dataesr/scientific-paragraphs-categorization huggingface dataset:
By comparing the high-resolution LCOGT spectra (Section 2. 2. 5, Table  2 ) and the model PHOENIX spectrum for an F5IV star used for photometric calibration (Sections 2. 2. 2, 2. 2. 3), we find no evidence to support the claim of any excess flux around the Ca II H or K lines beyond the 1% level. To compare the difference between the model PHOENIX spectrum and LCOGT spectra, each nightly spectrum was individually normalized using a scale factor, slope, and offset. Figure  4  shows the result of this comparison for the Ca II H line at λ = 3969. 5 Å. The top panels in Figure  4  show the data from all of the spectra in Table  2  compared to the model PHOENIX spectrum, while the bottom panels show the residuals between the data and model. The residuals from each spectra compared to the model are shown as gray points, while the red points show the residuals when the data are rebinned to the original NRES R ∼ 50,000.
Spectrum of HIP 5319 B Figure  5  shows the 2020 and 2021 CHARIS spectra (whose data can be found in Table  4 ) as well as MEC, 25  VAMPIRES, and NIRC2 photometric points. The MEC photometry and CHARIS spectra are flat in F ν units except for a broad peak in H band. Formally, the S/N of HIP 5319 B in each spectral channel is extremely high (S/N > 77). Outside of the H band, peak, consecutive wavelength channels show a "wavy" pattern, which may indicate the impact of spectrally correlated noise (see below). The two CHARIS spectra show broad agreement: due to the higher S/N for the 2020 epoch spectrum, we focus on it for subsequent analysis. HIP 5319 B's broadband near-IR colors (J-H ∼ 0. 57 ± 0. 03; H-K s ∼ 0. 24 ± 0. 03) resemble those of early-to-mid M dwarfs  (Pecaut & Mamajek 2013). HIP 5319 B is substantially fainter than the primary in the VAMPIRES 750 nm data (Δm ∼ 7.
110). The VAMPIRES filter does not correspond to any standard photometric bandpass with a published zero-point flux density but lies between the Johnson-Cousins R and I bands. Adopting again the standard colors from  Pecaut & Mamajek (2013)  and R-band optical photometry for the primary from the Simbad database, we estimate a pseudomagnitude of ≈13 at 750 nm. We compare HIP 5319 B's CHARIS spectrum with other low-mass objects in the Montreal Spectral Library 26  (e. g. ,  Gagné et al. 2015). Only the CHARIS spectrum was used because the wavelength range for the Montreal Spectral Library covers JHK, but is rather nonuniform otherwise. Following the methods described in  Greco & Brandt (2016) , we find that the CHARIS spectrum shows noise that is high spatially and spectrally correlated (A ρ ∼ 0.
Results Using a 1/M p Prior for Companion Mass For this companion, we used HGCA absolute astrometry measurements for the star and three epochs of relative astrometry from CHARIS, MEC, and NIRC2. There is no archival RV data for this target and so it is not included in the orvara fits. A Gaussian prior of 1. 4 ± 0. 05 M e was chosen based on literature values for the primary mass  (Casagrande et al. 2011) , while a log-flat (1/M) prior was chosen for the mass of HIP 5319 B, which is the default used by orvara. This choice is motivated by the shape of the initial mass function for low-mass objects and for planets, which says that low-mass objects are expected to occur more frequently than high-mass ones  (Chabrier 2003; Nielsen et al. 2019). Figure  8  shows the posterior distributions for the primary and secondary masses along with select orbital parameters.
However, adopting a Gaussian prior for the companion mass yields a higher mass of M 128 88 127 J -+ , which favors the interpretation of the companion as a low-mass star although the distribution's peak still falls in the substellar range. Future RV measurements, relative astrometry from direct imaging instruments and more precise astrometry from Gaia data releases will contribute to further constraining this companion's mass and orbital parameters, providing deeper clarity on this companion's identity. Atmospheric models of the companion show a best fit to an atmosphere with solar metallicity at T = 3200 K with a surface gravity log(g) = 5. 5, though solutions with comparably good fits exist with temperatures that range from 3100 to 3300 K and slightly lower surface gravities (log(g) = 4-4.
