dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017). We generated a 2D representation of the sample-specific networks using a uniform manifold approximation and projection for dimension reduction (UMAP) approach (Supplementary Figure  4 ), and compared it to the PCA results on gene expression data. As expected, samples from the same tissue cluster together on the UMAP, and the V2specific regulatory networks generated from V2 growing leaves from the formerly and newly generated datasets cluster together. Notably, while in the PCA husk samples were isolated and located close to silk and internode tissues (Supplementary Figure  3 ), in the UMAP the husk-specific regulatory network was clustered with regulatory networks of other types of mature leaves, thus indicating stronger similarity in terms of gene regulatory network than gene expression levels between tissues that share similar developmental stages.
8% of enhancers active in V2-IST. We then compared the networks structures between Husk and V2-IST using ALPACA (see section 4)  (Padi and Quackenbush, 2018). This allowed us to identify regulatory modules (i. e. , groups of TFs that were co-regulating groups of genes) in each tissue-specific network and to compare the modules between husk and V2-IST. We identified 71 modules in the V2-ISTspecific network, and 67 modules in the husk-specific network. Among them, respectively, 12 and 11 modules contained at least one TF and five genes, and were retained for further investigation. In order to identify shared and tissue-specific modules, we then compared their gene content between husk and V2-IST using the jaccard index. Nine of them had high jaccard indexes (greater than 0. 5), indicating that they were very similar between the husk and V2-IST networks (Supplementary Table  7 , and gray modules in Figures  2A, B ).
Transposable Elements Are a Source of TFBS Sequences in Tissue-Specific Enhancers We took advantage of the regulatory networks and modules we identified to investigate the role of transposable elements (TEs) in the tissue-specific regulation of gene expression. We first compared the TE sequence content of husk-specific and V2-IST-specific enhancers. To this end, we annotated TE in enhancers using a recently updated TE database  (Ou et al. , 2019). We found that of the 971 enhancers present in the prior, 555 (57. 2%) were included in, or partially overlapping at least one TE. On average, when an enhancer overlapped a TE, about 18. 7% of the enhancer sequence was covered by the corresponding TE sequence (ranging from 0. 4 to 100%). We then tested the relative enrichment in TE superfamilies of huskspecific and V2-IST-specific enhancers (Figure  4A ).
8, resampled χ 2 p = 0. 05). Most often, however, these MITEs did not overlap with any known TFBSs. Because the JASPAR database mostly contains TFBSs from A. thaliana, we hypothesized that this could be due to lack of a relevant maize TFBS motif in the database. Indeed, despite the identification of hundreds of maize TFs  (Zhou et al. , 2020) , JASPAR 2020 only contains 22 maize-specific binding motifs  (Khan et al. , 2018). To investigate whether the detected MITEs contain a putative TBFS motif, we searched their sequences for conserved motifs by performing an enrichment analysis for 9-to 15-mers (see section 4). We found that MITEs included in husk-specific enhancers are enriched in 3 potential 15 pb TFBS motifs (Supplementary  GTTCYCAAACTAGCC. By comparing these sequences to known plant TFBS motifs (JASPAR database), we found that they are most closely related to Arabidopsis HB-like, MYB-like, and MADS TFBS motifs, respectively (Supplementary Table  10b ).
This will be particularly useful to understand how to improve maize response to drought. In our attempt to characterize biological functions expressed in a tissue with limited biological characterization, we nevertheless encountered two limitations. First, the lack of functional annotation of maize genes in public databases prevented us to precisely annotate some of the husk-specific regulatory modules. Second, our TFBSs prediction in maize enhancers is based on motifs included in the JASPAR Plantae motifs database, which are mainly derived from ChIP-seq experiments performed in Arabidopsis thaliana. We validated our TFBS prediction for one TF using published DAP-seq data (the only one for which data were available), thus suggesting that the JASPAR database is relevant to detect TFBS in maize genome.
