dataesr/scientific-paragraphs-categorization huggingface dataset:
Work in the laboratory of S. Spicuglia was supported by recurrent funding from Institut National de la Santé et de la Recherche Médicale and Aix-Marseille University; Cancerop ôle PACA, AMIDEX [ANR-11-IDEX-0001-02]; INCA [PLBIO018-031 INCA 12619]; Ligue contre le Cancer (Equipe Labellisée 2018); ANR [ANR-18-CE12-0019, ANR-17-CE12-0035, ANR-22-CE45-0031-02]; Bettencourt Schueller Foundation (Prix coup d'élan pour la recherche francaise); S. H. and N. S. were supported by PhD fellowships from the Pakistani's government and Marseille Institute of Rare Diseases (MARMARA), respectively. Funding for open access charge: ANR.
ACKNOWLEDGEMENTS We thank the  Transcriptomics and Genomics Marseille-Luminy (TGML)  platform for sequencing the CapSTARRseq samples and the Marseille-Luminy cell biology platform for the management of cell culture. TGML is a member of the  France Genomique consortium  ( ANR-10-INBS-0009 ). Author contributions: S. Sp. and  S. Sa. conceived the project and secured funding. S. Sp. supervised the project. S. H. , N. S. and S. Sp. conceptualized and designed the experiments. S. H. performed the majority of the experimental work, with the help of LTMD and MT. N. S. performed the majority of the bioinformatic analyses, with the help of QF and GC. C. L. contributed to the analyses of repetitive sequences. T. S. performed analyses of Hi-C data. D. vE. and  S. Sa. contributed to the design of the CapSTARR-seq approach as well as bioinformatics identification of silencers.
FUNDING Work in the laboratory of S. Spicuglia was supported by recurrent funding from Institut National de la Santé et de la Recherche Médicale and Aix-Marseille University; Cancerop ôle PACA, AMIDEX [ANR-11-IDEX-0001-02]; INCA [PLBIO018-031 INCA 12619]; Ligue contre le Cancer (Equipe Labellisée 2018); ANR [ANR-18-CE12-0019, ANR-17-CE12-0035, ANR-22-CE45-0031-02]; Bettencourt Schueller Foundation (Prix coup d'élan pour la recherche francaise); S. H. and N. S. were supported by PhD fellowships from the Pakistani's government and Marseille Institute of Rare Diseases (MARMARA), respectively. Funding for open access charge: ANR.
Case #4: The supercritical methane reactor This process was first patented in 2006  [90]  and described in a publication in 2014  [91] ; a recent re-assessment of the process has been carried out  [88] , with a strong focus on thermal measurements. The detailed multi-physics model framework developed for the preceding cases has been adapted to the present one. The major modification was the chemical kinetic law, which has here to account for the reversibility of the deposition reaction. Indeed, very high pressures are bringing the system closer to thermochemical equilibrium between methane, hydrogen and carbon, so that the attack of carbon by hydrogen to give back hydrocarbons cannot be neglected any more. The proposed formalism is : 𝑅 = 𝜎 𝑣 (𝜀) 𝑘 𝑑𝑒 𝑝 (𝑇) 𝑃 𝑚 -𝑃 𝑚,𝑒𝑞 R𝑇 (22) where   is the surface/volume ratio and  , is the equilibrium partial pressure of methane, computed using a free enthalpy minimization program fed by a thermochemical database.
Acknowledgements This research was supported by funding from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation under Specific  Grant Agreement No. 945539  ( Human Brain  Project  SGA3 ) (M. F. and J. P. C. ) and by the  European Innovation Council Action Acronym CAVAA , No. 101071178  (M. F. ). We thank two anonymous reviewers for extremely useful comments to improve a first draft of the paper.
Establishment of ccRCC tumoroid cultures We generated renal tumor in mice by surgically implanting 786-O-Luc or ACHN-Luc cells under the renal capsula. 786-O cell line is a classically used ccRCC cell line that harbors VHL mutation and constitutively active HIFα, while ACHN cell line is de ned as papillary RCC without VHL mutation  [34, 35]. Human ccRCC specimen were collected fresh at the time of surgical resection at the Grenoble hospital and taken immediately to the laboratory for processing (Clinical trial NCT03571438). Several pieces of each tumor were snap frozen for RNA isolation and cryo-microtome sectioning. The remainder was mechanically and enzymatically dissociated. After counting, dissociated cells were seeded to perform tumoroid culture, using a scaffold-free tumoroid method (Fig.
