dataesr/scientific-paragraphs-categorization huggingface dataset:
Data & code availability Sample annotations for the expression and mutation analyses are in different sheets of Suppl. Table  12. Expression read counts for tumors and organoids (obtained using featureCount) will be available from GEO (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE165649) from the time of publication. The codes reproducing the results of this study are available from Github (https://github. com/HollandeLab/FOLFOX_Resistant_mCRC).
To test these predictions, we used a five-year acoustic dataset from bottom-moored hydrophones to extract call rates of each call type. In the absence of concurrent measurements of prey biomass, an ecosystem model was used to provide hindcast simulations of low trophic level (mesozooplankton) biomass for the area and period of acoustic recordings  [32, 33]. This approach allowed investigating the direct relationship between fin whale vocal behaviour and predicted prey biomass, avoiding interpretation of relationships with time-lagged prey proxies (i. e. chlorophyll).
Methods (a) Acoustic data collection and analyses Passive acoustic monitoring (PAM) data were collected at two locations off the Azores Archipelago (figure  1a ) using ecological acoustic recorders (EARs)  [34]  deployed at depths of approximately 200 m. The EAR consists of a Sensor Technology SQ26-01 hydrophone with a response sensitivity ranging from 193 to 194 dB re 1 V/μPa (depending on deployments) and a flat frequency response (±1. 5 dB) from 18 Hz to 28 kHz. Hydrophones recorded from March 2008 to October 2012 with several data gaps and duty cycles (electronic supplementary material, figure  S1 ). Despite gaps in the acoustic dataset, all seasons were well represented across the five sampled years. Acoustic recordings were analysed for two fin whale vocalizations: the 20-Hz call, a 1 s downsweep centred at 20 Hz  [15]  (figure  1b ) and the 40-Hz call, a 0.
Three months (representative of each season) with longer duty cycle recordings (1 h of continuous recordings) were manually inspected to identify song and non-song 20-Hz calls. Results showed that only 2. 5% of the files contained non-song 20-Hz calls (Oct: 0%; Nov: 3. 5% and Mar: 0%). Thus, we assumed that most 20-Hz calls analysed in this study were part of songs. Identification of 40-Hz calls using automatic detectors is challenging because of the frequency overlap with sei (Balaenoptera borealis) and blue whale (Balaenoptera musculus) calls  [37]. So, 40-Hz calls were detected by visually inspecting spectrograms of the entire dataset (2048-point FFT, Hanning window with 50% overlap) using Adobe Audition 3. 0 (Adobe Systems Inc. , San Jose, CA) and annotating each call. The 40-Hz call was identified from its acoustic characteristics  [20, 26] , which clearly differentiates it from the 20-Hz call because of the higher frequencies that downsweep from 75 Hz to 40 Hz over 0.
3-1 s. The 40-Hz call was also easily distinguished manually from blue whale D calls, previously identified in this dataset  [35] , as having a distinctly broader bandwidth and longer duration. A call rate index was calculated as the total number of 20-Hz or 40-Hz calls detected in a week divided by the recording time, in hours, during that week, to reduce potential bias from the different duty cycles. Hereafter, we will in general refer to 20-Hz or 40-Hz call rates, but these strictly mean the corresponding call rate index.
Source levels of 20-Hz and 40-Hz calls were calculated using calls localized by three EARs deployed in a nearby area. The propagation range-dependent acoustic model (RAM)  [45, 46]  was used for the calculation of TL. Ambient NL were calculated for the frequency band of each call type and for the quietest and noisiest months within the recording period (see electronic supplementary material, text S1B for more details). Finally, a sensitivity test of the scale of data integration was performed by analysing annual and monthly patterns of estimates of zooplankton biomass at a range of scales.
However, there are also species in which song traits do not correlate with male quality  [62]  and further research is needed to directly test this hypothesis in fin whales. The effect of year on singing activity was greatly influenced by 2012, which showed significantly lower call rates when compared to 2008, 2010 and 2011. The most plausible explanation is that decreased call rates in 2012 reflect lower numbers of fin whales in the area. This is partly supported by visual observations collected by the Fisheries Observer Program Data showing that fin whale encounter rates (sightings/100 km) were null in 2012, compared to 0. 9 in 2010 and 9. 2 in 2011. Year-to-year variability in fin whale numbers is usually attributed to environmental changes affecting prey distribution and abundance  [37, 63].
