dataesr/scientific-paragraphs-categorization huggingface dataset:
We did not choose the class of longevity initially, but these classes of longevity appear to be clinically relevant  3. Categorial data were examined as percentage ± SD and compared by χ 2 test or Fisher's exact test as appropriate. All variables used in the univariate analysis were entered into the multivariate models. A p value of <0. 05 was considered statistically significant. A multivariate logistic regression model was used to identify the independent predictors of structural bioprosthesis degeneration. Only bioprosthesis patients with known ABO blood group were included in the multivariate analyses. The analyses were conducted using SPSS-version18 or the statistical software system SEM (Silex Development, Mirefleurs, France). Downloaded from http://ahajournals. org by on April 13, 2022.
Most of the patients with bioprostheses implanted at 60 years or before will need to be re-operated after a period of 25 years for degenerative bioprosthesis  4. SVD data collected was available for 29 years so that we assume we have an unbiased full spectrum of bioprosthesis degenerative type anomalies for several periods of longevity. We have a group of 500 porcine bioprostheses that were implanted before the patients were 60 years old and for which most variables, especially the type of alteration, were known. This group thus constitutes our study cohort. However, in this cohort, information on the ABO blood group was missing for 79 bioprosthesis patients. During the period 1987-1998, we also explanted 82 bovine bioprostheses with intermediate and short longevity and 62 BP implanted when patients were < 60yrs.
The Bayesian skyline plot did not show a population expansion based on only our samples from the Black Sea. However, a population expansion was detected for samples from the Adriatic Sea (Fig. 3A ). When considering additional sequences available on GenBank, an expansion of population can also be distinguished for the Black Sea (Fig. 3B ) and the signal of expansion in the Adriatic Sea becomes stronger (steeper slope in Fig. 3B ). According to the molecular clock used, the age of expansion is datable at around 10,000-15,000 years ago, i. e. after the Last Glacial Maximum, for the Adriatic Sea population and around 5000-10,000 years ago for the Black Sea population.
Microsatellite data. A total of 786 specimens were analyzed at 11 loci. Heterozygosity values (Ho) ranged from 0. 356 to 0. 567 and mean allele numbers ranged from 5. 09 to 11. 55 (Table  1 ). Fis values were high and significant for all populations sampled, ranging from 12 to 33% (Table  1 ), and this signal was not due to a single or few loci (data not shown). All pairwise Fst values were significantly different from zero, except those comparing Ancona and Venice in the northern Adriatic Sea, and Costinesti2 and CostinestiB in the Black Sea (Table  3 ). Among basins genetic differentiation. The PCoA analysis showed the grouping of samples from the Western Mediterranean (Fusaro and Tunis) with samples from the Aegean Sea (Paralia Katerini) and samples from the Adriatic Sea (Fig. 4 ). Samples from the Black Sea clustered together.
The Structure analysis on the total dataset revealed K = 2 groups as the most likely partitioning (Fig. S1 ). Using the sampling localities as prior (ocean basin) did not change the results (data not shown). Noticeably, the Western Mediterranean and the Aegean Sea samples clustered with samples from the Adriatic Sea and samples from the Black Sea form another cluster (Fig. 5 ). The sample from Sile shows a mixed genetic make-up, with some individuals presenting a proportion of their genome with an Adriatic Sea affinity. Within basins genetic differentiation. For T. neritea in the Adriatic Sea, all the Fst values were significant, except for the Venice-Ancona pairwise comparison. The Structure plot revealed the most likely partitioning to be of K = 2 groups (Fig. 5B ) corresponding to a first group composed of the northern localities (Ancona + Venice + Nin) together with Otranto in the south; and a second group with the southern localities (Karaburun, Porto Cesareo, Torre Guaceto, Amvrakikos).