Gene expression of tumoroids and tumor tissues To evaluate genetic similarities between tumoroids and the corresponding tumor tissues, we performed RNA sequencing of cells for each experimental condition (see "Methods"). Since we injected human cancer cells into mice, cancer cells in the tumors were human while microenvironmental cells (i. e. Fibroblasts, Immune cells,…) were derived from mice. Thus, the RNA sequencing datasets generated from ACHN or 786-O tumor tissues (AxM and 7xM) and their respective tumoroids (AxTy and 7xTy) were mapped on human and mouse reference genome. We initially performed unsupervised hierarchical clustering analysis on gene expression values for human cancer cells and mice microenvironment cells (Fig. 4A  and 4B respectively). For cancer cells (Fig. 4A ), all ACHN tumoroids (AxTy) were grouped as their tumoral tissues (AxM).
From approximately 12,000 expressed genes, we carried out pairwise differential gene expression analyzes between a tissue and its tumoroids. Between 100 and 400 were identi ed as differentially expressed highlighting high similarities between tumoroids and their corresponding tumor tissues, either for human cancer cells (Fig. 4C ) or microenvironment mouse cells (Fig. 4D ). Moreover, an intersection size analysis (black dots and lines Fig. 4C, D ), considering sets of more than 10 genes, showed that many deregulated genes were shared between the tumoroids produced from the same tumor, whereas no common deregulation was observed between tumoroids derived from different tumors. Ontological enrichment analyzes were performed from these lists of differentially expressed genes. However, the low number of deregulated genes did not reveal pathways of interest for interpreting the differences between tissue and tumoroid (data not shown).
Patients and Clinical Samples All human renal carcinoma samples were obtained from patients, with their informed consent, and all procedures were approved by the ethics committee (Patient Protection Committee No. 2017 A0070251). Patients were recruited under the clinical trial, Comborein (NCT03572438)  [61]. Fresh renal tumor tissue was obtained from patients undergoing a partial or a total nephrectomy for cancer at the Urology Department, University Hospital Center of Grenoble, Alpes (CHUGA).
Invasion assay Three days-old spheroids and 14 days-old tumoroids were included in TissueSpec Kidney → hydrogel (Xylyx Bio, New York, USA). At day 0 and 7, cells were stained with Live and Dead viability kit containing Calcein and Ethidium Homodimer (Invitrogen). They were also stained with CMAC t-BOC-Leu-Met (Invitrogen) for calpain activity measurement. CMAC is a calpain peptidase substrate that will uoresce when cleaved by active calpain  [46]. Image analysis was performed using Icy and ImageJ softwares.
Fluorescence microscopy Qualitative imaging was performed thanks to the confocal microscope LSM 880 with AiryScan module (Zeiss) at the µLife platform, CEA Grenoble. Pictures were taken using the 40X oil objective. Quantitative imaging was performed thanks to the CX7 Cell Insight high throughput confocal microscope (ThermoFisher) at the CMBA platform, CEA Grenoble. Pictures were taken using the 20X air objective (NA 0. 45) for tumoroids and 10X air objective (NA 0. 4) for tissue slices. Image quanti cation was performed with the Thermo Scienti c HCS studio Cellomics Software by using the spot detector bio-application.
RNA extraction RNA extracts from tumoroids were obtained using the RNeasy Micro Kit (Qiagen) following manufacturer's instructions. Two tumoroids were used for each extract. RNA Extracts from tissues were obtained using the MirVana PARIS kit (ThermoFisher). Bulk RNA Barcoding (BRB)-sequencing and raw data preprocessing BRB-sequencing was perfomed at the Research Institute for Environmental and Occupational Health (Irset, Rennes, France) as previously described  [10, 62]. The rst read contains 16 bases that must have a quality score higher than 10. The rst 6 bp correspond to a unique sample-speci c barcode and the following 10 bp to a unique molecular identi er (UMI). The second reads were aligned to the human reference transcriptome from the UCSC website (release hg38) using BWA version 0.
