dataesr/scientific-paragraphs-categorization huggingface dataset:
The 5 km columns are composed by 15 lidar single shots (every 333 m) averaged together. When a layer is detected at 5 km horizontal resolution in the planetary boundary layer (PBL), the CALIOP L2 processing searches within the layer to identify those especially dense regions that can be confidently detected at single-shot resolution. In those cases where clouds are detected at single-shot resolution, the original 5 km profile is reaveraged to a nominal 5 km resolution, with the data from all single-shot layer detections being excluded from the newly averaged profile. This new profile is once again searched for layers whose presence would previously have been obscured by the very strong backscatter from boundary layer clouds. This allows, for example, the detection of aerosol layers at a nominal 5 km horizontal resolution with embedded small cumulus clouds detected at single-shot resolution.
The 5 km columns containing layers detected at single-shot resolution that were cleared from the original profile are not considered in this study. Indeed, the IIR observations can be strongly influenced by these large optical depth features. The CALIOP V4 dataset also reports top altitude z top , the estimated optical depth τ , and the CAD score calculated by the CAD algorithm for each detected layer. Nominal CAD scores range between -100 and 100. The layer is classified as cloud when the CAD score is positive and as aerosol when it is negative. The absolute value of the CAD score provides a confidence level for the classification. Here, we consider "confident" layers as those for which 70 ≤ |CAD score | ≤ 100 following the "high" confidence definition of  Liu et al. (2009)  and "ambiguous" layers as those for which 0 ≤ |CAD score | < 70.
First, we note that clear-sky columns are more frequent during daytime than nighttime. This is mainly due to the fact that the lidar detection sensitivity is much lower during daytime due to background solar noise making it more difficult to detect faint features (e. g. ,  Thorsen et al. , 2017; Toth et al. , 2018). For the same reason, more monolayer columns and fewer multilayer columns are found during daytime compared to nighttime. In the tropics, monolayer columns represent 54 % of daytime observations and 37 % of nighttime observations. In the midlatitudes, monolayer columns represent 57 % of daytime observations and 45 % of nighttime observations. High aerosol monolayers are very rare (0. 1 %-0. 4 %). Note that approximately half of the aerosol columns (difference between solid and transparent bars) will not be studied using the IIR due to the presence of dense clouds detected at full resolution (333 m) which have been cleared during the CALIOP L2 data processing.
IIR radiative transfer simulations To simulate the behavior of BTDs of ice clouds, liquid water clouds, and some types of aerosols (Sect. Radiative Transfer Database for Earth and Climate Observation (ARTDECO;  Dubuisson et al. , 2016) , a numeric tool for computing the optical properties of aerosols and clouds used for the 1-D simulation of Earth atmosphere radiances as observed with passive sensors from the UV (0. 2 µm) to the far infrared (50 µm). ARTDECO calculations also incorporate the IIR instrument functions. The 1-D radiative transfer computation is performed by the discrete ordinate method (DISORT 2. 1)  (Stamnes et al. , 1988). The cloud optical properties used for the ice cloud simulations are computed assuming the ice cloud is composed of a generalized mixture of ice crystal habits with its size distribution defined in the microphysical model of the Collection 6 data products distributed by the Moderate Resolution Imaging Spectroradiometer (MODIS) project  (Baum et al.
, 2011). Liquid clouds are assumed to be composed of spherical water droplets with a typical stratus size distribution as defined in  Stephens (1979). Optical properties of sea salt at an atmospheric relative humidity of 80 % obtained from the Optical Properties of Aerosols and Clouds (OPAC) database  (Hess et al. , 1998)  are used for the marine aerosol layer simulations. Dust layer simulations use optical properties derived from Saharan desert dust measured in Mauritania  (Di Biagio et al. , 2017). Simulations are performed for standard tropical and midlatitudes atmospheres  (McClatchey, 1972)  over an ocean surface with emissivities of 0. 971 at 8. 65 µm, 0. 984 at 10. 60 µm, and 0. 982 at 12. 05 µm, consistently with  Garnier et al. (2021).
IIR CAD algorithm Following the CALIOP V4 CAD method  (Liu et al. , 2009 (Liu et al. , , 2019)) , we develop a new IIR CAD algorithm. To do so, we build 2-D cloud and aerosol PDFs of the IIR signature observed in cloud and aerosol layers. These PDFs are built for several z top -τ ranges to account for the sensitivity of the IIR signature to these parameters (see previous section), by separating tropical and midlatitudes. The training dataset is based on cloud and aerosol layers classified as such with confidence by the CALIOP V4 algorithm (|CAD score,V4 | ≥ 70). This assumes that CALIOP misclassifications in confident layers are unlikely. The ability to discriminate clouds from aerosols is quantified through a so-called IIR CAD score, which characterizes the difference between the respective IIR signatures and the overlapping of the respective PDFs.
