dataesr/scientific-paragraphs-categorization huggingface dataset:
Regulation of transcription upon adi-pogenic induction is thus much sharper than what can be observed using more classical RNA-seq methods or QuantSeq. Our data also reveal a delay between transcriptional regulation and alteration of total mRNA levels. Finally, the effect of sumoylation inhibition on nascent transcripts (SLAMseq) was more pronounced than on total mRNA levels (QuantSeq). This is consistent with the mild alteration of total mRNA levels by ML-792 that was previously observed in another cellular system using classical mRNAseq  (43). The divergence between transcription and total mRNA data might be due to compensatory mechanisms regulating mRNA stability, which could also play an important role during adipogenesis. The investigation of the biological significance of the deviation between transcription and mRNA stability during adipogenesis will be the subject of our follow-up studies.
FUNDING Helse Sør-Øst RHF [2017064, 2019096];  Research Council of Norway  [301268, 261936, 314811];  Norwegian Cancer Society  [182524, 208012];  Research Council of Norway, Centers of Excellence  funding scheme [262652];  Novo Nordisk Foundation  [ NNF14CC0001 ,  NNF13OC0006477  (in part)];  Independent Research Fund Denmark  [ 4002-00051 ,  4183-00322A ,  8020-00220B ,  0135-00096B ];  Danish Cancer Society  [ R146-A9159-16-S2 ]; Sequencing was performed by the GenomEast platform, a member of the ' France Genomique' consortium  [ ANR-10-INBS-0009 ];  EPIC-XS , funded by the  Horizon 2020 programme of the European Union  [ 0000062 ,  EPIC-XS-823839 ]. Funding for open access charge:  Helse Sør-Øst RHF.
DATA AVAILABILITY The SLAMseq, QuantSeq, SUMO ChIPseq and RXR ChIPseq data have been deposited to the Gene Expression Omnibus with accession number GSE167222. The accession numbers for previously published PPAR␥ and RXR ChIPseq data are GSM340795, GSM340796, GSM340799, GSM340801, GSM340802 and GSM340805  (41). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD024144.
Status: Even though superconductivity has been studied intensively for more than a century, the vast majority of superconductivity research today is carried out in nearly the same manner as decades ago. That is, each study tends to focus on only a single material or small subset of materials, and discoveries are made more or less serendipitously. Recent increases in computing power, novel machine learning algorithms, and improved experimental capabilities offer new opportunities to revolutionize superconductor discovery. These will enable the rapid prediction of structures and properties of novel materials in an automated, high-throughput fashion and the efficient experimental testing of these predictions. High-throughput approaches to materials discovery have been successful in situations where a well-defined figure of merit depends directly on simple descriptors available in large databases, e.
The AD formula was based on solutions to the Eliashberg equations for a few simple models and materials and is known to deviate strongly from empirical T c's for high-temperature superconductors, e. g. , hydrides. In a proof of principle approach, we used the SISSO analytical machine learning approach to improve on the AD formula, training on the tiny AD dataset of 29 superconducting materials and testing with newer superconducting materials  [3]. Clearly, a reliable approach requires a much larger database to learn on, as discussed below. The search for phonon-mediated high-temperature superconductors rests on a simple principle, i. e. , maximizing the electron-phonon coupling strength and the phonon frequencies. Ashcroft proposed [4] dense metallic hydrogen as a candidate for high-temperature superconductivity by noting its high Debye temperature and moderate electron-phonon coupling strength.
Machine learning has the potential to accelerate the discovery of novel superconductors, like H 3S, by reducing the computational cost of obtaining Tc from first-principles calculations and narrowing down the list of candidate materials. Using data from the Supercon database, Stanev et al. carried out machine learning studies to establish connections between the superconducting transition temperatures and atomic properties. They discovered several properties that correlate with the superconducting transition temperatures and predicted a host of compounds as possible hightemperature superconductors  [6]. Hutcheon et al. constructed neural network models to predict superconductors with high Tc under lower pressure than required for superhydrides thus far  [7]. Discovering novel materials by machine learning methods, both with and without coupling to firstprinciples calculations, is progressing rapidly.
For a given material's system, once thermodynamically stable and metastable crystal structures at various conditions are known, their physical properties, including superconductivity, can be estimated and used to guide the experimental investigations of these materials. Many techniques and codes are available for predicting potentially stable phases and structural transitions, such as the semi-local methods of minima hopping, basin hopping, and simulated annealing and the global methods of genetic algorithms and particle swarm optimization. Chemical intuition, structurechemistry correlations, and data mining of open-access materials databases can also be used to provide starting structures to these methods. In our work, we have utilized the Genetic Algorithm for Structure and Phase Prediction (GASP) package  [8]  for exploring possible structural transitions to clathrate-like structures at high pressures in materials containing light elements.