7. 4. 4 with the nondefault parameter "-l 24". The second reads were also aligned to the mouse reference transcriptome from the UCSC website (release mm10) using BWA version 0. 7. 4. 4 with the non-default parameter "-l 24". Reads mapping to several positions in the genome were ltered out from the analysis. The pipeline is described in Reference [10. 1016/j. chemosphere. 2020. 128468]. After quality control and data preprocessing, a gene count matrix was generated by counting the number of unique UMIs associated with each gene (lines) for each sample (columns). The resulting UMI matrix was further normalized by using the rlog transformation implemented in the DeSeq2 package  [10. 1186/s13059-014-0550-8]. Raw and preprocessed data were deposited at the ArrayExpress repository under the accession number E-MTAB-XXXX.
Bioinformatics analysis The hcut method of the factoextra package on R (version R 4. 1) with 2-3 clusters is implemented on the original tumor tissue samples (A1M, A2M and 71M) and on the tumoroids samples (A1T1, A1T2, A1T3, A1T4, A2T1, A2T2, A2T3, A2T4, 71T1, 71T2, 71T3), for the human genome (11443 genes) and for the mouse genome (11914 genes) independently. This method computes hierarchical clustering and splits the tree into 3 clusters. It also accepts distance measurement methods based on pearson correlation. To visualize the results of the hierarchical clustering, we use the fviz_dend method of the R package factoextra (version R 4. 1). This method allows creating dendograms. We have chosen to perform the hierarchical clustering and the dendrogram with three clusters. Indeed, a preliminary analysis of the shape of the dendrogram gave us an indication on the number of clusters to retain and then we calculated the inertia of the clustering tree.
Acknowledgments We thank the animal unit staff ( Bama S. Magallon C. , and  Pointu H. ) at  Interdisciplinary Research Institute of Grenoble (IRIG ) for animal husbandry and  Thomas Darde  ( SciLicium ) for the sequencing and preprocessing of the BRB-seq data. Part of this work has been performed at the CMBA platform -IRIG-DS-BGE-Gen&Chem-CMBA,  CEA-Grenoble ,  F-38054   Grenoble , (a member of  GIS-IBISA and ChemBioFrance infrastructure ) supported by  GRAL , a program of the  Chemistry Biology Health Graduate School of Université Grenoble Alpes  ( ANR-17-EURE-0003 ) for the use of the High content screening CX7 microscope. We thank the microscopy facility MuLife of IRIG/DBSCI, funded by  CEA Nanobio  and  GRAL LabEX  ( ANR-10-LABX-49-01 ) nanced within the  University Grenoble Alpes  graduate school  CBH-EUR-GS  ( ANR-17-EURE-0003 ).
This work was supported by recurrent institutional funding from  INSERM, CEA , and  Ligue Comité de l'Isère, University Grenoble Alpes ,  Centre Hospitalier Universitaire de Grenoble-Alpes (CHUGA) ,  Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC). P. B. , F. J. and C. B. were funded by the  KATY  project which has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  101017453.
2  In fact, in Mazzone's time, native Mass Spectrometry (MS)  11  was in its infancy, and single particle ultra-high mass analysis methods, such as charge detection MS (CD-MS),  15  mass photometry,  16  or nanoresonator MS,  17  were mere curiosities, used only in specialized laboratories or not yet conceptualised. Since then, new instruments have been developed and analytical methods have advanced, meaning that it is now feasible to directly measure masses in the multi-million of Dalton (MDa) range, including those of viruses. 18, 19  Consequently, effective direct approaches to virus mass measurements are now available that could provide specific information on virus identity, help build useful databases, and contribute to investigations of virion structural integrity. 20  Incidentally, structural integrity is a strong indicator of virus infectivity, but is notoriously difficult to determine from gene sequencing data.
On the 'phylobaric' classification of HuReVs -Vaitson ÇUMAKU However, we were unable to find experimental mass values for the Arenaviridae family, and their pleomorphism 37 made it difficult to provide an estimate. In addition, we only found a single reported size measurement for Paramyxoviridae, and unique mass values for Togaviridae, Orthomyxoviridae, and Coronaviridae, precluding the definition of size (respectively mass) ranges for these families. (highlighted in orange). Mean biophysical masses (green symbols) were calculated using size and density data reported in the literature. Mean experimental reported mass values for each family (blue symbols) were extracted from the literature; error bars denote reported range for mass (vertical) and size (horizontal); blue dotted line: power law trend fit to mean values (y = 0.
