Biotechnology Information (NCBI) and are available under BioProject number PRJNA909065. Transcriptome assemblies, phylogenetic datasets, corresponding trees, and other supplementary materials are available from zenodo. org (DOI: 10. 5281/zenodo. 7790047).
C analyses. We are also indebted to Frank Knight, Mark Scherz, Miguel Vences, Andolalao Rakotoarison, Nico Avenant, Pierre-Henri Fabre, Quentin Martinez, NathalieDelsuc, Aude   Caizergues, Roxanne Schaub, Lionel Hautier, Fabien Condamine, Sérgio Ferreira-Cardoso,   and  François Catzeflis for their help with tissue sampling. Computational analyses benefited from the Montpellier Bioinformatics Biodiversity (MBB) platform. The JAGUARS collection is supported through a FEDER/ERDF grant attributed to Kwata NGO, funded by the European Union, the Collectivité Territoriale de Guyane, and the DEAL Guyane. This work has been supported by grants from the European Research Council (ConvergeAnt project: ERC-2015-CoG-683257) and Investissements d'Avenir of the Agence Nationale de la Recherche (CEBA: ANR-10-LABX-25-01; CEMEB: ANR-10-LABX-0004).
Our approach therefore tests the hypothesis that there are differences in measured and simulated soil parameters, and assemblages and relative abundance of nitrogen cycle-associated genes in the different plots at the Broadbalk site. We cannot conclude, based on statistical inference, that any differences observed result from different fertility management directly. However, it is reasonable to interpret our observations of between-plot differences in soil structure and relative gene abundance within the context of the broader data relating to SOC and Ntot stocks, held by the e-RA electronic database, and N2O emissions-all of which are temporally replicated-to suggest explanations for the patterns we observe.
10 µg of high-quality DNA was provided for sequencing for each of the samples. Shotgun metagenomic sequencing of DNA was performed using 150-base paired-end chemistry on an Illumina HiSeq 2500 sequencing platform by Beijing Novogene Bioinformatics Technology Co. Ltd. (Beijing, China). The generated raw sequences were limited to a minimum quality score of 25 and a minimum read length of 70 bases using Trimmomatic  50. Bioinformatic analysis of metagenome sequences -To assess general abundance of genes in metagenomes, we mapped individual metagenomic sequences to the RefSeq non-redundant (NR) protein database held at NCBI (downloaded August 22 nd , 2018) using DIAMOND version 0. 8. 27  53  in BLASTX mode imposing a bitscore cut-off of 55. For each sequence, only the match with the highest bitscore was considered.
Sequences not matching the NR database were considered currently unclassified. MEGAN Ultimate version 6. 10. 2  52  was used to associate metagenome sequences with Kyoto Encyclopaedia of Genes and Genomes 53 (KEGG) functional orthologs (ko) and modules (M). From all the reads binned to at least one KEGG orthologous group, we selected those associated with nitrogen metabolism (ko00910), amino acid metabolism (ko09105) and metabolism of other amino acids (ko09106) for detailed study of distribution differences between soils. Nitrous oxide is a product of autotrophic and heterotrophic nitrification pathways as well as denitrification. Emission rates differ markedly, both between processes and at different soil saturations  54. Emission rates of N2O due to denitrification are far greater than from either nitrification pathway  54.
Historical data relating to nitrogen inputs (as ammonium nitrate fertiliser or FYM, within seed grain and atmospheric deposition), off-takes (as harvested grain and straw), and soil nitrogen stocks were acquired from the e-RA managed database of data from Rothamsted's long-term experiments (data available from http://doi. org/10. 23637/rbk1-yldS10115-01). The end-of-season fate of annual nitrogen additions in the form of inorganic fertiliser or FYM introduced to the Broadbalk continuous wheat plots was separated into three pools: (1) that taken up for the current season's production (i. e. nitrogen content in straw and harvested grain); (2) the pool held within the system for potential use in the future (i. e. change in soil nitrogen stock); and (3) that lost from the system without being utilised (e.
Acknowledgements This research was supported by  UK Research and Innovation's (UKRI) Biotechnology and Biological Science Research Council (BBSRC) -funded Soil to Nutrition strategic program  ( BBS/E/C/000I0310  and  BBS/E/C/000I0320 ). The  Broadbalk Wheat Experiment  is part of the  Rothamsted Long-term Experiments National Capability  supported by  BBSRC  ( BBS/E/C/000J0300 ) and the  Lawes Agricultural Trust. LJG and RK were supported by the  Hartree National Centre for Digital Innovation , a collaboration between  UKRI's Science and Technology Facilities Council  and  IBM Research Europe. The authors a grateful to four anonymous reviewers for their time taken to provide helpful and encouraging comments on the original version of the manuscript. Their inputs have resulted in a muchimproved text.
