dataesr/scientific-paragraphs-categorization huggingface dataset:
[ B ] B Model simulation with the training dataset. Heatmaps depict model-observed (left) and -simulated (right) time-resolved RAS-OIS gene expression profiles at indicated time points for each model variable. Correlation coefficients between observed and simulated gene expression profiles are shown as a bar-plot for each line in the graph at the far right of heatmaps. Data are expressed as row Z-scores. [C] Histogram depicting Pearson coefficient correlation distribution between model-observed and -simulated gene expression profiles of RAS-OIS transcriptome time-course for each model variable. [D] Model simulation with validation dataset. Comparison of observed (left) and -predicted (right) RAS-OIS gene expression profiles at indicated time-point for control and AP1-cJUN knock-down. Correlation coefficients between observed and simulated gene expression profiles are shown as a bar-plot for each line in the graph at the far right of heatmaps.
TESS disco v ery photometry The transit signatures of TOI-836 b and TOI-836 c were originally identified by the TESS Science Processing Operations Center  (Jenkins et al. 2016  ) using an adaptive matched filter  (Jenkins 2002 ; Jenkins et al. 2010 Jenkins et al. , 2020 ) )  to search the Sector 11 light curve on 2019 June 5. The transit signatures were fitted with an initial limb-darkened transit model  (Li et al. 2019 ) , and passed all the diagnostic tests performed and reported in the Data Validation reports  (Twicken et al. 2018 ). The TESS Science Office reviewed the Data Validation reports and issued an alert for TOI-836 on 2019 June 17. Subsequent searches of the combined light curves from sectors 11 and 38 located the source of the transit events to within 3. 73 ± 2. 5 and 0. 98 ± 1.
For TOI-836 c the orbital period is 8. 60 d, and the transit depth is 1140 ppm, implying the candidate is potentially sub-Neptune in size. For this work, we use the Presearch Data Conditioning Simple Aperture Photometry (PDC-SAP) light curve produced by the SPOC pipeline. The PDC-SAP light curv es hav e non-astrophysical trends remo v ed from the raw Simple Aperture Photometry (SAP) light curves using the PDC algorithm  (Smith et al. 2012 ; Stumpe et al. 2012 Stumpe et al. , 2014 ) ). The PDC-SAP light curves for TOI-836 were retrieved from the Mikulski Archive for Space Telescopes (MAST) portal and used in our joint model in Section 3. To mitigate for the effects of stellar variability on the transit light curves in the Sector 11 and Sector 38 TESS data, we apply a Gaussian Process (GP) model using the PyMC3 and celerite packages.
We constrain this GP model for each sector using three 2 https:// github. com/jlillo/ tpfplotter hyperparameters as priors set up with log( s2 ) (a jitter term describing the excess white noise;  Salvatier , W iecki & Fonnesbeck 2016 )  and log( Sw4 ) as normal distributions with a mean equal to the variance of the flux of each sector and a standard deviation of 0. 1 for Sector 11 and 0. 05 for Sector 38 (this is done to prevent o v erfitting of the GP); and the same is applied to log( w0 ). log( Sw4 ) and log( w0 ) both represent terms that describe the non-periodic variability of the light curves  (Salvatier et al. 2016  ). These hyperparameter setups are identical to those described for TOI-431 in  Osborn et al. ( 2021 )  and informed by the exoplanet and PyMC3 documentation. These hyperparameters are then incorporated into the SHOTerm kernel within the exoplanet framework, representing a stochastically driven simple harmonic oscillator  (Foreman-Mackey et al.
2021a  ). The GP model is then subtracted from the PDC-SAP flux to reco v er a flattened light curve from which transit models of TOI-836 b and TOI-836 c can be drawn. The effect of this can be seen in the first and second panels of Fig. 2  for Sector 11 and Sector 38 of TESS , respectively. We also plot the phasefolded TESS data for TOI-836 b and TOI-836 c in Fig. 3  for both sectors. For all follow-up photometry, we convert each time system to TBJD ( TESS Barycentric Julian Date, BJD-2457000) for consistency, and normalize each light curve by dividing by the median of the out-of-transit flux data points and subtracting the mean of the outof-transit flux. The transits themselves are then modelled using a quadratic limb-darkened Keplerian orbit (with coefficients u 1 and u 2 ) according to  Kipping ( 2013b ) , with parameters including stellar radius (R * ) and mass (M * ) in Solar units, planetary orbital period ( P ) in days, transit ephemeris ( T c ) in TBJD, impact parameter ( b ), eccentricity ( e ), and argument of periastron ( ω) defined for each of TOI-836 b and TOI-836 c with priors informed by our spectral analysis and catalogue data (see Appendix Tables A1 , A2 , and A3 for details of the priors used).
