dataesr/scientific-paragraphs-categorization huggingface dataset:
Image analysis Image analysis and result generation are performed automatically with a provided command line script performing the steps described next. Nuclei were automatically segmented with Cellpose  52  and RNA detection was performed with FISH quant v. 2  53. Points detected in several channels at the same location were considered to be the result of auto-fluorescence and therefore removed. Custom written Python scripts were used to determine the celltype of a nucleus. Importantly, marker genes were chosen such that each cell type is identified with only one marker gene. First, RNAs of each gene were clustered with the algorithms OPTICS and DBSCAN  54  in their implementation in scikit-learn (https://scikit-learn. org/). We used the following functions and parameters: sklearn. cluster.
Acknowledgements This work has received financial support through an  Agence Nationale de la Recherche (ANR)  grant (Lustra to A. L. -V. , F. Mu. , T. W. , and C. F. ). It has also received support from  EDF  (to A. L. -V. and C. F. ), La Ligue Contre Le cancer (to A. L. -V. and S. C. A. ), the  Institut National du Cancer (INCa)  (to A. L. -V. ), the  Institut Curie ICGEx granting program  (to A. L. -V. ) and the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  666003  (to S. C. A. ). Furthermore, this work was supported by the  French government  under the management of  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute  to T. W.
). F. Mu. and C. W. acknowledge funding by  Institut Pasteur. The authors greatly acknowledge the  Multimodal Imaging Center-Light Microscopy Facility of the Institut Curie  ( CNRS UMS2016/InermUS43 / Institut Curie/Université Paris-Saclay ), as well as the  Cell and Tissue Imaging Platform-PICT-IBiSA  (member of  France-Bioimaging - ANR-10-INBS-04 ) of the  U934/UMR3215  of  Institut Curie  for help with light microscopy. The authors wish to thank  Christophe Alberti ,  Elodie Belloir ,  Cédric Lantoine , and  Virginie Dangles-Marie  from the animal core facility of  Institut Curie. The contribution of the bioinformatics core facility at U900  Inserm-Institut Curie  is gratefully acknowledged. High-throughput sequencing was performed by the  ICGex NGS platform of the Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  ITMO-Cancer Aviesan (Plan Cancer III ) and by the  SiRIC-Curie program  ( SiRIC Grant   INCa-DGOS-465  and  INCa-DGOS-Inserm_12554 ).
During 1980 -2020, temperature excess computed from ERA5-Land data lies largely within the envelope of the individual CMIP6 models (Supplementary Figure  8a , "ERA5-Land analysis" in Supplementary material). As such, the temperature excess findings from individual CMIP6 models are not implausible. As the ERA5-Land dataset is supported by the comprehensive assimilation of available observations, the similarity of the CMIP6 model results in terms of temperature excess demonstrates a successful validation of the models considered here. This is further corroborated by surface air temperature extremes from CMIP5 and CMIP6, that compare well with observation-based data sets, albeit with model-specific performance that varies in space and time  (Thorarinsdottir et al. , 2020). At the same time, the CMIP6-based ELI is only partly corroborated by the ERA5-Land reanalysis data from 1980 -2020 (Supplementary Figure  8b ), as globally and in half the regions of interest the reanalysisbased ELI exceeds the CMIP6 envelope.
Note that the ERA5-Land reanalysis is not supported as much by assimilated observations in the case of ELI compared to temperature excess, as ELI is based on ET and (root-zone) soil moisture which are not readily observed across the globe. This way, ERA5-Land estimates of the global ELI evolution are subject to uncertainty, and while it provides an independent reference for comparing the CMIP6 model results it is itself based on the land surface model dynamics underlying the ERA5-Land dataset. Next to that, differences could arise due to different land cover maps underlying respective simulations from ERA5-Land and the CMIP6 models. The tendency of temperature excess to be elevated in response to increasing ecosystem water limitation becomes even clearer when only grid cells where at least six out of eight CMIP6 models agree on the sign of the temperature excess trends are included.
