dataesr/scientific-paragraphs-categorization huggingface dataset:
Taxonomic Rank Analyses To identify phyla and genus differences among soils, normalized counts were analyzed using Likelihood Ratio Test (LR Test) on Generalized Linear Model (distribution: quasipoisson, link function: log), and pairwise comparisons of EMMs were then computed. To visualize community taxa and map similarities/ differences among NS and RS, heat trees were realized with the "heat_tree_matrix" function from the R "metacoder" package  (Foster et al. , 2017). Heat trees were realized on normalized and filtered (genera higher than 50/10,000) data, which were transformed into presence/absence data. October 2020 | Volume 11 | Article 536932.
Statistical Analyses The effects on hatching of the factors soil (A, D, F, and J), treatment (dose 1, dose 2, and control), and interaction between soil and treatment were tested using a Linear Mixed-Effects Model, (function lmer, "lme4" package;  Bates et al. , 2015)  with parametric analysis of variance (ANOVA) provided by R package "car"  (Fox et al. , 2012). Block effect was introduced as random effect in the model. Normality and homogeneity of variances were checked by performing Quantile-Quantile (QQ) plots of the residuals and the fitted models using function plotresid ("RVAideMemoire" package;  Herve, 2019). Pairwise comparisons of EMMs were performed ("emmeans" package;  Lenth, 2019) , applying the false discovery rate (FDR) correction for values of p.
Diversity and Structure of Microbial Communities The suicide hatching experiments were conducted on four NSs and four RSs obtained by inoculating the same soil matrix with the microbiota from the NSs. Two factors have been considered: (i) the type of soil, namely NS and RS, and (ii) the soil, namely A, D, F, and J corresponding to the global properties (biotic and abiotic) for NSs and to the microbiota for RSs. After normalization of metabarcoding data by rarefaction, 12,614 and 14,873 reads per sample were obtained for bacteria and fungi, respectively. Alpha diversity of native and recolonized soils was assessed by evaluating the number of ASV and calculating the Shannon index (Supplementary Figure  S2 ). In bacterial communities, the number of ASVs was influenced by the type (NS and RS) of soil (χ 2 = 335.
DATA AVAILABILITY STATEMENT The raw metabarcoding data sets are available on the European Nucleotide Archive database system under the project accession number PRJEB36768. Soil sample accession numbers range from ERS4307830 to ERS4307877 and sequencing runs from ERR3908564 to ERR3908611. An Excel file containing the raw data of the suicide hatching, in native and recolonized soils, is available at data. inrae. fr (doi: 10. 15454/THNCAC).
UK Biobank data The UK Biobank is a prospective cohort of approximately 500,000 people aged 40-69 who were recruited from 2006-2010 across the United Kingdom. A description of the study protocol and design can be found online. Briefly, participants were identified from National Health Service registers and lived within 25 miles of one of the 22 assessment centres. At recruitment participants completed a touchscreen questionnaire, had physical measurements and blood samples taken, and provided informed consent. Details of the blood sample assay methods and quality procedures can be found online. The study was approved by the North West Multi-Centre Research Ethics Committee (reference number 06/MRE08/65). We used information from the touchscreen 'food frequency' questionnaire (FFQ) to classify participants as either vegetarian or non-vegetarian.
We excluded WebQ records where energy intake was below or above pre-specified sex-specific limits (<3,349 kJ or >16,747 kJ for men and <2,093 kJ or >14,654 kJ for women; n=9,157), or where a record was non-representative due to illness or fasting (n=2,971), and WebQ data were only used if a participant completed three or more questionnaires (n=57,915). Estimated mean weekly intakes of total, red, and processed meat, and poultry were calculated by combining responses from both the FFQ and WebQ. Processing and analysis of genotype data Genome-wide genotype data are available for the whole cohort. We excluded participants based on non-white British ancestry (n=78,422), mismatch between genetic versus selfreported sex (n=308), sex chromosome aneuploidy (n=407), and relatedness (n=32,827). We also excluded single-nucleotide polymorphisms (SNPs) with MAF<0.
