dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Here we present the full spectrum of substrate motifs of the human serine/threonine kinome and provide an unbiased comprehensive framework to further explore their cellular functions. Globally, these motifs are substantially more diverse than expected, suggesting a broader substrate repertoire of the kinome. Hierarchical clustering of this dataset reorganized the kinome into at least 38 motif classes and introduced several shared motif features (Fig. 2  and Extended Data Figs. 2 3 4 ). The Ser/Thr kinases that we profiled were, almost without exception, strongly discriminatory against specific motif features. These findings suggest that fidelity in kinase signalling pathways is largely achieved through selective pressure on substrates to avoid phosphorylation by the majority of irrelevant kinases, and that this may occur by tuning the amino acid sequences surrounding the phosphorylation sites to be disfavoured by non-cognate kinases.
As this negative selection contributes substantially to proper substrate recognition, accurate identification of kinase-substrate relationships requires a comprehensive knowledge of kinase phosphorylation motifs-not only for an individual kinase of interest, but also for all other kinases in the human kinome that might compete for the same substrate pool. When this kinome-wide dataset was used to predict the specific kinases that are responsible for substrate phosphorylation solely based on the amino acid sequence surrounding the phosphorylation site, the results were highly accurate at identifying correct kinase-substrate relationships, even without knowledge of tissue specificity, scaffolding effects or subcellular localization. Including such additional information will probably further improve these predictive approaches  57, 58.
A limitation of using first-order peptide arrays in these experiments is that they do not directly measure the contributions of interpositional contacts within the substrate peptides, which we have previously shown can affect substrate selection for some tyrosine kinases  32  , albeit less so for Ser/Thr kinases  59. Moreover, we were unable to differentiate between positional selection of Ser or Thr residues and direct phosphorylation of neighbouring residues (for example, peptides containing more than one phospho-acceptor). Structural modelling approaches guided by kinase substrate motif data will potentially decipher this additional information to further improve predictions  60, 61. The examination of MS phosphoproteomic datasets using this global collection of motifs yielded potential biological insights and putative kinase substrates (Fig.
Plasmids For expression and purification from bacteria, the DNA sequences for the human Ser/Thr kinases, binding partners and chaperones listed below were codon-optimized for expression in Escherichia coli using GeneSmart prediction software (GenScript). Optimized coding sequences were synthesized as gBlocks (Integrated DNA Technologies) carrying 16 bp overhangs at the 5′ and 3′ ends to facilitate in-fusion cloning (Clontech) into pET expression vectors (EMD Millipore). pCDFDuet1 constructs were as follows: HSP90AA1-His 6 (full length), hereafter referred to as 'HSP90'; untagged HSP90(full length); His 6 -MO25a(full length); His 6 -ALPHAK3/ALPK1 N-terminal domain (1-474); and His 8 -CCNC(full length) in tandem with MED12-His 8 (1-100); untagged MEK5/MAP2K5(S311D, T315D; full length); and untagged CK2B(full length).
PSPA analysis Recombinant kinase was added to a 384-well plate containing peptide substrate library mixtures in solution phase at 50 μM (Anaspec, AS-62017-1 and AS-62335). The reaction was initiated with the addition of 50 μM ATP (50 μCi ml -1 γ-32 P-ATP, Perkin-Elmer) and incubated for 90 min. 1] [82] [83] [84]  ). After completion of the reaction, the solutions were spotted onto streptavidin-conjugated membranes (Promega, V2861), where the peptides tightly associated through their C-terminal biotinylation. The membranes were rinsed and then imaged using the Typhoon FLA 7000 phosphorimager (GE) to measure the extent of peptide phosphorylation. Raw data (GEL file) were quantified using ImageQuant (GE) to generate densitometry matrices (Supplementary Table  2 ). For the kinase ALPHAK3, spots were normalized to the surrounding background, owing to spatial variation in background signal.
Matrix clustering The dendrogram in Fig. 2  was generated using the normalized matrices with the 20 unmodified amino acids, as well as phosphothreonine and phosphotyrosine. The linkage matrix was computed using the SciPy package in Python (v. 3. 7. 6), using the Ward method. Results were converted to the Newick tree format and plotted using FigTree (v. 1. 4. 4).