Genome mass calculation The genome mass is the most readily accessible contribution to virion total mass. To compute genome masses, we used the average mass of individual nucleotides or nucleotide pairs for each genome type, the genome length in bases or base pairs (as reported by Geoghegan On the 'phylobaric' classification of HuReVs -Vaitson ÇUMAKU et al. 23  ) and the masses of end groups. (See SI# 1. 3. 2). Importantly, for viruses featuring segmented genomes (e. g. Hantanvirus), the sum of all the segments was calculated. This apparent limited capacity to discriminate based on genome mass could explain the fact that, even though genome mass is readily accessible from sources such as the NCBI Viral Genomes database  40  , no classification based solely on this characteristic has been proposed.
The primary reason was that these characteristics were determined using outdated methods, sometimes leading to overestimations, or overly imprecise estimations. Such inaccuracies cannot be ignored given the type of precision we seek to provide. Moreover, there was clear partiality in favour of a few virus families in terms of available data, which may be due to them being widely used models (e. g. Adenoviruses), or because their low risk-profile makes them easier to analyse in the laboratory (e. g. Adeno-Associated Viruses). Additional difficulties were associated with rather large discrepancies when trying to compare data acquired through non-identical methods, and sometimes even when comparing cited sources to their original reported values. These difficulties highlight the need for quality control when reporting data in the future, particularly if it is to be included in a suggested viral mass database.
Outlook and perspectives Although from our human perspective, viruses appear quite similar in size and mass, the scale of difference is sometimes equivalent to the difference between the smallest rodentsthe African pygmy mouse (a few grams)and the largest land animalthe African bush elephant (up to 10 tons). Over the past two decades, the mass spectrometry community has made major breakthroughs and modern instrumentations have reached the level of accuracy required for structural virology studies. A remarkable review by Wörner et al. 19   or study the assembly and disassembly of encapsidated viruses. 15  Echoing the words of Mazzone 2 in the conclusion of his book, we believe that a virus mass database is needed, and perhaps now more than ever. We envisage that a large open-access database, combined with future portable MS detectors capable of performing real-time mass measurements,  46  would improve biosafety applications for aerosol-transmissible viruses in locations where there is a high risk of propagation, e.
Acknowledgements Pr. R. Ruigrok  ( IBS, Grenoble ),  Drs S. Cianferani  ( CNRS, Strasbourg ),  I Michaud-Soret  ( CNRS, Grenoble ),  P. Boulanger  ( CNRS, Gif-sur- Yvette ) and  Y. Vandenbrouck  ( CEA, Grenoble ) are gratefully acknowledged for helpful scientific discussions.
Funding This research was funded in part by the  Cross-Disciplinary Program on Instrumentation and Detection [PTC-ID ] of the  French Alternative Energies and Atomic Energy Commission [CEA ] (Project  VIA-NEMS ); and by the  French National Research Agency [ANR ] through the  AERONEMS project  ( ANR-21-CE42-0028-01 ). VÇ's thesis fellowship was funded by the  Grenoble Alliance for Integrated Structural and Cell Biology , a programme of the  Chemistry Biology Health Graduate School, University Grenoble Alpes  ( ANR-17-EURE-0003 ).
One thus wants to learn a target function y ¼ f t ðnÞ. Actually, in practical applications we do not have a full definition of the function, but we have examples, in the form of a database of pairs input-output D ¼ fn l ; y l g, with l 2 f1; :::; Pg. Modern deep networks are based on artificial neurons organized in layers. Each neuron in layer L is a simple unit that receives a signal from the neurons in the previous layer, applies a nonlinear function and sends this processed signal to the neurons of the next layer (see Fig. 1 ). The activity of neuron i in layer r is given by x r i ¼ w r i X j W r ij x rÀ1 j ! ð20Þ where W r is a matrix of the ''synaptic efficacies'' between neurons in layer r À 1 and r. The nonlinear function w r i can be, for instance, a sigmoid or a rectified linear unit.