Data availability We use data from the UK Biobank resource under Application Number 3248 for this work. All bona fide researchers can apply to use the UK Biobank resource for health-related research that is in the public interest. Further information on the application process is available from the UK Biobank website (https://www. ukbiobank. ac. uk/register-apply/). or some other issue with the data. Further research and combining this data with other cohorts may eliminate significant results from this study 4) Results and Discussion. Please provide Manhattan and LocusZoom plots first. Please provide more details for additional tested traits: Do they come from UKBB? How were they selected? Are there any overlaps between samples? As mentioned above -please provide all the details about methodology, use other reviewers comments and take them step by step, then it would be easier to understand the data reported in the results section.
Minor Comments It is unclear how the 24-hour recall questionnaire from 57K individuals was combined with FFQ data to calculate weekly meat intake for all individuals (>360K). Does this mean that the majority of data is from FFQ, and a subset of contributing individuals have their weekly intake value derived from both questionnaires? If this is the case, why limit to just the 57K individuals that took the questionnaire three times, when ~200K took it at least once. Understandably, this data is more reliable for individuals that participated in the 24HR questionnaire more than once, but at minimum a clear description of how the 24HR questionnaire supplemented the FFQ data is necessary to understand this phenotype.
01. Contrasts between the Pre and 24hr timepoints were used to identify genes that could be exhibiting differential expression caused by wound response from the tissue sampling rather than response to darkness and recovery. Genes identified in these wound control contrasts were tagged but not excluded from the rest of the analysis. Dispersion was estimated independently for the wound control contrasts based on the Pre and 24hr timepoints only. This was done because using the dispersion estimates from the main analysis, including the Dark and Recovery timepoints resulted in biased p-value distributions due to the significant change in expression of most genes in the dark timepoint as compared to the other timepoints (68% of genes between wild type-Dark and wild type-Pre). Complete data can be accessed from the Gene Expression Omnibus (GEO) under the entry GSE135955.
Venn diagrams and gene ontology (GO) analysis to prioritize the DEG To find differences between the wild-type and phyB response to darkness and reillumination we looked at sets of genes that were significantly changed in these contrasts (darkened vs. pre or recovery vs. darkened) in one genotype but not the other. For each up or down Venn-diagram used in the GO analysis, all genes which either the wild-type or BrphyB mutant were greater (or less for down regulated Venn) than the log fold change cutoff of 2 were considered. These genes were then put into 4 categories: wild-type significant only, BrphyB significant only, both wild-type and BrphyB significant, and neither genotype significant. thaliana hits for each B. rapa protein. This output was further processed to limit each ortholog to the best hit, using a custom BASH script (available upon request).
Methods, Motif Enrichment A set of binding motifs for 619 A. thaliana transcription factors was downloaded from plantTFDB (http://planttfdb. cbi. pku. edu. cn/), (Jin et al. , 2014 (Jin et al. , , 2015 (Jin et al. , , 2017)). The RSAT matrix clustering tool (http://rsat. eead. csic. es/plants/matrix-clustering_form. cgi (Castro-Mondragon et al. , 2017)  was used with default parameters to group the motifs into 56 clusters based on similarity of aligned position weight matrices. Genes that were significantly differentially expressed from conditions pre to dark, and from dark to recovery were divided into groups depending on 3 factors: genotype, direction of regulation (up or down), and whether they are annotated with the GO term "chloroplast" (GO:0009507). The package "motifmatchr"  (Schep, 2019)  was used to count the number of genes in each set with promoter sequences with matches to each of the 619 motifs.
Together, these data suggest a shift from carbon fixation and nutrient acquisition to active redistribution of cellular resources (Fig. 2B , Table  S2 ). A recent experiment in A. thaliana found that the effect of darkening individual leaves was substantially similar to the effect of darkening whole plants, albeit with distinct timing for peak differences in gene expression between the two treatments  Law et al. , 2018). When we compared our transcriptomic response to this dataset, we found a substantial overlap. The highest similarity between the B. rapa dark response was to A. thaliana individually darkened leaves for 3 days (IDL_3D). Of the genes significantly up or down regulated in A. thaliana individually darkened leaves after 3 days, 46. 7% had B. rapa homologs also significantly up or down regulated (in the same direction) (Table  S3 ).