Kinase enrichment analysis The single phosphorylation sites (not including multi-phosphorylated peptides) in the analysed phosphoproteomics studies were scored by all of the characterized kinases (303 Ser/Thr kinases), and their ranks in the known phosphoproteome score distribution were determined as described above. For every non-duplicate, singly phosphorylated site, kinases that ranked within the top 15 kinases for the Ser/Thr kinases were considered to be biochemically favoured kinases for that phosphorylation site. For assessing kinase motif enrichment in phosphoproteomics datasets, we compared the percentage of phosphorylation sites for which each kinase was predicted among the upregulated/ downregulated (increased/decreased, respectively) phosphorylation sites (sites with |log 2 [fold change]| equal or greater than the log[fold change] threshold), versus the percentage of biochemically favoured phosphorylation sites for that kinase within the set of unregulated (unchanged) sites in this study (sites with |log 2 [fold change]| less than the log[fold change] threshold).
Comparative analyses between amino acids in the kinase domains and their substrate specificities For Extended Data Fig. 7 , kinases were sorted by their log 2 [S 0 /T 0 ] values. For the sequence logo, kinase domains of 290 available kinases were obtained from previously aligned kinase sequences  91. The alignments to residues Met1-Leu296 in CDK2 (Protein Data Bank (PDB): 1QMZ) were obtained for each kinase, and the frequencies of amino acids in increments of 15-kinases were calculated and plotted as a sequence logo.
Illustrations Experimental schema and illustrative models were generated using BioRender (https://biorender. com/). Kinome tree images were generated and modified using Coral (http://phanstiel-lab. med. unc. edu/ CORAL/). Structural illustrations were generated using PyMOL. Generic kinase domains in Figs. 1  and 3  were as follows: PKAα (PDB: 1ATP). The kinase and substrate structures in Fig. 3  were as follows: ATM (PDB: 7SIC)  92  and p53 (chimera of AlphaFold AF-P04637-F1-model_v2_1 (1-95)  61  and 2ATA(96-292)  92  ) (Fig. 3c ), and PHKG2 (PDB: 2Y7J)  92  and PYGM (PDB: 1ABB)  92  (Fig. 3b ).
Structural models of kinase-substrate complexes. a, Synthetic peptide from its complex with PAK4 (PDB: 2Q0N) modelled onto WNK3 (PDB: 5O26). Dotted circle highlights a shallow hydrophobic pocket accommodating a +3 Phe residue. b, GSK3 peptide from its complex with AKT2 (PDB: 1O6L) modelled onto CAMKK2 (PDB: 2ZV2). Circle indicates a hydrophobic pocket that could accommodate a -2 aliphatic residue. c, Monophosphorylated peptide from p63 bound to CK1δ (PDB: 6RU6) modelled onto GRK2 (PDB: 1YM7). Circle shows positive surface potential in the vicinity of the -2 and -3 pSer residues. d, p63 peptide bound to CK1δ (PDB: 6RU8) was modelled onto YANK1 (PDB: 4FR4) showing potential binding sites for -3 and +2 phosphorylated residues. Surface electrostatics are represented with Coulombic potential values were computed in ChimeraX and represented by scale bars (kcal/mol•e).
Acknowledgements We thank  M. Begley  and  T. DeFalco  for helpful discussions and technical assistance; and  T. Levy ,  S. Beausoleil ,  L. D'Amato ,  P. Lobo ,  M. Tran  and  C. Valencia  for technical Article support. T. M. Y. thanks  S. Yaron ,  N. Yaron ,  J. R. Haddad  and  S. Haddad  for their support; and  J. L. J. thanks  M. Bak-Johnson  for her support. This research was supported by  Leukemia & Lymphoma Society   FELLOW Award  (to J. L. J. and L. C. C. );  National Institute of Health  grants  P01 CA120964  (to L. C. C. ),  R35-CA197588  (to L. C. C. ),  P01-CA117969  (to L. C. C. ),  R35-ES028374  (to M. B. Y. ),  R01-CA226898  (to M. B. Y. ),  R01-GM104047  (to B. E. T. and M. B. Y. ),  U24 DK116204  (to B. E. T. and L. C. C. ) and  R35-GM139550  (to D. J. T. ); the joint  Cancer Research UK and Brain Tumour Charity funded Brain Tumour  Award  C42454/A28596  (to M.