For all visits, we use an exposure time of 60 s. See details set out in Table  2. The CHEOPS spacecraft is in a low-Earth orbit and thus parts of the observations are unobtainable because the telescope passes through the South Atlantic Anomaly (SAA), and as the amount of stray-light entering the telescope becomes higher than the accepted threshold, our observations are interrupted by Earth occultations. These effects that occur on orbital time-scales ( ∼98. 77 min) result in onboard rejections of images and manifest in a decrease in     observ ational ef ficiency, corresponding to 72 per cent, 55 per cent, 56 per cent, 54 per cent, and 96 per cent per visit, as can be seen in Fig. 4. For all visits, the data were automatically processed using the CHEOPS data reduction pipeline  (DRP v13; Hoyer et al.
2020 ) , that conducts image calibration, such as bias, gain, non-linearity, dark current, and flat fielding corrections, and performs rectifications of environmental and instrumental effects, for example cosmic-ray hits, smearing trails, and background variations. Aperture photometry is subsequently done on the corrected images using a set of standard apertures; R = 22. 5 arcsec (RINF), 25. 0 arcsec (DEFAULT), and 30. 0 arcsec (RSUP), and an additional aperture that aims to optimize the radius based on contamination level and instrumental noise (ROPT). For the CHEOPS observations of TOI-836, this radius is   et al. ( 2020 ) , that is subtracted from the light curves. Due to the orbit of CHEOPS and thus the rotating field of view, CHEOPS data include short-term, non-astrophysical flux trends due to nearby contaminants, background variations, or changes in instrumental environment that vary on the time-scale of the orbit of CHEOPS.
Whilst previous works have used linear decorrelation with instrumental basis vectors  (Bonfanti et al. 2021 ; Delrez et al. 2021 ; Leleu et al. 2021 )  or Gaussian process regression  (Lendl et al. 2020  ), a recent study has shown that a no v el PSF detrending method can also remo v e these roll angle trends  (Wilson et al. 2022 ). In brief, this method assesses PSF shape changes o v er a visit by conducting a principal component analysis on the autocorrelation function of the CHEOPS subarray images, as it was found that a myriad of causes of systematic variation within CHEOPS data affects the PSF shape. A leave-one-out-cross-validation (Celisse 2008 ) is used to select the most prominent components that are subsequently used to decorrelate the light curve produced by aperture photometry.
MEarth-South photometry A transit of TOI-836 c was observed using the MEarth-South telescope array  (Irwin et al. 2015a  ) at Cerro Tololo Inter-American Observatory (CTIO), Chile on 2019 July 3-4. Seven telescopes were operated defocused to a half-flux diameter of 12 pixels (10. 1 arcsec, giv en the pix el scale of 0. 84 arcsec pix -1 ), and an e xposure time of 32 s, observing continuously starting from twilight until the target set below 2 airmasses. Observations were made using an RG715 filter. A meridian flip occurred during the transit and has been taken into account in the analysis by allowing for a separate magnitude zero-point on either side of the meridian to remo v e an y residual flat fielding error. Data were reduced following standard procedures for MEarth-South data (e. g. Irwin et al.
9 arcsec pixel -1. We observed TOI-836 on 2021 April 8, during 5 h between BJD 2459313. 20 and 2459313. 41, and we detected the second half of the transit of TOI-836 c. We scheduled the observation using a custom scheduling tool that sends queries to the TESS Transit Finder. We set the exposure time to 25 s, the cadence was 50 s, and we collected 370 frames. The median full-width half maximum (FWHM) was 4. 06 arcsec and the airmass varied between 1. 57 and 1. 94. The details of the ASTEP observations are set out in Table  2. We performed differential aperture photometry using a custom data reduction pipeline based on the pipeline described in M ékarnia et al. (  2016 ) and adapted to TESS follow-up. We used an aperture radius of 10 pixels (9. 3 arcsec) and 8 comparison stars. The light curve RMS is 1.
