dataesr/scientific-paragraphs-categorization huggingface dataset:
[10, 12]  is applicable only to searches which consider a single narrow resonance. The simulation-based uncertainties from previous searches are used instead and their effect on the background estimation is incorporated using the methodology developed in a previous ATLAS search for non-resonant signatures in the dilepton channel  [11]. Finally, this analysis also relies on transformational and statistical methods developed in Ref. [15]  in order to deal with data featuring periodic structures. A brief description of the ATLAS detector is given in Section 3. The data and simulated event samples are discussed in Section 4 and an overview of the event selection in each analysis channel is described in Section 5. The signal modelling is presented in Section 6 and the background estimation methods are discussed briefly in Section 7.
Data and simulated event samples The data used for the search consists of the 𝑝 𝑝 collision data recorded by the ATLAS experiment at √ 𝑠 = 13 TeV during the 2015 to 2018 LHC data-taking period. After requiring stable beam conditions and data quality selections with all ATLAS subsystems operational  [21] , the data sample corresponds to an integrated luminosity of 139. 0 ± 2. 4 fb -1  [22]. The LUCID-2 detector  [23]  was used for the primary luminosity measurements. The events used in this analysis were recorded using a set of diphoton and dielectron triggers. Events in the diphoton channel were recorded using a diphoton trigger that required at least two energy clusters in the EM calorimeter with transverse energies (𝐸 T ) greater than 35 and 25 GeV for the 𝐸 T -ordered leading and subleading photon candidates, respectively.
Both of the clusters were required to satisfy photon identification criteria based on the shower shapes in the EM calorimeters. The triggers used in 2015 and 2016 required both of the photons to satisfy the loose identification requirement  [24]. In 2017 and 2018, due to the greater instantaneous luminosity, the diphoton trigger requirements were tightened and both of the photons were required to satisfy the medium identification requirement. The efficiency of the diphoton trigger relative to the event selection given in Section 5 is over 99% for the 2015-2016 data and above 98% for the 2017-2018 data  [25]. Events in the dielectron channel were collected using several dielectron triggers. The trigger used in 2015 required both of the electrons to satisfy the loose identification criteria and 𝐸 T thresholds of 12 GeV.
Generation of pseudo-experiments The analysis uses pseudo-datasets as inputs for the CWT, where one scalogram is generated for each pseudo-dataset. These scalograms are the basic ingredient for the statistical procedures used in this analysis and are also used for estimating the systematic uncertainties. The pseudo-datasets (or "toys") are generated with background-only models based on the functional forms from Section 7 and with signal-plus-background models using the signal models discussed in Section 6 and the background-only models from Section 7. These toys are produced with finely-binned (1 GeV) mass spectra separately for the diphoton and dielectron final states. For the toys containing a signal, a specific choice of CW/LD model parameters is used. The generated pseudo-datasets are hereafter called statistical-only toys (stat-toys) or systematic toys (syst-toys) depending on whether systematic variations are considered when constructing the toy.
The number of such weights in each uncertainty template is equal to the number of systematic variations considered. To replicate the procedures given in Refs. [10, 12] , the resulting uncertainty template is Poisson-fluctuated to generate one uncertainty pseudo-dataset. This uncertainty pseudo-dataset is then fit with the background model from either Eq. (  2 ) or Eq. (  3 ), depending on the analysis channel. This procedure results in one smooth alternative background description, replacing the fits done in Refs. [10, 12]. This alternative background description is then used to generate one respective background-only uncertainty pseudo-dataset. To make one syst-toy in the signal-plus-background case, a similar procedure is followed with minor modifications. The procedure starts from the signal systematic variations discussed in Section 8.
The nominal signal shape is described by Eq. (  1 ). The absolute systematic difference for each variation is calculated directly for the nominal signal shape. The signal uncertainty template is constructed from the nominal signal shape, summed with the Gaussian-weighted absolute systematic differences. Each signal-plus-background uncertainty template is constructed as the sum of one smooth alternative background description, as discussed above, and one signal uncertainty template. The result is Poisson-fluctuated to generate one respective signal-plus-background uncertainty pseudo-dataset. These procedures are repeated to generate background-only and signal-plus-background syst-toys ensembles, where each toy in these ensembles reflects a different (random) combination of all uncertainties.