We used the whole 2008-2019 time period for the training dataset. Examples of observed cloud and aerosol 2-D distributions are shown in Sect. 4. 1. The distributions are shown for the training dataset, that is for confident layers, as well as for ambiguous layers, to discuss the additional value of the IIR for cloud-aerosol discrimination. The derivation of the IIR CAD scores for the measure of this additional value is described in Sect. 4. 2, followed by presentation of CAD score masks in Sect. 4. 3.
In this study, our use of independent observations acquired by the IIR confirms the overall reliability and consistency of the lidar-only V4 CAD algorithm. For a few cases, the IIR can improve the CALIOP-based CAD by confirming part of the remaining ambiguous cloud layers and correct some aerosol layer misclassifications often embedded in larger cloud structures. IIR can also help with the cloud-aerosol classification where lidar signal is very noisy or when low laser energy shots occur. The preliminary version of a CAD score based on the IIR observations proposed in this paper could serve as a first step for the design of a future CAD making use of both the lidar and the IIR observations. To help further in the discrimination between cloud and aerosol layers, the collocated MODIS level-2 MOD04 aerosol data products based on multispectral analysis for identification of smoke or other non-dust aerosol layers may be complementary to the IIR analysis.
Acknowledgements. The AERIS infrastructure is gratefully acknowledged for maintenance of the CALIPSO database and computer resources for data processing. Financial support. This research has been supported by the  Centre National d'Etudes Spatiales  (grant no. 4500064868/DSO090 ) and the  National Aeronautics and Space Administration (STARSS III , grant no. NNL16AA05C ). Review statement. This paper was edited by  Andrew Sayer  and reviewed by  Bryan A. Baum  and one anonymous referee.
Data availability. The CALIPSO lidar level-2 5 km merged layer data version 4-20 are available at https://doi. org/10. 5067/CALIOP/CALIPSO/LID_L2_05KMMLAY-STANDARD-V4-20 (NASA, 2020a). The CALIPSO Infrared Imaging Radiometer level-2 track data version 4-20 are available at https://doi. org/10. 5067/CALIOP/CALIPSO/CAL_IIR_L2_Track-Standard-V4-20 (NASA, 2020b). The CALIPSO Level 2 merge layer data and the IIR Level 2 track products are also available from the AERIS/ICARE Data and Services Center (http://www. icare. univ-lille. fr, last access: 21 March 2022, AERIS/ICARE, 2022).
Background: The phenotype of an individual can be affected not only by the individual's own genotypes, known as direct genetic effects (DGE), but also by genotypes of interacting partners, indirect genetic effects (IGE). IGE have been detected using polygenic models in multiple species, including laboratory mice and humans. However, the underlying mechanisms remain largely unknown. Genomewide association studies of IGE (igeGWAS) can point to IGE genes, but have not yet been applied to non-familial IGE arising from "peers" and affecting biomedical phenotypes. In addition, the extent to which igeGWAS will identify loci not identified by dgeGWAS remains an open question. Finally, findings from igeGWAS have not been confirmed by experimental manipulation. Results: We leverage a dataset of 170 behavioral, physiological, and morphological phenotypes measured in 1812 genetically heterogeneous laboratory mice to study IGE arising between same-sex, adult, unrelated mice housed in the same cage.
c Experimental design. A list of the 170 phenotypes collected on each mouse is presented in Additional file 3: Table  S2  To address these issues, we leveraged a published dataset of 170 behavioral, physiological, and morphological phenotypes measured in 1812 male and female, genetically heterogeneous mice (Fig. 1c ), which we supplemented with previously unreported cage information (Additional file 1: Table  S1 ). For each phenotype, we investigated the relationship between DGE and IGE, using both polygenic analyses and GWAS. For 17 phenotypes, we fine-mapped IGE loci to identify putative causal genes underlying IGE. Finally, we tested one such gene using a knockout model.