Supported by the international  Gemini Observatory , a program of  NSF 's NOIRLab, which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation , on behalf of the  Gemini partnership of Argentina, Brazil, Canada, Chile , the  Republic of Korea , and the  United States of America. Some of the observations in the paper make use of the  High-Resolution Imaging instrument(s) 'Alopeke and Zorro. 'Alopeke and Zorro were funded by the  NASA   Exoplanet Exploration Program  and built at the  NASA Ames Research Center by Steve B. Howell, Nic Scott ,  Elliott P. Horch , and  Emmett Quigley. 'Alopeke and Zorro were mounted on the Gemini North and South telescopes of the international  Gemini Observatory , a program of  NSF 's NOIRLab, which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation.
on behalf of the Gemini partnership: the  National Science Foundation  ( United States ),  National Research Council (Canada) ,  Agencia Nacional de Investigaci ón y Desarrollo (Chile) ,  Ministerio de Ciencia, Tecnolog ía e Innovaci ón (Argentina) ,  Minist ério da Ci encia, Tecnologia, Inova c˜ oes e Comunica c˜ oes (Brazil) , and  Korea Astronomy and Space Science Institute (Republic of Korea). This work has been carried out within the framework of the  NCCR PlanetS  supported by the  Swiss National Science Foundation. FH is supported by an  STFC  studentship. The French group acknowledges financial support from the  French Programme National de Plan étologie (PNP, INSU). AO is supported by an  STFC  studentship. This work has been carried out within the framework of the  NCCR PlanetS  supported by the  Swiss National Science Foundation.
MNG acknowledges support from the  European Space Agency (ESA)  as an  ESA  Research Fello w. DJA ackno wledges support from the  STFC  via an  Ernest Rutherford Fellowship  ( ST/R00384X/1 ). PJW acknowledges support from  STFC  consolidated grant  ST/T000406/1. JSJ greatfully acknowledges support by  FONDECYT  grant  1201371  and from the  ANID BASAL  projects  ACE210002  and  FB210003. JL-B acknowledges financial support received from  'la Caixa' Foundation  ( ID 100010434 ) with fellowship code  LCF/BQ/PI20/11760023 , and the Projects No. PID2019-107061GB-C61  and No. MDM-2017-0737. EDM acknowledges the support from Funda c˜ ao para a Ci encia e a Tecnologia (FCT) by the  Investigador FCT  contract  IF/00849/2015/CP1273/CT0003. SH acknowledges CNES funding through the grant  837319. We acknowledge the support by FCT -Funda c˜ ao para a Ci encia e a Tecnologia through national funds and by  FEDER  through  COMPETE2020 -Programa Operacional Competitividade e Internacionaliza c˜  ao by these grants:  UID/FIS/04434/2019 ;  UIDB/04434/2020 ;  UIDP/04434/2020; PTDC/FIS-AST/32113/2017 & POCI-01-0145-FEDER-032113 ;  PTDC/FISAST/28953/2017 & POCI-01-0145-FEDER-028953.
SGS acknowledges the support from FCT through Investigador FCT contract nr. CEECIND/00826/2018  and  POPH/FSE (EC). SMO is supported by an  STFC  studentship. VA acknowledges the support from FCT by the  Investigador FCT  contract  IF/00650/2015/CP1273/CT0001. TGW, ACC, and KH acknowledge support from  STFC  consolidated grant numbers  ST/R000824/1  and  ST/V000861/1 , and  UKSA  grant  ST/R003203/1. YA and MJH acknowledge the support of the  Swiss National Fund  under grant  200020 172746. SCCB acknowledges support from FCT through FCT contracts nr. IF/01312/2014/CP1215/CT0004. XB and SC acknowledge their role as ESA-appointed CHEOPS science team members. ABr was supported by the  SNSA. 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 Li  ège through an  ARC grant for Concerted Research Actions  financed by the  Wallonia-Brussels Federation ; LD is an F.
R. S. -FNRS Postdoctoral Researcher. ODSD is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by national funds through FCT. B-OD acknowledges support from the  Swiss National Science Foundation  ( PP00P2-190080 ). 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 ). It has also been carried out in the frame of the  National Centre for Competence in Research PlanetS  supported by the  Swiss National Science Foundation  (SNSF). DE acknowledges financial support from the  Swiss National Science Foundation  for project 200021 200726. MF and CMP gratefully acknowledge the support of the  Swedish National Space Agency  ( DNR 65/19 ,  174/18 ).
