dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on the research conducted in the ESA-CCI programme, C3S provides (through the CDS) climate data records of land cover at yearly frequency, as well as a close-to-real-time global map of LAI with 10 to 20 d latency. Moreover, recent studies have identified errors in the diurnal cycle of land surface temperature over the Iberian Peninsula associated with the current land cover used in CHTESSEL and 1 h model time steps  (Johannsen et al. , 2019; Nogueira et al. , 2020). Therefore, the revision of the land cover and LAI to a new database as well as the introduction of their interannual variability is expected to provide more realistic land conditions as input to the surface scheme. There are also ongoing efforts to revise the vertical discretization of the vegetation roots and soil layers distribution (e.
g. Mueller-Quintino et al. , 2016; Stevens et al. , 2020) , which could complement the land cover and vegetation updates. Finally, the coupling of the offline simulations to an offline data assimilation system is a promising approach. The advantages are multiple, in particular the assimilation of lo- cal datasets (precipitation radar observations, local soil moisture networks, etc. ), permitting small adjustments of land state estimates. Undoubtedly, this will go accompanied by an increase in computational cost. Ongoing efforts to improve the parallelization of the land model will allow global highresolution simulations for several decades to be performed at affordable computational costs. All the components discussed above are currently under research, but they are not the only ones. ECMWF is currently working with a flexible, modular system called ECLand  (Boussetta et al.
Modular response analysis (MRA) is a widely used inference technique developed to uncover directions and strengths of connections in molecular networks under a steady-state condition by means of perturbation experiments. We devised several extensions of this methodology to search genomic data for new associations with a biological network inferred by MRA, to improve the predictive accuracy of MRA-inferred networks, and to estimate confidence intervals of MRA parameters from datasets with low numbers of replicates. The classical MRA computations and their extensions were implemented in a freely available R package called aiMeRA (https:// github. com/ bioin fo-ircm/ aiMeRA/). We illustrated the application of our package by assessing the crosstalk between estrogen and retinoic acid receptors, two nuclear receptors implicated in several hormone-driven cancers, such as breast cancer.
Based on new data generated for this study, our analysis revealed potential cross-inhibition mediated by the shared corepressors NRIP1 and LCoR. We designed aiMeRA for non-specialists and to allow biologists to perform their own analyses. Modular response analysis (MRA) was introduced to infer the directions and strengths of connections between components of a biological system in a steady-state  1. This approach can be applied to components at different levels of detail, e. g. , individual genes or subsystems, such as pathways or processes. MRA is based on the perturbation of individual components, the so-called modules. Various developments of MRA and related methods were recently reviewed  2. In this report, we applied and extended MRA methodology to explore the crosstalk between two nuclear receptors (NRs).
This resulted in the implementation of a new R 3 package called "aiMeRA". Estrogen receptors (ERs) belong to the NR superfamily, which function as transcription factors, and are activated upon ligand binding. Both ER isoforms (ERα and ERβ) are involved in the control of cell proliferation and exhibit essential functions in tissue development and homeostasis, particularly in organs related to reproduction  4. ERα overexpression is frequently observed in breast, ovarian, endometrial, and other hormone-driven tumors. The transcriptional activity of ERs is modulated by several coregulatory complexes, including coactivators and corepressors  4. In the presence of estrogens or any agonist ligand, ERs interact preferentially with coactivators, or with a specific subclass of corepressors, including the nuclear receptor-interacting protein 1 (NRIP1, often named RIP140) and the ligand-dependent corepressor (LCoR).
The first objective of this study was hence to explore the ER-RAR crosstalk at the transcriptional level by characterizing the ER-RAR-NRIP1-LCoR network by means of transcript abundance measurements and MRA. We considered a steady-state condition in a BC (MCF7-derived) cell line that would model BC cells under hormonal stimulation. We conducted perturbation experiments to generate quantitative PCR (qPCR) and mRNA sequencing (RNA-seq) data. Given the nature of ER and RAR, i. e. , transcription factors, and the general ability of MRA to perform predictions  16  , we introduced two extensions of MRA in a second part of this study. We exploited the RNA-seq data and tested whether the ER-NRIP1-LCoR network inferred by MRA could be used to search for novel genes under strong ER transcriptional control.