Conclusion Searches for periodic new phenomena in the dielectron and diphoton invariant mass spectra with the ATLAS experiment at the LHC are presented. The searches are conducted with 139 fb -1 of 𝑝 𝑝 collision data at √ 𝑠 = 13 TeV. The model-dependent result is optimised for spin-2 periodic resonances (KK gravitons) in the clockwork/linear-dilaton model. Model-independent results sensitive to more general periodic contributions are also presented. The data observed in the 𝑒𝑒 and 𝛾𝛾 invariant mass spectra are found to be consistent with the SM expectations. Without a significant excess, the 95% CL exclusion limits are set in the parameter space of the CW/LD model. This result excludes values of 𝑀 5 in the range 11 TeV to 1 TeV for values of 𝑘 in the range 100 GeV to 5 TeV. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT, Argentina ;  YerPhI, Armenia ;  ARC, Australia ;  BMWFW  and  FWF, Austria ;  ANAS ,  Azerbaĳan ;  CNPq  and  FAPESP ,  Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ;  CERN ;  ANID ,  Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias, Colombia ;  MEYS CR, Czech Republic ;  DNRF  and  DNSRC ,  Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU, France ;  SRNSFG, Georgia ;  BMBF ,  HGF  and.
MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Türkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom.
Analytical methods After experiment, the sample was cut following a section parallel to the shear plane and exposing the longitudinal tangential surfaces (maximum shear strain) for microstructural analyses using a scanning electron microscope (SEM; Merlin Compact Zeiss at ISTO). The whole section was imaged at high resolution using the SEM, and the images were segmented using the SPO2003 image-processing software  [Launeau and Robin, 1996, Launeau and Cruden, 1998] , in order to determine the two-dimensional (2D) contents, sizes, and ori-entations of the bubbles and the crystals, as detailed in Supplementary Section 1. 2. 1. A core sample bored perpendicularly to the shear plane was used for three-dimensional (3D) analyses using Xray computed tomography (XCT; Phoenix NanoTOM at ISTO). The 3D bulk porosity (Φ b_bulk_3D ) and the amount of bubbles connected to the sample borders (Φ b_connect_3D ) were determined by segmenting the XCT images using commercial software, such as VGStudio Max and Blob3D, as detailed in Supplementary Section 1.
Acknowledgements The authors greatly thank  I. di Carlo ,  S. Erdmann , and  P. Benoist  for assistance with the SEM and EMP; Camille Daffos  et al. M. Hatton  for the analyses using the elemental analyser;  A. Slodczyk  and  F. Faranda  for the analyses using Raman spectroscopy;  P. Penhoud  for the technical assistance with the X-ray microtomograph; and  M. Beaulieu  for the crystal block grinding using the Selfrag device at BRGM. This work is part of CD PhD thesis that was funded by the  French MNRT  grant. The study costs were provided by the  CNRS-INSU TelluS program , the  Agence Nationale de la Recherche  ( ANR-19-CE31-0007 ; LA), the  EQUIPEX PLANEX  project ( ANR-11-EQPX-0036 ; B. Scaillet), and the  LABEX VOLTAIRE  project ( ANR-10-LABX-100-01 ; B. Scaillet). The authors greatly thank  B.
Imaging mass cytometry: Data acquisition Images were acquired with the Hyperion Imaging System (Fluidigm, Standard Biotools) according to the manufacturer's instructions. After choosing the ROI in the section, the ROI was ablated with a UV laser at 200Hz. Data were exported as MCD files and visualized using the Fluidigm MCD ™ viewer 1. 0. 560. 6. The minimum and maximum thresholds were adapted for each marker and for each tissue for optimal visualization. Gamma was set to 1.
