dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample details were manually curated to be easily understandable by biologists. To make the data easily searchable, a user-friendly web interface was developed that provides versatile data mining web tools via on-the-fly generation of output graphics, such as expression bar plots, comprehensive in planta representations and heatmaps of hierarchically clustered expression data. In addition, it allows for the identification of co-expressed genes and the visualization of correlation networks of co-regulated gene groups. TomExpress provides one of the most complete free resources of publicly available tomato RNA-Seq data, and allows for the immediate interrogation of transcriptional programs that regulate vegetative and reproductive development in tomato under diverse conditions. The design of the pipeline developed in this project enables easy updating of the database with newly published RNA-Seq data, thereby allowing for continuous enrichment of the resource.
The high-quality reference genome sequences can be used as a backbone to guide RNA-Seq read mapping, thus helping to establish a precise and accurate gene expression value derived from the read counts mapped on each annotated gene. However, the production and processing of the huge RNA-Seq data in different laboratories generates non-intentional undesirable effects. In particular, bioinformatics tools and the statistical methods used to calculate and normalize expression counts for each gene are not standardized. Indeed, different mapping parameters, different genome sequences, or different gene models and gene IDs are used in the published RNA-Seq projects. In addition, each study focuses on a limited set of genes that are linked to the specific scientific question addressed. To our knowledge, a tomato database allowing for the integration of the whole set of expression data arising from different studies does not yet exist.
Another shortcoming relates to the description of the RNA-Seq experiments and the corresponding raw sequence data on the public databases SRA/ ENA. The annotations of the samples are often incomplete and need manual curation to be more comprehensive. To fill this gap and unify the whole tomato expression data set, the TomExpress platform (http://gbf. toulouse. inra. fr/tomexpress) has been developed with the aim of providing: (i) a standard pipeline to process and normalize all the tomato RNA-Seq raw sequence data; (ii) manually curated sample annotations; (iii) a dynamic database for sequence and experiment data handling; (iv) a userfriendly web interface to instantly access the whole set of data; and (v) several web tools to visualize and explore expression data (see Figure  1  for TomExpress screen shots).
RESULTS AND DISCUSSION RNA-Seq data source and manual curation of sample details SRA and ENA public databases dedicated to NGS were probed to identify the tomato RNA-Seq projects. In total, 29 studies were found, and the corresponding raw data and descriptions were downloaded (http://gbf. toulouse. inra. f r/tomexpress/www/projectsTomExpress. php). These studies provide sequences derived from 349 RNA samples corresponding to 222 conditions. On average, three biological replicates per condition and 10 million reads per sample were performed. The transcriptomic sequencing studies cover most tomato organs, including roots, stems, leaves, flowers, and an exhaustive list of fruit development and ripening stages. In addition to a large panel of wild-type tomato cultivars, these transcriptomic data also cover several mutant lines as well as responses to biotic interactions with viruses, pathogenic bacteria and fungal and mycorrhizal interactions.
Consistency and robustness of the TomExpress platform for the expression of ripening-associated genes To experimentally validate the data delivered by the TomExpress platform, the expression patterns of 12 ERF genes assessed either by qRT-PCR or obtained by in silico mining using the TomExpress tool were compared. These ERF genes were previously shown to be associated with ripening  (Liu et al. , 2016) ; their transcript levels were therefore assessed in tomato fruit tissues at four different ripening stages: namely, Mature Green (MG); Breaker (B); Orange (Or); and Red (R). Two methods were applied to quantify the correlation between two paired samples: the Spearman and Pearson correlation analyses. Pearson's coefficient quantifies a supposed linear relationship between given measures, while the less restrictive Spearman coefficient quantifies the intensity of a monotonic relationship.