MF acknowledges their role as ESA-appointed CHEOPS science team members. DG gratefully acknowledges financial support from the  CRT foundation  under Grant No. 2018. 2323'Gaseousor rocky?Unveiling the nature of small worlds'. DG acknowledges their role as ESA-appointed CHEOPS science team members. MG is an F. R. S. - FNRS Senior Research Associate. 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 Inv estissements d'Av enir supervised by the  Agence Nationale pour la Recherche. JL acknowledges their role as ESA-appointed CHEOPS science team members. ML acknowledges support of the  Swiss National Science Foundation  under grant number  PCEFP2 194576. PM acknowledges support from  STFC  research grant number  ST/M001040/1.
AC K N OW L E D G E M E N T S This work makes use of tpfplotter by  J. Lillo-Box  (publicly available in www. github. com/jlillo/tpfplotter),which also made use of the python packages astropy , lightkurve , matplotlib , and numpy. This research makes use of exoplanet (F oreman-Macke y et al. 2021b ) and its dependencies (Astropy Collaboration 2013 , 2018 ; Kipping 2013b ; Salvatier et al. 2016a ; Theano Development Team 2016 ;Kumar et al. 2019 ;Luger et al. 2019 ; Agol,  Luger & Foreman-Mackey 2020  ;  Foreman-Mackey  et al. 2021b ). This paper makes use of EXOFAST (Eastman et al. 2013(Eastman et al. , 2019 ) ) as provided by the  NASA Exoplanet Archive , which is operated by the  California Institute of Technology , under contract with the  National Aeronautics and Space Administration  under the  Exoplanet Exploration Program.
Malaria is a deadly disease caused by Apicomplexan parasites of the Plasmodium genus. Several species of the Plasmodium genus are known to be infectious to human, of which P. falciparum is the most virulent. Post-translational modifications (PTMs) of proteins coordinate cell signalling and hence, regulate many biological processes in P. falciparum homeostasis and host infection, of which the most highly studied is phosphorylation. Phosphosites on proteins can be identified by tandem mass spectrometry (MS) performed on enriched samples (phosphoproteomics), followed by downstream computational analyses. We have performed a large-scale meta-analysis of 11 publicly available phosphoproteomics datasets, to build a comprehensive atlas of phosphosites in the P. falciparum proteome, using robust pipelines aimed at strict control of false identifications.
A search of the P. falciparum proteome in the UniProt Knowledgebase (UniProtKB)  [17] , the world's most popular protein knowledge-base, returns 18 proteomes linked to different isolates of countries of origin, which are mostly well synchronised with PlasmoDB. Tandem mass spectrometry (MS/MS) is most often used in large scale phosphosite identification and localisation studies  [18]. Protein samples are purified and enzymatically digested, typically using trypsin. Samples are then enriched for phosphorylated peptides using reagents such as TiO2, or other metal ions that promote phosphate binding. Then liquid chromatography is used to separate peptides that are subsequently fragmented and analysed by MS/MS. Results from MS analyses can then be compared against protein sequence databases, with and without the mass shift for phosphorylation, via one of the many available search algorithms  [19, 20].
e. the pASTY method  [21]. An important benefit of pASTY searches is that it allows combination of results from multiple studies as FLR can provide objective comparable thresholds  [22]. For P. falciparum, PlasmoDB provides information on 16,118 phosphorylation sites, although phosphorylation sites have been loaded from multiple publications over the last 10 years. In our previous work examining databases containing human phosphosites, we estimated that there is a high proportion of false positive sites recorded, due to historically inadequate statistics associated with detection of sites by MS  [23]  and, prior to  [22] , lack of methods for calculating adequate statistics for controlling the FLR across studies. In this work, we aim to provide a high-quality mapping of P. falciparum phosphosites via a large-scale re-analysis of public phospho-enriched studies, underpinned by robust analysis pipelines enabling meta-analyses with FLR control within and across studies' results.
falciparum and human matches. Note that FLR threshold counts only considered PSMs that passed 1% FDR. The data is unequally distributed between the 11 studies, with four studies (PXD012143, PXD015833, PXD020381, PXD026474) contributing significantly more to the overall number of sites at every stage of the analysis. In fact, nearly 70% of all P. falciparum protein-sites at 5% FLR come from these four studies. 1A  shows the Gold-Silver-Bronze (GSB, see Methods) quality categorisation for P. falciparum protein-sites matching to P. falciparum (left panel) and human (right panel) proteins. If a sequence, and therefore the sites within the sequence, match to more than one protein sequence, these sites were counted for all mapping proteins in the proteome. In total, there were 28,495 protein-sites in P.