Data analysis Cell segmentation. Cell segmentation was done with the flexible multiplexed image segmentation pipeline based on pixel classification developed by Zanotelli et al. (44). Briefly, pixel classification and training were performed with Ilastik v1. 3. 3 to generate a probability. Then, a segmentation mask was created from the probability map using CellProfiler v4. 1. 3. For each cell identified, the mean intensity of each marker and the spatial coordinates were associated with the computeFeatures function in the R EBImage package  (45)  and exported in. fcs format using the R package flowCore  (46). Singlecell marker expression signals were summarized using the mean pixel values for each channel. Single-cell data were censored at the 99th percentile to remove outliers. Identification of zones of interest.
Tissue architecture and features were identified by a pathologist. For intestine section comparison, seven fcs file were generated: two files concerned the whole tissue sample (WT intestine and Apc D14/+ intestine with tumors) and five files concerned the Peyer's patch, the two intestinal tumors, and the two healthy zones (H WT represents the whole healthy tissue minus the Peyer's patch, and H Apc represents the whole Apc D14/+ tissue minus the two tumor areas). Cell clustering. The Phenograph algorithm in R was used to analyze the dataset after the 99th percentile normalization to eliminate outliers and minimize background noise. The aSMA, B220, CD19, b-catenin, CD3, CD4, CD8, CD11b, CD11c, CD31, CD68, E-cadherin, MPO, pancytokeratin, and vimentin signals were used for cell clustering with a k-value set at 60.
To further analyze CD4 + T cells, cells were subclustered using CD103, FOXP3, GATA-3, granzyme-B, Ki-67 and RORgt expression with a k value set at 30. Epithelial cells were subclustered using pan-cytokeratin, b-catenin, and E-cadherin with a kvalue set at 1000. Heatmaps. Heatmaps were generated using the zscore for each marker. Hierarchical clustering was performed using Euclidean distances and according to the Ward's linkage method in the R package pheatmap  (47). High-dimensional data visualization in 2D maps. The Barnes-Hut t-SNE method was used to generate 2D maps after concatenation and normalization to the 99th percentile of all areas with perplexity = 30, initial dimensions = 110, and theta = 0. 5. Principal Components Analysis. The frequencies of each cluster per zone were retrieved with the FlowJo software and analyzed with the PCA method proposed by FactoMineR  (48).
Acknowledgments We thank  F. Bernex  and  F. Boissière-Michot  for their expertise and advice. We acknowledge the  "Reśeau d'Histologie Expeŕimentale de Montpellier" -RHEM  facility supported by  SIRIC Montpellier Cancer Grant INCa_Inserm_DGOS_12553 , the european regional development foundation and the occitanian region ( FEDER-FSE 2014-2020 Languedoc Roussillon ),  REACT-EU (Recovery Assistance for Cohesion  and the  Territories of Europe) ,  IBiSA  and Ligue contre le cancer for processing our animal tissues, histology technics and expertise.
Funding This work was supported by the  INCa-Cance  ́ropo ̂le GSO 'programme emergence' and the  INCa   PLBIO18-332  and the  French National Research Agency  under the program " Investissements d'avenir " grant agreement  LabEx MAbImprove. The Imaging Mass Cytometry platform was financially supported by the  Region Occitanie, Regional European Development Funds and the SIRIC Montpellier Cancer  " Grant INCa-DGOS-Inserm_12553 ".
DOC concentration was measured on a Sievers© M9 TOC Analyser (GE, Boston, MA, USA). Sterile-ltered (0. 2 µm, Millipore) samples for major cations and anions were stored in the dark (4°C) and analysed using a Metrohm 930 Compact IC ex system. Benthic sediment mineralogy was determined using a X-TRA ThermoARL Diffractometer. Errors varied between 5% and 10% for the phyllosilicates and 5% for grain minerals. Raw data les were generated and transformed into calculated les by WinXRD 2. 0-6 (ThermoFischer). Relative abundances of the main mineral groups were estimated from the raw counts of mica, chlorite, amphibole, feldspars, calcite and quartz divided by the sum of counts.
