dataesr/scientific-paragraphs-categorization huggingface dataset:
Since expression values in CPM could not be calculated from these data, log 2 ((TPM/10) + 1) values were just transformed into log 2 (TPM + 1) to optimize the comparison between the different datasets. To avoid potential analytical bias due to scarcely detected genes, genes detected in less than 1% cells were filtered out. Malignant and normal cells were distinguished either according to the cell annotations provided for both SMART--seq2 datasets, or when absent as for 10X Genomics datasets, based on inference of copy--number variations (CNVs), a hallmark of malignant cells. Expression data in log 2 (CPM/100 + 1) values were used, which shrinks expression ranges, allowing to keep only the major gene expression variations, based on which malignant and normal cells can be distinguished. Data were processed using a three-step approach: CNV inference, marker gene expression and unsupervised cell clustering.
CONICSmat R package used to infer CNVs has the advantage of not requiring an a priori reference cell dataset  (Muller et al 2018). The default filtering and x and y, respectively. Only genes detected in at least 3% of GB cells were considered for this analysis. Genes coding for metabolism enzymes were identified using the list from KEGG  (Kanehisa et al 2017). Comparing list of differentially expressed genes between distinct datasets was performed with the venn R package on genes detected in ≥3% malignant cells in all datasets, and after updating gene symbols based on gene metadata files downloaded from HGNC and NCBI websites (Supplementary Table  6 ). Genes absent from these metadata files, or with ambiguous symbols (e. g. genes whose current approved symbol is the previous symbol of another gene), or whose symbol was previously associated with more than one gene were excluded from the lists.
Gene ontology analyses were carried out on the enrichR website (Chen et al 2013)  (Kuleshov et al 2016 )  (Xie et al 2021). We considered BH--adjusted p--value < 0. 05 (Fisher's Exact test) as the cut--off criterion for significance. Redundant terms were excluded. Graphs were generated using ggplot2 R package. R scripts used are provided in Datafile S1.
Trajectory inference analysis To model the path taken by cells with low motile potential to reach a high motile potential, STREAM python package was used  (Chen et al 2019). Briefly, the expression values of the ten elements of the motility signature were first extracted. Next, dimensions were reduced to four components using the spectral embedding algorithm (dimension_reduction STREAM function, other parameters are set to default). The components were then used for simultaneous tree structure learning and fitting using ElPiGraph (seed_elastic_principal_graph STREAM function, default parameters). The resulting trajectory structure was represented in a 2D subway map plot, where straight lines represent branches and each dot represents a single cell (plot_stream_sc STREAM function, dist_scale = 0.
GBOs were generated and cultured from a primary GB patient tumor (Supplementary Table  6 ) following the method developed by Jacob and collaborators  (Jacob et al 2020). In relevant experiments, cells were treated with 200µM I3--MT--3 or 1mM NAC (MedChemExpress and Sigma respectively) or their vehicles (DMSO and culture media respectively). measured by FACS analysis. The latter was performed on 10000--gated events using Vdye--labeled cells for detecting any non-viable cell (1µL/4--5*10 5 cells/mL, 30min, 4°C, eBiosciences). Violet laser (405nm) and Pacific Blue filter were used for Vdye detection, Blue laser (488nm) and FITC filter for CellROX Green reagent detection, and Red laser (640nm) and APC filter for CellROX Deep Red reagent detection. Data analysis and figure generation were performed using the FACS Diva software (BD Biosciences).
Measure of mitochondrial mass Cells, spheroids or GBO were incubated with 1µM MitoTracker Green reagent (Invitrogen) at 37°C for 30min and the fluorescence signal was assessed by microscopy or FACS. For FACS analysis, it was performed on 10000--gated events. Violet laser (405nm) and Pacific Blue filter were used for Vdye detection, and Blue laser (488nm) and FITC filter for MitoTracker Green reagent detection. Data analysis and figure generation were performed using the FACS Diva software (BD Biosciences).
The secondary antibody was anti--rabbit IgG (GE Healthcare, 1:30000). Signal detection was performed with SuperSignal™ West Femto Maximum Sensitivity Substrate chemiluminescence detection system (ThermoFisher, France). Densitometric analysis was achieved using ImageJ software. MPST levels were assessed by normalizing the chemiluminescent signal by the amount of proteins loaded assessed using Red Ponceau. Persulfidated protein levels were assessed following the Dimedone--Switch protocol, as described by Zivanovic and colleagues  (Zivanovic et al 2019). Briefly, this method relies on the indiscriminate labeling of persulfides, thiols, sulfenic acids, and amino groups with 4--chloro--7--nitrobenzofurazan (NBF--Cl, the NBF--adducts yielding a fluorescent signal detected at 488nm). Upon incubation with dimedone--based probes, only the persulfides exchange their labeling, thus enabling their identification (red signal detected at 800nm).