Availability of data Data relating to the Broadbalk and Highfield long-term experiments can be accessed via the electronic Rothamsted Archive (http://www. era. rothamsted. ac. uk/experiment/rbk1 and http://www. era. rothamsted. ac. uk/experiment/rrn1, respectively). Historical data relating to nitrogen inputs (as ammonium nitrate fertiliser or FYM, within seed grain and atmospheric deposition), offtakes (as harvested grain and straw), and soil nitrogen stocks are available from http://doi. org/10. 23637/rbk1-yldS10115-01. All soil images are available upon reasonable request from the corresponding author. Code relating to lattice Boltzmann simulation is available upon request from Xiaoxian Zhang, Rothamsted Research. Sequence data associated with this research have been deposited in the European Nucleotide Archive https://www.
The specific activities (or upper limits) were then calculated from data based on the sample's mass and measuring time, as well as the characteristic branching ratios of the gamma lines, as detailed in  [22]. The complementary analytic technique ICP-MS is among the most sensitive for the detection of trace elements. The intrinsic radioactivity of a batch of material can be found with a measurement of long-lived radionuclides. The sample is turned into an aqueous solution, introduced through a peristaltic pump, nebulized in a spray chamber, and then atomized and ionized in plasma. These ions are extracted into a system placed under high vacuum and separated in accordance with the charge-to-mass ratio. Sensitivities 1-10 µBq/kg are attainable for 238 U and 232 Th. Several live years of data, aggregated across all instruments, were acquired throughout the radioassay program of XENONnT.
4 tonnes of xenon in the detector, a final radon activity concentration of 4. 2 ( +0. 5  -0. 7 ) µBq/kg in the LXe target is expected, a factor of three lower than in XENON1T. XENONnT's novel radon distillation system will further reduce the radon concentration in LXe, allowing us to achieve the target activity of 1 µBq/kg. Imminent XENONnT TPC data will validate this post-distillation radon concentration projection. Special emphasis was placed on the cleaning and proper storage of all detector materials during the assembly of XENONnT. Dedicated cleaning procedures were defined for different materials in order to remove dust and lubricants introduced during the material production process. The cleaning agents were selected according to results obtained from gamma-ray screening. For some materials, an increased radon emanation rate was detected after their degreasing treatment, which was associated with a relatively high radium concentration in the cleaning agent.
Surface treatments are also important to mitigate background from long-lived radon daughters. For PTFE surfaces, the most critical component in this regard for XENONnT, a reduction up to a factor of six was established for the procedure described above. In order to avoid re-contamination of detector parts, a dedicated cleanroom infrastructure was built for material cleaning, storage, and detector assembly. ence Foundation, Fundacao para a Ciencia e a Tecnologia, Région des Pays de la Loire, Knut and Alice Wallenberg Foundation, Kavli Foundation, JSPS Kakenhi in Japan and Istituto Nazionale di Fisica Nucleare. This project has received funding/support from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860881-HIDDeN. Data processing is performed using infrastructures from the Open Science Grid, the European Grid Initiative and the Dutch national e-infrastructure with the support of SURF Cooperative.
Data Availability Statement This manuscript has no associated data or the data will not be deposited. [Authors' comment: The datasets generated during and/or analysed during the current study are available in the repository 'Supplemental data for material radiopurity control in the XENONnT experiment', https://zenodo. org/record/5767294. ] Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
Data and Methods MISR is aboard the Terra satellite, which is in a sun synchronous orbit with an equator crossing time of about 10:30 a. m. The instrument consists of nine cameras which view the Earth at nine different angels: nadir plus 26. 1, 45. 6, 60. 0, and 70. 5 degrees forward and aft along the direction of flight. Radiance measurements (images) are collected in four narrow spectral bands centered at 443, 555, 670, and 865 nm with a horizontal grid spacing of 275 m to 1. 1 km depending on the camera and wavelength  (Diner et al. , 1998). The MISR CTH-OD data set used in this study is based on stereo heights derived on a 1. 1 km grid (using image matching techniques) and optical depths retrieved from the observed visible radiances assuming a single layer one-dimensional cloud with a fixed effective radius and no aerosols  (Marchand et al.