Acknowledgements. We are grateful to  Marc Lammers , for providing the EARs and technical support, and to  Rui Prieto ,  Sérgio Gomes ,  Norberto Serpa , skilled skippers and crew that participated in the preparation and deployment of the EARs at DOP/IMAR. We also thank the reviewers that significantly improved the original manuscript. Data on fin whale sightings from 2010-2012 were kindly provided by the  Azores Fisheries Observer Program (POPA)  (www. popaobserver. org).
Funding. This work was supported by  Fundação para a Ciência e Tecnologia (FCT) Azores 2020 Operational Programme and Fundo Regional da Ciência e Tecnologia (FRCT)  through research projects  TRACE  ( PTDC/ MAR/74071/2006 ),  MAPCET  ( M2. 1. 2/F/012/2011 ) and  AWARENESS (PTDC/BIA-BMA/30514/2017 ), cofunded by  FEDER ,  COMPETE, QREN, POPH, ERDF ,  ESF , the  Lisbon Regional Operational Programme  and the  Portuguese Ministry for Science and Education. Okeanos R&D Centre  is supported by  FCT  through the strategic fund ( UIDB/05634/2020 ). M. R. was supported by a  DRCT doctoral grant  ( M3. 1. a/F/028/2015 ). S. P. J. was funded by  EC funds  ( SUMMER H2020-EU. 3. 2. 3. 1 ),I. C. by  FCT  through  AWARENESS -(PTDC/BIA-BMA/30514/2017 ). H. M. acknowledges support by  CMAF-CIO  (funded by  FCT , Portugal, through the projects  UID/MAT/00006/2013  and  UIDB/04561/2020 , respectively).
A. P. was supported by  AWARENESS  project ( PTDC/BIA-BMA/30514/ 2017 ) and UIDB/50019/2020 -I. D. L. and T. A. M. by  CEAUL  and the LMR  ACCURATE  project (contract no. N3943019C2176 ). M. A. S. was funded by  FCT  and  EC funds  ( IF/00943/2013 ,  SUMMER H2020-EU. 3. 2. 3. 1,GA 817806 ).
available in the electronic supplementary material and from the Dryad Digital Repository: https://doi. org/10. 5061/dryad. 00000003s [82]. Mesozooplankton model outputs (netcdf format) are available on the Copernicus Marine Service (https://marine. copernicus. eu/; GLOBAL_REANALYSIS_BIO_001_033) and are updated twice a year (next 2021 release will have resolution 1/12°x day).
Funding Open access funding provided by TU Wien (TUW). Artiom Alhayov acknowledges project 20. 80009. 5007. 22 "Intelligent information systems for solving ill-structured problems, processing knowledge and big data" by the National Agency for Research and Development. Sergiu Ivanov is partially supported by the Paris region via the project DIM RFSI n • 2018-03 "Modèles informatiques pour la reprogrammation cellulaire".
Acknowledgements We thank the  Centre Suisse de la Recherche Scientifique (CSRS) de Côte d'Ivoire  for providing the Tiassalé mosquito line. We thank  Fabrice Chandre ,  Pierrick Labbé ,  Muriel Raveton ,  Frederic Boyer  and  Vincent Corbel  for useful comments on the study design and the interpretation of results. We also thank  Thierry Gaude  for providing technical assistance in mosquito rearing and blood feeding.
5 trypsin solution (Sequenced Grade Modified Trypsin, ref V511A, Porcine, Promega). Digested peptides were extracted by incubation in 70% and 100% acetonitrile for 20 min each. Peptide extracts were evaporated and afterward reconstituted in 0. 1% formic acid. Peptide extracts were analyzed by liquid nanochromatography (nanoLC) nanoElute coupled with a TimsTOF Pro mass spectrometer (Bruker), as previous described by  [51]. Generated peaks were analyzed with the Mascot database search engine (MatrixScience version 2. 5. 01) for peptide and protein identification. The peaks were queried simultaneously in the: UniprotKB Human (release 20191016, 20656 sequences) database and a decoy database that was interrogated in parallel to estimate the number of false identifications and to calculate the threshold at which the scores of the identified peptides are valid.
Mass tolerance for MS and MS/MS was set at 15 ppm and 0. 05 Da, respectively. The enzyme selectivity was set to full trypsin with one miscleavage allowed. 14 Protein modifications were fixed carbamidomethylation of cysteines, variable oxidation of methionine, variable phosphorylation of serine, threonine or tyrosine. Identified proteins are validated with an FDR < 1% at PSM level and a peptide minimum score of 30, using Proline v2. 0 software  [52]. Proteins identified with the same set of peptides are automatically grouped together. Analysis of post-translational modifications was also executed using Proline v2. 0 Software. Localization confidence values for peptide ions were exported from Mascot  [53]  and a site probability is calculated for a particular modification according to the number of peptides confidently detected with this site modification.