Immunocytochemistry Cells were harvested, PBS washed, smeared on SuperFrost slides (ThermoScientific, France), and fixed with 4% paraformaldehyde at room temperature. Following fixation, cells were washed with PBS, and incubated for 60 min at room temperature in Antibody blocker/diluent (Enzo). MPST primary antibody (Sigma, 1:50) was incubated overnight at 4°C. Secondary antibody anti--rabbit A555 was incubated at room temperature for 1 h (1:500, Life Technologies). Nuclei were stained using DAPI (Sigma). Immunostaining was analyzed with a fluorescent microscope equipped with an ApoTome module (Zeiss). Immunofluorescent signals were analyzed with Zen software (Zeiss) using 2. 5 µm optical sections.
Statistical analyses R version 3. 6. 1 or Prism 7. 0 software (GraphPad) were used to generate plots and for statistical analyses. Significance level was set at p < 0. 05, unless otherwise indicated. The type of statistical test used is provided in the figure legends. All experiments were performed using independent biological samples. Mean ± SD are shown. f. FACS analysis of ROS levels using CellROX Deep Red reagent and mitochondrial mass using MitoTracker Green reagent. Cells classified based on their basal ROS levels. Mean ± SD, n = 3 independent biological samples, *: p < 0. 05, unpaired t--test with Figure legends Welch's correction. e. Cells with high mitochondrial mass have higher ROS production than cells with low mitochondrial mass. R633 and P3 PDC. FACS analysis of ROS levels using CellROX Deep Red reagent and mitochondrial mass using MitoTracker Green reagent.
Acknowledgements We are deeply grateful to  Dr Milos Filipovic  and  Thibaut Vignane  ( SULFAGING group, ISAS, Dortmund, Germany ) for their gift of reagents and for sharing protocols for assessing persulfidated protein levels. All our renewed thanks to  Dr François--Xavier Lejeune  ( ICM, Salpétrière Hospital, Paris ) for his advice on statistical analyses. We acknowledge the contribution of AniRA lentivectors production facility from the  CELPHEDIA Infrastructure  and  SFR Biosciences (UAR 3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL ), especially  Gisèle Froment ,  Didier Nègre  and  Caroline Costa. This work was supported by grants from  Région Ile--de--France  ( MSS fellowship ),  INCADGOS--Inserm_12560 :  SiRIC CURAMUS  (financially supported by the  French National Cancer Institute , the  French Ministry of Solidarity and Health and Inserm) , and  La Fondation pour la Recherche Médicale --Equipes FRM   2020.
05) Sheet4: results of the KEGG pathway analysis performed on metabolism genes upregulated in M--HIGH versus M--LOW cells from D--S dataset (BH--adj pval < 0. 05) Sheet5: results of the KEGG pathway analysis performed on metabolism genes upregulated in M--HIGH versus M--LOW cells from N--10X dataset (BH--adj pval < 0. 05) Sheet6: results of the KEGG pathway analysis performed on metabolism genes upregulated in M--HIGH versus M--LOW cells from PA--10X dataset (BH--adj pval < 0. 05) Supplementary Table 5, related to Fig. 2. Metabolism genes overexpressed in S3--S1 branch compared to each other branch Supplementary Table.
oRG:  genes  signing  for  outer  Radial  Glia  (oRG)--like  malignant  cell  population with increased invasive behavior in GB. TEAD: TEAD1--regulated genes involved in GB cell motility. p < 0. 001, hypergeometric test. i. Linear regression model between motility score and EMT, oRG and TEAD scores. p < 0. 0001. j. Higher EMT, oRG and TEAD scores in M HIGH versus M LOW cells. *: p < 0. 0001, Mann--Whitney test. b--g, and i--j: Results from N--S dataset analysis.
b. Inferred trajectory of glioblastoma cells from low to high motility. Cells colored by motility clusters (top panel) and motility score (bottom panel). MPST (Mercaptopyruvate Sulfurtransferase) marks the path crossroad between low and high motile potential. Results from N--S dataset analysis. c. MPST overexpression in  GB tissues compared to normal brain tissues. Expression in 163 glioblastoma (GB) versus 207  normal brain (Normal) tissues. Boxplot representation from GEPIA2 website. TCGA RNA--seq dataset. One--way ANOVA test, *: BH-- adjusted p--value < 0. 05. d.