Discussion In this study, we leveraged a published dataset of 170 behavioral, physiological, and morphological phenotypes measured in 1812 genetically heterogeneous mice housed in same-sex groups of three to characterize the relationship between DGE and IGE for the same phenotype and identify individual IGE loci. Using polygenic models, we showed that the genetic correlation ρ between DGE and IGE for the same phenotype is often significantly different from ±1, indicating IGE loci are different from DGE loci for the same phenotype. Consistently, we found that none of the 24 significant IGE loci identified for 17 phenotypes using igeGWAS overlapped with significant DGE loci identified for the same phenotypes using dgeGWAS. We fine-mapped seven significant IGE loci to a single putative causal gene and experimentally validated IGE from one of them, Epha4, on stress-coping strategy and wound healing using a knockout model.
Phenotypes and experimental variables Phenotypes and experimental variables (covariates) for 1934 male and female Crl: CFW(SW)-US_P08 (CFW) mice were retrieved from http://wp. cs. ucl. ac. uk/ outbredmice/. Phenotypes were normalized using the boxcox function (MASS package  [65] ) in R; phenotypes that could not be normalized satisfactorily (transformation parameter lambda outside of -2 to 2 interval) were excluded. Because data for some phenotypes were missing for some mice, the sample size varied. The sample size for each phenotype after all filtering (see below) is indicated in Additional file 3: Table  S2. The subset of covariates used for each phenotype, which always included sex, is indicated in Additional file 3: Table  S2. For those phenotypes where body weight was included as a covariate, we checked that this did not lead to systematically increased (or decreased) estimates of the aggregate contribution of IGE (collider bias).
Cage information Mice were 4 to 7 weeks old when they arrived at the phenotyping facility and were housed in same-sex groups of three mice. They were left undisturbed for 9 to 12 weeks during their time in quarantine and spent 4 weeks together during phenotyping. assignments were not included in the publicly available dataset but were provided by the authors upon request and are now provided in Additional file 1: Table  S1. Cage assignments were recorded at eleven time points throughout the study and showed that a few mice were taken out of their original cages and singly housed, presumably because they were too aggressive. We only included in our analyses mice that had the same two cage mates throughout the experiment. We further excluded a subset of mice based on their genotype-based genetic similarity, as described below.
Identification of significant associations We used a genome-wide permutation strategy to control the FDR for each phenotype, as done by Nicod et al. [36]. This strategy takes into account the specific patterns of linkage disequilibrium present in the sample and identifies significant associations for each phenotype independently of the results for the other phenotypes in dataset. More precisely, for each phenotype and for type of genetic effect (direct and indirect), we performed 100 "permuted GWAS" by permuting the rows of the matrix of social (respectively direct) genotypes and testing each variant at a time using the permuted genotypes together with the un-permuted phenotypes, un-permuted covariates, un-permuted GRM, and un-permuted matrix of direct (respectively social) genotypes (for conditioning)  [40, 41].
Gene expression in the hippocampus of an independent sample of CFW mice Gene expression in the hippocampus of an independent sample of 79 male CFW mice, initially published in Parker et al. [45] , was available from GeneNetwork (http:// genenetwork. org/) (  [69, 70] , (https://pubmed. ncbi. nlm. nih. gov/33472826/)). The data are accessible by selecting Mouse as Species, CFW Outbred GWAS as Group, Hippocampus mRNA as Type, and UCSD CFW Hippocampus (Jan17) RNA-Seq Log2 Zscore as Dataset. To retrieve the genes whose expression is most highly correlated with that of Epha4, we entered "Epha4" in the Get Any field. Following the selection of the Epha4 record (click on ENSMUSG00000026235), we used Calculate Correlations with Sample r as Method, UCSD CFW Hippocampus (Jan17) RNA-Seq Log2 Z-score as Database, and Spearman rank as correlation Type.
Healing from an ear punch Both ears of FVB mice were punched with a 2-mm-diameter ear punch scissor just before the mice were paired with an Epha4 or a Dlgap1 cage mate at weaning. Following the same protocol as in the CFW study  [36] , the ears were collected 2 months later after sacrifice and stored in 10% buffered formalin phosphate until analysis. To measure the area of the hole, each ear was mounted on a histology slide and photos were taken from a fixed distance. Images were analyzed with the ImageJ software  [72]  and the average across the two ears calculated.
Acknowledgements The authors want to thank  Drs. Na Cai ,  Robert W. Davies , and  Richard Mott  for facilitating access to the genotype data, and  Dr. Elena Pasquale  for generously providing mice from the EphA4 knockout line, which was produced thanks to funding from  NIH  grant  NS087070. The authors also want to thank  Dr. Jason Wolf  and anonymous reviewers to their comments, which contributed to improving the manuscript.