3 Extended Data Fig. 5 |. 6 | 356 Structural models of kinase-substrate complexes. a, Synthetic peptide from its complex with PAK4 (PDB: 2Q0N) modelled onto WNK3 (PDB: 5O26). Dotted circle highlights a shallow hydrophobic pocket accommodating a +3 Phe residue. b, GSK3 peptide from its complex with AKT2 (PDB: 1O6L) modelled onto CAMKK2 (PDB: 2ZV2). Circle indicates a hydrophobic pocket that could accommodate a -2 aliphatic residue. c, Monophosphorylated peptide from p63 bound to CK1δ (PDB: 6RU6) modelled onto GRK2 (PDB: 1YM7). Circle shows positive surface potential in the vicinity of the -2 and -3 pSer residues. d, p63 peptide bound to CK1δ (PDB: 6RU8) was modelled onto YANK1 (PDB: 4FR4) showing potential binding sites for -3 and +2 phosphorylated residues. Surface electrostatics are represented with Coulombic potential values were computed in ChimeraX and represented by scale bars (kcal/mol•e).
Data reduction The N2CLS data are reduced using the "may21a" 1 version of the PIIC data reduction pipeline developed and supported by IRAM  (Zylka 2013). Our data reduction script is adapted from the default template provided with the PIIC software. We use the options "deepfield" and "weakSou", which are designed to recover faint sources from the NIKA2 timeline data without prior information on source positions. We used the PIIC iterative procedure with five iterations to recover the bright source fluxes by subtracting an estimate of the sky (so-called "source map") constructed by thresholding the previous iteration map at 4σ. The procedure converges rapidly and five iterations are sufficient. At each iteration, following the default PIIC parameters, the emission of the sky is subtracted from the timeline using the 16 best-correlated KIDs.
As for COSMOS, N2CLS is 2. 7 and 2. 2 times deeper than AzTEC  (Aretxaga et al. 2011 ) and MAMBO  (Bertoldi et al. 2007 ) at 1. 1/1. 2 mm, respectively, and 1. 6 times deeper than GISMO at 2 mm on a four times larger area  (Magnelli et al. 2019). Similarly, in COSMOS field, N2CLS also reaches a depth comparable to the deepest SCUBA2 observation at 850 µm (S2COSMOS, see  Simpson et al. 2019). To obtain realizations of the instrument noise as closely as possible to the noise in real observations, we generate halfdifference maps from the original scans. The half-difference maps, or null maps, are built by opposing half of the single-scan maps, that is, multiplying them by -1, and co-adding all of them. The opposed single-scan maps are selected randomly from the full list. The random selection and coadding operations are carried out by the HalfDifference module in nikamap  (Beelen 2023)   2.
Source extraction The source detection of the N2CLS survey is made on the matched-filter PIIC maps produced by the dedicated Python package nikamap. For the matched filter, the kernels are fixed to circular 2D Gaussian with an FWHM equal to the corresponding beam sizes. The absolute level of the matched-filter maps is undefined, and any baseline residual in the PIIC maps could introduce a shift in the matched-filter signal map. Moreover, the PIIC re-gridding introduces a correlation in the weight maps, which needs to be taken into account. In order to retrieve a signal-to-noise ratio standard deviation of unity, and a null mean, assuming a Gaussian noise distribution, we perform a Gaussian fitting on the S/N pixel histogram values between -3 and 1. 5 in order to avoid contamination by the sources.
The nikamap package incorporates the find_peak algorithm from the photutils package  (Bradley et al. 2022 ) to identify peaks above a certain threshold in the matched-filter S/N maps. We set an extraction box size to 5 pixels, allowing only the brightest source in a 2. 5-pixel distance on both axes to be extracted. The box size is chosen to be matched with the FWHM of the PSF. The values at the position of detection in the beam matched-filter S/N maps provide the S/N of the point sources. We finally perform PSFfitting photometry on the original PIIC signal maps (which are shown in Appendix A) with the nikamap package based on the BasicPSFPhotometry module in photutils. The position of the sources is fixed and corresponds to the results of find_peak. The PSFs used in this process are two-dimensional circular Gaussians that have their FWHMs defined by the beam described in the PIIC data products.
The backgrounds of the maps are estimated by the MedianBackground module in photutils and are removed during the analysis. For the source detection in the N2CLS observations, we further refine the choice of detection threshold in both fields and bands according to the purity analysis presented in Sect. 3. 2. We use the source fluxes from the PSF fitting in the following analysis, which provide more robust flux measurements on slightly blended sources than the peak flux. As for the flux uncertainties, we provide the pixel value of the noise map after corrections (Eq. (  1 )), which accounts for the noise correlations between nearby pixels. However, we note that this does not take into account the additional uncertainties caused by degeneracies between the individual fluxes of heavily blended galaxies, but this is not a problem because we do not deblend sources closer than 2.