At the beginning of each forward and reverse reads, 17 and 21 nucleotides were removed, respectively. Forward and reverse reads were truncated at 280 and 220 bases. These steps resulted in a total of 77,774,860 reads across 418 samples. Taxonomy was then assigned against the SILVA reference database (v138)  57  using the algorithm classify-sklearn. The ASV and taxonomy tables and metadata were then transferred to the R (v4. 1) environment for subsequent statistical analyses. Eukaryote, Archaea, Mitochondria, Chloroplast, unclassi ed, singleton and doubleton related sequences were discarded, resulting in a total of 45,923,727 reads across 418 samples and a total of 133,524 ASVs. Rarefaction curves were generated to evaluate the quality of the sequencing depth in providing a fair measure of alpha diversity (Extended data Fig.
13 ). The number of reads per sample varied from 10'812 to 262'747. We then applied a prevalence threshold to our dataset so that only ASVs present in at least 50% of the three patches (i. e. , ecological replicates) were kept across our samples. This resulted in a total of 51,481 ASVs across 418 samples. Replicate samples from the same patch were then agglomerated (i. e. , averaged and converted to integers) to reach a total of 148 samples corresponding to 148 GFSs. Single replicates were kept to maximise the sample size. Rarefaction threshold was then set at 20,023 reads per sample across 148 samples. The nal ASV table included a total of 45,737 ASVs across 148 GFSs. A phylogenetic tree was produced based on 45,737 ASVs in QIIME2 with the plugin q2-phylogeny that uses mafft and FastTree for alignment and phylogenetic tree generation, respectively  58, 59.
Data analyses across GFSs within a given mountain range. The type of MEM variables used are principal coordinates of neighbour matrices (PCNM)  69, 70. MEM analysis produces a set of orthogonal variables based on the geographic coordinates of the GFSs locations  70. These variables are used as explanatory variables in the db-RDA to model spatial structure in community data. Speci cally, MEM variables from the spatial matrix were arranged in blocks with each block attributed to a speci c mountain range. Pools from the other mountain ranges were attributed the value of 0. The environmental model was computed using a forward selection procedure applied to a set of environmental variables  71. Prior to variation partitioning, the community data matrix was log-transformed  72. We included streamwater temperature, pH, speci c conductivity, turbidity, DOC, chlorophyll-a, DIN, SRP, benthic sediment mineral composition and glacier metrics in the environmental models.
e. , negative median scores across all pairs of GFS) indicative of homogeneous environmental selection. Phyloscores and the taxonomies of major clades under homogeneous environmental selection were displayed on the phylogenetic tree using ggtreeExtra. Finally, the number of ASVs per genus was compared to the collective contribution of members of the respective genus to global beta-diversity and the relative contribution of taxa under homogeneous environmental selection to highlight that turnover within clades under homogeneous selection shapes global biogeographic signatures. To unravel the phylogenetic depth at which different aspects of beta-diversity arise, we consecutively agglomerated phylogenetic tips using the tree_glom function in the speedyseq package (v. 0. 5. 3. 9018). As reference, we used the mean phylogenetic distance between ASVs (i.
Acknowledgments This research was supported by The  NOMIS Foundation  to T. J. B. We are most grateful to  A. McIntosh  and  L. Morris  in  New Zealand ,  J. Abermann  and  T. Juul-Pedersen  in Greenland,  O. Solomina  and  T. Kuderina Maratovna  in Russia,  V. Crespo-Pérez  and  P. Andino Guarderasin Ecuador ,  S. Leth Jørgensen  in Norway,  S. Sharma  and  P. Joshi  in Nepal,  N. Shaidyldaeva-Myktybekovna  and  R. Kenzhebaev  in  Kyrgyzstan ,  J. Nattabi Kigongo ,  R. Nalwanga  and  C. Masembe  in Uganda,  M. Gonzlaléz  and  J. Luis Rodriguez  in Chile, and  C. Kuhle  and  P. Tomco  in  Alaska  for their logistical support. This work would not have been possible without the tireless efforts of the many porters and guides in Nepal, Uganda and Kyrgyzstan. We acknowledge the laboratory support of  E.
