dataesr/scientific-paragraphs-categorization huggingface dataset:
Prioritization of functional SNPs using annotation tools Analysis of SNP enrichment among ATAC-Seq peaks was performed using GREGOR. 28 The sentinel SNPs from loci associated with p-value < 10 -5 were used as reference for MVPassociated SNPs. We included in the analysis SNPs in high LD (r 2 <0. 7) in European samples from 1000 Genomes. To use homogenous peak sets and mitigate the difference in signal-to-noise ratio between samples, 500bp windows centered on peak summits were used for this analysis. Next, to prioritize the most possible functional SNPs at each of the risk loci, we annotated each SNP with overlapping mitral valve ATAC-Seq peaks (narrowpeak from MCAS2 output + 100bp on each side), presence of overlapping H3K27ac histone mark from heart left ventricle, heart right atrium, ascending aorta or primary fibroblasts (peaks from ENCODE datasets: ENCFF052BXS, ENCFF172RFM, ENCFF208DZK, ENCFF222SST, ENCFF222WPT, ENCFF283CXG, ENCFF332RJQ, ENCFF385NNX, ENCFF401EEA, ENCFF491RWJ, ENCFF617BNF, ENCFF670ZUM, ENCFF686LUV, ENCFF824SPW, ENCFF946SRJ, ENCFF964UAX, and ENCFF984MJS) and RegulomeDB score.
29 ional analyses using GWAS data at IGFBP5/TNS1 and SMG6/SRR loci Using genotype data from our previous study  12  , we performed an updated association analysis on chromosome 2 and chromosome 17 using newly imputed SNPs obtained from the HRC  30  as imputation reference panel through Michigan imputation server  31. We used results from our GWAS meta-analysis as described previously. 12 We used the GCTA-COJO function to perform conditional analyses to identify secondary or causal signals on loci. 32 The COJO analysis used the MVP-France case control study as reference panel to calculate linkage disequilibrium (LD) between SNPs. To avoid collinearity issues, SNPs in high LD (r 2 >0. 9) with the tested SNP were excluded before analyses.
Comparison of chromatin accessibility profiles between mitral valve, heart tissues, and fibroblast cells To determine whether mitral valve ATAC-Seq allows the identification of valve specific regulatory elements, we compared our data to primary cultures of fibroblasts and heart tissue. We determined chromatin accessibility in human dermal fibroblasts (HDF, two samples) and human cardiac fibroblasts (HCF, one sample), and reanalyzed through our pipeline ATAC-Seq experiments on heart left ventricle (Heart LV, two samples) and heart right atrium auricular region (Heart RAAR, one sample) from ENCODE. We obtained more than 130,000 peaks in all three fibroblasts samples, and 20,000 to 40,000 peaks from heart tissue (Figure  2A ). We then annotated the genomic localization of ATAC-Seq peaks obtained in all samples.
Mitral valve specific open chromatin peaks are enriched for MVP associated loci Mitral valve ATAC-Seq data provide potential regulatory elements that may participate to the pathogenesis of MVP. To investigate further this idea, we tested the enrichment for associated SNPs from existing MVP GWAS results among different ATAC-Seq datasets. 12 We used the Genomic Regulatory Elements and GWAS Overlap algorithm (GREGOR)  28  that compares the number of trait-associated SNPs overlapping predefined genomic regions (here, ATAC-Seq peaks) with randomized SNP matched by regions (1Mb window) and frequency. Figure  4A  shows that MVP-associated SNPs (p < 10 -5 ) were significantly enriched (p < 0. 05) in ATAC-Seq peaks from all mitral valve samples whereas significant enrichment was found for only one of the other samples (Heart LV-2).
Identification of potential causal variants at MVP-associated loci Given the enrichment for regulatory elements near genes involved in myxomatous valve biology, we sought to use the mitral valve ATAC-Seq datasets to narrow down a list of potential causal SNPs at MVP-associated loci. We selected associated SNPs (p < 0. 001) from the six loci reaching genome-wide significance in our previous study  12  and filtered for colocalization with mitral valve ATAC-Seq peaks (Table  1 ). Between three and 20 SNPs were identified at each locus, representing a list of potential causal SNPs. We annotated these SNPs using RegulomeDB scores from 1 (most likely to affect TF binding and expression of a target gene) to 6 (least likely), and for the presence of H3K27ac enhancer/promoter marks in heart tissue (left ventricle or right atrium), ascending aorta, and dermal fibroblasts (Table  1 ).