5). The best-fit models show radii between 3. 25 and 3. 62 R J and log(L/L e ) = -1. 94 ± 0. 04. The mass inferred from atmospheric modeling is poorly constrained. This work highlights the need to have an updated inventory of system measurements when interpreting companions imaged around accelerating stars. While much older data suggested that HIP 5319 is a RS CVn (short-period) binary, our RV data rule out stellar companions with an orbital period less than 30 days whose presence would affect our conclusions about HIP 5319 B's mass and orbital properties. Similarly, our HIP 5319 spectra find no evidence for Ca HK emission that could reveal evidence of HIP 5319ʼs youth. Other system measurements whose values may impact derived companion masses and orbits include spectral type/luminosity, projected rotation rate, lithium abundances, x-ray activity, etc.
T. C. was supported by a  NASA Senior Postdoctoral Fellowship  and  NASA/Keck  grant  LK-2663-948181. T. B. gratefully acknowledges support from the Heising-Simons foundation and from  NASA  under grant # 80NSSC18K0439. M. T. is supported by  JSPS KAKENHI  grant #  18H05442. N. Sk. and V. D. acknowledge support from  NASA  (grant # 80NSSC19K0336 ). S. S. is supported by a grant from the  Heising-Simons Foundation. N. Z. was supported throughout this work by a  NASA Space Technology Research Fellowship. K. K. D. is supported by an  NSF   Astronomy and Astrophysics Postdoctoral Fellowship  under award AST-1801983. Facilities:  Subaru/SCExAO ,  Keck/NIRC2 ,  Las Cumbres Observatory Global Telescopes (LCOGT). Software: MKID Data Reduction Pipeline (Walter et al. 2020, Steiger et al. 2022), orvara (Brandt et al.
We recognize the limitations of the 2D-COSY technique in terms of its relatively longer acquisition time (∼17 min). However, future modifications of this pulse sequence would benefit from implementing acceleration techniques like matched accumulation or sparse sampling to reduce overall scan time. Quantification of metabolic resonances detected on 2D-COSY could also be improved by implementing a prior-knowledgebased fitting approach analogous to linear combination (LC)model fitting on 1D-MRS rather than using a peak integration method that is generally used. ProFit is one such 2D prior-knowledge-based fitting algorithm, and adopting this program could improve the reliable quantification of brain tumor metabolites  (52, 71, 72). Future developments also include designing multivoxel-based 2D-COSY sequences using concentric circular echoplanar encoding or spiral encoding schemes for facilitating faster data with greater anatomical coverage and higher spatial resolution.
FUNDING This work was supported by funding obtained from  University Research Foundation (URF) ,  Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA  (PI: SC, PhD, DABMP), extramural research grants [( BT/RLF/Re-entry/16/2015 ), and ( CRG/2021/000668 )] from  Department of Biotechnology (DBT)  and  Science & Technology (DST), Govt. of India , respectively (PI: MK).
ORCHIDEE-PCH 4 was calibrated and evaluated on 14 peatland sites distributed on both the Eurasian and American continents in the northern boreal and temperate regions. Data assimilation approaches were employed to optimized parameters at each site and at all sites simultaneously. Results show that methanogenesis is sensitive to temperature and substrate availability over the top 75 cm of soil depth. Methane emissions estimated using single site optimization (SSO) of model parameters are underestimated by 9 g CH 4 m -2 yr -1 on average (i. e. , 50 % higher than the site average of yearly methane emissions). While using the multi-site optimization (MSO), methane emissions are overestimated by 5 g CH 4 m -2 yr -1 on average across all investigated sites (i. e. , 37 % lower than the site average of yearly methane emissions).