Then, we used the same ER-NRIP1-LCoR network to predict the expression levels of E2-regulated and ER target genes that were not perturbed during the construction of the MRA-inferred network. These variations from classical MRA inference, but also a new procedure to estimate confidence intervals (CIs) around MRA parameter estimates from a very low number of replicates, are available in the R package developed in this study. Based on the application of multivariate calculus, we also present a straightforward mathematical derivation of MRA. To put our work in perspective, other authors have extended the classical MRA approach, e. g. , by introducing Bayesian variable selection to better infer pathway topologies  17  , by pruning edges for a similar purpose  16  , or by dealing with incomplete perturbation sets  [18] [19] [20].
SPC can be used for normally distributed data, which will be the case here, but also in a nonparametric manner. In addition to SPC, our bootstrap procedure was also influenced by recent results discussing the propagation of noise in the data across MRA calculations  27. These results showed that CI estimations on MRA parameters are best obtained by considering the noise on global response coefficients. Accordingly, a global response matrix R is computed for each of the 6 replicates of the complete set of qPCR measures, i. e. a sample of 6 values is available for each R j,k. SPC allows for the estimation of its intra-transfection standard deviation s intra j,k considering the replicate structure: three biological replicates ( k t = 3 ) with two technical replicates ( n r = 2 ) each. According to SPC methodology, the standard deviation estimator is constructed by computing the mean of the 3 sample ranges, i.
e. , the mean the absolute values of the differences between the two replicates of each transfection in our particular configuration, divided by Hartley's constant d 2 (n r ). Namely, with Ra i j,k = max 0≤r≤n r R i,r j,k -min 0≤r≤n r R i,r j,k , where r is the technical replicate index and i the transfection index. Note that d 2 (2) = 1. 128 28 ( n r = 2 here). We did not use SPC to exclude samples outside the expected variability for qPCR data, but we did for the even more sparse RNA-seq data; see the specific Results subsection for the details. Lastly, one million R matrices were generated sampling normal distributions, one distribution for each R j,k with mean equal to the average of the 6 raw data replicates and specific variance obtained by SPC. Note that, in practice, different R j,k displayed very different variances in some cases on the contrary to Thomaseth et al.
study  27. The 95% CIs were obtained from the 2. 5th and 97. 5th percentiles. Departure from the assumed normal noise in the data was tested by Kolmogorov-Smirnov tests (data not shown), which detected no significant departure. This does not prove normality (no such test exists in statistics), especially a small number of replicates, but it clearly supports that the estimated 95% CIs are reasonable first approximation. Cell culture and perturbation experiments. We used MELN cells, an MCF7-derived cell line stably transfected with the estrogen-responsive luciferase reporter gene ERE-βGlob-Luc-SV-Neo  29. The cell line was authenticated by short tandem repeat profiling and tested for mycoplasma contamination. MELN cells were cultured in phenol red-free Dulbecco's modified Eagle medium (Gibco) containing 5% dextran-charcoal treated FCS (Invitrogen) and antibiotics (Gibco).
s intra j,k = k t i=1 Ra i j,k k t d 2 (n r ) , mRNA sequencing. For two of the triplicates, in each condition, RNA was extracted as above described. Libraries were prepared with Illumina TruSeq kit and submitted to NextSeq500 sequencing (1 × 75 bp/40 M reads). The first 13 and last 7 bps were cut by an in-house Perl script to eliminate compositional bias. Cut reads were submitted to sickle to eliminate remaining low-quality regions. Alignments were performed against the human genome (hg38) with TopHat v2. 10 30 and read counts extracted with HTSeq-Count  31. The read count matrix was normalized with edgeR  32  TMM algorithm. Data are available from GEO under GSE143956. aiMeRA library implementation. We implemented the MRA method according to the mathematical formulation above as an R library.
The same reasoning applies to LCoR inhibition of NRIP1 that became an activation, as shown in Fig. 2A. The strong inhibition of ERα by LCoR, which in turn activates NRIP1 in Fig. 2B , results in a strong indirect inhibitory action counterbalancing the moderate direct positive connection coefficient. Perturbation magnitudes are reported in Fig. 2C. Finally, we assessed the validity of the inferred network by checking its predictive power in a validation experiment where the activity of each module was measured under double siNRIP1 & siLCoR perturbation. In Fig. 2D , reasonable fidelity of the predictions was noted, with relative errors proportional to the CI sizes, i. e. , with data variability. Connection coefficients whose 95% CI excluded 0 were marked by an asterisk (Fig. 2A, B, E, F ); the sign of such coefficients was known at 5% significance.