Acknowledgements We thank  Prof. Mark Stitt  and  Dr. Virginie Mengin  for sharing their insights and expertise, as well as the other members of the  PHYTOCAL consortium, Nemhauser and Imaizumi labs  for feedback and discussion. We also thank the undergraduate researchers from  Dr Arsovski 's Spring 2018 Special Field Topics class who carried out protocol optimization and preliminary measurements of phenotypes presented here:  Ericka Budinich ,  Jonas Hill ,  Sean Hoeger ,  Katrin Hosseini ,  Nikhil Kaza ,  Winnie Kwong ,  Kellen Larsen ,  Andrew Lui ,  Rohan Menon ,  Claudia Moroney ,  Anita Nguyen ,  Arthur Sargent ,  Emma Stevens , and  Nanami Tsumura. This work was supported by the  National Science Foundation participation in the ERA-CAPS program  ( IOS-1539834 ).
The use of a coronagraph to mask out the host star jointly with an extreme adaptive optics system is mandatory for reaching these challenging contrasts. Exoplanet hunting facilities dedicated to direct observations such as VLT/SPHERE  (Beuzit et al. 2008 (Beuzit et al. , 2019)) , GEMINI/GPI  (Macintosh et al. 2014) , Subaru/SCExAO  (Jovanovic et al. 2015) , Keck/VORTEX  (Howard et al. 2010) , and Magellan/MagAO  (Morzinski et al. 2014 ) are equipped with these cutting-edge optical systems. Several observation strategies can be used for direct imaging. The most popular observation mode is angular differential imaging (ADI,  Marois et al. 2006). It consists of tracking the target star and maintaining the telescope pupil stable over time while the field of view rotates. Such observational sequence produces 3D datasets (2D + time) in which the speckles (structured background resulting from instrumental aberrations) are quasi-static, while the signature of the companions describes an apparent circular motion along around its host star.
Spectral differential imaging (SDI,  Racine et al. 1999 ) consists of recording simultaneously images in several spectral channels using an integral field spectrograph (IFS). Reduced 3D datacubes (2D + spectral) are obtained by mapping raw observations of the IFS cameras into a multi-spectral cube  (Pavlov et al. 2008 ). In the reduced datacubes, the stellar speckles that are due to diffraction are very similar from one wavelength to the other, apart from their chromatic scaling  (Perrin et al. 2003). After compensating for this scaling, speckles are aligned and can be combined to cancel out thus revealing the presence of off-axis sources whose positions are achromatic. A natural extension of ADI and SDI is to use them simultaneously. This hybrid observation mode called angular and spectral differential imaging (ASDI) produces 4D datasets (2D + time + spectral), combining the properties of both ADI and SDI.
Using ASDI datasets such as the ones obtained with the VLT/SPHERE-IFS instrument brings a spectral diversity  (Macintosh et al. 2014; Gerard et al. 2019)  compared to simple ADI. The discrimination between the signal from off-axis sources and the background signal due to stellar speckles is thus improved. In addition, ASDI datasets allow both the detection and the characterization of the detected exoplanets  (Beuzit et al. 2019). Such a characterization is performed by fitting orbit models and exoplanet atmospheric models to the estimated astrometry and photometry  (Vigan et al. 2010). Physical information such as age, effective temperature, composition or surface gravity can be derived for the detected exoplanets  (Müller et al. 2018; Cheetham et al. 2019; Mawet et al. 2019; Claudi et al.
Several adaptations of this general principle have been proposed in the literature, such as the ALOCI  (Currie et al. 2012a,b) , TLOCI  (Marois et al. 2013 (Marois et al. , 2014)) , or MLOCI  (Wahhaj et al. 2015)  algorithms. Among these, the TLOCI algorithm has become one of the gold-standard methods to process ADI datasets. It differs from the standard LOCI algorithm on the construction of the reference stellar PSF. Instead of only minimizing the residual noise, TLOCI also maximizes the exoplanet throughput. Another group of methods models the fluctuations of the stellar speckles (i. e. , the on-axis PSF) by a low-dimensional subspace. The exoplanet signal is thus captured on the subspace orthogonal to the subspace that captures fluctuations of the stellar speckles. The data are projected on an orthogonal basis created by principal components analysis (PCA).
