dataesr/scientific-paragraphs-categorization huggingface dataset:
From these results, we see that SPT cluster data are driving the constraining power, as it is shown from the shift of Ω m contours toward lower values and σ 8 contours toward larger values for the SPTcl + PvSPLIT baseline combination, with respect to PvFULL constraints. We stress again that for the SPTcl + PvSPLIT baseline combination we are not including BAO+BBN data set and part of the X-ray+WL mass calibrations when considering Planck data, therefore losing part of the constraining power that leads to the tight bounds obtained for the PvFULL analysis (as further discussed in Appendix A). We now focus on the Planck scaling relation parameters Regarding the mass slope α SZ , we find a = -+ 1. 49 SZ 0. 10 0. 07 , which is ∼4σ away with respect to the value obtained when adopting the X-ray calibration, α SZ = 1.
(2019)  We analyze the possible redshift, mass, and noise dependence for the mass bias ( ) b 1 M , as defined in Equation (  22 ). The results are obtained from the fit of ( ) • = - M b M 1 M SZ th th 500 to Planck M SZ masses, starting again from the SPTcl + PvSPLIT (including M 500 evaluation) chain. We report these trends in Figure  5  (blue curves) and the results for the fit in Table  6. While we find a value for the amplitude that is consistent with the constraints for ( ) -b 1 SZ , having = - + A 0. 69 bias 0. 09 0. 04 , we find also Note. We refer to the text for the full data set description. The Markov Chain Monte Carlo chain for the SPTcl + PvSPLIT combination is available at https://pole. uchicago. edu/public/data/sptplanck_cluster. strong evidence for mass and redshift evolution.
edu/public/data/sptplanck_ cluster. We report the first entries in Table  5 , eighth column. As for the marginalized masses, for the 27 clusters in common with the SPT-SZ catalog, our mass estimation is in agreement within 2σ with the estimates from  Bocquet et al. (2019) , as further discussed in Appendix B. We show in Figure  6  the Planck-evaluated masses M 500 , as a function of redshift, in comparison with cluster masses from the SPT-SZ 2500 deg 2 catalog  (Bocquet et al. 2019). As a reference, we also add clusters from recent SPT observations: the 79 clusters from the SPTpol 100 deg 2 sample  (Huang et al. 2020 ) and the 448 clusters from the SPTpol Extended (SPT-ECS) sample  (Bleem et al. 2020). SZ ) parameters. The contours represent the 68% and 95% CL. We compare results for different data set combinations: SPTcl + PvSPLIT in green (baseline results of this analysis), PvFULL in orange, and SPTcl in blue.
Figure2. We show the one-dimensional and two-dimensional probability distributions for the cosmological (Ω m , σ 8 ) and Planck scaling relation (α SZ , ( ) b 1 SZ ) parameters. The contours represent the 68% and 95% CL. We compare results for different data set combinations: SPTcl + PvSPLIT in green (baseline results of this analysis), PvFULL in orange, and SPTcl in blue. We refer to the text for the complete description of the data sets.
The authors thank the anonymous referee for the useful comments they provided to improve the presentation and discussion of the analysis. L. S. and M. C. are supported by  ERC-StG "ClustersXCosmo " grant agreement  716762. A. S. is supported by the  ERC-StG ClustersXCosmo ' grant agreement  716762  and by the  FARE-MIUR grant ClustersXEuclid '  R165SBKTMA  and  INFN InDark Grant. T. S. acknowledges support from the  German Federal Ministry for Economic Affairs und Energy (BMWi)  provided through  DLR  under projects  50OR2002  and  50OR2106 , as well as support provided by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under grant  415537506. The Melbourne group acknowledges support from the  Australian Research Councilʼs Discovery Projects scheme  ( DP200101068 ).
This research made use of computation facilities of CINECA, within the projects  INA17_C5B32 ,  INA20_C6B51 ,  INA21_C8B43 , and at the  Observatory of Trieste (Bertocco  et al. 2020; Taffoni et al. 2020); observations obtained with Planck (http://www. esa. int/Planck), an  ESA  science mission with instruments and contributions directly funded by  ESA Member States ,  NASA , and  Canada ; the SZ-Cluster Database (http://szcluster-db. ias. u-psud. fr)operated by the  Integrated Data and Operation Centre (IDOC) at the Institut d'Astrophysique Spatiale (IAS)  under contract with  CNES  and  CNRS. The  South Pole Telescope program  is supported by the  National Science Foundation (NSF)  through grants  PLR-1248097  and  OPP-1852617. Partial support is also provided by the  NSF Physics Frontier Center  grant  PHY-1125897  to the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Kavli Foundation , and the  Gordon and Betty Moore Foundation  through grant  GBMF #947  to the  University of Chicago.