Nevertheless, we are still likely to be missing a number of tissuespecific functions regulated by maize-specific TFs. While some progress toward the identification of maize TFBS motifs has been made through the use of sequence conservation analyses  (Tian et al. , 2019) , this limitation can only be circumvented by addition of new maize ChIP-seq-or DAP-seq-derived motifs to the TFBS databases, which is likely to occur in the near future. Despite these two caveats, we were able to provide candidate TFs and genes playing a key role in the expression of husk-specific biological functions. Our approach, coupled with the rapidly increasing data available on maize-specific TFBS motifs  (Burgess et al. , 2019) , will thus improve our capacity to concomitantly identify enhancers, TFs and genes involved in the regulation of the expression of biological functions in poorly characterized tissues.
Our results support the role of TEs as functional actors in the tissue-specific regulation of biological functions involved in leaf differentiation. We find that a substantial amount (∼ 60%) of the enhancers analyzed include TE sequences. This is higher than estimated in previous studies  (Oka et al. , 2017; Zhao et al. , 2018)  and likely arises from the fact that we used an upgraded maize TE database  (Ou et al. , 2019) , which allowed a more in depth characterization of TEs within enhancers. Several studies have shown that TEs can modify gene regulation under stress conditions, for instance in rice  (Naito et al. , 2009)  and in maize  (Makarevitch et al. , 2015). But the underlying mechanisms are still unclear. Several cases of TE-driven enhancers involved in the regulation of specific genes have been described in plants.
1% of reads (median) were mapped unambiguously to a gene for the Oka et al. dataset (ranging from 87. 9 to 92. 1%), and 95. 8% for the AMAIZING Gene Atlas dataset (ranging from 92. 8 to 97. 7%). Counts per gene from both datasets were then pooled and normalized together using the tissue-aware smooth quantile normalization from the R bioconductor YARN package version 1. 1. 1 (Paulson et al. , 2017) , using the normalizeTissueAware function with the method = "qsmooth" option. Data were then corrected for single-end/paired-end batch effect using the removeBatchEffect function from the R bioconductor limma package version 3. 28. 2.
Enhancer Definition and TFBS Identification Candidate enhancer sequences were extracted from the bed files containing coordinates of enhancers from  Oka et al. (2017)  and the AGPv4 maize genome sequence using bedtools getfasta. They were scanned for Transcription Factor Binding Sites (TFBS) using the FIMO software from the MEME v. 5. 0. 5 suite  (Grant et al. , 2011)  using default parameters. To this end, we retrieved the MEME core plants position frequency matrix files corresponding to the binding sites of 489 transcription factors available in JASPAR database (accession October 31, 2019)  (Khan et al. , 2018). Matches with a q-value (Benjamini-Hochberg corrected p-value) lower than 0. 01 were retained. Significance of the TFBS results was tested by comparing the number of bases covered by TFBSs in original candidate enhancer sequences to this of random sequences obtained by shuffling enhancers dinucleotides.
To this end, for each enhancer, we generated 1,000 random sequences using the BiasAway software  (Worsley Hunt et al. , 2014)  and computed resampling p-values by counting the number of random sequences for which TFBS coverage exceeded the one of the original enhancer. We compared the TFBS motif content of husk and V2-IST enhancers by using the AME software from the MEME suite version 5. 0. 5  (McLeay and Bailey, 2010). Enrichment in particular TFBSs among husk enhancers was estimated by setting husk as primary and V2-IST as background sequences. This procedure was swapped to obtain V2-IST enhancer TFBS enrichment. We tested enrichment for motifs using the Fisher exact test, and p-values were corrected for multiple testing using the Bonferroni method. E-value threshold was set to default (E ≤ 10).
TF-Gene Network Building We built tissue-specific regulatory networks using the PANDA and LIONESS softwares  (Glass et al. , 2013; Kuijjer et al. , 2019). PANDA represents regulatory relationships between TFs and genes as a bipartite network, nodes being either TFs or genes, and edge weight being proportional to the strength of the TF-gene regulatory relationship. The method requires as input a prior representing potential regulatory relationships between TFs and genes. The prior is a gene × TFs matrix of zeros and ones, were ones indicate the presence of a putative TFBS in a cis-regulatory region of the gene, and zero its absence. This prior edge matrix is then updated using a protein-protein interaction (PPI) matrix that represents interactions between TFs and a gene co-expression matrix.