Soil sampling We used pooled data from all soil sampling campaigns that were conducted throughout the experiments, the latest sampling being in 2021. In Embu and Machanga, regular soil sampling campaigns were done every two to three years since the first experimental year (2002), while in Sidada and Aludeka, regular soil samplings were only initiated in 2018, due to budget constraints. With the exception of the 2021 sampling, only topsoil (0-15 cm) samples from the cropped plots were taken, by combining a composite sample of six transect insertions along the two diagonals. During the 2021 sampling campaign, soil samples were taken down to 50 cm depth (the depth intervals were 0-15-30-50 cm) and the bare plots were also sampled. The initial aim was to sample even deeper, but due the soil being too hard and crumbly (Embu) or too shallow (Aludeka), it turned out unfeasible to sample from deeper layers.
Calculation of SOC stocks based on equivalent soil mass As the measurements conducted in 2021 consisted of both SOC and BD, the equivalent soil mass approach was used to estimate SOC stocks across normalized soil masses  (Lee et al. , 2009) , in addition to SOC concentrations. For this, the approach of Wendt and Hauser (2013) was followed, by fitting a cubic spline to measured data in order to re-scale to equivalent soil masses. First, the soil masses and SOC stocks were computed for each soil depth layer that was sampled. Then, cumulative SOC stocks and soil masses were calculated for combined depth layers to increasing depth (0-15, 0-30 and 0-50 cm) by summing the SOC stocks and soil masses of the depth layer with the values from all above layers. Next, a cubic spline with no intercept was fitted to each individual sampled soil core with cumulative SOC stocks as dependent and cumulative soil masses and the squared value thereof as independent variables.
This resulted in the removal of the random slope in the models but a random intercept was kept. The nested random intercepts were dataset/blockInSite/Plot and dataset/Site/blockInSite/Plot, for the models without and with site as random effect, respectively. The random effect of "dataset" accounted for potential systematic effects of the soil sampling in different years. After selecting an appropriate random effects structure and visually checking for normality and homoscedasticity (a site-specific variance had to be included), interactions of fixed effects were removed one by one until only significant fixed effects remained, starting at the highest order of interaction. Selection of random effects was done using restricted maximum likelihood, that of fixed effects with maximum likelihood and the final model was then fit again with restricted maximum likelihood  (Zuur et al.
, 2009). Additionally, an alternative version of the site-specific model, using relative SOC data (percent of initial SOC) as dependent variable, was created to compare SOC changes within the same organic resource treatments across sites. All statistical analyses of the SOC stock data from 2021, based on equivalent soil mass, and pH data from 2018, were done in a similar way as the analyses of SOC concentration. Fixed effects were the organic resource and mineral N treatments, the site and the cropping status (cropped vs bare; in the case of SOC stocks data only) and interactions of all were allowed. Models contained a random nested blockInSite/Plot effect and allowed for variance heterogeneity between sites, while no random effect for "dataset" and no interaction with time was needed, given the single time point.
Statistical software and definitions The R software version 4. 0. 4 (R Core Team, 2021) with the 'nlme' package  (Pinheiro et al. , 2021)  was used for all statistical analyses. The post-hoc pairwise comparison of different treatments at different times was done with the 'cld' function (Piepho, Abbreviations: CT, control; SD, saw dust; MS, maize stover; TD, Tithonia; CC, Calliandra; FYM, farmyard manure. A BC CDE AB AB AB DE F BCD AB E no input 1. 2 t C ha -1 yr -1 4 t C ha -1 yr -1 C T S D M S T D C C F Y M S D M S T D C C F Y M -2. 0 -1. 5 -1.
2004 ) of package 'emmeans'  (Lenth, 2021)  using the 'containment' method to estimate degrees of freedom. Estimated least square means were computed for SOC at different time points, for the temporal trends of SOC concentrations and for SOC stocks from 2021 and pH data at the time of measurement. Note that in the flow text, the term significant refers to the p < 0. 05 threshold, if not specified otherwise. 3 Results.