Discussion As expected for species characterized by a direct cycle without a pelagic larval stage, we observed genetically divergent populations even at local scales within small seas such as the Adriatic Sea or the Black Sea. The close investigation of this species within its native range demonstrates complex diversity patterns, genetic structure and historical changes that improve our understanding of patterns in the current and future introduced areas. The mitochondrial dataset confirms a strong geographic clustering of haplotypes (Fig. 2 ), as already noted in  46, 54, 55. Five mitochondrial clades correspond to marine ecoregions as defined in 56 : (i) South European Atlantic shelf and Western Mediterranean, (ii) Adriatic Sea, (iii) Aegean Sea and (iv) the Black Sea and (v) the 5th one is composed of specimens from both the Adriatic Sea and the Western Mediterranean Sea (Gulf of Lion).
Likewise, the high private allele number at the Aegean location suggests a refugial area. Therefore, our dataset seems consistent with the hypothesis that past climate changes impacted the genetic structure of T. neritea. Furthermore, the Bayesian skyline plots revealed population expansions in both seas when enough sequences are available (Fig. 3 ). Interestingly, the signal of population expansion is detectable even in the face of the strong genetic structure of T. neritea, a feature known to potentially hinder the detection of demographic expansion  60. This expansion is dated from just after the Last Glacial Maximum for the Adriatic Sea population, as also seen for other marine invertebrates and fish in the region  53, 61  including other nassariid gastropod species  62. In the specific case of the Black Sea, this basin achieved its current marine status only ca.
e. complete introgression), a phenomenon known in many organisms  [90] [91] [92]  and mitochondrial introgression has been reported for mollusks  93, 94. Alternatively, if only male specimens were introduced, the nuclear dataset would be of foreign origin while the mitochondrial marker would remain from the geographic location sampled. However, no data on sex ratio is available for T. neritea to support this second hypothesis. Further study is warranted to verify if this population shows any effects of admixture (i. e. mixing of specimens from different genetic clusters), such as hybrid vigor. Rius & Darling  95  recently highlighted the unknown evolutionary trajectory for such admixed populations. Human activities are responsible for the translocation of vast amounts of organisms, altering natural patterns of dispersal and gene flow.
Molecular analyses. Mitochondrial sequences. A portion of the Cytochrome oxidase subunit 1 (COI) was amplified for a subset of 128 specimens including all locations, using the specific primers Cy2 5′-GTT AAA ATT TCG ATC TGT TA-3′ and CyR 5′-GGA TTA GTT GGT ACAGC-3′  45. PCR mixture and cycling parameters were as given in this publication. The 42 haplotypes available from GenBank  [45] [46] [47] 55  together with their frequencies were added to this newly generated dataset in order to place our samples in a broader context. Not bly, a group of these haplotypes lies in the middle of the haplotype network, but have been only recorded in the invaded area and their source remains unknown  54. Microsatellite markers. A set of 14 microsatellite markers were screened for this study, 7 markers are from Simon-Bouhet et al.
A total of 10,000,000 generations sampled every 100th generation were run. As no mutation rate is calibrated for Tritia neritea, a mean mutation rate ranging from 0. 012 to 0. 016 (substitutions per site per My), used for the COI gene in Protostomia  103  and in limpets  104  was applied. Microsatellite markers. The presence of null alleles, scoring errors or large allele drop-out were verified using Micro-checker  105. As null alleles are common in mollusks  106  , we decided to discard the three microsatellite markers that presented an estimated frequency of null alleles above 0. 10. Further analyses were run on the dataset with 11 loci. Summary statistics were computed in Genetix v. 4. 05. 02  107  : the mean number of alleles (A), the expected (He) and observed (Ho) heterozygosities, the inbreeding coefficient (F is ) and the fixation index (F st ).
The correction for multiple comparisons of Benjamini & Hochberg 108 was applied to the F st p values. A Principal Coordinate Analysis (PCoA) was computed in GenAlEx v6. 5  109. The most likely number of clusters present in our dataset was estimated using Structure v2. 3  110. This analysis was run both with and without prior on the location. After several first runs, the parameters were set as follows: a burn-in period of 100,000 iterations followed by 500,000 recorded iterations for K = 1 to K = 8 clusters and 15 iterations per K values. The most probable number of clusters present in this dataset was determined using the Evanno's ΔK approach  111  using Structure Harvester online  112. Finally, a pattern of Isolation-by-distance was tested by a Mantel test in Genetix using the genetic distance [F st /(1 -F st )]  113  and the log coastal geographical distance (in km) between each pair of locali-ties within basins.