12ndidate SNPs for causality from Table1were used at each locus to interrogate potential eQTL association from GTEx v8 database. For each eGene identified, the strongest association is reported (strongest tissue, SNP eQTL p-value, and effect size). Effect size sign was adjusted to represent the effect of MVP risk allele. P-values are for MVP association reported from the discovery and replication results of each locus. 12Last column indicates whether the TSS of indicated eGenes is located either within 5kb of a potential causal SNP or within 5kb of a fragment that interacts topologically with potential causal SNPs, from our 4C-Seq experiments.
Uncertain Linear Models Resulting from the Nonlinear Battery Model The nonlinear model (  24 ) was linearized around operating points using a numerical method (Matlab linmod command). The Bode diagrams of the resulting set of linear models of the transfer function J sr (s)/I ch (s) are shown in Figure  5. Among this set, one was chosen as a nominal model, with the other linear model thus defining an uncertain linear model. Figure  5  highlights a strong dependence of the frequency response of the transfer function J sr (s)/I ch (s) to the operating point. The resulting gain and phase uncertainty are taken into account in the next section for the design of a fast charging robust controller. Appl. Sci. 2020, 10, x FOR PEER REVIEW 11 of 19.
Uncertain Linear Models Resulting from the Nonlinear Battery Model The nonlinear model (  24 ) was linearized around operating points using a numerical method (Matlab linmod command). The Bode diagrams of the resulting set of linear models of the transfer function Jsr(s)/Ich(s) are shown in Figure  5. Among this set, one was chosen as a nominal model, with the other linear model thus defining an uncertain linear model. Figure  5.
While explaining the fast orbital expansion of Rhea, 2 predicted a similar expansion rate (and smaller tidal Q) for Titan, making the monitoring of Titan's orbit a strong case for testing their model. Contrary to most tidal theories where the tidal Q is constant for all moons, resonance locking predicts the tidal Q for outer moons is much smaller. To measure the migration rates of Saturn's moon, we use two independent methods. In The radiometric data analysis strategy was based on the classical approach used by the Cassini Radio Science Team in the past for gravity science experiments  [12] [13] [14] [15] [16] [17]. Our solution was obtained using JPL's orbit determination program MONTE  18  , using a linearized weighted least squares filter that allowed us to determine corrections to an a-priori dynamical model taking into account all the relevant accelerations that affected the orbit of Titan and the trajectory of the Cassini spacecraft.
Acknowledgments V. L. 's research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. This work has been supported by the  ENCELADE team of the International Space Science Institute (ISSI). Support for this work was provided by the  Italian Space Agency (LGC, MZ, PT, and DM)  through the Agreement 2017-10-H. O in the context of the  NASA/ESA/ASI Cassini/Huygens mission. JF's research is funded in part by a  Rose Hills Innovator Grant. N. C. and C. M. were supported by the  UK Science and Technology Facilities Council  (Grant No. ST/M001202/1 ) and are grateful to them for financial assistance. N. C. thanks the  Scientific Council of the Paris Observatory  for funding. Q. Z. 's research was supported by the  National Natural Science Foundation of China  (Grant No.
, 2004; Chen et al. , 2015; Litt et al. , 2012). As reconstructions are expressed in the same units, this advantage can be further used to produce regional to continental syntheses, extract synoptic-scale climate change patterns and, ultimately, inform past atmospheric dynamics (e. g. Davis et al. , 2003; Simonis et al. , 2012). In particular, numerous broad-scale compilations   (Williams et al. , 2018a) , including its regional constituent databases [the European Pollen Database  (Fyfe et al. , 2009) , the North American Pollen Database  (Grimm, 2000) , and the Latin American Pollen Database  (Flantua et al. , 2015) ], the Indo-Pacific Pollen Database  (Hope, 2018) , the African Pollen Database  (Vincens et al. , 2007)  and the ACER database  (Sánchez Goñi et al. , 2017). (B) Locations where temperature reconstructions have been generated from fossil pollen data.