1  and 2 ). Assuming Poisson uncertainties, this is compatible at 2 σ with the 23 and 27 sources detected in the real data (Sect. 3. 2). In both fields, 87% of these sources are associated with a counterpart in the blob catalog. Most of these 2 mm detections are therefore associated with objects present in the simulation, and are not pure noise artifacts. This suggests that the combination of instrument noise, data reduction pipeline, and source extraction procedures could produce this apparent excess of 2 mm-only sources. We checked whether or not an increase in the S/N threshold improves the situation. For an S/N threshold corresponding to 95% purity, we have 2 and 11 sources in the real GOODS-N and COSMOS catalogs, while the end-to-end simulations contain on average 6 and 8 sources per field.
2022 ); -To characterize the performance of our analysis pipeline, we performed 117 end-to-end simulations of each field based on the SIDES model of galaxy evolution  (Béthermin et al. 2017; Gkogkou et al. 2023). Here, we took advantage of the simulation mode of the PIIC pipeline, which accepts SIDES maps as input models to be injected into real NIKA2 timeline data. A half-difference method was applied to these timelines to remove only the true astrophysical signal but not the injected one. Maps and catalogs from the simulations were then produced following the data reduction and source detection procedures identical to that applied to N2CLS data and maps; -We then compared the output source catalogs of these endto-end simulations with the input ones to determine the performance of our source-extraction algorithm.
Because of the angular resolution of NIKA2, for the input catalogs, we use the blobs extracted from the noiseless maps rather than the individual galaxies. For each field and wavelength, we determined the sample purity as a function of the S/N threshold, and the completeness as a function of the source flux and the local noise level. With the S/N thresholds of 80% purity, we detect 120 and 195 sources at 1. 2 mm in GOODS-N and COSMOS, respectively, and 67 and 76 sources at 2 mm. In the 1. 2 mm maps of GOODS-N and COSMOS, we detect sources as faint as 0. 4 mJy and 1. 7 mJy in uncorrected PSF fluxes. At 2 mm, we reach limiting uncorrected PSF fluxes of 0. 1 mJy and 0. 5 mJy in GOODS-N and COSMOS, respectively; -We also computed the ratio between the output (measured) and the input (simulated) flux densities, taking into account the effects of both data reduction (flux filtering) and source extraction (flux boosting).
2020 ) models. The semi-empirical models agree at low flux density (<1. 5 mJy), but tend to under-predict the counts at bright flux density (>1. 5 mJy). We discussed several possible causes such as the lack of strong lensing in some models, a deficit of high-SFR galaxies, and the dearth of objects with cold-dust SEDs. In contrast, the semi-analytical model of  Lagos et al. (2020)  over-predicts the counts at low flux while agreeing at higher flux. The measurements and the models of mm source counts are now close to convergence. Stronger constraints will come from a full characterization of N2CLS sources and will allow us to test our models in greater detail. The upcoming follow-up observations with NOEMA/ALMA will pinpoint the location of galaxies contributing to the observed N2CLS flux. The rich ancillary data and ongoing JWST observations, such as COSMOS-Web  (Casey et al.
2022) , will help to identify the multiwavelength counterparts of N2CLS sources, construct their full SED, and determine their redshift distribution and physical properties. Thanks to its volume-complete flux selection, the N2CLS sample is an ideal reference sample with which to perform this full characterization of DSFGs. We expect to reach a depth in COSMOS that is 1. 5 times greater than that achieved here. The final COSMOS catalogs will be released together with the full COSMOS data release in a forthcoming paper. The identification and properties of sources with S /N > 4 in GOODS-N will be detailed in  Berta et al. (in prep. ) together with the complete source catalog. Also in GOODS-N, Ponthieu et al. (in prep. ) will discuss the confusion noise due to distant galaxies for the IRAM 30m telescope.
Acknowledgements. The authors would like to thank the anonymous referee for the comments on the manuscript, as well as  Gergö Popping  and  Claudia Lagos  for providing their model prediction on mm number counts. L. B. warmly acknowledges financial support from  IRAM  for his first year of  Ph. D Thesis  and the support from the  China Scholarship Council  grant ( CSC No. 201906190213 ). We acknowledge financial support from the  "Programme National de Cosmologie and Galaxies" (PNCG)  funded by  CNRS/INSU-IN2P3-INP ,  CEA  and  CNES, France , from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  CONCERTO , grant agreement No  788212 ) and from the  Excellence Initiative of Aix-Marseille University-A*Midex , a  French "Investissements d'Avenir" programme.