This is the principle behind the widely used KLIP algorithm  (Soummer et al. 2012; Absil et al. 2013 ) which builds a basis of the subspace capturing the stellar PSF by performing a Karhunen-Loève transform that is, a PCA 1  of the images from the reference library. To obtain a model of the stellar PSF to subtract in order to attenuate the speckles, the science data is projected onto a predetermined number of modes. Interestingly, as demonstrated by  Savransky (2015) , the (A,M,T)LOCI-and KLIP-type methods can be seen as two closely related instances of the general class of algorithms called Blind Source Separation (BSS). The LLSG method  (Gonzalez et al. 2016 ) is also based on subspace approaches since it decomposes the datasets into low-rank, sparse and Gaussian components. Other ADI processing methods are based on a statistical framework.
2018a ) is also based on a maximum likelihood approach but with a more consistent statistical model self-calibrated on the data and accounting for the spatial covariances of the speckles at the scale of small patches. Recently, some works have also applied deep learning techniques to direct imaging  (Gonzalez et al. 2018; Yip et al. 2019). Most of these algorithms are subject to different limitations. Generally, they are not fully unsupervised so that the tuning of several parameters is often mandatory to reach the best performance of the method. Such tuning is time-consuming and should ideally be repeated for each dataset since it depends on the dataset properties (considered spectral bands, number of temporal and spectral frames, quality of the observations, amount of parallactic rotation, etc.
, control over the probability of false alarms), -characterization of the sources detected: subpixel astrometry and unbiased estimation of the relative spectrum 2  , with reliable confidence intervals, -ability to process the whole field of view covered by the instrument without artifacts, -computation of a map reporting the contrast achieved for a source to be detected at a given detection threshold. In this paper, we attempt to address these different points by deriving an algorithm from a data-driven statistical modeling of ASDI observations. The proposed algorithm, named PACO ASDI, is an extension of our ADI exoplanet detection method PACO introduced in  Flasseur et al. (2018a). PACO can be used to process independently each spectral channel of ASDI datasets  (Flasseur et al. 2018b ). The main methodological adaptations of PACO ASDI for joint ASDI processing are the following: (i) modeling of local covariances based both on temporal and spectral information (see Sect.
3. 1), -multi-wavelength detection, by combining the singlewavelength detection maps; this is achieved by learning the spectral correlations between single-wavelength detection maps (steps -) and introducing a (coarse) prior information on the spectrum of the source (detailed in Sect. 3. 2), -once a source has been detected, its astrometry and photometry is estimated during a characterization step (step , described in Sect. 4), by iteratively refining the source parameters (angular location, spectrum and total flux) and the statistical model of the background (spatial and spectral correlations). Statistical modeling of the spatial, temporal, and spectral fluctuations of ASDI datasets is a guiding thread throughout the paper for grounding the detection and estimation method and to obtain reliable indications on the probability of false alarms, astrometric, and photometric confidence intervals.
Those statistical guarantees are essential to the astronomers to automatize the detection and analysis of ASDI datasets, for the scientific exploitation of the results, and also for characterizing the performance of the instrument (detection limits and photometric accuracy depending on the observation strategy, the observation conditions, and the performance of the adaptive optics + coronagraph). This paper is organized as follows. We describe in Sect. 2 our statistical modeling of the background fluctuations for ASDI datasets. In Sect. 3, we explain how to obtain single wavelength and combined detection maps at a controlled probability of false alarms. Section 4 details our parameter-free and regularized spectrum estimation procedure applied to the detected sources. In Sect. 5, we illustrate on VLT/SPHERE-IFS datasets the performance of the proposed PACO ASDI algorithm in terms of detection maps, achievable contrast, and spectrum estimation.
Local multivariate Gaussian model Our modeling of the spatial covariances in ADI datasets with PACO algorithm led to two conclusions: (i) to account for the nonstationarity of the background, local modeling is necessary; and (ii) given the limited number of samples available at any given location, a trade-off must be found between the size of the covariance matrices and the estimation variance. To extend the modeling from ADI datasets to the 4D spatiotemporo-spectral datacubes of ASDI, we keep a local Gaussian modeling: parameters of the Gaussians are estimated by analyzing patches extracted at a given location. For the nth pixel 3  of the field of view (identified by its 2D angular direction θ n on the sky with respect to the stellar center), we extract T • L patches (where T is the number of temporal frames and L is the number of spectral channels), each made of K pixels.