While searching for potential stable crystal structures at high pressures, it is essential to also consider the stability of the competing phases that might destabilize the stoichiometry of the material of interest. To this end, the GASP package can perform grand canonical searches, meaning it can search for stable crystal structures over the entire composition range of interest and generate a high-pressure convex hull diagram. While the discovery of the high-pressure superconducting hydrides has revolutionized the field of electron-phonon superconductivity scientifically, it is of little practical utility unless methods can be found to engineer materials at high pressure that remain metastable under ambient conditions. One approach has been to search for hydrides that are superconductors at lower pressures  [9].
Current and Future Challenges Machine learning and high-throughput methods work best when applied to large datasets. Given the improvement of first-principles based predictions of Tc for electron-phonon based superconductors in recent years, one could imagine constructing a database to enable machine learning of higher-T c superconductors by calculating α 2 F and associated moments, e. g. , for all 16,414 materials in the SuperCon database. However, such a program is prohibitively expensive since each α 2 F calculation typically requires O(100-1000) core-hours of computing time even for elemental superconductors with a modern package such as Quantum Espresso. Purely electronic descriptors like the Fermi level density of states, N(0), are much cheaper and can enable extensions of machine learning approaches such as that attempted by Stanev et al.
Recently, we extended the approach to study a much larger sample of α 2 F's generated from a) EPW electron-phonon calculations for 50 real materials and 5,000 artificial α 2 F's of bimodal form. The solution of the isotropic Eliashberg equation, given α 2 F, is relatively inexpensive. Using the resulting dataset, which is two orders of magnitude larger than that of the previous work  [3] , we obtained a predictive model with improved performance over the AD formula for systems with higher T c. Figure  1a  illustrates the performance of the AD formula on low-Tc materials such as elemental metals, hydrides such as LaH 10 and H3S, and the bimodal einsteins used to augment the training dataset. The systematic underprediction of TC with the AD formula is absent from predictions made with a new machine-learned equation, shown in Figure  1b.
Coupled with the Eliashberg equations, this approach augments the dataset with arbitrarilymany training samples that are qualitatively realistic compared to model systems. For unconventional superconductors, a more promising approach is to use empirical knowledge about the normal state of systems that support unconventional superconductivity. For example, unconventional superconductivity is often found near phase transitions where magnetism disappears via doping or applied pressure. Such "soft" magnetic states can be identified by highthroughput DFT calculations of magnetic candidate materials (mostly metallic compounds in materials database with transition metal ions), calculating how magnetism responds to applied pressure. Promising materials where magnetization decreases rapidly can then be synthesized and studied further by doping and/or pressure.
Concluding Remarks We have outlined an approach to superconductor discovery that seeks to identify the electronic properties of a material that are essential for high T c via machine learning techniques. One thrust is to develop improved equations for T c of the Allen-Dynes type capable of accounting for large-λ materials like high-pressure superconducting hydrides. We discussed the challenges of creating sufficiently large databases of Eliashberg α 2 F's using expensive electron-phonon calculations for real materials and addressed them by the creation of artificial α 2 F's with modeling of simple physically motivated form, and the use of autoencoders, both equally good for learning Eliashberg theory. We also discussed future directions, including structure prediction utilizing surrogate machine learning models of energy landscapes to inform studies of metastable materials synthesized under high pressure, and extension of current methods to unconventional superconductors.
Raman Measurements A confocal Raman microscope (SENTERRA, Bruker Optics, Germany) was used for the measurements of the different filters. This device was equipped with two gratings (400 and 1200 lines/mm), a CCD camera cooled to -60 • C and a BX51 Olympus microscope with multiple objectives (LMPLFLN 100x/0. 8 objective, laser spot = 1. 12 µm). The analyses were performed at 785 nm, and the laser power was approximately 25 mW on the sample. The spectral resolution was approximately 8 cm -1 , and integration time was 30 s with two co-additions. Raman spectra were acquired using OPUS software (Bruker Optics, Ettlingen, Germany). For field work, we used a portable Raman device (QE Pro-Raman spectrometer, Ocean Optics, Netherlands), hereafter referred to as the 'Portable Fiber-Optic system'. OmniDriver and SPAM (spectral processing and maths, respectively) libraries were used for the Portable Fiber-Optic system.
Part 4. Data mining for toxicity analysis Pre-processing The raw data had a spectral range from 181 to 4045 cm -1. The first pre-processing step was to cut the raw spectra in the defined spectral zone of interest: between 550 and 1780 cm -1 for E. coli MG1655. The baseline correction was processed by an elastic concave method (64 • and 10 iterations) using OPUS software. Data processing was then performed with MATLAB (version 2019) using the SAISIR package  [47]. The spectra were normalized using the probabilistic quotient normalization (PQN) method with respect to the median spectrum of each sample group.