We used the non-adiabatic oscillation code MAD  (Dupret 2001)  and found that only radial and non-radial modes of δ Scuti  Notes. ( a) The filekey is the unique identifier associated with each dataset processed by the DRP. ( b) N exp /N int denotes the image stacking order. type are predicted to be excited by the κ mechanism  (Pamyatnykh 1999)  in this model: the predicted pulsation periods range from ∼0. 50 to 2. 53 h. We did not observe strong evidence for sizable δ Scuti-like pulsations in the CHEOPS light curves described in Sect. 3. 1, although at the high end of the period range each light curve only covers a few periods of the expected signal. Moreover, the 98. 77 minutes duration of an orbit of the CHEOPS satellite falls within this range, meaning that known instrumental signals occur on this timescale.
For the orbit in question, the centroiding algorithm could not lock the pointing on the target and the payload was consequently not in the loop. This issue occurred right after an interruption where the target was not visible for 40 minutes. Updates to the centroiding algorithm have improved its performance since then, and this issue has not been observed again. When fitting an occultation model to this dataset alone, the measured occultation depth was very sensitive to the combination of auxiliary observing parameters used in the baseline model (see  Sect. 4. 3). This is because the erroneous orbit fell very close to the end of the observation, with the ten integrations in the final orbit proving insufficient to discriminate between a range of baseline models. This is demonstrated in the bottom panel of Fig.
Light curve fitting A good model for jointly fitting the CHEOPS and Spitzer data has to take account of a number of signals present: the asymmetric transits of the oblate star, the flux deficit during the occultations, the phase-dependent modulation as the visible hemisphere of the tidally locked planet rotates with respect to the observer, the instrumental trends that are routinely present in CHEOPS photometry (e. g. Lendl et al. 2020; Bonfanti et al. 2021) , and any other time-correlated trends. To incorporate each of these signals, we created a custom model to simultaneously fit the light curves based on the existing tools available in pycheops 4  (see  Maxted et al. 2021) , a package that allows simultaneous modelling of transiting and occulting planets or eclipsing binaries along with the most common systematic trends present in CHEOPS datasets.
Instrumental model We modelled systematic trends in the CHEOPS light curves using a set of linear basis vectors of auxiliary observation parameters that are output by the DRP, such as sky background, contamination, detector smearing correction, detector xy-coordinates, and a 'glint' function (see  Maxted et al. , in prep. ) created in pycheops by fitting a spline function to the out-oftransit or occultation flux as a function of telescope roll angle. We found that linear decorrelation only using a glint function per CHEOPS dataset and no other parameters optimised the A75, page 8 of 26 Bayes factor (Kass & Raftery 1995, see the following section), with the exception of the final CHEOPS occultation where sky background was also included. As discussed in Sect. 2. 2, we observed low-frequency timecorrelated trends in the CHEOPS light curves that we are unable to attribute to astronomical or instrumental sources.
2021). This novel approach enabled us to separately derive the A g , spherical albedo A s , Bond albedo A B , and phase integral q of MASCARA-1. This approach to fitting multi-wavelength occultation and phase curve data in the optical and IR also provides a template for the analysis of spectrally resolved James Webb Space Telescope (JWST) data. Due to the limited phase coverage of the CHEOPS data, we fitted the data from both bandpasses with a single set of phase curve parameters, which implicitly assumed that they share a common albedo, and that the spectral energy distribution of the planet is well represented by a blackbody. Although a similar strategy may still be necessary when analysing occultation data, the use of a unique set of parameters per wavelength bin in the exquisite-precision spectrally resolved phase curves that JWST will acquire will allow for variations in thermal emission and reflection to be taken into account in the light curve fitting.
A75, page 9 of  26 A&A 658 ,  A75 (2022 ) Acknowledgements. CHEOPS is an ESA mission in partnership with Switzerland with important contributions to the payload and the ground segment from  Austria, Belgium, France, Germany, Hungary, Italy, Portugal, Spain, Sweden , and the  United Kingdom. The CHEOPS Consortium would like to gratefully acknowledge the support received by all the agencies, offices, universities, and industries involved. Their flexibility and willingness to explore new approaches were essential to the success of this mission. M. J. H. and Y. A. acknowledge the support of the  Swiss National Fund  under grant  200020_172746. S. H. gratefully acknowledges CNES funding through the grant  837319. D. K. acknowledges partial financial support from the  Center for Space and Habitability (CSH) , the  PlanetS National Center of Competence in Research (NCCR) , and the  Swiss National Science Foundation  and the Swiss-based  MERAC Foundation.