While the correlation between ELI and heat wave temperatures is robust across models, we find substantial differences between individual models in terms of the strength of this link (e. g. Figure  4  and Supplementary  Figures 5, 6, 7 and 11). This could be related to a different representation of land-atmosphere interactions in general, which could be due to e. g. different soil moisture layers and depths, as well as different underlying soil and vegetation types. Additionally, models might use different vegetation water stress functions, some of which are poorly constrained by theory  (De Kauwe et al. , 2017; Martínez-de la Torre et al. , 2019; Ukkola, Kauwe, et al. , 2016). Further, not all models include dynamic vegetation, irrigation and land use change (Table  1 ). Another reason might be that measurements of soil moisture and terrestrial evaporation are scarce, such that large-scale observational constraints for these key quantities have been lacking and are only recently available following the advent of machine-learning techniques to efficiently interpolate global gridded datasets from the available in-situ measurements  (Jung et al.
Given this situation, future research should focus on the link between ELI and heat wave intensities using observation-based datasets, particularly as longer-term interpolations or reconstructions of key variables become available. This can help to corroborate model-based findings, and to constrain the variable relevance of ELI across models. Finally, we focus on the intensity of the heat extremes by considering temperature only rather than more impact-relevant indices. Heat stress for humans is dependent not only on temperature, but also on wind speed and humidity  (Buzan & Huber, 2020; Matthews, 2018). Through reduced evaporative cooling and increased entrainment of dry air above the atmospheric boundary layer, the lethality of heat extremes above dry soils can be reduced  (Wouters et al.
Acknowledgements R. O. is supported through funding from the  German Research Foundation  ( Emmy Noether Grant   391059971 ). We thank the respective climate modelling groups for making their model output available within the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble. Further, I want to acknowledge the fruitful discussions within the  Hydrosphere-Biosphere-Climate Interactions group   in the Biogeochemical Integration Department  of the  Max Planck Institute for Biogeochemistry  that have contributed to the interpretation of the results and design of the figures. A special thanks to  Sujan Koirala  for making the scripts to download CMIP6 data from Google cloud CMIP6 public data publicly available and supporting whenever issues came up. Another special thanks to  Ulrich Weber  for downloading and aggregating the reanalysis data used in this study.
The scripts to acquire and aggregate CMIP6 data are publicly available (https://doi. org/10. 5281/zenodo. 5900393, (Koirala, 2022) ). All the code written and used in this analysis will be made available from a code repository on Zenodo. https://doi. org/10. 5194/egusphere-2023-1925 Preprint. Discussion started: 1 September 2023 c Author(s) 2023. CC BY 4. 0 License.
19)  [124] [125] [126]. This is correlated with higher migration and invasion propensities, and limited apoptosis. Li et al. have shown that the signalling cascade PAK4/LIMK1/cofilin is activated in OS and associated with metastasis and poor survival  [125]. PAK4 knockdown or silencing in MG63 diminishes the cell viability, migration, and invasion, and limits the growth of subcutaneous transplanted tumour in nude mice  [125]. Different peptides or proteins (Insulin, EGF, and VEGF) have been shown to promote Jensen et al. performed an RNA interference screening on three different AML cell lines (U937, U60 and OCI-AML3). They identified LIMK1 as the only gene whose transcripts reduced the cell viability in all these cell lines when targeted by four independent shRNAs. Furthermore, by analysing The Cancer Genome Atlas (TCGA) AML databank, they pointed out a significant association between the high expression of LIMK1 and a shorter survival, and data from the Microarray Innovations in Leukaemia and TCGA AML allowed them to correlate the high LIMK1 mRNA level with the normal karyotype and KMT2A-rearrangement AML.
The KMT2A gene encodes for the histone-lysine Nmethyltransferase 2A, which works as a positive regulator of gene transcription. As these genetic subtypes are well characterised for recurrent driver alterations, authors have established a significant correlation between a high LIMK1 expression, FLT3 and NMP1 mutations, and KMT2A-rearrangements by further analysing TCGA AML databank. Suppression by shRNA or KO by the CRISPR-Cas9 gene-editing tool of LIMK1 or LIMK2 reduced the colony formation, decreased proliferation, and induced the morphological changes of different AML cell lines and patient-derived xenograft (PDX) samples, indicat-ing a role for LIMKs in leukaemia tumorigenesis. The suppression of LIMK1 by shRNA was also associated with the upregulation of several tumour suppressor genes (EGR1, BTG2, and BIN1) and the downregulation of mitosis-associated genes (HOXA9-associated genes, which are transcription factors).
