dataesr/scientific-paragraphs-categorization huggingface dataset:
A score quantifying fitting success was determined by the difference between the correlation of training sample and that of the test sample, which was used in this particular study to evaluate the performance of selecting the sparsity parameters. After the sparsity parameters are selected based on the data, a bootstrapping stability selection  (Meinshausen and Bühlmann, 2010)  approach was used to select a stable subset of variables that most commonly occurred among the 200 bootstrapped samples. The frequency cutoff was set to be 0. 6 based on  Meinshausen and Bühlmann's work (2010). The new algorithmic methodology revealed consistent brain regions and genetic variants with the past studies, such as (i) hippocampus and fusiform in the fMRI data  (Kircher and Thienel, 2005) , (ii) SNPs related to brain development including BSX that has influence on methylation level  (Park et al.
The CCA solution is constraint by the sample as well as the number of variables. As a cautionary note, the canonical correlation effect sizes obtained from the training data limit statements about how the obtained CCA solution at hand would perform on future or other data. In a similar vein, smaller datasets offering measurements from only a few dozen individuals or observations may have difficulty in fully profiting from the strengths of multivariate procedure such as CCA. Moreover, the ground-truth effects in areas like psychology, neuroscience, and genetics are often small, which are hard to detect with insufficient sampling of the variability components. One practical remedy that can alleviate modeling challenges in small datasets is using data reduction methods such as PCA or other data-reduction method for preprocessing each variable before applying CCA (e.
g. Smith et al. , 2015)  or to adopt a sparse variant of CCA (see below). Reducing the variable sets according to their most important directions of linear variation can facilitate the CCA approach and the ensuing solution, including canonical variates, can be translated back to and interpreted within the original variable space. These considerations illustrate why CCA applications have long been less attractive in the context of many neuroscience studies, while its appeal and feasibility are now steadily growing as evermore rich, multi-modal, and open datasets become available  (Davis et al. , 2014). A second limitation concerns the scope of the statistical relationships that CCA can discover and quantify in the underlying data. As a linear model, classical CCA imposes the assumption of additivity on the underlying relationships to unearth relevant linked co-variation patterns, thus ignoring more complicated variablevariable interactions that may exist in the data.
, 2013). One advantage of this CCA variant is that it has a more principled definition of the variation to be expected in the data and so has more opportunity to produce synthetic but plausible observations once the model has been fit. Additionally, because probabilistic CCA allows for the introduction of prior knowledge into the model specification, an advantageous aspect of many Bayesian models, this approach has been shown to yield more convincing results in small biomedical datasets which would otherwise be challenging to handle using ordinary CCA (e. g. Fujiwara et al. , 2009; Huopaniemi et al. , 2009). ii. Sparse CCA  (SCCA Witten et al. , 2009 ) is a variant for identifying parsimonious sources of variation by encouraging exactly-zero contributions from many variables in each variable set.
PRACTICAL CONSIDERATIONS After these conceptual considerations, we next consider the implementation of CCA. The computation of CCA solutions is possible by built-in libraries in MATLAB (canocorr), R (cancor or the PMA package), and the Python machine-learning library scikit-learn (sklearn. cross_decomposition. CCA). The sparse CCA mentioned in the examples is implemented in R package PMA. These code implementations provide comprehensive documentation for how to deploy CCA. For readers interested in reading more on detailed technical comparisons and discussions of CCA variants, please refer to the texts in Table  1.
Missing data is a common occurrence in large dataset. It is recommended to exclude observations with too many missing variables (e. g. those missing a whole domain of a questionnaire). Alternatively, missing variables can be "filled in" with mean or median when the proportion of missing data is small, or more sophisticated dataimputation techniques. Besides unwarranted extreme and missing values, it is often necessary to account for potential nuisance influences on the variable sets. Deconfounding procedures are a preprocessing step in many neuroimaging data analysis settings to reduce the risk of finding non-meaningful modes of variation (such as motion). The same procedures that are commonly applied prior to the use of linear-regression analyses can also be useful in the context of CCA. Note that deconfounding is typically performed as an independent preceding step because the CCA model itself has no explicit noise component.
g. Smith et al. , 2015). Following many iterations of this process, the extracted perturbed mode from the permutation datasets serve to compute the chance level of associations between the two variable sets. Each canonical mode whose original canonical correlation exceeds the 95% level (significance at p < 0. 05) or 99. 9% level (significance at p < 0. 001) can be certified as robust under the null hypothesis of absent linkage between the left and right variable set. If the investigator wishes to add an explicit correction for multiple comparisons, the pvalue threshold can for instance be divided by the number of modes (i. e. , Bonferroni's method) or false-discovery rate (FDR) can be used to reduce possible type I errors. This approach hence yields one p-value for each of the originally obtained CCA modes.