Reads are then aligned to the same reference sequence using TopHat2/Bowtie2 alignment tools. For each gene, the number of read counts mapped on the exons part are calculated using HTSeq-count tool. The whole counts are normalized between the RNA-Seq projects. Sample details downloaded from ENA are manually curated and integrated in the database together with the normalized expression data. coefficient is low for ERF. C1 and ERF. F4, the expression profiles provided by in silico mining are similar to those obtained experimentally. It is worth mentioning here that the TomExpress data correspond to the mean value of normalized counts of expression data achieved with different tomato cultivars; by contrast, the qPCR data refer to only the Microtom cultivar. Therefore, the differences may reflect a potential specificity of this cultivar.
Database features and data mining tools To handle, explore and make searchable the whole set of processed data, the TomExpress platform was developed with integrated and versatile data visualization and mining web tools via a friendly dedicated web interface. Data search browser. The TomExpress home page provides access to the whole expression data set through a comprehensive form page. Users can target specific genes by specifying their iTAG gene IDs or search genes by key words in iTAG gene descriptions or gene names. They can check expressions by selecting specific developmental stages or treatments that are easily understandable on this form. Finally, they can check expressions in wild-type cultivars only or can include mutant lines if desired.
Data visualization table. When users submit their desired search options and parameters to the TomExpress platform, the expression results corresponding to the specified genes and experimental conditions are first shown in an interactive web table. The first column presents the list of genes in study; from this column, the users can be directed to the Sol Genomics Network (SGN) web page of the corresponding gene by clicking on the SGN logo that gives all details concerning the sequence information and the structural annotation of the gene(s). In the same way, a second external link can direct users to the MapMan functional annotation. The first line of the table presents the list of global conditions chosen by the user; for each row of this line, a dedicated link offers access to the sample details, the published work related to the gene(s) when available, and to the row data table in the SRA or ENA databases.
, 2013). The correlation map is interactive, allowing users to view additional information on gene annotations and on correlation values. Correlation data are also visualized as a heatmap after a hierarchical clustering to highlight the positively and negatively correlated groups. Finally, a web table with the pairs of co-expressed genes is displayed. This table displays the correlation values and the functional annotation details of each of the co-expressed genes. Export of processed expression data and figures. All data tables and figures processed by the TomExpress platform built-in web tools are freely downloadable for any further custom analysis or direct integration in scientific presentations or other types of documents. Of note, unlike the web version of the pictures that is limited to a resolution adapted for screen visualization, the resolution of the downloadable version of pictures was increased to meet requirements for hard-copy printing.
TomExpress a versatile tool: case study and application As shown below, in addition to the mining and visualization of expression data in a holistic way, TomExpress provides versatile tools that allow for gaining insight into a particular biological process just by in silico search. Furthermore, the TomExpress platform offers powerful tools to define the most appropriate reference genes to be used in quantitative RT-PCR experiments aiming to assess transcript accumulation in specific plant tissues or organs. Finally, the expression and clustering analysis tools offered by TomExpress can also reveal potential redundancy among gene family members. TomExpress is a powerful tool to find appropriate RT-PCR reference genes for different plant tissues. Data normalization is always instrumental for accurate expression monitoring of individual genes by real-time RT-PCR at the transcript level.
Interestingly, TomExpress can be used to identify and validate new candidates, such as the GAGA-binding transcriptional activator (Soly-c04g008380), which displays a nearly constant expression in various fruit tissues (Figure  5 ), clearly showing that this gene is much more suitable as an internal reference in qRT-PCR studies dealing with tomato fruit, flower and seeds. Indeed, while ubiquitin transcript accumulation varies up to six times in reproductive tomato tissues, the abundance of GAGA-binding transcriptional activator transcript levels vary two times at most. Moreover, when comparing tomato vegetative and reproductive tissues, the variation in transcript levels exceeds 10 times in extreme cases. Altogether, TomExpress proves to be effective in finding more suitable reference genes than ubiquitin for gene expression profiling in tomato.