Since this review is primarily intended for non-experts, we provide high-level descriptions of each approach, and refer readers to original publications for theoretical details. Section 5 provides general guidelines on how to avoid commonly encountered challenges and perform reliable reconstructions. Section 6 provides a list of currently available software packages, scripts and data used to apply the various techniques described. In Section 7, we discuss potential avenues for future methodological developments and propose a list of possible axes of development for the integration of pollen-based quantitative reconstruction techniques in multi-disciplinary research.
, 1973; Imbrie and Kipp, 1971 ) led to the development of the first generation of modern and fossil pollen databases, and the development of spatiallycomprehensive reconstructions of palaeoclimates during the 1970s and 1980s (e. g. Bartlein and Webb, 1985; Huntley and Prentice, 1988 Webb and Clark, 1977). Shortly thereafter, global temperature reconstructions of Holocene terrestrial (pollen) and oceanic (foraminifera) surfaces were used to evaluate the first generation of climate models  (COHMAP Members, 1988; Wright, 1993). During the third phase (~1990 -~2010) of quantitative palaeoclimatology, site-specific reconstructions based on fossil pollen became increasingly prevalent, providing a wealth of new information regarding the evolution of past climates. This extensive use of fossil pollen has fostered 1) the development and refinement of many of the reconstruction methods in use today (see Section 4) and 2) the continuous expansion of their applicability, which have eventually led to the global synthesis of  Bartlein et al.
, 2014; Izumi and Bartlein, 2016; Parnell et al. , 2008; Salonen et al. , 2019). The emerging open-data era, illustrated by the growth of online databases and data repositories (e. g. Williams et al. , 2018b)  and the advance of informatics and data standards (e. g. Khider et al. , 2019; McKay and Emile-geay, 2016) , has also influenced how pollen data are used to reconstruct past climates. As such, many studies have recently addressed fundamental palaeoclimatic questions by making use of extensive networks of fossil pollen data, including  Marsicek et al. (2018)  addressing the Holocene temperature conundrum  (Liu et al. , 2014) ,  Routson et al. (2019)  analysing the effects of latitudinal temperature gradients on moisture transport in the Northern Hemisphere, and  Kaufman et al. (2020)  reconstructing mean global temperature trends during the Holocene (pollen-derived records contributing to almost 40% of records in the analysed data collection).
g. Birks et al. , 2010; Imbrie and Kipp, 1971 ): 1. The taxa observed in both modern and past samples are systematically related to the climate in which they live. This assumption assumes approximate dynamic equilibrium between the observed pollen assemblage and climate. 2. The climate variables to be reconstructed are important determinants, or systematically related to important determinants of the vegetation and the associated pollen. This assumption assumes that the relationship has remained stable throughout the record (i. e. the uniformitarian principle). 3. Unstudied environmental variables have had a minor influence on the fossil data over the studied period of time and at the spatial scale of the pollen record, or their relationship with the variable of  interest remained the same as in the calibration dataset.
4. The taxa in the modern calibration dataset are the same biological entities as in the fossil data, and their ecological responses have not changed over the studied period of time (uniformitarian principle). 5. The specified mathematical model has sufficient flexibility to represent the complexity of the ecological responses to the climate variables of interest. These assumptions have long been recognised, but the difficulty of testing them has sometimes turned them into what  Jackson (2012)  referred to as unknown knowns, namely assumptions that are often taken for granted without even knowing the nature of how these factors interact. Juggins (2013)  discussed how the violation of assumptions 2 and 3 could lead to spurious reconstructions. We thus re-emphasise the need to proceed with suitable care when undertaking pollen-based climate reconstructions.