Statistical analysis Statistical analysis, comparing multiple groups was performed using two-way ANOVA on rank test with Tuckey's post hoc test in GraphPad Prism 7 software. TransAM NF-B activation assays were analyzed using a 2-way ANOVA test with a Sidak correction for multiple analysis. RT 2 profiler PCR array was performed using the online software provided by the manufacturer (https://dataanalysis2. qiagen. com/pcr). In short, the p-values were calculated based on a student t-test of the replicate 2^(-DC T ) values for each gene in the control group and treatment group. The p-value calculation used is based on a parametric, unpaired, two sample equal variance, two tailed distribution. P-values < 0. 05 were considered as significant. Figure legends.
ARC contre le Cancer (NB), Ligue nationale contre le cancer comités de Loire-Atlantique, Maine et Loire, Vendée (JG, NB), Région Pays de la Loire et Nantes Métropole under Connect Talent Grant (JG), the National Research Agency under the Programme d'Investissement d'Avenir (ANR-16-IDEX-0007), and the SIRIC ILIAD (INCa-DGOS-Inserm_12558). AT and SR hold post-doctoral fellowships from la Fondation ARC contre le Cancer. TD is a PhD fellow funded by Nantes Métropole. This work was also supported by grants from Biogenouest, Infrastructures en Biologie Santé et Agronomie (IBiSA) and Conseil Régional de Bretagne awarded to CP.
Fondation ARC contre le Cancer (NB), Ligue nationale contre le cancer comités de Loire-Atlantique, Maine et Loire, Vendée (JG, NB), Région Pays de la Loire et Nantes Métropole under Connect Talent Grant (JG), the National Research Agency under the Programme d'Investissement d'Avenir (ANR-16-IDEX-0007), and the SIRIC ILIAD (INCa-DGOS-Inserm_12558). AT and SR hold post-doctoral fellowships from la Fondation ARC contre le Cancer. TD is a PhD fellow funded by Nantes Métropole. This work was also supported by grants from Biogenouest, Infrastructures en Biologie Santé et Agronomie (IBiSA) and Conseil Régional de Bretagne awarded to CP.
ACKNOWLEDGEMENTS CS is supported by grants from  Fondation Française pour la Recherche contre le Myélome et les Gammapathies monoclonales (FFRMG) et Limousin committees of Ligue nationale contre le cancer. VB is supported by the  Paul Teschan Research Fund. PWS is supported by a  Merit Award  ( 1 I01 CX001326 ) from the  US Department of Veterans Affairs Clinical Sciences R&D (CSRD) Service  and a  National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases George M. O'Brien Kidney and Urological Research Centers Program  grant ( P30 DK079337 ).
Thermal shift assay with nano differential scanning fluorimetry (nanoDSF) Protiated (H) and deuterated (D) protein samples were diluted to ~0. 2 mg/mL concentration in protiated and deuterated buffers, respectively. Each sample was loaded into capillaries (Prometheus NT. 48 Series nanoDSF grade standard capillaries) in duplicate and placed into a Prometheus Nano Temper NT. 48 (Nano Temper Technologies GmbH; Germany). The nanoDSF method detects thermal unfolding of proteins through a change in fluorescence when intrinsic tryptophan and tyrosine residues are exposed to solvent. OmpF contains 2 and tryptophan and tyrosine residues, respectively. A temperature ramp of • C/min from 20 to 95 • C was used. The melting temperatures (Tm) were calculated using the software PR ThermControl and an XLSX file with processed data was exported for further analysis  [12].
Mass spectrometry The level of D incorporation in OmpF purified from cells grown in media with different carbon sources was measured by mass spectrometry analysis. This was done by calculating the mass difference between H and D samples. The intact mass of purified H and D OmpF samples was analyzed by MALDI TOF/TOF mass spectrometer (Bruker Auto flex Speed TOF/TOF MALDI MS) in positive reflector mode. All samples were prepared in protiated buffer with a protein concentration of 1 mg/ml 1 μl of protein sample was diluted with 2 μl of 0. 1% TFA. From this mixture, 1 μl of sample was mixed with 0. 5 μl of matrix (5 mg/ml α-cyano-4-hydroxy cinnamic acid and 80% acetonitrile) and spotted on a MALDI stainless steel plate. The obtained spectra were calibrated using Intact BSA (Bruker Daltonics) software where four signals from the internal standard bovine serum albumin (BSA) were used for calibration [M+H] + , m/z 66431 (average mass); [M+2H] + 33216 (average mass); [M+3H] + , m/z 22144 (average mass); [M+4H] + , m/z 16607 (average mass).