Statistical analysis The statistics were also performed with MATLAB (version 2019) using the SAISIR package  [47]. The statistical analysis strategy was based on independent component analysis (ICA), which was performed using the JADE algorithm  [48]. The significance of differences between groups was tested by ANOVA with a statistical significance threshold of p-value < 0. 05. To quantify the classification results of the spectra, stepwise factorial discriminant analysis (sFDA) procedures were then performed on scores from the ICA selected after the ANOVA analysis. Each discriminant model was calculated using a crossvalidation procedure (random selection of 2/3 of scores for the calibration model and 1/3 of scores for the validation test). The final classification rates corresponded to an average of 600 sFDA iterations.
Acknowledgments Financial support of  NWO-TTW   ViDi grant  (project  ADMIRE 17509 ) is acknowledged. Luyu Wang  gratefully acknowledges the financial support by  China Scholarship Council  (No. 201904910310 ). The authors would like to thank all the members of the  DARSim  ( Delft Advanced Reservoir Simulation ) research group and ADMIRE project members at  TU Delft , for the fruitful discussions.
This situation needs to be better assessed and our objective is to measure this food market biodiversity concerning legumes through data information from markets. We began tackling this problem by using Mintel Global-New-Products-Database (GNPD) 1 which tracks food product innovations, considered as all new products launched on the market, in over 80 countries. Mintel-GNPD with more than 3,600,000 registered products, is currently the only database that has such a global coverage 2  , with detailed information at product level  (Solis, 2016) , allowing us to focus on North America and Europe markets considered as central markets for food innovation in western countries. Based on these data, we identified around 100,000 products launched on markets between 2010-2019 which had at least one legume in their ingredients, based on soya and 14 pulses species (Text Box 1).
The food product innovation database used Mintel's GNPD (Text Box 2) is a data base in which food product innovations launched into a considered market are assigned into one of the five different categories: new product, new variety/range extension, new packaging, new formulation, and relaunch. Generally speaking, a new product refers to a product that is completely new to the firm introducing it, that is to say, the firm is positioning a new family of products  (OECD and Eurostat, 2018). A new variety or range extension however refers to the development of a family of products already in place in the firm. After launching a new product or a new variety/range extension, if sales are not satisfactory, firms might adopt strategies involving new formulations, new packaging or relaunching the product.
Text Box 2. The Mintel Global-New-Product-Database on foods and drinks Mintel is a private company that collects data on all types of food innovations according to standardised and rigorous protocols involving a global network of expert shoppers across 86 countries in 2020. Most countries have been covered over the last decade as shown on the map here-below. New launches are added daily and the number of shoppers employed by Mintel regularly increases overtime, leading to a better coverage inside each country and through new countries included. Various channels are investigated: from supermarkets to specialised shops and various secondary sources of information are mobilised (trade shows, websites, press…). Even though these longitudinal data must be analysed with caution as each country's coverage is not exactly the same, they do show general trends on a global scale and at regions level worldwide.
Coverage country map by MINTEL shoppers from 1996 to 2020 (source: personal communication from Mintel). Mintel-GNPD data contain over 80 information fields for each new product retrieved based on information printed on packaging (such as the company trading the product) and reported in their database. For our analysis we have focused on product subcategories and developed specific language processing methods to filter data and identify the various legume species used in these products.
Species identification in food products When we retrieved data from MINTEL, there was no species category from Mintel GNPD for directly retrieving a dataset of food products containing pulses or soya. We then used queries to search products that Mintel linked to common terms on legume species in all Food and Drink product categories (except alcoholic beverages and mineral or source waters) and concerning launches from January 1st, 2010 to December 31st, 2019. The Figure  1  sums up the building of the datasets and hereafter explained. Using the data search tool of Mintel, we firstly built two datasets on the worldwide area: one with 58,649 product innovations containing pulse species (caught up by key-terms like pea, bean, lentil, chickpea) 3  and another one with 243,827 product innovations containing soya.
Secondly, each product in the Mintel database having a unique identifier, we matched the two datasets built and identified almost 29,000 products were common, ie. containing both pulses and soya. Based on these datasets we applied text-mining filters to keep only products launched in North America (USA, Canada) and Europe (108,894 products retained, and among them around 12,000 have both soya and pulses ingredients). We then checked occurrence frequencies for soya and the 14 pulses species we selected (Text Box 1) in the ingredients data of those products. However, those species present several common names which increases synonymy, and consequently the risk of over or under representation of the diversity of species used. Moreover, we were confronted with methodological problems as there is no program that automatically decomposes products' ingredients lists.