Indexed libraries were quality controlled with a Bioanalyzer 2100 (Agilent Technologies) and sequenced on an MGISEQ-2000RS at BGI Europe (Copenhagen, Denmark), generating 150-bp paired-end reads. Data processing and taxonomy assignment for 16S profiling. Adapter sequences were trimmed from 39 ends of demultiplexed forward and reverse reads using AdapterRemoval v2. 3. 1  (66)  with default parameters. The DADA2 v1. 17. 3  (67)  package was used for the remainder of the processing steps with R v4. 0. 2, including quality filtering and trimming, dereplication, inference of ASVs, and chimera filtering (Text S1). Taxonomy was then assigned at the genus level using a custom database based on the SILVA nonredundant SSU v138 training set provided by DADA2 (Text S1). Species assignments at 100% sequence identity were performed where possible.
Species accumulation curves indicated that sequencing reached saturation for most samples (see additional figures at https://github. com/jcbrealey/cestode_microbiome). The mock samples had a genus-level composition that matched the theoretical bacterial community composition (see additional figures on GitHub). There was good concordance between PCR replicates of both the mock and the salmon and cestode samples (see additional figures on GitHub). We therefore merged the ASV count data for each replicate in a sample using the phyloseq function merge_samples. Statistical analysis. For all analyses, cestode index was treated as an ordered factor, while sample type (salmon gut, cestode wash, or cestode body) was treated as an unordered factor. Associations between cestode index and gutted fish weight were evaluated in linear regression, including feed type and sex as covariables.
For visualization of 16S data, relative abundance was calculated at the ASV level and summed at the genus level. To account for the compositional nature of microbiome sequence data  (71) , for statistical analyses, ASV and genus counts were transformed with a pseudocount of 1 and the centered log ratio (CLR) calculated using the R package microbiome (http://microbiome. github. io/ microbiome). Nonmetric multidimensional scaling (NMDS) was performed using CLR-normalized abundance values and Euclidean distance with the ordinate function in phyloseq. The NMDS stress value, which is a measure of the degree to which the distance between samples in the reduced dimensional space corresponds with the actual distance between samples (similar to a goodness-of-fit value), has been included in the figure legend of each NMDS plot.
PERMANOVAs were performed on the Euclidean distances using the adonis function in the R package vegan (https://github. com/vegandevs/vegan). Homogeneity of group dispersions was assessed using the betadisper and permutest functions. Species richness was calculated using the specnumber function in vegan and compared among groups using linear regression. Shannon diversity was calculated using a Hill numbers framework with the R package hillR (q = 1) (72). MaAsLin2 (73) was used to identify changes in ASV-and genus-level relative abundances associated with sample type (fixed effect, salmon gut as reference level), with individual ID and sampling date included as random effects. To identify changes in relative abundance associated with cestode index, MaAsLin2 was run with cestode index, feed type, sex, and size category (small category as reference level) as fixed effects and sampling date as a random effect.
The same model was also run using cestode presence as a binary variable (yes/no) instead of cestode index, but no taxon was associated with feed type, sex, or size at false-discovery rate (FDR) of ,0. 2. When interpreting statistical tests, we have used "language of evidence" as outlined in Muff et al. (74). Thus, P values and FDRs of ,0. 05 are interpreted as providing moderate or strong evidence for the respective association, while values between 0. 05 to 0. 2 are interpreted as providing little or weak evidence. Phylogenetic tree based on 16S profiling. ASVs classified as Mycoplasmataceae (unknown genus), Mycoplasma, or Ureaplasma were placed in a phylogenetic tree with a reference set of mycoplasma 16S sequences (Mycoplasma, Ureaplasma) and Bacillus pumilus as an outgroup to root the tree (accessions available at https://github.