The total number of pollen taxa used in a reconstruction is most often a trade-off between using too few and too many taxa. Some simple rules can guide the selection of taxa, including: ▪ As terrestrial and aquatic taxa can be influenced by distinctly different sets of environmental variables, it is recommended to exclude all non-terrestrial pollen taxa and to rescale the sum of terrestrial plant taxa to 100% before reconstructing climate. ▪ All the taxa observed in the fossil record should be present in the modern dataset and in significant proportions (e. g. presence in at least 20-25 samples), as a large number of modern calibration samples of pollen or plant occurrences are required to establish a taxon's response to climate. ▪ Cosmopolitan taxa or pollen taxa identified at a low taxonomic resolution may be problematic because their signal-to-noise ratio is often low, but also because estimating a reliable pollen-climate relationship may be complex (e.
g. Guisan and Zimmermann, 2000). These bioclimatic variables are strongly correlated with monthly or seasonal mean values commonly used in climatology (e. g. winter temperature with number of frost days). Climate variables are thus commonly used as proxies for these more specific variables because they have the advantage of providing a common scale with other biotic and abiotic palaeoclimatic sensors (e. g. isotopes, biomarkers), hence improving comparability among palaeoclimate datasets, instrumental data, and climate model simulations. The variable selection should be as independent as possible from the researcher's expectations. In other words, it is not because a researcher is primarily interested in studying temperature change that temperature can or should be reconstructed from all pollen records or for all time periods.
2 for details on how to calculate these) can identify whether a variable is a poor predictor of the pollen data. However, such analyses, alone are insufficient to affirm that a certain variable can be reconstructed, since good performance indices can also be obtained with potentially confounding, covarying variables (i. e. variables that correlate with the studied taxa in the modern settings but have no causal relationship with them). In such a situation, a confounding variable correlates with the studied taxa only because it is correlated with another variable, which ultimately drives the observed distributions of taxa. If the relationship between the driving and confounding variables changes over time (breaking Assumptions 1 and 3), reconstructions may become biased. For example, January and July temperature are usually positively correlated in spatial datasets today and, as such, both can appear as significant drivers of vegetation.
However, in many regions they are likely to have been negatively correlated during the Holocene due to orbital forcing. In this trivial example, the positive modern correlation between the two temperature variables in calibration datasets will potentially induce a positive correlation between reconstructed temperatures based on a pollen record, whereas in reality these may have been negatively correlated. Atmospheric CO 2 imposes an additional, non-climatic control on vegetation dynamics and structure that must be accounted for when interpreting reconstructions (e. g. Cowling and Sykes, 1999). Atmospheric CO 2 concentration influences plant water-use efficiency, so that lower CO 2 concentrations, such as during the LGM (e. g. Monnin et al. , 2001) , induce plants to consume more water to maintain the same rate of photosynthesis (e.
Probability Density Functions (pdfs) The philosophy of the MCR approach was integrated into a probabilistic framework by  Kühl et al. (2002)  and  Schölzel, 2005) , who developed the first climate reconstruction statistical technique employing probability density functions (pdfs). In the pdf techniques, the pollen-climate relationships are described by conditional probability density functions that represent the dependence of the different taxa on climate  (Kühl et al. , 2002). A pdf describes the probability of observing any climate value considering the presence of one taxon, with the constraint that all probabilities (i. e. the area under the curve) sum to one. In contrast to MCR that focuses on the estimation of the minimum and maximum climate values only, the pdf approach uses all the information contained in the species' distributions to estimate entire climate responses and is, therefore, significantly less sensitive to the structure and range limit of the modern calibration dataset than MCR.
5 ). Two pdf techniques have been developed for pollen data: the approach by  Kühl et al. (2002)  and the CREST (Climate REconstruction SofTware) technique  (Chevalier et al. , 2014). Based on similar fundamentals, the implementation of these two techniques differ in some Assumptions that reflect the characteristics of the environments for which they were developed (Europe for  Kühl et al. (2002)  and the southern African subtropics for  Chevalier et al. (2014) ). As  Iversen (1944)  pointed out that for some plants, higher winter temperatures can compensate for lower summer temperatures and vice versa (see also  Jeffree and Jeffree, 1994) ,  Kühl et al. (2002)  argued that pdfs should preferably be bi-or tri-dimensional in order to account for the covariance of climate variables. However, fitting multidimensional pdfs for all taxa assumes that they are sensitive to all the variables to be reconstructed.