Significance was obtained using a random permutation procedure implemented in Genetix (5,000 permutations). Finally, a parentage analysis was run using the software Colony  114. Even if the number of loci is low for this kind of analyses  84  , and given that heterozygote deficiency is often detected in mollusk population genetic analyses  71  , we wanted to verify any sign of family structure in our dataset. This relatedness analysis was performed on the whole dataset including the 786 multilocus genotypes and with the following input parameters: a mating system with female and male polygamy, with possible inbreeding and without clones. The method was composed of 3 runs set as 'long' , with a full likelihood method and a likelihood precision that was set to 'high'.
Acknowledgements This project was funded by the  European FP7   CoCoNet  project (Ocean. 2011-4,grant agreement # 287844 ) and we are grateful to the whole CoCoNET consortium. We are grateful to the following people for their critical help with logistics and field work '  Antheus srl (Lecce, Italy )';  S Bevilacqua ,  G Guarnieri ,  S Fraschetti  and  T Terlizzi  ( University of Salento, Italy );  L Angeletti  and  M Sigovini  ( ISMAR, Italy );  D Shamrey  ( IBSS, Sevastopol );  A Anastasopoulou ,  MA Pancucci-Papadopoulou  and  S Reizopoulou  ( HCMR, Greece ) and  E Hajdëri  ( Catholic University 'Our Lady of Good Counsel' , Tirana ). Thank you to  J Almany  for English corrections. This is ISMAR-CNR scientific contribution  n1987. E Boissin  was supported by a  European Marie Curie postdoctoral fellowship   MC-CIG-618480.
5-7 km/pixel. We cannot rule out the existence of thinner layers that cannot be resolved at the resolutions available at these latitudes. At other latitudes, the layer multiplicity and regular vertical distribution is not present. For example, from midlatitudes to the equator, the number of layers reduces to 1-3 even at high resolution of 1-2 km pixel -1. A general study of the haze layers we have observed at Saturn's limb along the whole Cassini mission will be presented elsewhere. Radiative transfer modeling. The properties of the aerosol and haze layers were studied using two datasets. A first analysis was performed on multiwavelength images taken on 29-30 June 2015 with the Hubble Space Telescope (HST), 15 days after the Cassini limb images. The HST images  17  were obtained under a phase angle α = 3.
Methods Cassini images. The Cassini ISS images here analyzed are public data retrieved from the Atmospheres node of the Planetary Data System (PDS) NASA. Images are available from the following filters: UV1, UV2, UV3, BL1, RED, MT1, MT2, MT3, CB1, CB2, and CB3 (see details in ref. 14  and Supplementary Table  1 ). The maximum resolution achieved was in the range of 2-6 km/pixel, but images with ultraviolet filters were binned (UV1 ×4, UV2-UV3 ×2). During the observations, the phase angle varied from 29. 1°to 30. 5°(corresponding to a scattering angle θ ∼ 150°, see Supplementary Fig. 2 ). The images were calibrated with the CISSCAL software  43  and navigated to determine the limb location and obtain reflectivity scans across the limb using the software PLIA  44. HST images. Images used in this work are the same as in ref.
17  , where all relevant information is detailed. The following filters were available: F225W, F336W, F410M, F502N, F547M, F689M, FQ727N, FQ750N, FQ889N, and FQ937N (details given in Supplementary Table  1 ). All HST images were calibrated in absolute reflectivity as explained in ref. 45. Image navigation and reflectivity scans were obtained using the software LAIA  46. Reflectivity was evaluated at 35 points at the latitude of interest from -80°to +80°from the central meridian, providing coverage of all the available illumination and viewing geometries within a planeparallel approximation. Radiative transfer. The radiative transfer analysis used two different codes: one for the spherical geometry case  21  and the other, the NEMESIS code, for nadir case  47. For the interpretation of the limb-viewing observations, we used a Monte Carlo model  21  that solves the multiple-scattering problem in a spherical-shell atmosphere.