com/jcbrealey/cestode_microbiome). Using QIIME v2-2020. 8 (75) , sequences were aligned with MAFFT (76), and uninformative positions were masked (--p-max-gap-frequency 0. 2, --p-min-conservation 0. 4). A bootstrapped, maximum-likelihood phylogenetic tree was constructed with RAxML rapid bootstrapping  (77, 78) , using the GTRGAMMA substitution model and 1,000 bootstrap replicates. The resulting tree was visualized in FigTree v1. 4. 3 (https://github. com/rambaut/figtree/). Metagenomic data processing and MAG generation. Processing steps of the shotgun metagenomics data included adapter removal  (66) , PCR duplicate removal  (79) , and filtering of reads mapping to the sequencing control phiX174, human, salmon  (80) , and cestode reference genomes (81-84) (Text S1). A coassembly of reads from all samples was then performed with MEGAHIT v1.
1. 1  (85) , and reads for each sample were mapped back to the coassembly with bowtie2 v2. 3. 4. 3 (86) (Text S1). The majority of the remaining analyses were performed through Anvi'o v6. 2 and v7. 0 (87) as previously described  (9) , including contig taxonomy assignment  (88) , gene calling  (89) , identification of singlecopy genes  (90) , gene annotation  (91) (92) (93) , and manual binning and curation of three MAGs (Text S1). MAG quality statistics were generated using CheckM v1. 1. 3 (94) , which also placed all three MAGs in the Mycoplasmataceae. More specific MAG taxonomic identification was carried out by extracting each 16S gene and matching it to ASVs from the 16S amplicon sequencing data set. To confirm the 16S gene associated with each MAG, single assembly and binning were performed as described above for select samples (162E, 166E, and 361E).
Pangenome comparisons and phylogenomics. The three generated mycoplasma MAGs were compared to selected Mycoplasma and Ureaplasma reference genomes (accessions available at https:// github. com/jcbrealey/cestode_microbiome) and related salmonid Mycoplasma genomes, including "Candidatus Mycoplasma salmoninae salar," generated from gut content samples obtained from eight host salmon originating from the same sample cohort analyzed in this study, "Candidatus Mycoplasma salmoninae mykiss" from rainbow trout (Oncorhynchus mykiss), and "Candidatus Mycoplasma lavaretus" from European whitefish (Coregonus lavaretus)  (9). The reference FASTA reads were annotated with the Anvi'o platform, as described above, before performing comparative pangenome analyses with Anvi'o (Text S1). Pairwise average nucleotide identity (ANI) was then calculated for each genome/MAG with pyANI  (95).
Gene cluster and functional annotation were extracted for each genome. Additional functional annotation was performed with RAST  (96, 97) , taking into account Mycoplasma readthrough of the stop codon  (98). Exploratory analysis was then performed in R v4. 0. 2. To construct a phylogenomic tree, 38 additional Mycoplasma reference genomes were processed in Anvi'o in the same manner as above using B. pumilus as an outgroup (accessions available at https:// github. com/jcbrealey/cestode_microbiome). Concatenated amino acid sequences for all HMM hits for each genome were used to generate a maximum-likelihood phylogeny using RAxML v8. 2. 11  (77, 78)  (Text S1). The resulting tree was visualized in FigTree v1. 4.
ACKNOWLEDGMENTS The work was funded by the  FHF-Norwegian Seafood Research Fund ("HoloFish ," grant no. 901436 ), the  Danish National Research Foundation  ( CEH-DNRF143 ) and the  European Union 's  Horizon 2020 Action "HoloFood " ( 817729 ) to MTL and MDM. This study is part of the  Parasite Microbiome Project. We thank the following people for their assistance with this project:  Cathrine Kalgraff ,  Marcus Thomas Pius Gilbert ,  Mikkel Sinding ,  Filipe Vieira ,  Maria Karm ,  Martin Nielsen ,  Even Fjaere  and  Annette Bernhard  for assisting with sampling in the field,  Luisa dos Santos Bay Nielsen  for assistance with data generation,  Tom Delmont  for metagenome assembly and Anvi'o advice, and  Håkon Hansen  for discussions on Eubothrium infections in salmon. Harald Sveier is employed at  Lerøy Seafood Group , who produces, markets, and sells farmed Atlantic salmon, including the fish used in the current investigation.