No connection coefficient from X to another module can be estimated due to the absence of perturbation data on X, i. e. we assume that X does not influence NRIP1, LCoR, or ERα transcriptional activity significantly. The connection coefficients from NRIP1, LCoR, or ERα towards X can be learned in the E2-stimulated basal condition by applying elementary perturbations, as described above. One separate network is constructed for each gene X considered. The mRNA abundance of gene X reports an n + 1 th module activity and, by design, r i,n+1 = 0 , ∀i ∈ {1,. , n} , since no perturbation of X is available. From Eq. (  7 ), we can compute r n+1,j , ∀j ∈ {1,. , n} , by solving the following system: The performance of this type of MRA-inferred network that we call udMRA was assessed by its ability to predict the activity of the module n + 1 under the dual siNRIP1/siLCoR condition, as we did above for the con- ventional MRA-derived networks (Figs.
2D,H  and 3C ). To avoid trivially successful predictions on genes that would not vary, we limited the benchmark to the 884 E2-regulated/ERα-targeted genes that were significantly regulated upon siNRIP1 or siLCoR perturbation (under E2 stimulation); 60 genes fulfilled this condition (Suppl. Table  5 ). For comparison, the relative errors observed after applying udMRA or naïve predictions are depicted in Fig. 4E. udMRA yielded significantly better estimates of X expression, which is consistent with the complex dependency pattern between gene X expression and the modules NRIP1, LCoR, and ERα (Suppl. Fig. 2 ). It is worth considering whether perturbation magnitudes during double siRNA interference on the same biological system remain identical; mathematically, this can be formulated as to whether filling the vector c in Eq.
(  11 ) with 1 values at the indices of the perturbed module, as conducted so far, is the best option. Equation (  11 ) is written such that we can test different values. We examined the optimal set of coefficients a and b applied to the siNRIP1 and siLCoR perturbations (at the corresponding indices in vector c ). This was based on the constraint that the prediction errors of luciferase, NRIP1, and LCoR expression (as in Fig. 2D ) must be minimal; this yielded a = 1 , and b = 0. 4. Subsequently, these values were used in the udMRA network to more accurately predict the expression of the 60 benchmarked genes. Indeed, the relative error estimates confirmed that this modified inference procedure called udMRA. ab achieved greater accuracy (Fig. 4E ). aiMeRA usage. The R package was designed to be generally applicable; it relies on the formulae presented here, and is able to process any quantitative input, including biological and technical replicates.
We included a functionality to facilitate the definition of MRA underlying network topologies (Fig. 5A ). The design of a network can be specified through the execution of a few generic R functions and network plots can be generated within R directly (Fig. 5B ). There is the possibility to export graphs in the graphML or gml formats for loading into Cytoscape  34  or yED graph editor. More details are provided in the package documentation and Supplementary Material. aiMeRA is available from GitHub and submission to Bioconductor is pending.
Discussion MRA inference 1,2 is a widely used technique to uncover the directions and strengths of connections (connection coefficients or local response coefficients) between components of a biological system-the so-called modulesfrom systematic perturbation data (Fig. 1 ). We have developed an R package that implements classical MRA computations along with several extensions devoted to genomic data. The package was entirely implemented in R. The development of our MRA package came along with the opportunity to explore the crosstalk between ER and RAR, two important NRs involved in several tumors, such as BC. We generated unique qPCR and RNA-seq data using BC MELN cells, which are derived from MCF7 cells (see Methods) and a well-established BC model. The transcriptional activity of ER and RAR is modulated by the shared corepressors NRIP1 and LCoR.
Considering networks of growing complexity, i. e. , ER-NRIP1-LCoR, RAR-NRIP1-LCoR, and ER-RAR-NRIP1-LCoR, we showed that our MRA inference R package could recapitulate well-known interactions between these molecules by exploiting qPCR data (Figs. 2, 3 ). We successfully confirmed the known predictive power of MRAinferred networks as a means to assess their validity in the validation experiments presented. Using original experimental data, the most complex network, ER-RAR-NRIP1-LCoR, enabled us to confront the hypothesis we stated in the Introduction section. Specifically, ER-RAR crosstalk has been described in previous studies as having potential mutual repressive consequences  [11] [12] [13] [14] [15]. A spatial competition mechanism to bind DNA was proposed in one instance  14. We reasoned that the sharing of corepressors (NRIP1 and LCoR) may contribute to cross-suppression.