This analysis strategy issued a perturbed set of canonical variates under the assumption that, one-by-one, a particular input dimension may not have been important to obtain the original canonical modes. The degree of alteration in the canonical correlations was quantified by computing Pearson's correlation coefficient between the original and perturbed canonical variates. In addition to these point estimates after variable deletion, the induced statistical uncertainty was quantified by carrying out bootstrapping analysis. "Shaken up" bootstrap datasets were generated from the original participant sample by randomly drawing individuals with replacement. In each of these alternative datasets, the perturbed CCA was fitted and evaluated in identical fashion. This robustness assessment provided population-level uncertainty intervals and hence enabled extrapolation of statements on variable importance on data we would observe in the future.
CODE AVAILABILITY MATLAB, Python, and R code of several published CCA project can be found on several different repositories. Analysis by  Smith and colleagues (2015)  is available providing details of the CCA implementation in MATLAB and the permutation test (https://www. fmrib. ox. ac. uk/datasets/HCP-CCA/). The Python code of nested crossvalidation scheme of  Wang and colleague (2018a)  is available on GitHub (https://github. com/htwangtw/patterns-of-thought) along with pre-modeling data scaling. Xia and colleagues (2018)  made their R codes on grid search parameter selection and permutation test procedures on GitHub (https://github. com/cedricx/sCCA/tree/master/sCCA/code/final). We provide a reusable Conda environment file containing the key Python and R libraries for CCA and the model selection for users to explore the methods (https://gist.
CONCLUSIONS In contemporary biomedical research, complex multivariate relationships are expected among body, brain, cognition, and genes. These together are likely to provide insight into the cause of diseases and other societal problems. CCA provides a simple, effective method for describing the correspondences between two variable sets that can be instrumental in describing complex relationships in neuroimaging. The appeal of CCA is likely to increase as the detail and quality of multi-modal datasets in neuroscience and other biomedical sciences increases. CCA has already started to be useful in two of the currently largest brain-imaging collections -the Human Connectome Project and UK Biobank. In many of these applications, CCA serves as the centerpiece of the analysis workflow. Given its versatility, CCA has the capacity to become a core building block of more elaborated data analysis pipelines  (Calhoun and Sui, 2016; Correa et al.
INTRODUCTION T cell development in the thymus and differentiation to functionally distinct lineages in peripheral lymphoid tissues require a complex interplay of transcription factors, chromatin modifiers, and genomic regulatory elements to ensure proper cell fate commitment  (Rothenberg, 2014). Although many studies have described transcription factors (TFs) important for differentiation and putative regulatory elements in the CD4 lineage (for example, see  Cauchy et al. , 2016; Fu et al. , 2012; and Hollenhorst et al. , 2007)  or described thymic partial epigenetic landscape  (Zhang et al. , 2012) , none has defined the robust set of enhancers that critically define CD4 + T cell identity through their life cycle. During these last years, the combinatorial analysis of genomewide transcriptomic and epigenomic datasets identified H3K27ac deposition by p300 and other histone acetyltransferase (HAT) and H3K4me1 in the presence of low H3K4me3 as the hallmarks of active enhancers in the genome of virtually all cell types in metazoans  (De Santa et al.
DISCUSSION In this article, we describe a global epigenomic landscape of murine T cell differentiation in the thymus and in in vitro differentiated T helper cell populations. Besides providing a large and robust resource for scientists to further study the dynamic regulome of T cells, our observations emphasize novel findings on the role of regulatory elements hallmarked by enhancer signature. First, we used Pol II recruitment as a critical hallmark of active enhancers, an approach that we validated by genome-wide enhancer assay. Our rationale was supported by previous descriptions of enhancer transcription as a strong determinant of enhancer activity  (Core et al. , 2014; De Santa et al. , 2010; Kim et al. , 2010; Koch et al. , 2011). Although other studies have described dynamics of open regions or used combination of one or more epigenetic marks to describe T cell p-enhancers, our large dataset offers a more accurate picture of dynamic activity of p-enhancers during differentiation.