5 for λ ≤ 450 nm, and 0. 9× the single-scattering albedos proposed for Titan's stratospheric particles for λ > 450 nm  22. Other choices in how the haze optical properties are described in the radiative transfer model will have an impact on the inferred number densities. We consider our estimated values to be valid first-order descriptions that could eventually be refined by, for instance, combining observations at other phase angles. The NEMESIS code uses an optimal estimator scheme to find the most likely values of atmospheric parameters starting from a priori assumption. Our a priori assumption for this work was inherited from previous studies at neighboring latitudes  49  , where all the details in the initialization of the model atmosphere can be found. Further details on the goodness of fit and retrieved parameters from the nadir observations can be found in the companion Supplementary Table  2  and Supplementary Figs.
Acknowledgements This work has been supported by the  Spanish  project  AYA2015-65041-P (MINECO/ FEDER ,  UE ) and  Grupos Gobierno Vasco   IT1366-19. We acknowledge the  Cassini ISS team  for obtaining the limb images. This work used data acquired from the  NASA/ESA HST Space Telescope , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-26555. We acknowledge the three orbits assigned by the Director Discretionary time from HST for this research ( GO/DD Program 14064, PI A. Sánchez-Lavega ).
Code availability All radiative transfer calculations under spherical geometry were conducted with a novel preconditioned backward Monte Carlo (PBMC) model [AGM2015]. A plane-parallel version of the model is freely available through https://doi. org/10. 1051/0004-6361/ 201424042. This version contains instructions to run the model, test cases, and model outputs that can be compared with calculations tabulated in ref. [AGM2015]. In the analysis of the Mars plume, we used a version of the PBMC suitable for spherical-shell atmospheres. The spherical-shell model is available on request from A. G. M. (tonhingm@gmail. com). The radiative transfer code NEMESIS (http://users. ox. ac. uk/ ~atmp0035/nemesis. html) is available upon request from Patrick Irwin (patrick. irwin@physics. ox. ac. uk). The navigation software LAIA is available at http://www.
Exposure of cells or organisms to chemicals can trigger a series of effects at the regulatory pathway level, which involve changes of levels, interactions, and feedback loops of biomolecules of different types. A single-omics technique, e. g. , transcriptomics, will detect biomolecules of one type and thus can only capture changes in a small subset of the biological cascade. Therefore, although applying single-omics analyses can lead to the identification of biomarkers for certain exposures, they cannot provide a systemic understanding of toxicity pathways or adverse outcome pathways. Integration of multiple omics data sets promises a substantial improvement in detecting this pathway response to a toxicant, by an increase of information as such and especially by a systemic understanding. Here, we report the findings of a thorough evaluation of the prospects and challenges of multi-omics data integration in toxicological research.
We review the availability of such data, discuss options for experimental design, evaluate methods for integration and analysis of multi-omics data, discuss best practices, and identify knowledge gaps. Re-analyzing published data, we demonstrate that multi-omics data integration can considerably improve the confidence in detecting a pathway response. Finally, we argue that more data need to be generated from studies with a multi-omics-focused design, to define which omics layers contribute most to the identification of a pathway response to a toxicant.
Electronic supplementary material The online version of this article (https ://doi. org/10. 1007/s0020 4-020-02656 -y) contains supplementary material, which is available to authorized users. 1 3 reproducibility of omics data acquisition and data analysis, and to define best practices  (Buesen et al. 2017). A single-omics technique, e. g. , transcriptomics, will detect biomolecules of one type, also called one layer, and thus captures changes only for a small subset of the components of a particular pathway. Therefore, applying singleomics analyses in response to a toxicant in a non-continuous design led to the identification of biomarkers for certain exposures but not to a systemic understanding of toxicity pathways or adverse outcome pathways (AOPs) in the past. A necessity to evaluate the integration of different omics layers for defining AOPs has, therefore, also been identified at the ECETOC workshop  (Buesen et al.