Availability of data and materials Genotype and phenotype data from Nicod et al. [36]  and Davies et al. [37]  are available from http://wp. cs. ucl. ac. uk/ outbredmice/. Cage information is provided in Additional file 1: Table  S1. CFW hippocampus data is available in GeneNetwork (https://genenetwork. org) as FAIR data and can be analysed reproducibly using the online GWAS webservice. GeneNetwork contains over 20-years of experimental mouse and rat data. The code used for variance decomposition and GWAS in this study is available from https://github. com/Baud-lab under an Apache 2. 0 license and from zenodo  [73, 74].
Section 2 gives an overview of both volcanoes and some key information on the presented eruptions. In Sect. 3, we introduce the data sets, models and their respective setup. The early phase of the injected Raikoke plume and the global transport of the Raikoke and Ulawun plumes are analyzed in Sect. 4, and the resulting climate impact is estimated in Sect. 5. Finally conclusions are drawn. 2 Raikoke and Ulawun eruptions in 2019.
OMPS The Ozone Mapping Profiler Suite Limb Profiler (OMPS-LP) is onboard the Suomi National Polar-orbiting Partnership satellite that was launched in October 2011. It was originally designed for height-resolved atmospheric ozone observations  (Loughman et al. , 2018; Bhartia and Torres, 2019). Aerosol extinction measurements at 675 nm are provided from 10 to 40 km altitude on a 1 km vertical grid. Three slits separated horizontally by 4. 25 • result in three measured profiles at each point in time separated by 250 km of the tangent points at the Earth's surface. The vertical resolution is ≈ 1. 6 km. Here, we use the aerosol extinction profile measurements from 2017 onwards of the NASA OMPS data product version 1. 5  (Rault and Loughman, 2013). A near-global coverage is produced within 3-4 d.
Tropopause values are included in the data set from the MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) forward processing  (Randles et al. , 2017; Gelaro et al. , 2017). To avoid removing enhanced aerosol layers that are mistakenly identified as clouds, we use the unfiltered OMPS data set. The influence of stratospheric clouds for the interpretation of this transport study about the Australian fire plume is expected to be negligible and not further analyzed. With its high sampling rate, we use the OMPS data set to study the global transport of the respective volcanic plumes in the lower stratosphere.
In the context of other recent events (2017-2020) Figure  8  shows mean sAOD estimations for OMPS, SAGE III/ISS and in situ LOAC observations from France. The mean sAOD from the OMPS and SAGE III/ISS aerosol extinction observations are at 675 and 676 nm, respectively. The dense sampling, reaching high latitudes from OMPS, gives confidence in the representation of the overall AOD evolution (Fig. 8a ). While we present 3 d averages for the OMPS data set, we calculate 30 d averages for SAGE III/ISS to account for the much sparser sampling of SAGE III/ISS. The timing and total value of sAOD enhancements for OMPS and SAGE III/ISS (Fig. 8a  and b ) following the Canadian wildfires in 2017, the Ambae eruptions in 2018 and the Raikoke/Ulawun eruption in the different latitude bands agree very well.
Acknowledgements. The authors are grateful to the providers of LibRadtran and the  National Aeronautics and Space Administration (NASA) ,  SAGE III/ISS  and OMPS teams. The IASI-related activities were supported by  CNES  ( TOSCA IASI  project). The authors are grateful to  Tong Zhu  from  SSAI  and  Talat Khattatov  for technical support. Furthermore, the authors would like to thank  Jim Haywood  and an anonymous reviewer for their suggestions, which improved the quality of the paper. Financial support. This research has been supported by the  ANR (Agence Nationale de La Recherche)  under grants  ANR-17-CE01-0015  ( TTL-Xing ) and  ANR-10-LABX-100-01  ( French Labex VOLTAIRE  managed by the  University of Orleans). Corinna Kloss  was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -409585735.
Data availability. The aerosol extinction data sets from SAGE III-ISS v5. 1 are available at https://eosweb. larc. nasa. gov (last access: 4 November 2020) (NASA EARTHDATA, 2020a) and OMPS v1. 5 at https://daac. gsfc. nasa. gov/ (last access: 4 November 2020) (NASA EARTHDATA, 2020b). The model and simulation data may be requested from the corresponding author: the CLaMS model data (f. ploeger@fz-juelich. de) and the UVSPEC input and output files for the radiative forcing calculations (pasquale. sellitto@lisa. u-pec. fr). Himawari-8 and IASI Level 1c data are provided by the AERIS/ICARE data center (https://en. aeris-data. fr/direct-access-icare/, last access: 4 November 2020) (AERIS, 2020); the ERA5 data are available from the Copernicus Climate Change Service (https://climate. copernicus.