A. C. C. and T. G. W. acknowledge support from  STFC  consolidated grant number  ST/M001296/1. P. M. acknowledges support from  STFC  research grant number  ST/M001040/1. This work was also partially supported by a grant from the  Simons Foundation (PI Queloz , grant number  327127 ). B. -O. D. acknowledges support from the  Swiss National Science Foundation  ( PP00P2-190080 ). S. S. has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 833925 , project  STAREX ). A. Br. was supported by the  SNSA. This work was supported by  FCT -Fundação para a Ciência e a Tecnologia  through national funds and by  FEDER  through  COMPETE2020 -Programa Operacional  Competitividade e Internacionalizacão by these grants:  UID/FIS/04434/2019 ,  UIDB/04434/2020 ,  UIDP/04434/2020 ,  PTDC/FIS-AST/32113/2017  and  POCI-01-0145-FEDER-032113 ,  PTDC/FIS-AST/28953/2017  and  POCI-01-0145-FEDER-028953 ,  PTDC/FIS-AST/28987/2017  and  POCI-01-0145-FEDER-028987 ,  O.
D. S. D. is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by national funds through FCT. We acknowledge support from the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund  through grants  ESP2016-80435-C2-1-R ,  ESP2016-80435-C2-2-R, PGC2018-098153-B-C33 ,  PGC2018-098153-B-C31 ,  ESP2017-87676-C5-1-R , MDM-2017-0737  Unidad de Excelencia Maria de Maeztu-Centro de Astrobiologí a (INTA-CSIC) , as well as the support of the  Generalitat de Catalunya/CERCA  programme. The MOC activities have been supported by the  ESA  contract No. 4000124370. S. C. C. B. acknowledges support from FCT through FCT contracts nr. IF/01312/2014/CP1215/CT0004. X. B. , S. C. , D. G. , M. F. , and J. L. acknowledge their roles as ESA-appointed CHEOPS science team members.
This project was supported by the  CNES. The Belgian participation to CHEOPS has been supported by the  Belgian Federal Science Policy Office (BELSPO)  in the framework of the  PRODEX Program , and by the  University of Liège  through an  ARC grant for Concerted Research Actions  financed by the  Wallonia-Brussels Federation. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  FOUR ACES. grant agreement no. 724427 ). D. E. acknowledges financial support from the  Swiss National Science Foundation  for project  200021_200726. C. M. P. and M. F. gratefully acknowledge the support of the  Swedish National Space Agency  ( DNR 65/19 ,  174/18 ). D. G. gratefully acknowledges financial support from the  CRT foundation  under Grant No.
2018. 2323'Gaseousor rocky?Unveiling the nature of small worlds'. M. G. is an F. R. S. - FNRS Senior Research Associate. KGI is the  ESA   CHEOPS  Project Scientist and is responsible for the  ESA CHEOPS Guest Observers Programme. She does not participate in, or contribute to, the definition of the  Guaranteed Time Programme  of the CHEOPS mission through which observations described in this paper have been taken, nor to any aspect of target selection for the programme. This work was granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche. Acknowledges support from the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund  through grant  PGC2018-098153-B-C33 , as well as the support of the  Generalitat de Catalunya/CERCA  programme.
, 2020a; b, Pérez-Palazón et al. , 2018). Improved resolution. Higher spatial resolution datasets were a common demand in almost all study cases. The resolution of CS' information, usually provided at pan-European scales, was seen as a disadvantage for decision-making at such local scale as the case studies. Dealing with this issue, one study case used a new dataset at higher resolution than the previously used (EURO-CORDEX 11x11 km (EUR-11) vs 50x50 km (EUR-44),  (Berg et al. , 2019)  and a new method for downscaling meteorological forcing that runs through the impact models  (Berg et al. , 2021; Schmith et al. , 2021). In the Spanish case for the water allocation, downscaling was directly performed to streamflow instead of meteorological forcing data at watershed scale lumping the scale effects usually found in small mountain-coastal watersheds.