Taken together, the data and tools offer an opportunity to biologists to discover new stories, hoping to gain insight from functional genomics in an accelerated way. Indeed, the functional annotation of many tomato genes has not yet been established. The identification of co-expressed groups can help to assign a biological process to these genes. To validate the predicted functional annotation of a particular gene of interest and promising candidate genes in its co-expressed group, biologists can use a reverse genetic approach. The current version of TomExpress holds approximately 349 RNA-Seq samples from public and private data. The pipeline developed in this project allows the database to be easily updated with newly published RNA-Seq data, allowing this resource to be enriched and up-to-date with supplemental samples.
As TomExpress is fully dedicated to processing tomato RNA-Seq data, it makes this database complementary to other databases more specialized on microarray technology or covering a subset of public tomato RNA-Seq data, such as TED  (Fei et al. , 2006)  and the Botany Array Resource (BAR;  Toufighi et al. , 2005)  databases. Other omics data generated using NGS technologies are publicly available. Part of these omics data, such as epigenomics or ChiP-Seq, can help biologists to understand how transcriptomes change. Integration of these data into TomExpress would help to define combinatory epigenetic and/or transcription factors that may orchestrate the different transcriptional programs that define cells, tissues and organs in different conditions.
Figure2. The TomExpress data processing and integration workflow. Public or in-house private data are downloaded and subjected to a quality control step. Reads are then aligned to the same reference sequence using TopHat2/Bowtie2 alignment tools. For each gene, the number of read counts mapped on the exons part are calculated using HTSeq-count tool. The whole counts are normalized between the RNA-Seq projects. Sample details downloaded from ENA are manually curated and integrated in the database together with the normalized expression data.
ACKNOWLEDGEMENTS This research was supported by the  'Laboratoire d'Excellence' (LABEX)  entitled  TULIP  ( ANR-10-LABX-41 ), by the  ANR   TomEpiSet  project and the  TomGEM H2020  project, and benefited from networking activities within the European  COST Action   FA1106. The authors are grateful to  Christophe Klopp  from  GenoToul bioinformatics  for his help.
1 ). Full exploration of the generality of resolution extension/suppression requires follow-up studies. Angular refinement. The experimentally measured tilt angles may not always coincide with the true orientations of the projections. This could be the result of many causes including instrument misalignment, slipping, beam-induced motion, vibration, thermal effects, or software error. To achieve high-resolution 3D reconstruction, we implement an angular refinement procedure to reduce the tilt angle error, which consists the following four steps. (i) An initial 3D reconstruction is computed using the experimentally measured tilt angles. (ii) For the j th projection, a series of 2D projections are calculated from the 3D reconstruction by varying the three Euler angles: 𝜙 ∈ [𝜙 𝑗 -𝛿𝜙, 𝜙 𝑗 + 𝛿𝜙], 𝜃 ∈ [ 𝜃 𝑗 -𝛿𝜃, 𝜃 𝑗 + 𝛿𝜃], 𝜓 ∈ [𝜓 𝑗 -𝛿𝜓, 𝜓 𝑗 + 𝛿𝜓 ], where (  ,   ,   ) are the current best fit for the Euler angles of the j th projection.
Results Numerical simulations on the reconstruction of a biological vesicle. Numerical simulations on the 3D reconstruction of a 64x64x64 voxel vesicle model (Figs. 2a-c ) were performed using GENFIRE, EST, FBP and SIRT. Simulated projections were obtained by first calculating 2D Fourier slices of the 3D model for given angles. The corresponding real-space projections were then computed by applying the inverse FFT to the Fourier slices. This code is also included in the GENFIRE package and can be accessed graphically using the Projection Calculator. To evaluate the performance of various reconstruction algorithms with noise, we calculated 71 projections with the tilt angles ranging -70. 1º to +70. 1º. Noise was added to the projections at levels similar to that observed in cryo-EM images of cellular structures.