2, Thermo Fischer Scientific), or IL17A (eBio17B7, Thermo Fischer Scientific) by using the Cytofix/Cytoperm Fixation/Permeabilization Cell Reports 32, 108048, August 18, 2020 e2 Resource Kit (BD Biosciences). Flow cytometry was performed by using a MACSQuant analyzer 10 (Myltenyi) and the data were analyzed by using FlowJo software (Tree Star).
5 at all stages) for both groups and the randomized sets for the -one promoter group-shown are representative of the several groups. The numbers of genes at each differentiation stage are DN 597, DP 500, nCD4 515, Th1 561, Th2 539, Th17 548. ChIP-seq Average Metagene Profiles ChIP-seq values from wiggle files were retrieved with in-house R scripts for selected genes and enhancer regions. Then we used an algorithm as described previously  (Koch et al. , 2011)  to rescale the genes to same length by interpolating the values on 1000 points and build a matrix on which each column is averaged and resulting values are used to plot average metagene profiles. To generate average signal profiles, we selected the mm9 genes or identified enhancer regions that do not have any other annotation around boundaries within the profiled distance (Figures  2  and S2 ).
Removal of the annotations too close to each other is necessary to avoid mixing signals from close-by annotations that can cause misinterpretation of the results. Profiles around TSS shown in Figures  6  and S4  were performed on the most distal promoters for genes with multiple TSS. 4C-seq data processing and call of long-distance interactions All bait sequence (including and downstream of the primer sequence, up to but not including the GATC DpnII site) are trimmed by the demultiplexing Sabre tool (https://github. com/najoshi/sabre), allowing two mismatches, before mapping to the mm9 genome with Bowtie  (Langmead et al. , 2009)  and processing with the utility functions of the 4See browser  (Ben Zouari et al. , 2020). 4C profiles are given as the running mean values over sliding windows of 21 fragments.
Data and Code AvailabilityAll high throughput sequencing data used in this study have been deposited at GEO under accession number GSE144586. Wiggle files of ChIP-seq and RNA-seq data can be viewed on a dedicated UCSC genome browser (http://genome-euro. ucsc. edu/cgi-bin/ hgHubConnect?redirect=manual&source=genome. ucsc. edu) Track Hub using this link (https://ndownloader. figshare. com/files/ 22633064?private_link=3df2f43ff40a9ae78875).
ACKNOWLEDGMENTS We thank  Chales Lecellier  for his help and advice on TFBS analyses. This work was supported in J. -C. A. 's lab by grants from  Fondation pour la Recherche Medicale (FRM)  '' Amorcage Jeunes Equipes ''  AJE20130728183 ,  Agence Nationale de la Recherche  ( ANR) isplice  ( ANR-11-BSV8-0013 ),  ANR   ChromaTin  ( ANR-10-BLAN-1326 ),  ITMO INCA ''Dys3Dpoly ,'' ESGI 2011 (FP7 funding for high-throughput sequencing), and  Ligue Regionale contre le Cancer (Herault). L. P. was supported by a grant from  FRM  (4eme annee these FDT201904007910). The T. S. lab was supported by funds from the  European Research Council (ERC)  ( H2020 , Starting Grant  678624 -CHROMTOPOL-OGY ), the  ATIP-Avenir program , and the grant  ANR-10-LABX-0030-INRT , under program  ANR-10-IDEX-0002-02. A. M.
Software and Algorithms PASHA Fenouil et al. , 2016 https://cran. r-project. org/web/packages/ Pasha/ Bowtie2 Langmead and Salzberg, 2012 http://bowtie-bio. sourceforge. net/bowtie2/ index. shtml TopHat2 Kim et al. , 2013 http://ccb. jhu. edu/software.