This work is based on observations carried out under project numbers  192-16  with the IRAM 30 m telescope. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). We would like to thank the  IRAM  staff for their support during the campaigns. The NIKA2 dilution cryostat has been designed and built at the  Institut Néel. In particular, we acknowledge the crucial contribution of the Cryogenics Group, and in particular  Gregory Garde ,  Henri Rodenas ,  Jean-Paul Leggeri ,  Philippe Camus. This work has been partially funded by the  Foundation Nanoscience Grenoble  and the  LabEx FOCUS   ANR-11-LABX-0013. This work is supported by the  French National Research Agency  under the contracts "MKIDS", " NIKA " and  ANR-15-CE31-0017  and in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ).
This work has benefited from the support of the  European Research Council Advanced Grant ORISTARS  under the European Union's  Seventh Framework Programme  (Grant Agreement no. 291294 ). E. A. acknowledges funding from the  French Programme  d'investissements d'avenir through the  Enigmass Labex. A. R. acknowledges financial support from the  Italian Ministry of University  and Research -Project Proposal  CIR01_00010. The NIKA2 data were processed using the Pointing and Imaging In Continuum (PIIC) software, developed by  Robert Zylka  at the  Institut de Radioastronomie Millimetrique (IRAM)  and distributed by IRAM via the GILDAS pages. PIIC is the extension of the MOPSIC data reduction software to the case of NIKA2 data. This work made use of Astropy, a community-developed core Python package and an ecosystem of tools and resources for astronomy (Astropy Collaboration 2013, 2018, 2022).
The Cluster is organized into three action teams: S1-01: Longterm solar variability; S1-02: Worst-case scenario for extreme solar events; and S1-03: Data sets of historical observations of solar and geomagnetic activity. In this paper, the leads of each Action Team and the research community were asked to provide a review of the current understanding of the research field, identifying the key outstanding questions and the current knowledge gaps, as well as the future research developments. In Section 2 we review the observational and modeling aspects of sunspot time series, solar total and spectral irradiance, large-scale heliospheric parameters, and extreme solar events. Section 3 discusses the physical understanding of long-term variability, including solar cycle predictions and forcing on planetary environments.
Observational and modeling/theoretical aspects Solar variability can be probed through the observation of various parameters available in different periods (length of the raw dataset) and timescales. The longest time series available is an index based on the number of spots and sunspot groups that appear on the Sun, called the sunspot number  (Owens, 2013) : it is available from the beginning of the 17th century. It enables the study of the long-term behavior of the Sun. Other solar-activity indices include, e. g. , the radio flux F10. 7  (Tapping and Charrois, 1994) , coronal index, etc. The spectral solar irradiance and the total solar irradiance have been directly measured since 1978 (see, e. g. ,  Ermolli et al. , 2013; Kopp, 2016; DeLand et al. , 2019 and references therein)  and can be modeled before that using other long-term parameters, e.
g. , sunspot (areas, positions, sunspot number) and plage (from whitelight or Ca II K images) observations or cosmogenic isotope data  (Solanki et al. , 2013). The solar wind and heliospheric magnetic field have been directly measured by satellites for only a few decades (since late 1950s -early 1960s,  Obridko and Vaisberg, 2017) , but can be modeled over centuries and millennia through proxy data sources, e. g. , cosmogenic radionuclides. They are used, for example, to understand and anticipate solar eruptive events. Here we discuss only extreme solar events because of the focus on longer timescales of the ISWAT S1 Cluster. In this section we present the available observations, their compilation, and different modeling approaches used to extend these observations back in time and/or through data gaps, and to estimate their uncertainties.
Because of the amount of work necessary to actually gather and compile all the data required for SN calculations,  Hoyt and Schatten (1998a) ;  Hoyt and Schatten (1998b)  created an index based on the number of groups which does not take the number of individual spots into account. In order for GN to be comparable with SN,  Hoyt and Schatten (1998a) ;  Hoyt and Schatten (1998b)  introduced a linear relationship where GN has to be multiplied by 12. 08 to reach the level of SN. We note, however, that the approach of using SN as a proxy for GN needs to be taken with caution because the number of sunspots per group varies with phase of the solar cycle (e. g. ,  Tlatov, 2013). Georgieva et al. (2017)  devised a correction function for the GN series that takes into account the changes in the number of sunspots per sunspot group from 1700-2017.