GENFIRE reconstruction on experimental data of a porous material. To perform a quantitative comparison between GENFIRE and other iterative algorithms on experimental data, we acquired a tomographic tilt series of a Mo3Si alloy annealed at 1100° C. Mo-Si and Mo-Si-B alloys are resistant to oxidation and creep and are among potential candidates with high melting temperatures to replace currently widely used Ni-based superalloys  50, 51. The experiment was conducted on an FEI TitanX 60-300 in STEM mode equipped with a Gatan high-angle annular dark field detector. The microscope was operated at 200 keV with electron beam current ~40 pA, a convergence semi-angle of 10 mrad, and a camera length of 91 mm. A total of 129 projections were collected with a tilt range from -58 and +70 in 1 increments.
The tilt series was separately reconstructed with GENFIRE and FBP (Fig. 6 ). The GENFIRE reconstruction was performed for 100 iterations with a loose cubic support, while the FBP reconstruction was computed using IMOD  54. Several lowcontrast features are visible in the GENFIRE reconstruction that are difficult, if not impossible, to identify with FBP. Of particular interest in this dataset was the interactions between the marine cyanobacterium and cyanophages. Fig. 6  shows a slice through the reconstructed volumes capturing the penetration of a cyanophage into the cell membrane during the infection process. This interaction has caused a local depression in the cell membrane, and the shown cross section passes through this depression as well as the viral capsid and appendage (Figs. 6c-j ).
Acknowledgements We thank  M. Taylor  and  J. Perepezko  for the preparation of the Mo3Si alloy sample. This work was supported by  STROBE: A National Science Foundation Science & Technology Center  under Grant No. DMR 1548924 , the  Office of Basic Energy Sciences of the US DOE  ( DE-SC0010378 ), the  NSF   DMREF program  ( DMR-1437263 ), and the  DARPA   PULSE program  through a grant from  AMRDEC. Work at the Molecular Foundry was supported by the  Office of Science, Office of Basic Energy Sciences , of the  U. S. Department of Energy  under Contract No. DE-AC02-05CH11231.
Viviant unpublished data). The first step of the map creation involved computing the Euclidean distance of each cell to the island. Then, all cells more than 500 km away or within the 50 km wide Kerguelen plateau were set to zero abundance. The distance to resources (Dist) represents the inner limit of the highest possible abundance so that there is a gradient of probability (from 0. 0 to 1. 0) from the edge of the plateau to this limit (S1 Fig). We defined nine possible distances to resources (every 50 km between 100 and 500 km). Maps of prey were randomly drawn using the Gaussian field method (GaussRF function of the R software  [41] ; RandomField package  [42] ). To this end, for each map the parameter "scale" of GaussRF defined an aggregation level. We defined eight arbitrary levels of prey aggregation (Aggreg) from dispersed to highly aggregated (i.
Simulation model Entities, state variables and scales. The time step for the simulations was fixed to one hour and day/night alternation (16/8 hours) was taken into account. For each female, four main attributes were embedded in the data structure and were updated every hour: (1) location of the female (X seal , Y seal ), (2) her energy (MJ), (3) her decision whether or not to fish and (4) her decision to return to the island or to continue foraging. The pup energy was updated similarly. Females and pups parameters are characterized by several state variables listed Table  1. Process overview and scheduling. Several processes can be involved every hour, depending on the period and females' location. MarCPFS processes are shown in the flow chart diagram (Fig  1 ). During the prospective prenatal period (15 days), the female explores the environment map and memorizes the richest resources locations.
One of the two types of memory was assigned to each female. We considered the following two cases: no memorization at all (Mem0); memorization of the best patch location discovered during the prospective prenatal period and updating of this location throughout the pup-rearing period (Mem1). The initial values of the foraging behavior such as the mean duration of the trip and the mean duration ashore to suckle the pup were obtained from the literature (  [29, 38] ; Table  2 ). We calculated the initial mass of the female from data collected on animals just before their departure from the island (C. Guinet, unpublished data). Equation (n = 147, R 2 = 0. 49) for initial female mass (kg) was: W seal ¼ 0:5246 Â length À 29:595 The female body length was fixed for each simulation (85, 100, 115, 130 or 145 cm).