This relies on a message-passing algorithm that verifies both the "responsibility" and the "availability" of each edge  (Glass et al. , 2013). The final PANDA output is an "aggregate" network model representing gene regulation in a specific dataset. LIONESS is a mathematical framework that extracts networks for individual samples from such an aggregate regulatory network  (Kuijjer et al. , 2019). In this study, the prior TF-gene interaction matrix was obtained by crossing enhancer coordinates with gene coordinates. The gene-enhancer maximal distance was set up to 250kb. This threshold is based on analysis of the enhancernearest gene distance distributions of  Ricci et al. (2019) and Lu et al. (2019) , which show that 99% of distances are below 250 kb. This allows us to capture a large fraction of distal cis-regulatory elements, while keeping the number of genes and enhancers to analyze at a computationally acceptable amount.
All enhancers within 250 kb upstream or downstream of a gene transcription start site were annotated as a potential regulator, and prior edges between this gene and each transcription factor mapping to those enhancers were set to 1. All other prior edges were set to 0. The co-expression matrix was obtained from the mRNA-seq normalized data from the 45 samples (13 tissues). In absence of a detailed protein-protein interaction matrix for plants, we used an identity matrix. Using PANDA and LIONESS, we generated 46 networks: a global network (PANDA), and 45 sample-specific ones (LIONESS). Raw sample-specific edges weights (EW) were log-transformed We then used the online version of Tomtom  (Gupta et al. , 2007)  to compare these motifs with known TFBS motifs available in the JASPAR 2018 non-redundant core plants database.
ACKNOWLEDGMENTS We thank  Alain Charcosset  for the Amaizing program coordination,  Harry Belcram  and  Agnès Rousselet  for help in tissue grinding and extractions,  Peter Rogowsky  for hypocotyl and root experiment coordination,  Jérôme Laplaige  for root and hypocotyl sampling and extraction,  Sylvie Coursol  for internode experiment coordination,  Sandrine Chaignon  for internode sampling and extractions, and  Carine Palaffre  for grain experiment set up and sample collection. We thank the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  for providing computing and storage resources,  Shujun Ou  for providing an upgraded TE maize database that we used for TE annotation,  Anthony Mathelier  for help with TFBS identification, and  Megha Padi  for assistance with ALPACA. This manuscript has been released as a pre-print at https://www.
DATA AVAILABILITY STATEMENT All steps of the experiment, from growth conditions to bioinformatic analyses, were managed in CATdb database  (Gagnot et al. , 2007)  (http://tools. ips2. u-psud. fr/CATdb/) with Project ID NGS2017 06 AMAIZING, experiment Amaizing B73 WW. This project has been submitted from CATdb into the international repository GEO (Gene Expression Omnibus,  Edgar et al. , 2002;   http://www. ncbi. nlm. nih. gov/geo) with ProjetID GSE151455. These data are available upon request to Clementine Vitte, and will be made publicly available upon article acceptance.
The goal of the ESSnuSB H2020 Design Study is to organise European physicists and accelerator engineers in co-operation with the ESS Laboratory and the Garpenberg Mining Company to study and produce a Conceptual Design Report for the ESSnuSB project. In the work package five of the ESSnuSB project we developed an event display based on the graphics technology developed at Orsay from 2010 (developments done at LAL up to 2020 and now continued in the new Orsay IN2P3 IJCLab laboratory that absorbed LAL). This technology permits to have programs running natively on most of today's interactive devices as desktop/laptop under Linux, macOS or Windows-10 but also on iOS/Android smartphones and tablets. Since 2019, with the same core technology we can run in most web browsers by using the WebAssembly and WebGL technologies.
Main ideas EsbRootView is fully written in C++ (98 flavour) with good part of the code being pure header. Being able to run on most interactive devices, including iOS and Android, is made possible by the fact that since C++98 and GL-ES (OpenGL for Embedded Systems, a graphics rendering standard), are available for all of them. A key point is that at Orsay we have light portable code (inlib/rroot classes) to read ROOT files and then we can read on all our devices the files describing the detectors and the events issued from the ESSnuSB detector simulations. We have implemented an event model with a one_event class managing instances of Es-bMCTrack, EsbDetectorPoint, EsbHit, EsbFgdHit classes describing data issued from the simulation. All the items of the event model received one or more graphical representations.