Despite known limitations, presence-only data have been used with pdf techniques to reconstruct modern environmental parameters successfully  (Chevalier et al. , 2014)  and to produce reconstructions of past conditions consistent with independent proxy records (e. g. Chevalier et al. , 2020; Cordova et al. , 2017; Litt et al. , 2009; Neumann et al. , 2007). As they have not been employed in many regions, more applications are necessary to fully assess their strengths and weaknesses. Currently, it is suggested that the definition of the pdfs is robust provided that the climate space of the plants' ranges is fully sampled  (Chevalier et al. , 2014). The recent release of a global calibration dataset enabling using pdf techniques from anywhere vegetation currently grows  (Chevalier, 2019)  could promote testing this hypothesis across various environmental gradients.
Partial Least Square (PLS) regression Linear regression approaches are prone to violations of their underlying statistical assumptions, such as the linearity of the response of the taxa and the independence of the predictor variables (pollen taxon abundances show a high degree of multicollinearity in pollen datasets;  Birks, 1995). Partial Least Squares regression (PLS) -a dimensionality reduction approach  (Höskuldsson, 1988; Wold et al. , 2001 ) -has been proposed as an alternative to the classical linear regression-based approaches to account for the covariance of the predictors (i. e. many pollen taxa respond similarly to climate variability;  Birks, 1998 , Birks, 1995). The covariance is removed by combining the predictors together to estimate composite components that are independent of each other (i.
e. their correlation is zero) and maximise the covariance with the response variable to be reconstructed. The components are then linearly combined to model the relationship between vegetation change and climate. While adding many components improves the apparent statistical performance of the transfer function, it can also lead to an overfitted model that has poor predictive power on the fossil data, despite high performance statistics with the training set(s)  (Birks, 1998; Naes, 2002). The optimal number of components can be determined by cross validation (see Section 5. 2). Usually, between one and three components are used in the transfer function. As with classical linear regression, the PLS technique is most suitable for calibration datasets covering short environmental gradients where linear responses can be expected.
, 1993; ter Braak and Looman, 1986). As species tend to occur with maximum abundance near the climatic conditions they are most adapted to or competitive in, wA estimates such climate optima from a calibration dataset by calculating the average climatological conditions a species occurs in weighted by the abundance of this species (i. e. by calculating a weighted average of the climate values where the taxon is observed; Fig. 6 ). To reconstruct climate from fossil data, the optima of pollen types in a given fossil pollen assemblage are weighted by their percentage and averaged, assuming that the most abundant types were closest to their optimal climatic conditions at the time the pollen assemblage was deposited  (Birks, 1998; Brewer et al. , 2007a; ter Braak and Looman, 1986 ). An extension of WA weights the relevance of individual taxa based on the width of their distribution, with widely distributed taxa receiving lower weight than narrowly dispersed ones (ter Braak and van Dame, 1989), thus assuming that taxa with narrower distributions are more reliable indicators.
2), as selecting too many components will result in a model with poor predictive power. The final model should represent the minimal adequate model (i. e. having fewest components possible), a low root-mean-square error in cross-validation and low maximum bias. In practise, WA-PLS models based on two or three components perform best, as their inclusion still usually represents an improvement from a one-component WA-PLS model, while avoiding the risk of overfitting. Additionally, WA and WA-PLS provide robust solutions to account for many other intrinsic characteristics of palaeoecological datasets, such as the high number of taxa, many zero values (i. e. absences), data expressed as percentages and covariance among taxa (e. g. Birks, 1998; ter Braak and Juggins, 1993). WA-PLS has been widely used with pollen to reconstruct late Pleistocene and Holocene precipitation in China  (Chen et al.
The Modern Analogue Technique (MAT) MAT was first proposed in palaeoclimatological studies by  Hutson (1978)  for marine data and by  Overpeck et al. (1985)  and  Guiot and Pons (1986)  for pollen data. MAT is a specific version of a broader statistical technique called 'k-nearest-neighbours' (k-NN;  Cover and Hart, 1967)  adapted to palaeoecological datasets. The approach is based on the measure of the degree of similarity ('degree of analogy') between fossil pollen assemblages and elements of a collection of modern assemblages  (Jackson and Williams, 2004). The climate values associated with the most similar pollen assemblage(s) (i. e. the closest modern analogue(s)) are averaged, possibly using a weight defined by the measure of similarity, to estimate the climate related to fossil assemblages (Fig.