This PFT is defined as a flood-tolerant C 3 grass with reduced productivity due to the lack of nutrients and with a reduced rooting depth. For the present study, ORCHIDEE-PEAT v2. 0  (Qiu et al. , 2019)  has been further enriched with a module simulating methane production, oxidation, and transport in northern peatlands; it is named ORCHIDEE-PCH 4. To achieve this, the methane scheme described by  Khvorostyanov et al. (2008a, b)  was revised according to high-latitude processes and peatland ecosystem features. This early version was an idealized 1D soil model that accounted for heat and gas transport as well as soil organic carbon decomposition and production of CO 2 and CH 4 driven by soil water content and temperature in the soil column. In that early version, only a moss layer that serves as a thermal insulator was considered for the vegetation above ground  (Khvorostyanov et al.
(2019). Site simulations were then constrained at the grid cell scale with a half-hourly time series of meteoro-logical conditions, e. g. , air temperature, wind speed, wind direction, longwave incoming radiation, shortwave incoming radiation, specific humidity, atmospheric pressure, and precipitation. These time series are flux tower measurements that were gap-filled by the 6-hourly CRU-NCEP 0. 5 • global climate forcing dataset  (Qiu et al. , 2018). Other variables measured on a half-hourly time step at sites, e. g. , CO 2 and energy (latent heat: LE; sensible heat: H ) fluxes, water table position, soil temperature, and snow depth, served for the calibration of peatland soil and vegetation phenotype characteristics such as the maximum rate of carboxylation (Vcmax). Optimized Vcmax values  (Qiu et al.
This demonstrates the complexity of the interactions of the methane cycle with environmental condi-tions considered at various scales and the need for more detailed on-site studies. Code availability. The source code (https://doi. org/10. 14768/ d385219a-787a-439c-b128-2e2d30a21f87, Salmon, 2021) is available online via https://forge. ipsl. jussieu. fr/orchidee/wiki/ GroupActivities/CodeAvalaibilityPublication/ORCHIDEE_ mict_peat_ch4 (last access: 24 March 2022). Readers interested in running the model should follow the guidelines at http://orchidee. ipsl. fr/index. php/you-orchidee (last access: 24 March 2022). The optimization tool is available through a dedicated website for data assimilation with ORCHIDEE (https://orchidas. lsce. ipsl. fr, Bastrikov, 2018). Supplement. The supplement related to this article is available online at: https://doi.
org/10. 5194/gmd-15-2813-2022-supplement. Author contributions. ES revised and modified the implementation of the methane module in ORCHIDEE-PEAT, performed model optimization simulations in ORCHIDEE-PCH 4 employing the OR-CHIDEE data assimilation system, investigated simulation results, and prepared the paper with contributions from all co-authors. FJ, BG, CG, PC, SG, and FLD conceptualized, secured funding for, and supervised the project. CQ, DZ, and BG provided ORCHIDEE-PEAT and assisted with model setup and development. LJ, FJ, and BG assisted in implementing ORCHIDEE-PCH 4 and investigating simulation results. VB and PP provided the model and the expertise on the ORCHIDEE data assimilation system and contributed to the interpretation of simulation results. CG, SG, FLD, MA, MSBH, JC, BHC, HC, CWE, ESE, LBF, KF, DH, JK, OK, NK, LK, AL, LM, WP, TS, and KZ produced and provided the quality field dataset employed to constrain and validate ORCHIDEE-PEAT and ORCHIDEE-PCH 4.