X-ray data collection X-ray data collection for the protiated OmpF crystals grown in M9 medium was performed at FIP, BM30 beamline at ESRF, France. For all other protein crystals, data was collected on the BioMAX beamline at MAXIV laboratory, Lund. Crystals were flash cooled in liquid nitrogen to collect data at cryogenic temperature without addition of a cryoprotectant.
Acknowledgements The authors would like to thank  Lund Protein Production Platform (LP3)  for remote data collection of OmpF crystals at MAXIV. The production of deuterated materials and crystallization was supported the  ESS Deuteration and Macromolecular Crystallization platform, Lund, Sweden. We would like to thank  Monika Spano  for hosting SA during her secondment as part of the  RAMP ITN Network at University Grenoble Alpes, France  and  David Cobessi  for assisting in data collection at  ESRF. We would also like to thank  Vinardas Kelpsas  for useful discussions and acknowledge technical support from  Katja Bernfur  for Maldi-TOF MS analysis. This project received funding from the  European Union 's  Horizon 2020 research and innovation programme, RAMP-ITN  under the  Marie Skłodowska-Curie  grant agreement No.
The absence of DNA contamination was checked by PCR targeting the tuf gene. Total RNA was quantified using a Nan-oDrop 1000 and RNA quality was analyzed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer's instructions. RNA samples were stored at -80 • C. They were reverse-transcribed to cDNA and labeled as previously described  [35]. A complete description of the array developed for S. xylosus C2a is available at the NCBI Gene Expression Omnibus (GEO) database under platform accession number GPL19201 and the complete genome of S. xylosus C2a is available under accession number LN554884. Significant differences in the probe set intensities between the different conditions were identified using a linear model with an empirical Bayes method using all information probes to moderate the standard errors of the estimated log-fold changes  [36].
Recently, as a proof of concept, we reported a new experimental approach  [5]  where the stability towards breakage under mechanical perturbation for different types of amyloid fibrils were analyzed and quantitatively compared. We determined the division rates and the type of fragmentation kernels associated to each type of amyloid fibrils. These data suggested that the proteins that are involved in diseases may be overall less stable toward breakage and generate larger numbers of small active particles than their non-disease associated counterparts. In the context of the experimental data presented in  [5] , and as pointed out in  [9] , the experimental context may have a considerable impact on the loci at which the fibril is more likely to break up. Therefore, it is important to develop a general method based on a common mathematical platform, which can be applied to the analysis and comparison of experimental data from a wide range of amyloid systems and conditions.
Data analysis We detail here how to use the theory and the Matlab code provided in the SI to estimate the parameters from a real experiment. First, the user should provide a set of n measurements of the size distribution in the suspension at different times t n. See Fig  6 , upper panel for an example of such data for t n = 5, 10, 20, 30, 40. We also refer to S1, Fig 13 upper panel for an example of such data for t n = 0, 1, 2, 5, 8, 13, 18 and obtained numerically with the same parameters except for the initial condition. In this last example, the experimental size distribution is a probability distribution that usually consists in a sum a dirac masses (the size of the sample at initial time is here N = 200), that we turn into its best fit density distribution, using here ksdensity (MatLab command).
Numerical simulations To develop, explore and test a new protocol to extract the division law properties γ, α and κ from experimental data based on our inversion formulae shown in the Theory section, we proceeded as follows. From an initial distribution profile, and given a set of parameters γ, α and κ, we created simulated data of size distributions u using a numerical scheme, which is described in the next paragraph and implemented in Matlab. The three initial conditions we considered are a gaussian centered at x = 1 with standard deviation σ = 10 -2 that we refer to as the peaked gaussian, a gaussian centered at x = 1 with standard deviation σ = 1 that we refer to as the spread gaussian, and a decreasing exponential f 0 (x) = exp(-x). We observed numerically that the scheme is converging, and we validated it on test cases of known analytical formulae (Table  1 ).