The publication associated with dataset PXD015833 specifically investigated substrate specificity and concluded that some of the FIKK family have a preference for a basic motif near to pS/pT site -with arginine enriched in the minus position (group 6 in our analysis)  [30]. We could speculate that the motifs groups identified in Figure  2  are driven by different kinase groups in Supp. Table  2 , but without new experimental data, robust conclusions are not possible. This is an area requiring further work. Conservation analysis Based on unique phosphorylated protein-sites at 5% FLR in P. falciparum 3D7, we investigated the existence of their orthologs in other species of Plasmodium. Conservation for each site, defined as the proportion of species containing the same amino acid at that position in the multiple sequence alignment, can be found as supplementary files (Supp File 3.
Data for this analysis including the clusters can be found in Supp. File 4. Figure  4. A: Heatmap of the agreement in sequence conservation between 23 species of Plasmodium: P. gorilla clade G1(PPRFG01), P. reichenowi (PRCDC), P. blacklocki G01 (PBLACG01), P. billcollinsi G01 (PBILCG01), P. adleri G01 (PADL01), P. gaboni (PGSY75), P. malariae (PmUG01), P. brasilianum strain Bolivian I (MKS88), P. ovale (PocGH01), P. relictum (PRELSG), P. gallinaceum (PGAL8A), P. chabaudi AS (PCHAS), P. vinckei vinckei CY (PVVCY), P. yoelii 17X (PY17X), P. berghei ANKA (PBANKA), P. cynomolgi (PcyM), P. vivax (PVP01), P. knowlesi (PKNH), P. coatneyi Hackeri (PCOAH), P. fragile strain nilgiri (AK88), P. inui San Antonio 1 (C922), P. vivax-like (PVL) and the reference P. falciparum (PF3D7). The heatmap displays the mean conservation (agreement) of phosphosites per protein with three clusters for species and five for proteins.
File 6). We also investigated conservation based on SNP analysis within the Plasmodium falciparum species, using data sets derived from whole genome sequencing of different isolates downloaded from PlasmoDB (Supp. File 7). Out of 26,006 phosphosites, 25,587 did not have any recorded single amino acid variants (SAAVs recorded in PlasmoDB), indicating very high conservation of phosphosites within the species sampled (98. 3%). Analysis of the total serines within the proteome found 4,660 SAAVs with major allele frequency (AF) <1 (from 261,791 total serines), i. e. 98. 2% conservation. This indicates that that pS is no more likely or less likely to be mutated than other serines. On average, 97. 9% threonines in the proteome are conserved (i. e. have no SAAV in this analysis), compared to 98. 7% for pT sites.
Disorder and site conservation data is available as Supp. File 8. Figure  6. A: Boxplot of the disorder scores (from metapredict) for phosphosites (pS, pT and pY) versus disorder scores for all S, T and Y residues in the P. falciparum proteome. B: Density functions for percentage of conservation by residue for ordered and disordered regions (<0. 3 metapredict score).
Protein structural context The release of AlphaFold2 (AF2) has had a very significant impact on the ability to understand the three-dimensional (3D) structure of proteins across both model and non-model organisms  [34]. 3D structure predictions for P. falciparum proteins are available via AlphaFold database, UniProtKB and PlasmoDB (via mapping to UniProt identifiers). We have mapped all the phosphosites onto AF2 structures, also incorporating the conservation scores (across the Plasmodium genus), enabling visual exploration of the relationship between order/disorder (which can be clearly visualized) on structures, conservation and positions of phosphosites. In Supp. File 9, we have created a static html page with a table of the phosphoproteins identified, with hyperlinks so that every phosphoprotein's structure and phosphosites can be visualized via the online iCn3D viewer  [35] , and links to their corresponding record in UniProtKB (see section below on data access).
Open access data availability The Plasmodium falciparum "phosphosite build" is part of a wider project, called PTMeXchange, aimed at high quality re-analysis of MS/MS data sets enriched for particular PTMs, and providing simple public access to the resulting data sets. The build is provided via PRIDE/ProteomeXchange with identifier PXD046874, where researchers can download the phosphosites identified per study in simple tab-separated text files. The data has also been loaded into UniProtKB (Figure  8A ), enabling phosphosite evidence to be explored alongside other protein features and AF2 models, with links to the raw evidence. We have also released the phosphosite build within PeptideAtlas (https://peptideatlas. org/builds/pfalciparum/phospho/), enabling more detailed exploration of the evidence within each protein, peptidoform and spectrum for a given site (Figure  8B ).