�x n+1 x n+1 q k = n j=1 r n+1,j �x j x j q k , k ∈ {1,. , n}. Following this classical application of MRA, we showed that almost identical MRA-derived networks could be trained from qPCR and RNA-seq data obtained in the same conditions. By employing concepts of SPC theory, we introduced a new procedure that estimates data variance over a gene population when only a few experimental replicates are available. Such a method could be applied with proteomics or phospho-proteomics data when restricted to limited replicates, which is often the case. Access to genomic data related to the ER-RAR crosstalk motivated an extension of MRA. Since ER acts as a transcription factor upon ligand binding, MRA enabled us to infer a core transcriptional regulatory network around it, i. e. , the ER-NRIP1-LCoR network.
Acknowledgements We thank  Simon Cabello-Aguilar  and  Meriem Mekedem  for useful discussions during the development of the project. JC was supported by the  Agence Nationale de la Recherche  [ ANR-16-CE17-0002-02 ], the  Groupement des Entreprises Françaises dans la Lutte Contre le Cancer (GEFLUC) , and the  Fondation ARC contre le Cancer  [PJA 20151203332].
1. Currents and turbulence beneath forced surface waves with the time-dependent Stokes drift u S 5 e 2kz a 2 k ffiffiffiffiffi ffi gk p (1 2 e 2t 2 /2Tw )x with wavenumber k 5 2p/100 m 21 , growth time scale T w 5 4 h, equilibrium amplitude a 5 2 m, and gravitational acceleration g 5 9. 81 m s 22. (left) Contours of w L and (right) the horizontally averaged horizontal velocity components hu L i and hy L i are shown after half an inertial period at t 5 p/f. The tendency of the depth integral of the Stokes drift, › t U S 5 Ð 0 2h u S dz, is a stress accompanying the growth of swell that forces a vertically sheared inertial oscillation and drives turbulent mixing and boundary layer deepening. More details about the physics, numerics, and software can be found in sections 2 and 3, in Table  1 , and at https://github.
The eddy viscosity n e and eddy diffusivity k e in (20) are modeled with the anisotropic minimum dissipation (AMD) formalism introduced by  Rozema et al. (2015)  and  Abkar et al. (2016) , refined by  Verstappen (2018) , and validated and described in detail for ocean-relevant scenarios by  Vreugdenhil and Taylor (2018). Additional details about (20) are given in appendix A. We solve (  9 )-(  11 ) numerically with Oceananigans, a software package developed by the authors in the Julia programming language that runs on graphics processing units (GPUs)  (Bezanson et al. 2012; Besard et al. 2018). The simulations in this paper use second-order finite volume spatial discretization, second-order Adams-Bashforth time discretization, a pressure projection method to ensure = Á u L 5 0, and a fast method based on the fast Fourier transform to solve the pressure Poisson equation discretized with second-order differences on a regular grid  (Schumann and Sweet 1988).
Oceananigans code and documentation are hosted at https://github. com/ CliMA/Oceananigans. jl. Our simulations are performed in two rectangular domains: one ''regular'' size domain with dimensions 128 m 3 128 m 3 64 m in x, y, z, grid spacings 0. 5 m 3 0. 5 m 3 0. 25 m, and resolution 256 3 , and a second ''large'' domain of dimension 192 m 3 192 m 3 96 m with 0. 75 m 3 0. 75 m 3 0. 25 m grid spacing and resolution 256 3 256 3 384. The domains are horizontally periodic in x, y and have rigid top and bottom boundaries, where we impose w L 5 0. To absorb internal waves radiated downward from the turbulent surface boundary layer, we implement bottom sponge layers of the form F f 5 e 2(z1H)/d m(f y 2 f) , (21) for each variable f 2 (u, y, w, b), where H is the depth of the domain, m 5 1/60 s 21 , › 5 4 m, b y 5 N 2 z, and u y 5 y y 5 w y 5 0.