Subsequently, we first used the numerical scheme to explore the role of the γ, α and κ parameters on the behaviour of the system. This provided insights on how and when (whether at early or late times) each parameter affects the trajectory of the system. Then, we use the distribution f of the simulated data obtained with the scheme to test our method to the γ, α and κ parameters by comparing the estimated parameters with their known values used to generate the simulated datasets. We also added noise to the simulated data to see how it affects our estimates. We underline that the method of estimating γ, α and κ based on our analytical inversion formulae is only valid in the cases where the experiments under consideration is well described by the model  (3). Therefore, in addition, we also provided some a posteriori tests to confirm or infirm the validity of the model.
The panel inside shows the relative difference between the theoretical and numerical solutions. The relative error becomes large for large x since each solution itself is very small. Upper left: Illustration of on an example of size distribution profiles for γ = 1. 3, α = 1, and κ is a two-peaked Gaussian kernel, the initial condition is a spread Gaussian function. Notice that noisy data cannot be represented on a graph, since they formally consists in a sum of delta functions, instead we represent an estimate of the density using the matlab function ksdensity. Upper right: To visualize the profiles more precisely, we rescale the profiles using the real value for γ (i. e γ = 1. 3) and formula  (3). What is plotted is for each time t the function t -1/γ f (t, t -1γ x), where f (t,. ) is the distribution profile at time t.
As for a standard STD experiment, 1D 1 H-NMR spectra were determined for both ligand and protein. In addition, mixed spectra containing both protein and excess ligand (P + L) were determined with and without excitation irradiation at frequencies corresponding to prominent resonance within the protein but far from any ligand (pulse 'on') or where the centre of the pulse was moved to avoid ligand and protein (pulse 'off', labeled '1D in Figures). Deconvolved spectra for ligand determined in the presence of protein were matched with high accuracy by uSTA (Figure  1E ). Moreover, uSTA generated highly consistent binding 'heat maps', comprising atom-specific magnetization transfer efficiencies (proton data mapped onto heteroatoms by taking a local 1/r 6 average to enable visual comparison) that described the pose of ligand bound to protein (Figure  1F  see also Supplementary Figures  S8, S11 ).
A typical set of values used was 0. 1s, 0. 3s, 0. 5s, 0. 7s, 0. 9s, 1. 1s, 1. 3s, 1. 5s, 1. 7s, 1. 9s, 2. 0s, 2. 5s, 3. 0s, 3. 5s, 4. 0s and 5. 0s. Off and on-resonance spectra were acquired for 16 saturation times, giving a total acquisition time of 8. 7 hrs The experiment was acquired as a pseudo 3D experiment, with each spectrum being acquired at a chosen set of excitation frequencies and mixing times. Relaxation delays were set to 10 s for BSA + tryptophan STDs, and were 5 s otherwise. uSTA Data Analysis.
NMR Methods NMR spectra with a range of excitation frequencies and mixing times were acquired on ligand only, protein only, and mixed protein/ligand samples (Supplementary Figure  S6 ). To analyse an STD dataset, two projections were created by summing over all 1D spectra, and summing over all corresponding STD spectra. These two projections provide exceptionally high signal to noise, suitable for detailed analysis and reliable peak detection. The UnidecNMR algorithm was first executed on the raw spectra, to identify peak positions and intensities. Having identified possible peak positions, the algorithm then analyses the STD spectra but only allowing resonances in places already identified in the 1D spectrum. Both analyses are conducted using the protein only baselines for accurate effective subtraction of the protein baseline without the need to use relaxation filters (Supplementary Figure  S8 ).
In practice, raw unformatted FIDs are submitted to the uSTA pipeline, and the various steps are performed largely automatically, where a user needs to manually adjust processing settings such as phasing and choosing which regions to focus on, iteratively adjust the peak shape to get a good match between the final reconvolved spectrum and the raw data, and input manual atomic assignments for each observed multiplet. The uSTA analysis pipeline then provides a user with a report that shows the results of the various stages of analysis, and uses pymol to render the surfaces. The final transfer efficiencies delivered by the program can be combined with a folder containing a series of HADDOCK models to provide final structural models (Figure  5 ).
Modelling of the N-terminal Domain of SARS-CoV-2 with Glycans. We modelled the structure of the N-terminal domain (NTD) on Protein Data Bank (PDB) entry 7c2l (18) since it provided significantly better coverage of the area of interest when compared to the majority of the templates available on the PDB as of July 15th 2020. The models were created with Modeller (63), using the 'automodel' protocol without refining the 'loop'. We generated 10 models and ranked them by their DOPE score  (64) , selecting the top 5 for ensemble docking.