For the geometries, we can read the TGeo classes stored in the ROOT files and we have graphical representations of them in our inlib library. To view geometries, we have nothing specific to ESSnuSB. To let the user customise things, we have a default Bourne shell like scripting, the "insh", which is described below in section 6. About our event model, we are aware that this duplicates the event model code found in the EsbRoot framework used for the simulation, but for the moment this one is not portable, in particular because it is tied to CERN-ROOT  [2] , and then not usable for us. Being able to have a portable unique event model shared with the simulation and analysis would be a nice target to aim for the mid and long range term of the ESSnuSB project.
For example a shape node (cube, sphere, polyhedron), when traversed, will give to the render_action the graphics primitives (points, lines, segments, triangles) representing that shape. A camera node will give a projection matrix, a matrix node will give a model matrix. A common graphics engine being GL-ES, we have the exlib::sg::GL_action class that does that for GL-ES. But we have also various render_action to do offscreen rendering, web rendering (using WebGL) or to handle private company specific engine, such as the Apple/Metal one. Note that we keep within the namespace "inlib" only code that relies on the STD/STL and on some few system functions not being in C++98. exlib::Windows::viewer classes for X11 or Windows. By windowing we mean the code to create a window to draw in and get events as click or touch on it.
scripting with insh EsbRootView is equipped with the core of a Bourne like shell called "insh" for INlib SHell. It had been introduced to have a way to customise things by using a scripting syntax known by any UNIX pedestrian and easily implementable by a "doer" in a light way. insh has the basic mechanisms found on any bash shell: variables, env variables, dollar replacement of a variable, backquoting, execute other scripts, source other scripts and, obviously, execute commands with options. For example, in a "next event" insh script, you may see: # this is a comment. event_index='event_index' gui_show_console ${event_index} scene_clear_dynamic event_vis MCTrack -color=red event_vis WCDetectorPoint -color=green.
The startup, event, anim. insh scripts The "setup" main menu item permits to choose which detector to work with. Depending on the selection, dedicated startup. insh, event. insh, anim. insh scripts are copied from internal versions coming with the installation (from the "resources" directory) into the user Documents/EsbRootView directory. The startup. insh script is executed at each startup of EsbRootView, the event. insh when doing a "next event" and the anim. insh when applying the "scene anims" main menu item. These scripts are good starting points to customise the general overall behaviour of EsbRootView.
Windows On Windows, we use the standard WIN32 API for the windowing, the Microsoft opengl32. lib for the rendering and VisualC++ for the compiler (the cl. exe command). VisualC++ can be installed and used with a free version of VisualStudio brought from Microsoft web sites. The build is done under CYGWIN by using our "bush" set of bash scripts. The usage of the VisualC++ from here is done by using a "bush/vc++" script encapsulating the usage of the cl. exe command in a way that the arguments passed to bush/vc++ have the same syntax than for a g++ command. Note that today we do not use DirectX for the rendering, but our architecture would permit to have a straight exlib::Windows::DirectX_action class for the rendering if one day or another Microsoft would deprecate its OpenGL implementation.
iOS On iOS we use the UIKit for the windowing, the Apple GL-ES for the rendering and the clang++ compiler coming with Xcode. We have not yet an Apple/Metal version for the rendering. It is in the job list, but in principle the exlib::metal::action done for macOS should be reusable "as is" for iOS. Note that here we use Xcode on a Mac to build the application. Xcode is more or less mandatory if wanting to deliver for the iOS App Store or upload, in a simple way, the cross compiled application on an iPhone or iPad. Today EsbRootView had not been deposited on the macOS and iOS App Stores, but it is in the job list to do so.
Android To build for Android, we use the SDK and NDK developments kits to cross compile the application from a Mac. We use some minimal java for the windowing (to create an "activity") but use the C EGL library of the NDK for the rendering. We build the "EsbRootView. apk" by using the SDK tools (ant and GNUmake) along the adb program to upload the apk on a device connected to the Mac. Today EsbRootView had not been deposited yet on the Google Play Store, but it is in the job list to do so.