This learning is called the training phase. In order to train a network, one defines a ''loss function'' L(W) which measures the errors made by the machine with parameters W on the database. For instance, one could use a quadratic loss L D ðWÞ ¼ X P l¼1 y l À f ðW; n l Þ ð Þ 2 ð21Þ but many other choices are possible. Then the training phase consists in finding the W that minimizes the loss. In practice, people use a form of gradient descent called stochastic gradient descent, in which one moves in the W landscape using iteratively noisy versions of the gradient computed from partial sums involving some batches of l indices. Once the learning has been done, one can use the couplings W Ã which have been found during training, and see how well the network generalize when it is presented some new data that it has never seen.
In recent applications of deep networks like the large language model Chat-GPT, the total number of weights can be of order 10 11 , and the total number of operations used in order to train a network on extremely large databases of basically all available text is easy to remember; it is of the order of 10 24 , a ''mole of operations. '' So, the distribution (  22 ) is a measure in a large N-dimensional space. The elementary variables, the weights, are real-valued variables with a measure q. They are coupled through an energy which is the loss L D ðWÞ. This energy is a complicated function of the variables W, and it depends on a large set of parameters, namely all the input-output pairs D. Therefore, the measure  (22)  has all the ingredients of a statistical physics systems with a quenched disorder which is the data.
Having in mind our discussion of disordered systems, we can immediately identify several questions. One can work on a given data base (a given sample) and ask about the landscape for learning, and for generalization. But clearly, for theoretical studies, one would like to have an ensemble of samples, which is a probability measure in the space of inputs, and for each input the corresponding output. With such a setup of a data ensemble, one can draw a database by choosing inputs independently at random from the ensemble, one can study the property of self-averageness (which properties of the optimal network W Ã are dependent on the precise realization of the database, and which ones are not-in the large N limit ?). The test loss becomes easy to define: it is the expectation value of the loss, over pairs of input-output generated from the data ensemble.
Working with a single dataset and with a data ensemble are two rather different approaches. In many practical applications the engineer's approach is to use a single dataset, and the definition of an ensemble is not obvious. For instance, if one wants to identify if there is a cat or a dog on an image, so far what is done is use huge databases of images of cats and dogs, randomly choosing part of them for training, and another part for the test phase. However, in such a single database setup it is not easy to develop a theory: on the one hand, there is a risk of developing a theory which is too much tailored to this precise database, and from which one cannot draw general conclusions; also, one cannot use probabilities to compute the generalization error. So, the use of data ensembles is clearly welcome from a theoretical point of view, but then one faces the difficulty of defining the ensemble in such a way that it will include some essential features of real databases, but it should be smooth enough so that one can interpolate through it reasonably (the ensemble which would use a probability law that is a sum of d peaks on each point of a database is useless), and simple enough that it can be studied.
So, the question of finding good ensembles is a fundamental question of how to model the ''world'', i. e. , the set of all possible inputs that can be presented. Here by modeling one intends it in a physics' approach, namely being able to identify the key features that should be incorporated into the ensemble, neglecting less important ''details''. Interestingly, this quest for modeling of data meets with an important recent direction of development of machine learning, which are generative models. In parallel to, and in symbiosis with, the supervised machine learning that I have briefly exposed, very significant progress has been made on generative models. These aim at generating data 'similar to' a given database. Among the processes that have been explored, one can mention generative adversarial networks (GAN), or the very physical generative diffusion models which take a database, degrade it using a Langevin process until it has been transformed into pure noise, and then reverse the Langevin process to reconstruct artificial data from noise (see  [49, 51, 52] ); for a recent review see  [57] , and for a statistical physics perspective:  [5] ).
Perceptrons Training a neural network in supervised learning amounts to finding the ground state of a strongly disordered system. One can thus ask what properties of spin glasses one can find in neural networks. Early studies on perceptrons have provided important benchmarks. Two main categories of tasks have been studied: learning arbitrary labels, or learning from a ''teacher rule''. In both cases, the database consists of P independent input datapoints each consisting of d i. i. d. numbers from a distribution q 0 ðnÞ. In the case of learning from arbitrary labels, for each input one generates a desired output which is drawn randomly, independently from the input. In this case one studies only the training phase, generalization has no meaning. In the case of learning a teacher rule, one generates the output from a ''teacher'' set of weights, W t , through y ¼ f ðW t ; nÞ.