An analysis of the gene expression data from TCGA and REMBRANDT, and a clinically annotated dataset from several groups confirmed these data: the clinical features of GBM are correlated with changes in LIMK expression. LIMKs are upregulated at the mRNA and protein levels in GBM, and patients with gliomas that exhibit downregulated LIMKs have a better overall survival  [60, 137, 138]. The upregulation of LIMKs is also observed in different GBM cell lines (U87, T98G and U118)  [137, 138]. As they have been shown to be upregulated in GBM cell lines and patients, and as they play important roles in cell polarisation, migration, and invasion, LIM kinases were thought to be suitable therapeutic targets for the treatment of GBM by inhibition. The knockdown of both the LIM kinases with shRNA significantly reduced the invasion of the GBM cell lines and of the human GBM tumour-initiating cells (TICs), which is a more clinically proximal culture model, as it forms infiltrative tumours when xenografted in mice.
Funding: This research was funded by the  Centre National de la Recherche Scientifique , the  Ministère de l'Enseignement Supérieur et de la Recherche, University of Orleans, La Ligue contre le Cancer  (grant number  PM/FP/202-251 ), the  French Association Neurofibromatosis et Recklinghausen , the  French Agence Nationale de la recherche  (grant number  ANR-19-CE18-0016-02 ), and the  region Centre Val de Loire. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
The solutions of Lane-Emden (LE) type equations arising in astrophysics for the polytropic gas spheres, the isothermal gas sphere, and the white dwarf stars are revisited. We explore the potentiality of recent methods of deep learning using neural networks constrained by the physics and called Physics-Informed Neural Networks (PINNs). The basics of PINNs is introduced for solving each equation individually. The method consists in constraining the equation residual at some collocation dataset in addition to the boundary data via a minimization procedure. When the training process is complete, a learned differentiable function is obtained that can generate solution at any value of the variable. The novelty of this study is the additional possibility of learning solutions for several equations collectively with the same network, e.
Introduction Machine learning and more precisely deep learning techniques based on Neural Networks (NNs) are actually widely used to solve problems in a variety of domains including computer vision, language processing, game theory, etc. (see Le  Cun et al. , 2015 and references therein). The idea of applying NNs to solve differential equations was first proposed by  Lagaris et al. (1998) , but it was not immediately put into practice in the following years due to the lack of computational resources. Leveraging prior knowledge of the physics in the learning process of NNs with the aim to solve partial differential equations (PDEs) was introduced only recently  (Raissi et al. , 2017 (Raissi et al. , , 2019)). Benefitting also from technical progress on automatic differentiation and the facilitated use of Python open source software libraries like Tensorflow or Pytorch, promising new methods so-called Physics-Informed Neural Networks (PINNs) were born.
In classical NNs, a supervised approach is generally considered that consists in finding a mapping function between given input objects and their associated output values. This is done by using knowledge about a dataset containing several input/output pairs. This dataset is used to parameterize the NN such that it minimizes the error between solutions predicted by the NN and exact known solutions in the dataset of the so-called training data. The convergence is achieved by minimizing a loss function which expression is a measure of the error (e. g. the mean squared error). In other words, this is an efficient non-linear approximation procedure. However, in cases of PDEs the training dataset is generally drastically reduced to the sole knowledge of the initial/boundary conditions. PINNs approach consists in enhancing classical NNs by defining some other set of data, called collocation points, at which the estimated solution must additionally ensure the equation.
( 8 ) The latter expression assumes that a set of N data data is available for y(x) taken at different x i , i. e. input/output pairs (x i , y data i ) are known that are generally called training data in the literature. For the purely non linear approximation problem, the loss function L is simply L data. Finally, a gradient descent algorithm is used until convergence towards the minimum is obtained for a predefined accuracy (or a given maximum iteration number) as The goal is to calibrate the trainable parameters θ (weight matrices and bias vectors) of the network such that y θ (x) approximates the target solution y(x). However, in cases of differential equations the training dataset is generally drastically reduced to the sole knowledge of the initial/boundary conditions. θ i+1 = θ i -η∇ θ L(θ i ),.