Data and Code AvailabilityAll high throughput sequencing data used in this study have been deposited at GEO under accession number GSE144586. Wiggle files of ChIP-seq and RNA-seq data can be viewed on a dedicated UCSC genome browser (http://genome-euro. ucsc. edu/cgi-bin/ hgHubConnect?redirect=manual&source=genome. ucsc. edu) Track Hub using this link (https://ndownloader. figshare. com/files/ 22633064?private_link=3df2f43ff40a9ae78875). REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Rabbit polyclonal anti-H3K4me1 Abcam ab8895 Rabbit polyclonal anti-H3K4me3 Abcam ab8580 Rabbit polyclonal anti-H3K27ac Abcam ab4729 Rabbit polyclonal anti-H3K27me3 Millipore 07-449 Rabbit polyclonal anti-H3K79me2 Abcam ab3594 Rabbit polyclonal anti-RNA Polymerase II Santa Cruz Biotech Sc-899 Purified Rat Anti-Mouse CD4 -Clone GK1.
5 BD Biosciences 553727 Hamster Anti-Mouse TCR b Chain-Clone BD Biosciences 553174 H57-597 Rat Anti-Mouse CD44 -Clone 1M7 BD Biosciences 553133 Rat Anti-Mouse CD62L -Clone MEL-14 BD Biosciences 553152 Mouse Anti-Mouse I-A[b] BD Biosciences 553551 Chemicals, Peptides, and Recombinant Proteins RNaseIII Thermo Fisher AM2290 Turbo DNA-Free Thermo Fisher AM1907 Deposited Data ChIP-seq, RNA-seq, CapSTARR-seq and This study GSE144586 4C-seq data ChIP-seq data Cauchy et al. , 2016 GSE56395 ChIP-seq data Fenouil et al. , 2016 GSE38577 ChIP-seq data Koch et al. , 2011 GSE29362 MNase-seq data Cauchy et al. , 2016 GSE56395 Experimental Models: Cell Lines P5424 Dr Salvatore Spicuglia, TAGC Aix-Marseille N/A University, France Experimental Models: Organisms/Strains C57BL/6 wild-type IGMM N/A RAG 2À/À mice IGMM N/A.
Small-scale ocean dynamics around New Caledonia (22 • S) in the Southwest Pacific Ocean occur in regions with substantial mesoscale eddies, complex bathymetry, complex intertwined currents, islands, and strong internal tides. Using second order structure functions applied to observational ADCP and TSG dataset, these small-scale dynamics are characterised in the range of scales 3-100 km in order to determine the turbulent regime at work. A Helmholtz decomposition is used to analyse the contribution of rotational and divergent motions. A surface intensified regime is shown to be at work south and east of New Caledonia, involving substantial rotational motions such as submesoscale structures generated by mixed layer instabilities and frontogenesis. This regime is however absent north of New Caledonia, where mesoscale eddies are weaker and surface available potential energy is smaller at small scales.
, 2007)  and on biogeochemistry processes such as primary production  (Mahadevan, 2016; Lévy et al. , 2012). In addition to observations from satellite and modelling studies, there exist substantial databases of high-resolution in situ observations, which may be useful sources of information on small-scale oceanic features. One possible way to extract and characterise the small-scale content of in situ observations is to use a scale-dependent analysis. In particular, several studies have used spectral analysis through the usual Fourier transform to characterise the small-scale distribution of KE, based on horizontal velocities from Shipboard Acoustic Doppler Current Profiler (SADCP) (e. g. ,  Callies and Ferrari, 2013; Bühler et al. , 2014; Rocha et al. , 2015; Qiu et al. , 2017; Chereskin et al.
, 2019)  and tracer variance from temperature, salinity and chlorophyl measurements (e. g. ,  Hodges and Rudnick, 2006; Cole et al. , 2010; Cole and Rudnick, 2012; Kolodziejczyk et al. , 2015). This type of characterisation has gained popularity, particularly as the SWOT SSH requirements are defined in terms of spectra. Yet, spectral analysis is limited to data with uniform sampling and often requires a priori an interpolation step when applied to in situ observations. In parallel to these spectral analysis, the computation of structure functions (SFs) is a classic method to study the scale dependence of a physical quantity and has been used in early studies of turbulence to confront theories and experimental setups. SFs have been used to characterize the atmospheric KE spectrum in the upper troposphere and lower stratosphere  (Cho and Lindborg, 2001)  based on opportunity measurements made by commercial aircraft, while providing insights into the direction of KE cascades  (Lindborg and Cho, 2001).