2017). Consequently, we hypothesize that a substantial improvement in detecting the pathway response to a toxicant can be achieved by using multi-omics data in a time-and concentration-resolved design [see also  Escher et al. (2017) ]. The CEFIC LRI-funded project "XomeTox -evaluating multi-omics integration for assessing rodent thyroid toxicity" investigates the utility of multi-omics data integration for applications in toxicology and develops best practices aiming at regulatory application. 1 Here, we report the findings of the first phase of XomeTox, which has evaluated the current status of multi-omics data in toxicological research, the availability of such data, of repositories for exchanging these data, and of methods for integration and analysis of multi-omics data to derive where possible best practices and identify knowledge gaps.
2009). RNA-seq is Fig. 1  a Sketch of a regulatory pathway illustrating the diversity of biomolecules involved. The flow of information as a response to an event is not confined to a single-omics layer. Response times vary considerably within and between omics layers as indicated by the clock symbols (details, see Fig. 2a ). b Subset of the components of a pathway that are detected by a specific omics approach and number of data points returned during measurements for (i) phosphoproteomics, (ii) transcriptomics, (iii) proteomics and (iv) metabolomics ◂ 1 http://cefic -lri. org/proje cts/c5-xomet ox-evalu ating -multi -omics -integ ratio n-for-asses sing-roden t-thyro id-toxic ity/. 1 3 per se un-targeted, but can be run in targeted mode to reduce costs and increase throughput. Compared to the other omics layers discussed in this section, transcriptomics stands out by its comprehensiveness in terms of coverage, i.
Prior to the use of LC-MS/MS, proteomics frequently relied on 2D-gel electrophoresis, which separated proteins by mass and pI, subsequently detected differentially expressed spots using the DIGE method and identified these proteins using MS  (Unlü et al. 1997). However, apart from the high manual effort and limited throughput, DIGE-based proteomics data sets are of limited use for multi-omics approaches, as quantitative information is only collected for strongly differentially expressed proteins and missing for the larger part of the proteome, which hampers integration with the other omics layers. Metabolomics. The metabolome is different to the other omes discussed here, as it is a collection of chemically highly heterogeneous molecules. The metabolome is typically defined as the complete complement of all small molecule metabolites ( < 1500Da ) found in a specific cell, organ or organism  (Wishart 2007).
Thanks to initiatives like the Human Metabolome Project, the endogenous metabolome of human and animal model organisms has been mapped to a large extent. However, entries in databases for food-or environment-derived metabolites detected, e. g. , in human serum are continuously growing, depending on the ever increasing sensitivity of mass spectrometry and improvements in databases. Metabolome measurements rely either on nuclear magnetic resonance (NMR)-based detection or on gas or liquid chromatography hyphenated to MS. NMRbased approaches have the advantage of the inherent capacity of quantification and constitute the gold standard for the structural elucidation of unknown metabolites. MS-based approaches outperform NMR in terms of sensitivity and can be run in an untargeted or targeted fashion.
For addressing the question in focus of this review, i. e. , to facilitate the detection of pathway responses to chemical exposure, omics layers should be chosen to optimally interrogate the suspected pathways of interest, in case these are known. A tool like MOD-Finder might be used to assemble prior knowledge for a chemical of interest (see section "Multi-omics data for toxicological research questions"). The XomeTox model problem of thyroid toxicity calls for an investigation using a multi-omics approach as the complete AOP can only be addressed at different omics levels: thyroid hormones are detected by metabolomics, while liver enzyme levels are most directly assessed by proteomics. Tumorigenesis in the thyroid or neurodevelopmental effects are potentially indicated by changes on the transcriptome level and manifest in altered protein activity level.
Important switches in many pathways are determined by phosphoproteomics. Finally, non-coding RNAs, which have been shown to be important regulators in tumorigenesis  [e. g, Boll et al. (2013) ] can only be detected by transcriptomics, but require integration with other omics data sets for their functional annotation. In case no prior information is available, we propose to include the four layers discussed above and to consider additionally including epigenomics if the study aims at transgenerational or long-term effects. Transcriptomics using long RNA-seq and LC-MS/MS based proteomics may convey similar information on the overall pathway response. However, transcriptomics includes information on regulatory RNAs and is much more comprehensive than proteomics. Proteomics on the other hand is much closer to the phenotype and subsumes several regulatory principles that lead to changes on the protein level, without remarkable changes on the transcriptome level.