Acknowledgments. This paper's seed was planted by arguments between the lead author and Sean R. Haney about the intricacies and obscurities of Stokes drift while Sean was a postdoctoral researcher at the Scripps Institution of Oceanography. Besides being an amazing surfer and a generous friend, Sean had a unique ability to make arguments fun and productive. Sean passed away too soon due to illness on 1 January 2021 and is deeply missed. This paper is dedicated to him. Be careful, Sean. In addition to Sean's inspiration, this work benefited from the constructive criticism of two reviewers and amiable banter with Stephen Belcher, Keaton Burns, Navid Constantinou, Baylor Fox-Kemper,  Ramsey Harcourt, Qing Li, Brodie Pearson, Nick Pizzo, Brandon Reichl, and William Young. Our large-eddy simulations of (  9 )-(  11 ) use a downgradient hypothesis to model the subfilter stress tensor T ij appearing in (9), T ij 5 22(n e 1 n)å L ij 1 nd 3j › z u S , ( A1 ) where n is the molecular viscosity, n e is an eddy viscosity that is a nonlinear function of resolved velocity field, d ij is the Kronecker delta, and  )  is the Lagrangian-mean rate of strain tensor.
Eagle is an R package for multi-locus association mapping on a genome-wide scale. It is unlike other multi-locus packages in that it is easy to use for R users and non-users alike. It has two modes of use, command line and graphical user interface. Eagle is fully documented and has its own supporting website, http://eagle. r-forge. r-project. org/index. html. Eagle is a significant improvement over the method-ofchoice, single-locus association mapping. It has greater power to detect SNP-trait associations. It is based on model selection, linear mixed models, and a clever idea on how random effects can be used to identify SNP-trait associations. Through an example with real mouse data, we demonstrate Eagle's ability to bring clarity and increased insight to single-locus findings. Initially, we see Eagle complementing single-locus analyses.
By replacing these libraries with their multi-threaded counterparts, certain linear algebra operations become parallelized, implicitly. The Eagle package has been structured, purposely, to make extensive use of these implicitly parallelized operations. In the parts of Eagle where this has not been possible, we have instead written Cþþ routines and parallelized the code explicitly through openMP  (Dagum and Menon 1998). Eagle differs most from competing packages in its ease-of-use for non-R users. Considerable effort has been invested into making Eagle equally usable to R and non-R users. Eagle is available on CRAN (https://cran. r-proj ect. org/). The package comes with a browser-based graphical user interface (GUI). A user need only issue a single R command, OpenGUI(), to harness the full functionality of Eagle.
Notation Suppose genotypes are collected on L loci from n g individuals/ lines/strains. The genotypes are coded as À1, 0, and 1 corresponding to SNP genotypes AA, AB, and BB, respectively. Ideally, missing genotypes are imputed prior to analysis. If not, missing genotypes are set to 0 by Eagle. Let M ðngÂLÞ ¼ ½m 1 m 2. m L be the matrix of SNP genotype data where the vector m ðngÂ1Þ j contains the genotypes À1, 0, and 1 for the jth SNP. Furthermore, let y ðnÂ1Þ contain the quantitative trait data. Examples of the quantitative trait data include flowering time and grain yield in plants, weight gain and feed efficiency in animals, and blood pressure and cholesterol levels in humans. Here, n can be larger than n g if multiple measurements, as is common in plant studies, are recorded on the same line/strain.
The relationship matrix is calculated from pedigree records or from SNP data. However, Eagle models u g differently and this is where the innovation lies. Instead of relying on the relatedness between individuals to model the genetic background, Eagle accounts for genetic background by modeling the association between all the SNPs and trait simultaneously. A similar idea exists in genomic selection  (Goddard 2009). For a detailed explanation of how u g is formed and its statistical justification, the interested reader is referred to  Verbyla et al. (2007 Verbyla et al. ( , 2012)). Briefly, the genetic effects are modeled as u g ¼ X s k¼1 m S k a S k þ M ÀS a ÀS (2) where m ðngÂ1Þ S k is the vector of genotypes for the kth selected SNP, a S k is the additive effect of the kth selected SNP, M ðngÂLÀsÞ ÀS is the matrix of SNP genotypes with the data for the SNPs in S removed, and a ðLÀsÂ1Þ ÀS is a random effect whose distribution is a ÀS $ Nð0; r 2 a I ðLÀsÂLÀsÞ Þ.