This 3D immersive experience could help valorize the iconographic cultural heritage like photographic collections to better understand and analyze the past, with applications in humanities and tourism for instance. To maximize outreach and replicability, the proposed approach should leverage web technologies, which means rendering using a WebGL framework to enable visualization in any recent web browser and access to geospatial datasets using standard OGC web services. If required, any joint analysis of the different datasets should occur on the client at rendering time, rather than at preprocessing time, so that the image and geospatial dataset providers may expose images, 3D city models (terrain and buildings), and point cloud datasets through existing standard web services such as IIIF, WMTS, WFS, and 3D tiles, with no application-dependent preprocessing and no coupling between the different datasets.
Related Work The georeferencing of such iconographic cultural heritage collections is still an active topic of research, which may be addressed by an interactive process, using user selection of point correspondences between the historic image and the current city model, point cloud or images  (Blettery et al. , 2021). Presentation of historic image collections has been addressed in a 3D web context by providing interactive web geovisualization applications  (Bruschke et al. , 2017 , Paiz-Reyes et al. , 2021)  that help the user understand the coverage of the dataset and discover photographs of interest through interactive navigation. These are however only reprojecting a few image textures at a time. Due to the heterogeneity of the photographs in terms of color, details, and time, the precomputation of a texture by merging all photographs into a single texture  (Waechter et al.
, 2014)  used to give a historical appearance to the current city model is likely to give poor results. This context thus calls for an image-based rendering approach that can provide view-dependent texturing by virtually replacing the cameras with projectors that project their images back to the scene, with blending weights depending on the view camera parameters  (Brédif, 2013). Using densely sampled image collections and heavy joint precomputations on all images and the scene geometry, very high quality and real-time immersive navigation is achievable  (Hedman et al. , 2016). However, even if such datasets were available, it would require some duplication of the scene geometry for each texturing image and it would introduce coupling between the scene geometry and the image datasets. In contrast, we propose to render the geometry of the current 3D model without any pre-processing, by reprojecting all available images of the collection on the first surface of the current 3D model, using a multi-texturing shadow-mapping approach  (Brédif, 2013).
Progressive Depth Map Updates Even when the scene is static, the massive data volumes of the scene geometry (terrain, buildings, point clouds) call for level of detail approaches that only stream to the client the part of the dataset that is currently visible. Therefore, the rendered 3D scene is continuously updated as different levels of detail are selected or deselected for rendering. Thus, the depth map approach, used for projecting images only on the first scene surface as seen from the texturing camera, requires the depth map of the scene from a texturing camera to be updated whenever any visible part of the scene is loaded or unloaded. This workload is linear in the number of projective textures, thus, when this becomes the limiting factor, these depth map updates directly limit the rendering frame rate.
Computation of the reprojection pi in texture i of the screen-space point p provided by FragCoord. The first line (JS) contains the uniforms computed on the CPU in javascript,  (VS)  shows the varyings evaluated in the vertex shader, and (FS) provides the evaluation of the texture coordinate pi in the fragment shader. P and N are the position and normal point attributes, N defaulting to (P -C) if unavailable. components. Assuming no other varyings, the limit on the number of textures is then : ⌊MAX VARYING COMPONENTS/9⌋, a typical value being ⌊124/9⌋ = 12. Instead of exhausting the available varying components by computing all Hi homographies in the vertex shader, we propose to compute a single vec3 N ′ = M -T N N T (P -C) in the vertex shader, at the cost of a slight increase in computation in the fragment shader (Table  1 ).
Texture Visibility Estimate The proposed approach does not require any joint preprocessing of the various datasets (3D city models, terrain, point clouds, images. ). This prevents the computation costs of preprocessing and ensures flexibility. However, there might be inconsistencies due to diachrony and semantics across datasets. For instance, a building may be non-existent, or even in construction, on a photograph while its current is present in the 3D city model (Figure  3 ). Also, a mobile mapping point cloud samples points on the ground and on buildings, but also on transient objects like vehicles, pedestrians, and trees. If a semantic classification of the point cloud is available, we propose to only consider non-transient classes for texturing and depth map estimation. This will prevent respectively texturing a transient object with photographs (which are very unlikely to contain the color of a coherent transient object at the same location), and casting a shadow on further non-transient objects.
RESULTS The proposed approach has been implemented using iTowns, a Three. js-based framework written in Javascript/WebGL for visualizing 3D geospatial data. The geospatial datasets are accessed using standard web services. The application connects to a WMTS server to retrieve elevation and orthophotographic tiles for the terrain. The LOD 1 buildings are accessed by WFS tiles of 2DZ polygons (and extruded in 3D in the web client) and the point clouds are served a 3DTiles dataset. For now, the image files are served over HTTP as the resolution is moderate, but any image server could be implemented (IIIF, IIP. ). Image parameters are available as a WFS service where the feature is the 3D point location of the image center and the other extrinsic and intrinsic parameters are available as feature properties.