In particular, the quality of the patches of juvenile Pollock on which Northern fur seals feed, their structuring into clusters and inter-patch distance, were found to play major roles. In order to obtain more detailed and accurate results, we have already engaged in coupling MarCPFS with a physicalbiogeochemical model of the ocean dynamics (including the micronekton dynamics: SEAPO-DYM,  [76] ). This coupling, after modifications and new calibration of our model, should allow us to verify that some of the prominent results we presented can be, to some extent, generalized to other central place foragers such as seabirds. Simulation code is available on request. Synthetic simulation results are available on the MarCPFS DataBase: http://www2. sophia. inra. fr/MarCPFS_Database.
Acknowledgments Some of the data used in this study were collected with the support of  Institut Paul Emile Victor  ( IPEV Program 109 ) and  Terres Australes et Antarctiques Francaises. We thank these organizations and all the fieldworkers involved in the collection of data in the Kerguelen archipelago. The MarCPFS DataBase was kindly developped by  Roger Boll. We would like to warmly thank  Dr Celine Feillet  for reviewing the English and anonymous reviewers who provided helpful suggestions and constructive comments. The simulation code was developped in C-ANSI and built thanks to the open sources cross-platform IDE CodeBlocks ( 13. 12 ) and the Gnu Compiler gcc 4. 8. 2.
One of the most challenging and recurring problems when modelling plasmas is the lack of data on key atomic and molecular reactions that drive plasma processes. Even when there are data for some reactions, complete and validated datasets of chemistries are rarely available. This hinders research on plasma processes and curbs development of industrial applications. The QDB project aims to address this problem by providing a platform for provision, exchange, and validation of chemistry datasets. A new data model developed for QDB is presented. QDB collates published data on both electron scattering and heavy-particle reactions. These data are formed into reaction sets, which are then validated against experimental data where possible. This process produces both complete chemistry sets and identifies key reactions that are currently unreported in the literature.
Introduction Realistic plasma models of many processes rest on the availability of reliable atomic and molecular data, so that the models are able to replicate the processes that drive the plasma at the submicroscopic level. Particularly for low-temperature plasmas, which are substantially molecular in composition, the set of possible processes, which we refer to as reactions below, can be very large. For low-temperature plasmas, accurate and comprehensive reaction datasets enable complex modelling of plasma-using technologies that empower our technology-based society  [1]. Assembling appropriate datasets is therefore of critical importance. For a given plasma composition, there are sets of species that are present in the plasma and a set of processes, generally called reactions, that will link the species or different states of the species.
Assembling plasma chemistries is far from straightforward. While there may be several chemistries available for relatively simple systems such as molecular nitrogen plasmas  [3, 4, 5, 6, 7] , they generally do not exist for more complex problems such as the chemical mixtures typically used in etching and other technological plasmas. Indeed, given that reactions involving molecular radicals frequently remain completely uncharacterised  [8] , it is often a challenge to assemble a complete reaction set for these chemistries. Here we present the Quantemol database (QDB). There are a growing number of databases aimed at supplying the needs of plasma modellers. For example, the recent LXCat project of Pitchford et al  [9]  aims to provide a web-based platform for data needed to model low-temperature plasmas.
In practice LXCat considers electron collision processes but not heavy-particle (chemical) reactions. While both QDB and LXCat are set up to accept and provide multiple datasets for a single process if they are available, QDB aims to recommend a dataset for a particular application while LXCat leaves this choice to its users. The Phys4Entry database provides (ro)vibrationally resolved collisional data, including heterogeneous processes, for modeling re-entry plasmas  [10]. For low-temperature, astronomical plasmas KIDA  [11, 12]  and BASECOL  [13]  provide data on chemical reactions and collisional excitation, respectively. QDB aims to provide a repository for cross sections and/or rates for key reactions needed for models of low-temperature, i. e. molecular, plasmas. QDB collects data on both electron scattering and heavy-particle reactions and aims to facilitate and encourage peer-to-peer data sharing by its users.