The MISR simulator, hereafter referred to as MISR-COSP (because it is part of the Cloud feedback model intercomparison project Observation Simulator Package (COSP) that is being run by many modeling centers) is used to produce a MISR-like cloud field that is consistent with the treatment of radiation in each model  (Swales et al. , 2018). Details on the MISR simulator can be found in  Marchand et al. (2010)  and  Marchand and Ackerman (2010)  with evaluation of both MISR observations and MISR-COSP output against CloudSat and CALIPSO (radar and lidar) given by  Hillman et al. (2017). Currently the MISR CTH-OD data set is only generated over ocean, and for comparison MISR-COSP output is restricted to oceans. Similarly, the MISR-COSP only records cloud in the portion of the globe that is sunlit, because MISR is only capable of observing cloud on the daytime portion of its orbit.
Specifically, synthetic MISR projections are produced for CESM2 and IPSL (for comparison with direct MISR simulator output), as well as MRI-ESM2-0, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, and BCC-CSM2-MR. Primary references for each of these models is given in Table  1. In the following analysis, these data are first deseasonalized (by subtracting the mean seasonal cycle from the full length of each data set). The model AMIP output used here are available for download through the ESGF CMIP6 database (https://esgf-node. llnl. gov/projects/cmip6/) and MISR observation data are available from the NASA Langley archive (http:// asdc. larc. nasa. gov/project/MISR/). The SSP5-8. 5 simulation with the MISR-COSP simulator was performed as an independent run by each modeling center, and is available at: https://atmos.
The Weighted Cloud Top Height (WCTH) Metric MISR and the MISR simulator parse cloud occurrence into a fixed set of CTH and (total column) optical depth bins. To assess a trend in CTH, we calculate a cloud-fraction weighted average CTH (WCTH) from the histogram data as given below in Equation 1. Here we define high clouds as those above 9 km in the tropics (between -20° and 20° latitude), above 7 km in the subtropics (±20° to ±40° latitude), and above 5 km for all latitudes poleward of ±40° in both hemispheres. The MISR histogram is first integrated over (some desired range of) optical depth to obtain the cloud occurrence (  CF ) associated with each MISR height bin (with height  CTH ). WCTH(𝑡𝑡) = ∑ 𝑧𝑧𝑧thresh (CTH𝑧𝑧 × CF𝑧𝑧(𝑡𝑡)) ∑ 𝑧𝑧𝑧thresh (CF𝑧𝑧(𝑡𝑡)) (1) The WCTH describes the average height of high clouds, when a high cloud is present, and is independent of the total amount of high cloud (and is conceptually similar to the weighted cloud height (WCH) metric from T19).
To a good approximation (see Supporting Information) the increase in the standard deviation in the monthly WCTH measured by MISR due to sampling is given by 𝐴𝐴 𝐴𝐴internal∕ √ 2 * 𝑛𝑛eff -2 , where  internal is the standard deviation of the internal variability in WCTH and  eff is the effective number of independent samples collected by MISR. This suggests the increase in observed variability resulting from the MISR sampling would be less than 10%. The differences in the top panel of Figure  1  are much greater than 10% (at most latitudes) and we conclude that the differences in WCTH standard deviation seen in Figure  1  are likely a result of differences in internal variability (between models and between the models and reality) and not a sampling effect. In later analysis the data is separated into the different MISR optical depth ranges.
We note that at all latitudes the variability of WCTH in the lowest optical depth range (OD < 0. 3) is very large in the MISR observations and these data are not trustworthy. MISR does not typically or consistently detect clouds with optical depths less than 0. 3  (Hillman et al. , 2017; Marchand et al. , 2010) , and the MISR simulator assumes that all high clouds with optical depth less than 0. 3 will not be detected  (Marchand & Ackerman, 2010). Consequently, clouds with an optical depth below 0. 3 are not used in the subsequent analysis. There is also substantially higher variance in the MISR observations at latitudes poleward of ±60° that is related to low solar zenith angles and a lack of direct sunlight in the winter months, and we restrict the analysis to latitudes equatorward of ±60°.
The model simulations employ a MISR simulator which produces histograms of cloud-top-height and optical depth consistent with those obtained from the MISR instrument, and a scheme was developed to calculate a weighted cloud-top-height (WCTH) metric for high clouds based on the histogram data. This metric, in combination with ∆mean and slope statistics, and bootstrap resampling is used to determine a Time of Emergence (TOE) for the detection of change in WCTH with 95% confidence (in both statistics). The models were found to often underestimate variability in the MISR observation over the time period of the current observational record, and the model variability was scaled to match that found in the MISR data over the current observational record in the analysis. The TOE analysis is performed using two CMIP6 models: CESM2 and IPSL-CM6A-LR, both of which generated MISR simulator output while running the ScenarioMIP SSP5-8.