The basics of PINNs for solving an ODE In order to tackle the limitation due to the reduced training dataset, PINNs approach considers a second dataset called collocation data aiming at evaluating the residual of the equation written as F [x, y(x), y x (x), y xx (x)] = 0, x ∈ [0, D] , (10) for second order ODE as considered in this work. We use the notation y x = dy dx , and y xx = dyx dx , D defining the right boundary of the integration interval which may vary from case to case in this work. Consequently, a second loss function associated to the physics (i. e. the equation) can be defined as L F (θ ) = 1 N c Nc ∑ j=1 ⏐ ⏐ F [x j , y θ (x j ), y x,θ (x j ), y xx,θ (x j )] ⏐ ⏐ 2 , (11) that must be evaluated at a set of N c data points located at x j (generally called collocation points, j ∈ [1, N c ]).
Our PINN algorithm Taking into account the initial/boundary conditions can be done in different ways in the PINNs variants found in the literature. In the vanilla-PINNs, the condition y(x = 0) = y 0 is generally imposed through the training dataset via L data (θ ) as a soft constraint, thus N data = 1 (in the single input case), contrary to other PINNs variants where this condition is incorporated into L F (θ ) thus as a hard constraint. In this work we follow the first option. Moreover, taking into account the first derivative at x = 0, that is y x = 0 in this study, must be also done. For our PINN algorithm we have found efficient to use the first collocation point at x 1 = 0 in order to constrain the condition on y x,θ (x 1 ) to be close to zero (as required for LE boundary conditions), thus constituting a second soft constraint.
Indeed, the different snapshots taken at different epochs show the progression of the training process that is stopped after 32000 steps. Note that a very modest number of collocation points (here N c = 10) is enough to obtain an average absolute error (square root of the MSE deduced from Fig. 4 ) significantly smaller than 10 -3. The MSE is evaluated using the standard expression, MSE = 1 N eval ∑ N eval i=1 ⏐ ⏐ y θ (x i ) -y eval i ⏐ ⏐ 2 , where the evaluation y θ (x i ) is done on N eval = 300 points uniformly distributed within the whole space interval, and y eval i is the expected exact solution at x = x i. This dataset introduced to test the accuracy of the method must not be confused with the collocation dataset at which the loss function is evaluated and used to make the progress of the training.
Conclusions We show that PINNs are promising numerical tools for solving Lane-Emden type and other similar differential equations. Compared to traditional numerical methods, they present some advantages listed below. 1. They are meshless methods in the sense that the solution is not computed on a given grid. Indeed, a dataset of collocation points is introduced with the aim to minimize the equation residual, but the solution can be directly evaluated (via a learned function) at any variable points (i. e. x value). The required number of collocation points is very reduced compared to classical integration schemes for a similar accuracy. 2. Once trained, the solution and derivatives can be quasiinstantaneously generated in the trained spatial domain. The solution obtained with our method is valid over the entire domain without the need for interpolation (unlike RK tabular solutions).
first used a first PINN algorithm solving LE equations for x varying in the range x ∈ [0, 7] and n ∈ [0, 5]. A corresponding set of uniformly distributed collocation data is taken with 32 points for x and 6 points for n (i. e. n = 0, 1, 2, 3, 4, and 5). Thus, N c = 32×6. The training dataset representing the boundary conditions for the solution at x = 0 is now N data = 1 × 6. The results at the end of the training process are plotted in Fig. 8. The chosen combination of parameters is, η = 1 × 10 -4 , ω F = 8 × 10 -2 , and ω d = 1 × 10 -2. The NN is chosen to have 4 hidden layers (two times larger than for individual PINN modelling) with 20 neurons for each layer. First, Fig. 8 shows that the n = 0, 1 and 5values that are learned by the network are perfectly superposed with the exact solutions over the whole radius range.
125 within the coding sequence (CDS). Using different functional in vitro assays, including original ones, we demonstrated that these variants alter endoglin levels and function, thus arguing in favor of a pathogenic effect of these variants. Interestingly, additional very rare non-coding variants creating uAUGs in the 5'UTR of ENG have been previously identified in HHT patients and reported in the literature  2, [21] [22] [23]  and in databases (https://www. ncbi. nlm. nih. gov/clinvar/?gr=1; https:// www. hgmd. cf. ac. uk/ac/all. php; https://arup. utah. edu/database/ ENG/ENG_display. php; Table  1 ). Three of these variants (c. -142A>T; c. -127C>T and c. -10C>T) are predicted to generate uoORFs also ending at the same stop codon at position c. 125. Two of these variants (c. -142A>T and c.