Because of the weighting by factors 1/ σ2 n, ,t , some patches have more importance than others and an equivalent number P of patches is used in the shrinkage formula, step 3 of Algorithm 1, see also  Flasseur et al. (2020). The closed form expression of P is derived in Appendix B. The rationale behind this equivalent number of patches comes from the variance reduction when performing the weighted mean. Figure  2a  depicts the observed intensities in some frames of an ASDI dataset. Fluctuations can be noted both through time and through the wavelengths. In Fig. 2b , maps of the time and wavelength-specific scaling factors σ 2 n, ,t are displayed for 16 pairs ( , t). At a given location n, large values of this scaling factor compared to other frames t or other wavelengths indicate that the corresponding patches have a moderate or negligible weight when estimating the mean background and the spatial covariance matrix.
In each case, 1000 different random draws were used as an initialization. The graphs report the normalized distance to the solution found with a constant initialization after a large number of iterations. Convergence to the same solution is observed experimentally in all cases. An insert also gives the evolution of each scaling factor σ 2 n, ,t with the iterations, until the convergence criterion is reached. A satisfactory convergence is reached in about 10 iterations. At large angular separations, as in 3c, only the shortest wavelengths are available after the speckles are aligned by spectral zooming. The convergence is even faster in this case. Figure  4  evaluates the statistical accuracy of our modeling of the background on HR 8799 ASDI dataset. The left column gives the values and empirical distributions of the collection of mean-subtracted patches {r n, ,t -m n, } ∈1:L, t∈1:T , at a location n near the coronagraph (rows a and b), at a location farther from the coronagraph (rows c and d), and for all patches from the field of view (row e).
p GLRT + H 0 = 1 2 L δ 0 (GLRT + ) + L-1 =0 L! 2 L !(L -)! χ 2 L-GLRT + , (14) where δ 0 is a Dirac mass centered in 0 and χ 2 L-is a Chi-square distribution with Ldegrees of freedom. Figure  7a  displays the GLRT + obtained with Eq. (  13 ) on an ASDI dataset of HR 8799 obtained with SPHERE-IFS. Three point-sources can be detected in this dataset, at the locations marked c, d, and e. To perform an automated detection, it is necessary to set a threshold corresponding to a fixed probability of false alarm. The empirical distribution of GLRT + values, excluding the three regions that contain the point-sources, is shown at the right of the detection map of Fig. 7a. This empirical distribution is compared to the theoretical distribution of GLRT + under H 0 , drawn in dashed line. A strong mismatch is observed.
Datasets description In this section, we assess the performance of the proposed PACO ASDI algorithm both in terms of source detection and spectrum estimation. The results are compared to two standard algorithms: TLOCI and KLIP. These two algorithms are currently used for the exploitation of the SPHERE science data  (Lagrange et al. 2019a; Mesa et al. 2019b; Gratton et al. 2019; Gibbs et al. 2019; Maire et al. 2019 ). In the following, they are applied both in ADI and ASDI mode. We used the SpeCal  (Galicher et al. 2018 ) implementation of TLOCI-A(S)DI and KLIP-A(S)DI algorithms which is the current reference and well documented post-processing standard of the SPHERE consortium. The TLOCI implementation is based on the algorithm as described in  Galicher et al. (2011) ,  Marois et al. (2014).
In particular, it implements a frame selection strategy for the estimation of the stellar PSF to mitigate the self-subtraction of the putative sources. A flat spectral template was considered in our experiments. The KLIP implementation is based on the algorithm as described in  Soummer et al. (2012). No frame selection strategy was implemented to minimize the self-subtraction of the putative sources when conducting the PCA. There are several variants of these implementations, especially for the KLIP-ASDI method. In this paper, we used only the publicly available variant of the SPHERE consortium. For all algorithms from the SpeCal software, the S/N maps are computed after annular normalization of the residual images (obtained after subtraction of the estimated on-axis PSF) by the empirical standard deviation of the noise.