A total of 255 control cases, randomly selected from healthy volunteer cases enrolled in the Biochemistry Department, were matched to those of the BCa patients group according to the age range (64. 28 ± 11. 54), sex, and geographic origin. Epidemiological, clinical and anatomopathological data of BCa patients were collected from the medical records of the urology department and the histological reports from the pathology department of the Charles Nicolle Hospital in Tunis (Table  1 ). Epidemiological data (age, sex, smoking status) and pathological data (grade, stage, histological type, type, and follow-up of treatment) were recorded and made available for the study (Table  1 ). Histological reviews of the tumors were performed con rmed by a trained pathologist in urological oncology. The BCa tumors of the 249 patients were classi ed as 45 Specimens of High-Grade (HG) NMIBC, 119 specimens of Low-Grade (LG) NMIBC, and 66 specimens of MIBC.
Panel Design We have selected a panel of 139 polymorphisms from 97 genes. These SNP were selected according to their eventual implications in poor-prognosis BCa such as those affecting the xenobiotic metabolism, DNA repair, treatment response, and in ammatory reaction. The design of the panel was done, after consulting published data and available databases (dbSNP, UCSC Genome Browser, and GWAS). In a second step, the implication of the SNPs selected in our SNP panel in BCa susceptibility was con rmed by the following tools: Kyoto Encyclopedia of Genes and Genomes (KEGG) available on the website https://www. genome. jp/kegg/pathway. html [18, 19] , Panther classi cation system available on the website ( http://pantherdb. org/tools/gxIdsList. do?list=upload_1&organism=Homo%20sapiens)  [20] , STRING database available on the website https://string-db.
org/ ,AmiGO2  [21] , Reactome Pathway Database  [22]  and GWAS  [23]. In addition, we constructed candidate pathways based on our de ned panel consisting of 97 genes. The pathways were de ned according to the STRING protein-protein interaction networks. A complete list of the studied variants was summarized in Table  2. Pathways and protein-protein interaction, in which the studied genes were implicated, were presented and described in Fig. 2.
Bioinformatics Analyses Primary analyses were done using the Torrent_Suite 5. 6. 0 and included signal processing, base calling, reads alignment to the human genome reference 19 (Genome Reference Consortium GRCh37), quality control of mapping and coverage analysis, and variant calling. Subsequently, a list of detected sequence variants, including SNPs and small insertions/deletions, was imported into (Thermo Fisher Scienti c) for annotation. The GeneRead data analysis work ow runs the GATK Variant Annotator program using the TVC output to populate the INFO eld in the VCF le with the parameters necessary for downstream ltering. The INFO eld of the GATK Uni ed Genotyper output already contains all the necessary parameters. Alignments were visually veri ed with the Integrative Genomics Viewer (IGV) v2.
Abbreviations ACTRT3 Actin Related Protein T3; 3q26. 2 APOBR Apolipoprotein B Receptor ARNT Aryl hydrocarbon receptor nuclear translocator BCa Bladder cancer BER base excision repair BLNK B cell linker COMT Catechol-O-methyltransferase CYP1B1 cytochrome P450 family 1 subfamily B member 1 CYP3A4 Cytochrome P450 3A4 dbSNP database of single nucleotide polymorphisms DNA deoxyribonucleic acid DRC DNA Repair Capacity DSBR double-strand break repair EDTA ethylene diamine tetra-acetic acid ERCC4 Excision Repair Cross-Complementation group 4 GSTM1 Glutathione-s-transferase M1 GWAS Genome-Wide Association Study GSS Glutathione Synthase GSTA4 Glutathione S-transferase Alpha 4 HG.
3. A combination of SnpEff v4. 3, SnpSift v4. 3 were used for annotation and ltration, and PLINK and in-house algorithms (R packages …) were used for analysis. QC ltered variants were then merged, annotated, and ltered using various databases including, ClinVar (http://www. ncbi. nlm. nih. gov/clinvar/), HGMD (http://www. hgmd. cf. ac. uk/ac/index. php), and dbSNP (https://www. ncbi. nlm. nih. gov/snp/).