As discussed in the preceding paragraph, this is largely due our need to restrict the analysis to oceans, which T19 did not need to do using CloudSat observations (as the radar high cloud detection has little sensitivity to surface conditions). Regarding the detection time, we note that T19 did not apply any variance adjustment in the results presented in their primary manuscript (and rely directly on the model simulated variance). They did this because the relatively short CloudSat data record made it difficult to quantify the observational variance with sufficient accuracy. However, in their Supporting Information (Figures  S15  and S16 ) they do present results based on the observed CloudSat variability and find this delayed detection by as much as 5 years, which is broadly consistent with what we find here when relying on the model variance.
It is logical to expect that models with lower ECS and TCR would have a later TOE. Based on analysis of feedback processes, the historical record, and the paleoclimate record  Sherwood et al. (2020)  estimate that the true ECS is likely lower than is produced by CESM2 and IPSL-CM6A-LR. To investigate the model dependence of TOE, we created synthetic MISR WCTH time series combining MISR output from AMIP simulations, with temperature data from SSP5-8. 5 simulations (from models that performed the SSP5-8. 5 experiment, but did not produce MISR output for this run) and a Fixed-Anvil-Temperature assumption. In effect, the MISR simulated cloud profile found in the AMIP run remains fixed with temperature and shifts up to maintain the same mean temperature in the SSP5-8. 5 simulation. The synthetic WCTH projections showed that, as expected, models that experience more warming also tend to have faster rising cloud tops.
Acknowledgments This research was supported by the  MISR  project at the  NASA Jet Propulsion Laboratory  (under contract no. 1318945 ). The authors wish to acknowledge and thank the many modelling centers who contributed data to the Coupled Model Intercomparison Project that was necessary for this study, specifically  A. Feofilov  from  LMD  for running the IPSL model simulations, as well as  Catherine Moroney ,  Steve Protack  and everyone at  NASA JPL and LaRC  who produce the MISR CTH-OD data. Author Brian Medeiros acknowledges support by the  U. S. Department of Energy  under Award Number  DE-SC0022070  and  National Science Foundation  (NSF) IA 1947282; the  National Center for Atmospheric Research , which is a major facility sponsored by the  NSF  under Cooperative Agreement No. 1852977 ; and the  National Oceanic and Atmospheric Administration  under award  NA20OAR4310392.
, 1995)  and ABTS  (Re et al. , 1999)  assays to estimate the total antioxidant activity. Data was analysed using R v4. 1. 2 (R Core Team, 2020) and the package lme4  (Bates et al. , 2014) , using two modelling strategies. First, for each parameter, we fit a linear model specifying producer, sampling timepoint, storage light conditions and their interactions as fixed factors. Second, for each parameter, we fit a linear-mixed effect model specifying storage timepoint, light condition, and their interactions as fixed factors, and producer as a random effect. The package emmeans was used to infer pairwise contrasts (corrected using false discovery rate, FDR).
), and 1 μl of the purified amplicons was used for a second PCR to integrate Illumina adaptors using the Nextera XT index Kit (Illumina). Amplicons were purified a second time as reported above, quantified using a Qubit 3. 0 Fluorometer (Thermo Fisher) and pooled at equimolar ratio. The pooled library was then sequenced on a Illumina MiSeq instrument using the 300PE chemistry. Paired-end reads were processed using the DADA2 v1. 22  (Callahan et al. , 2016)  pipeline implemented in R v4. 1. 2 (R Core Team, 2020) to remove low-quality data, identify ASVs and remove chimeras. Taxonomy was assigned using UNITE v8. 3 database  (Nilsson et al. , 2019). Data was analysed using R v4. 1. 2 (R Core Team, 2020) with the packages phyloseq (McMurdie & Holmes, 2013), vegan  (Dixon, 2003) , DESeq2  (Love et al.
, 2014)  and lme4  (Bates et al. , 2014). The diversity of microbial communities was estimated for each sample using Faith's phylogenetic diversity index  (Faith, 1992) , and tests were performed by using two different modelling strategies. First, we fit a linear model specifying producer, sampling timepoint, storage light conditions and their interactions as fixed factors. Second, we fit a linear-mixed effect model specifying sampling timepoint, storage light conditions, and their interactions as fixed factors and producer as a random effect. The package emmeans was used to infer pairwise contrasts (corrected using false discovery rate, FDR). Similarly, we tested the influence of producer, sampling timepoint, and storage light conditions on the structure of fungal microbiomes in our system using a multivariate approach.