Better culling could be implemented on the CPU by testing the bounding boxes of scene geometries against the frustums of texturing cameras, similar to  (Hedman et al. , 2016)  by leveraging the existing bounding volume hierarchies of geospatial data access services (e. g. quad-trees or octrees). This culling at the object level on the CPU could then be refined at the fragment level on the GPU using an acceleration structure that would prevent from iterating over all the textures selected by the CPU, but only to those who have the 3D position of the current fragment in their frustum. The handling of diachrony between datasets definitely deserves further research. An interesting approach is the modeling of spatio-temporal city models  (Schindler and Dellaert, 2012) , which models not only a temporal snapshot of the city but which compactly encodes and is able to deliver the geometry of the city at any date.
Bonjean et al. (2020)  unveiled the galaxy distribution and their properties in the SDSS filaments, such as star formation rate, galaxy types, and stellar mass. Tanimura et al. (2020a, hereafter T20y)  detected the WHIM gas at a significance of ∼4σ with the tSZ effect and at a significance of ∼8σ in the CMB lensing signal and measured both the gas density and temperature. In addition, more recently,  Tanimura et al. (2020b, hereafter T20x)  detected the X-ray emission from the WHIM gas for the first time in large cosmic filaments at a significance of ∼4σ using the ROSAT data 1  provided by  Snowden et al. (1994 Snowden et al. ( , 1997)). The authors constrained both the gas temperature of T ∼ 0. 9 +1. 0 -0. 6 keV and its overdensity of δ ∼ 30 ± 15 at the center of the filaments. They further forecast the detection of the X-ray emission from filaments in the SRG/eROSITA data assuming different hypotheses for the gas densities and temperatures.
An actual observational study with SRG/eROSITA was performed for the Abell 3391/95 galaxy cluster system in  Reiprich et al. (2021) , and the authors discovered evidence of X-ray emission from warm gas in the bridge region, which extends over 15 Mpc. As an extension of T20x work, we use the publicly available SRG/eROSITA data in this study to assess an X-ray emission associated with large cosmic filaments. Based on this, we estimate the gas density and temperature in the filaments. The paper is organized as follows: Section 2 summarizes the datasets we used in our analyses. Section 3 explains the stacking method for measuring the X-ray emission from filaments. Section 4 presents the model we used to interpret our measurements. The possible systematic uncertainties in our measurements are discussed in Sect.
eROSITA Final Equatorial Depth Survey The extended ROentgen Survey with an Imaging Telescope Array (eROSITA;  Predehl et al. 2010 ) is achieving full-sky X-ray surveys with its instrument on board the Russian Spektrum-Roentgen-Gamma satellite 2  (SRG). After completing four years of observation, eROSITA will achieve eight full-sky surveys, resulting in a sky-averaged exposure time of ∼2 ks. This will allow the detection of about 100 000 galaxy clusters (SRG/eROSITA Science Book:  Merloni et al. 2012). In this analysis, we use the eROSITA Final Equatorial Depth Survey 3  (eFEDS;  Brunner et al. 2022). It has been car-ried out as a part of the calibration and performance verification (Cal-PV) program between mid-September and mid-December 2019 (about 360 ks, or 100 h, in total) with all the seven SRG/eROSITA Telescope Modules (TM 1-7) in operation.
The telescope modules performed scanning observations of ∼140 sq. deg, composed of four individual rectangular rasterscan fields of ∼35 sq. deg each. However, due to an unrecognized malfunction of the camera electronics, 28% of the TM6 data of eFEDS field I and 48% and 43% of the TM5 and TM6 data, respectively, of eFEDS field II could not be used, resulting in a reduced exposure depth in the affected areas of up to ∼30%. Because the data in these fields have higher noise than other eFEDS fields, we discarded the data in these fields from our analysis. A set of calibrated data products from eROSITA, the Early Data Release (EDR) 3 , is made public. They consist of calibrated event files with energies between 0. 2-10 keV and image, exposure, and background maps in the energy range of 0. 2-0. 6 keV, 0.