Conclusions First, The AQUACLEW' proposed framework has proved to improve the usability of already existing CS tackling and overcoming the water issues in decision-making through a lengthy co-development approach through a modelling chain with several steps and an active communication with continuous exchanges between users and developers. However, this approach has shown a very demanding interaction with a very exclusive target-oriented user. Hence, despite their potential, it might not be feasible to be widely applied due to the need of knowledge developers for a limited number of users. Therefore, a deeper effort should be made in educational aspects to develop deeper user The users' difficulties in understanding CS' information since the language used, English, is different to their mother tongue.
Acknowledgements  R. Pimentel  acknowledges fundings by the  Juan de la Cierva Incorporación Programme  of the  Spanish Ministry of Science and Innovation  ( IJC2018-038093-I ). R. Pimentel is a member of DAUCO, Unit of Excellence ref. CEX2019-000968-M, with financial support from the  Spanish Ministry of Science and Innovation , the  Spanish State Research Agency , through the  Severo Ochoa and María de Maeztu Program for Centres and Units of Excellence in R&D.
While efficient, the techniques seem relatively difficult to generalize to high-order. In [BDK + 20], the authors describe a first-order masked implementation of Saber, with only a 2. 5x overhead factor. They introduce an optimized arithmetic to arithmetic conversion algorithm (A2A) for performing logical shifts. Their algorithm is based on securely propagating the carry with a pre-computed table, by adapting the techniques from  [CT03, Deb12]. Their A2A conversion only applies to first-order masking. In this paper, we describe in Section 5 a generalization to high-order masking of the A2A computation of the logical shift, based on table recomputation. In [FBR + 21], the authors describe a first-order masking of Kyber and Saber, for both software and hardware implementations. For the masking of the Compress function in Kyber, they describe a technique based on modulus switching.
Ring-LWE encryption We first recall the principle of ring-LWE encryption  [LPR10]. We then consider Module-LWE (M-LWE) encryption and two finalists of the NIST PQC standardization: Kyber and Saber. The two independent candidates share a lot of similarities as they are both M-LWE-based encryption schemes. For any positive integer q, we define r = r mod q to be the unique element r in the range [0, q[ such that r = r (mod q). For an even (resp. odd) positive integer q, we define r = r mod ± q to be the unique element r in the range -q/2 < r ≤ q/2 (resp. -(q -1)/2 ≤ r ≤ (q -1)/2) such that r = r (mod q). For x ∈ Q, we denote by x the rounding of x to the nearest integer, with ties being rounded up. Ring-LWE IND-CPA encryption. Let R and R q denote the rings Z[X]/(X d + 1) and Z q [X]/(X d + 1) respectively, for some d ∈ Z and an integer q.
Module-LWE IND-CPA encryption. A public-key encryption scheme based on the module learning-with-errors problem (M-LWE) in module lattices  [LS15]  is parameterized by a ring R q , a module rank l and a distribution χ outputting "small" elements in R. In Kyber and Saber, we use R = Z[X]/(X d + 1) and R q = Z q [X]/(X d + 1), and χ is a distribution outputting polynomials with coefficients independently drawn from a centered binomial distribution of fixed parameter. We denote vectors and matrices by boldfaced variables. Let s and e be elements of R l sampled from χ l and A a uniformly random element of R l×l q. The public key is t = A • s + e ∈ R l q and the secret key is s. To CPA-encrypt a message m ∈ R with binary coefficients, one computes (c 1 , c 2 ) ∈ R l q × R q such that c 1 = A T • r + e 1 c 2 = t T • r + e 2 + q/2 • m where r and e 1 are sampled from χ l and e 2 from χ.
To ensure a negligible decryption error, we must therefore ensure that all coefficients of u belong to the set R q,∆ considered in the previous section, except with negligible probability. Application to Kyber. The authors of the Kyber submission provide a Python script computing a tight upper bound on the decryption error probability δ. Following  [HHK17] , we say that PKE = (KeyGen, Enc, Dec) is (1-δ) correct if E[max m∈M Pr[Dec(sk, Enc(pk, m)) = m]] ≥ 1 -δ, where the probability is over the randomness of Enc, and the expectation is over (pk, sk) ← KeyGen(). More precisely, for an encryption of 0, the authors compute an upper-bound on the probability that any coefficient of u = c 2 -s T • c 1 is greater than q/4 in absolute value. From the definition of the set R q,∆ in (10), it suffices to rerun the script with the bound q • (1/4 -∆) instead, in order to obtain the new decryption failure probability.