Therefore, we chose to match any expressions in the ingredients list referring to legumes through those key-terms: soy, pea, bean and the familiar names of the species we studied (Text Box 1). Then we identified 2,500 distinct expressions from which we built a species glossary indexing each legume expression with its scientific name, checked in the "Catalogue of Life" 4. An extraction of this glossary is provided in the Supplementary Materials (Table  2 ). With this indexation we were able to detect 'fresh pulses' and around 500 'false friends' from which products were wrongly indexed in Mintel's database. For instance, the terms "lentil" or "lens" were sometimes associated in expressions that do not refer to Lens Culinaris species but to a specific process applied to other pulses such as "yellow split lentils"; a process applied to chickpea seeds that consists of dehulling and splitting them in order to obtain something similar to a lentil.
Or another frequent case concerns the term "bean" associated with other species such as the expressions 'coffee bean' or 'cocoa bean' which were retrieved by the Mintel search request. This first methodological investigation suggests that delineation of data in Mintel GNPD according to their filters, contains a significant number of ambiguities, therefore it is advisable to pay careful attention when using those data without post data processing. Finally, a total of 750 expressions related to soya were identified and 1,500 expressions related to pulses species; those remaining concern "fresh pulses" use, other pulses or generic terms leading to undetermined legume species. We used this glossary to index all the products of our datasets. From this first indexation, we delineated a dataset of 107,266 products containing pulses ingredients and/or soya ingredients.
Another delineation was created for the objectives of our analysis by excluding products with just one soya ingredient, that being soya lecithin; a well-known additive and minor ingredient that we excluded from our analysis 5 : 622 products were excluded. Hence, the statistical results (Section 3) are presented from the two types of dataset built: (i) including products having both soya and pulses ingredients (12, 088 products), (ii) or by considering separately products with only soya and no pulse ingredients (80,290) and those with only pulse and no soya ingredients (14,266); all having been launched in North America and Europe over the last decade. Fig. 1. The datasets building of food product innovations containing legume species 2. 3 The market segmentation of food product innovations Food product innovations concern a large variety of market segments according to their main uses and functionalities.
Type of food product innovations and top companies When considering pulse and soya datasets separately, and at a global scale, we observed that product innovations with soya represent almost six times more than pulses. Nevertheless, the growth rate is not similar: over the last decade, the number of food innovations has tripled for those products using pulses and doubled for those using soya. But over recent years, those products using pulses continued to grow while ones using soya were more stagnant and even started to slow down. Therefore, even if pulses know a renewed interest, the food uses of legumes by firms remain largely on soya uses. Range/extension innovations are more numerous (Figure  2 ). This could mean that more and more incumbent firms are investigating legumes, as extension of product families concerns more those kinds of firms.
Nevertheless, without public R&D support on pulses for food, achieving an equal level of development with soya is unlikely given the fact that the current more numerous innovations and market segments invested for soya reinforce soya knowledge-base in foods. 4. 2 Developing such analyses reduces information asymmetries between firms competing on markets to stimulate innovation This global analysis also helps to detect new developments on markets or alternative products that could meet consumer expectations, and firms looking for such information in order to reinforce their positions on the market. On the one hand, such analyses require expensive subscription data and methodological data-mining skills that small and medium firms cannot manage in the same way as major firms. On the other hand, existing market analyses on that subject are rare and costly, and more importantly they are not based on any particular strict methodologies to delineate the different pulse species as we did.
We consider that analysing the presence of pulse ingredients in food products launched on the market is a way to reveal how agrifood firms give interest to pulses. This analysis required to develop links between plant thesauri and databases on food products such as Mintel-GNPD; and now to go further in parsing techniques from computing for implementing automatic processing and analysing deeply ingredient lists from packaging, so that products can be automatically indexed with species. Through parsing techniques this analysis could be improved in order to gain in efficiency and depth  (Salord et al. forthcoming). In particular, we can add a deeper analysis of the type of ingredients for distinguishing whole-grain uses, more refined ingredients and fractions (such as protein, fibre, starch ones) by mobilizing thesaurus on food processing like, for instance, the National Agricultural Library Thesaurus 6.
" Our study tends to indicate that for the moment the second pathway does not generally concern pulses; one reason being that meat-substitutes using pulses is still under-developed compared to soya. But again, only sophisticated techniques for analysing ingredient types could help answer those questions. Third, additional information from Mintel data could be used, since any information found on packaging is available, such as claims on how products were marketed. Combining this with an analysis of ingredient lists will provide a better assessment concerning the 'reality' of sustainable and healthy claims. In addition, geographical information (if any) on the ingredients could help assessing links between production and consumption regarding the challenge of regional food systems development by adapting legume species to appropriate pedoclimates regions, and more largely by combining agricultural research and food research  (El Bilali, 2019).