WebAssembly For visualisation in the WWW, we use WebAssembly  [4]. In most web browsers there is a virtual machine (the wasm) that permits to execute a C++ application locally. By installing the emscripten SDK on a Mac or a Linux, someone can cross compile (with the emc++ compiler) a C++ application to produce a wasm binary. The windowing is done by using a "canvas" of the web browser, and the rendering is done by using WebGL. We did a exlib::wasm::render class that produces, then from the C++ in the wasm binary, a textual javascript code to order the rendering of primitives in the web browser by using WebGL. The SDK provides an index. html file along some javascript files to create the WebGL canvas, download and activate the wasm binary. The overall build is done again with our "bush" set of bash scripts.
At the end we have the wasm binary, several html and javascript files that can be deposited in static web pages. There is no need to have dedicated web servers to deliver this web application. There is a WebAssembly version of EsbRootView accessible from the EsbRootView section of the "softinex portale" under the gbarrand. github. io web pages. ("softinex"  [5]  is the entity that gather all our applications done with our inlib and exlib libraries). After having passed the startup, that needs to download and activate the wasm binary, the user experience is rather good. Anyway the overall reactivity of the web app is not so good than the native application. This is due to the fact that here we are pilling up layers as running inside a virtual machine, doing "javascript string programming" to do the WebGL and then having the WebGL activating the native GL-ES from javascript.
Web pages and repositories The web pages of EsbRootView are at https://gbarrand. github. io under the section "EsbRootView" at left. The source code of stable releases is on github at https://github. com/gbarrand/EsbRootView. git. The license is a customisation of the FreeType license. For each release, we arrange to provide binaries at least for Linux, macOS and Windows. A WebAssembly version is available through the web pages.
Conclusions EsbRootView is probably the first HEP event display written in C++ able to exploit natively all the nice devices that we have in our hands today, including desktops, laptops but also smartphones and tablets. Its flexible architecture permits also to have a web version done on the same C++ core as for the other platforms. It permits to have animations that strengthen the intuition about what happens in the detectors. Its scripting mechanism, inspired by the Bourne shell permits a strong customisation with a short learning curve. Up until now, EsbRootView proceeds well. The only downside that we see is the inability to have common event and detector models, along a coworking IO service, shared with the other parts of the ESSnuSB software. The lack of portability of the IO service and of frameworks in general is a quite common problem in HEP that, we think, refrains this field to enjoy fully all aspects of computing technologies that we have today.
Context. The analysis of exoplanetary atmospheres by means of high-resolution spectroscopy is an expanding research field which provides information on chemical composition, thermal structure, atmospheric dynamics and orbital velocity of exoplanets. Aims. In this work, we aim at the detection of the light reflected by the exoplanet 51 Peg b employing optical high-resolution spectroscopy. Methods. To detect the light reflected by the planetary dayside we use optical HARPS and HARPS-N spectra taken near the superior conjunction of the planet, when the flux contrast between the planet and the star is maximum. To search for the weak planetary signal, we cross-correlate the observed spectra with a high S/N stellar spectrum. Results. We homogeneously analyze the available datasets and derive a 10 -5 upper limit on the planet-to-star flux contrast in the optical.
Such detection would imply that 51 Peg b has an unusually high geometric albedo for the class of hot Jupiters (HJs), and makes it stand out in the search for elusive correlations between atmospheric properties and stellar irradiation  (Heng & Demory 2013). Moreover, the same optical spectra have been reanalyzed by Di  Marcantonio et al. (2019) , who do not reproduce the claimed signal, yet cautioning that their method could not reach the accuracy level needed for the detection of the signal. In this perspective, we here reanalyze and extend the previous analysis of optical spectra using all the available data in the HARPS and HARPS-N archives. Firstly, in Sect. 2 we provide a detailed mathematical framework, while in Sect. 3 we describe the analyzed datasets. In Sect. 4 we refine the ephemeris of 51 Peg b using the full set of radial velocity data available in the literature, complemented with the newest measurements.