To do so, we use a scale-dependent analysis based on both SFs, applied to existing in situ datasets including SADCP and TSG measurements. In order to anticipate the future altimetric swath observations and to design joint experiments where in situ observing systems are deployed under SWOT groundtracks, we address here three main issues: (1) How are KE and tracer variance distributed in the range of wavelength scale 3-100 km around New Caledonia? (2) Are there spatial and seasonal variations of those distributions? (3) What kind of dynamical information can we infer from those distributions? Section 2 describes the data we used from different observing systems: upper ocean velocities from shipboard ADCPs, surface temperature and salinity from TSGs, and sea level from altimetric data. Section 3 presents the computation of structure functions applied on SADCP and TSG data.
Horizontal velocities from Shipboard Acoustic Doppler Current Profiler Horizontal velocity can be reliably measured by Shipboard Acoustic Doppler Current Profilers (SADCP) mounted on the hull of vessels during transits or research cruises. We use the historical transects around New Caledonia gathered into one dataset by  Cravatte et al. (2015)  in order to describe the regional circulation. This database consists of a collection of 109 cruises that have acquired measurements of longitudinal u and transverse u ⊥ velocities, from 1990 to 2014 (see Table  1  of  Cravatte et al. , 2015,  for a full description). The SADCP data come from different institutes: IRD (73), JASADCP database from the University of  Hawaii (11) ,  CSIRO (23) , JAMSTEC data center (2). Most of the data are provided by 150 kHz SADCP instruments and cover an average range of 25 to 300 m depth, with an average vertical resolution of 8 meters and an average temporal resolution of 5 minutes.
Some SADCP instruments also provide measurements closer to the surface and down to 500 m depth. The available SADCP sections are shown in the bottom left panel of Fig. 2. More details about data quality control and data filtering applied at this stage are given in  Cravatte et al. (2015). The SADCP database has been edited to retain only data meeting certain requirements for computing structure functions. We have only considered high-frequency SADCP: shiptracks with minimal temporal sampling slower than 15 minutes were discarded from the analysis. The ship cruising speed must be faster than 4 m. s -1 (around 8 knots); this condition aims at avoiding the subsampling of internal waves whose average phase velocity is around 1 m. s -1 in the ocean. The segments were defined so that the ship travels almost in a straight line, with a 5 • variation of the ship heading allowed.
Sea surface temperature and salinity from thermosalinograph data High-resolution near-surface temperature and salinity have been collected by the French Sea Surface Salinity Observation Service (SSS-SNO) from voluntary observing ships (mainly merchant ships) using thermosalinograph (TSG) instruments  (Alory et al. , 2015). Tracers are measured near surface because sea water is pumped between 5 and 10 meter depth on the side of the hull, depending on the ship configuration. Measurements are made every 15 seconds and are averaged every 5 minutes by taking the median. The average spatial resolution associated with this 5-minute temporal resolution is around 2. 5 km for a 20-knot cruise speed. The TSG data can be downloaded from the LEGOS Sea Surface Salinity database 2. Only delayed-time data flagged as "Good" and "Probably Good" have been extracted and used in this study.
Delayed-time data undergo a more severe quality control than real-time data using the TSG-QC software 3. Flags are automatically attributed and are checked using visual inspection. Corrections of TSG timeseries are applied if needed by comparison with daily water samples and ARGO data. More details about measurements, processing and quality control of TSG data may be found in  Alory et al. (2015). The SSS-SNO network is global but has a better coverage in the tropical Pacific and in the North Atlantic. Thus, this dataset is particularly suited to study high-resolution surface tracers in the South West Pacific. Available sections around New Caledonia are presented in Fig. 2 's bottom right panel. Although the temperature and salinity are measured at 5-10 meter depth, we will assume in the rest of the study that they are representative of the tracer behaviour at the top of the mixed layer and we will use the terms sea surface temperature (SST) and sea surface salinity (SSS).
Surface to interior oceanic properties from ARGO climatology The climatologies of temperature and salinity over the first 500 meters of the water column are taken from the Roemmich-Gilson Argo Climatology (Roemmich and Gilson, 2009) computed over the period 2004-2016 4. This ARGO climatology is used to better characterise and understand the seasonal variations of the surface and interior oceanic properties jointly with the results obtained from the TSG and SADCP datasets. Exponential profiles of the vertical stratification N (z) are fitted to determine an e-folding scale b and a surface extrapolated buoyancy frequency N 0. These quantities will be used in the following to determine the empirical model for IGWs.