-127C>T) have been analyzed in vitro in published studies and have been associated with a decrease of endoglin levels  2, 21, 22. While c. -127C>T and c. -10C>T have been reported as pathogenic in public databases, the c. -142A>T is absent from databases but suggested to be pathogenic in Ruiz-Llorente et al. 21. An additional uAUG-creating variant (c. -9G>A) has been reported in the 5'UTR of ENG 2. As the created uAUG is in frame with the CDS, it is predicted to generate an elongated CDS. This variant has been associated with a very mild decrease of the endoglin levels in vitro 2 and its clinical interpretation is considered as "conflicting" or "pathogenic mild" in HGMD and HHT databases, respectively. By deploying our functional assays on these four previously reported uAUGcreating ENG variants, our study provides additional arguments supporting a pathogenic role of these variants and sheds new light on the non-canonical regulation of ENG.
-142A>T that parallel those we observed for ENG c. -127C>T, classified as pathogenic in HHT database, and combined with published data  21  add strong support for this variant being pathogenic. The observed effects are concordant with those obtained by other groups  2, 21, 22. However slight difference in the quantified endoglin levels could be observed and may be due to differences in the used constructs and cells 2,21,22. 16. Anti-Myc and anti-βactin correspond to the used antibodies for the target and the reference proteins, respectively. kDa kilodalton, M protein ladder, WT wild type, C-negative control corresponding to pcDNA3. 1-empty vector. Shown results are representative of 5 independent experiments. Uncropped blots are shown in Supplementary Fig. 4. All blots were processed in parallel and derive from the same experiments.
RNA isolation and RT-qPCR analysis In order to evaluate ENG transcript levels in transfected HeLa cells, total RNA was isolated from the collected pellets by using the RNeasy mini kit (Qiagen) following the manufacturer's instructions. Extracted RNA was quantified and equal quantities were used for reverse transcription reaction, performed with the M-MLV reverse transcriptase (Promega). Then 20 ng of cDNA of each sample was used for the qPCR reaction (duplicate/sample) with ENG-or α-tubulin-specific primers (Supplementary Table  4 ) and the GoTaq qPCR Master mix (Promega), in the presence of CXR reference dye, in a final volume of 10 µl and 40 cycles of amplification on QuantStudio3 Real-Time PCR System (ThermoFisher). QuantStudio design and analysis software was used to analyze the results and transcript levels were normalized to the reference α-tubulin gene.
ACKNOWLEDGEMENTS This project was supported by the  GENMED Laboratory of Excellence on Medical Genomics  [ ANR-10-LABX-0013 ], a research program managed by the  National Research Agency (ANR)  as part of the  French Investment for the Future , the  EPIDEMIOM-VT Senior Chair  from the  University of Bordeaux initiative of excellence IdEX , the  INSERM GOLD Cross-Cutting program , and the  Lefoulon-Delalande Foundation.
-68G>A NA NA uoORF NA NA T and PAVM No This study (195 nts) c. -10C>T rs756994701 0. 0000066 uoORF HGMD Likely pathogenic/ E and PAVM Yes 23 (138 nts) HHT pathogenic database ClinVar c. -9G>A rs368423516 0. 00041 eCDS HGMD Conflicting Infrequent E and T Yes 2 (+9 nts) HHT interpretations/ (heterozygous carriers) database pathogenic mild Daily E and T ClinVar (homozygous carriers) a Variant position in the L-ENG NM_001114753. 3 transcript (c. 1 corresponds to the A of the main AUG). Bolded variants correspond to newly identified variants in HHT patients and analyzed here for the first time.
Words with exception spelling Exception words do not obey usual phoneme-to-grapheme correspondences, and spelling them out therefore requires access to stored lexical knowledge of orthography (e. g. ,  YACHT or OIGNON in French). By presenting TTS individuals with exception words, we wished to determine whether such lexical knowledge was reflected in the content of subtitles. We selected 10 French exception words that had at least 3 phonemes, and generated the corresponding audios files using the ttsreader. com speech generation software.