Data availability. Measured eddy covariance fluxes and related meteorological data can be obtained from the SNO-T (https://sourcesup. renater. fr/www/si-snot/, Doc SNO-T, 2022) for FR-Lag; from the European flux database cluster (http://www. europe-fluxdata. eu/home/sites-list, European Fluxes Database Cluster, 2022) for DE-Hmm (http://www. europe-fluxdata. eu/home/site-details?id=DE-Hmm, last access: 25 March 2022), DE-Spw (http://www. europe-fluxdata. eu/home/site-details?id=DE-Spw, last access: 25 March 2022), PL-Kpt (http://www. europe-fluxdata. eu/home/site-details?id=PL-Kpt, last access: 25 March 2022), PL-Wet (http://www. europe-fluxdata. eu/home/site-details?id=PL-wet, last access: 25 March 2022), and RU-Che (http://www. europe-fluxdata. eu/home/site-details? id=RU-Che, last access: 25 March 2022); from the FLUXNET database (http://fluxnet.
ornl. gov/, ORNL DAAC, 2022) for DE-Sfn, DE-Zrk, FI-Lom, and DK-Nuf; from the AmeriFlux database (http://ameriflux. lbl. gov/, AmeriFlux, 2022) for CA-Wp1 (https: //ameriflux. lbl. gov/sites/siteinfo/CA-WP1, last access: 25 March 2022), US-Bog (https://ameriflux. lbl. gov/sites/siteinfo/US-BZB, last access: 25 March 2022), US-Los (https://ameriflux. lbl. gov/ sites/siteinfo/US-Los, last access: 25 March 2022), and US-Wpt (https://ameriflux. lbl. gov/sites/siteinfo/US-WPT, last access: 25 March 2022); and from the investigators upon request. Model outputs are available upon request.
Acknowledgements. The modeling work was supported by the  European Union 's  Horizon 2020 Project CRESCENDO  under contract  641816  and  Labex   VOLTAIRE ANR-10-LABX-100-01. The authors acknowledge the support of staff at each site. Research at  US-Los  was supported by the  AmeriFlux Network Management Project  under contract no. 7544821  to the ChEAS core site cluster. Funding for the measurements in Biebrza National Park was provided by the  Polish National Science Centre  under projects  UMO-2015/17/B/ST10/02187  and  UMO-2020/37/B/ST10/01219. US-Bog  was supported by the  United States National Science Foundation :  NSF   OPP 1107892 ,  1503912 ,  1936712. Lawrence B. Flanagan  acknowledges funding from the  Natural Sciences and Engineering Research Council of Canada (NSERC) , the  FLUXNET-Canada Network (NSERC , the  Canadian Foundation for Climate and Atmospheric Sciences -CFCAS , and  BIOCAP Canada) , and the  Canadian Carbon Program (CFCAS).
Fieldwork at FR-Lag was funded as part of the  Labex VOLTAIRE  and the  PIVOTS  project of the  Région Centre -Val de Loire  ( ARD 2020 program  and  CPER 2015-2020 ) in the framework of the  French Peatland Observatory, SNO Tourbières , endorsed by  CNRS-INSU. Research work at DE-Hmm was funded by the  Deutsche Forschungsgemeinschaft  under  Germany's Excellence Strategy -EXC 177  " CliSAP -Integrated Climate System Analysis and Prediction " -contributing to the  Center for Earth System Research and Sustainability (CEN) of Universität Hamburg. The work at PL-Wet is based on use of  Large Research Infrastructure CzeCOS  supported by the  Ministry of Education, Youth and Sports of CR  within the  CzeCOS program  under grant number  LM2018123. Natalia Kowalska  acknowledges the support by  SustES -Adaptation strategies for sustainable ecosystem services and food security  under adverse environmental conditions ( CZ.
02. 1. 01/0. 0/0. 0/16_019/0000797 ). Financial support. This research has been supported by  Horizon 2020 (CRESCENDO  (grant no. 641816 )) and  Labex VOLTAIRE  (grant no. ANR-10-LABX-100-01 ). Review statement. This paper was edited by  Carlos Sierra  and reviewed by two anonymous referees.
, 2017; Guven-Maiorov et al. , 2017; Lasso et al. , 2019) ) providing the predictions through a database (e. g. ,  (Lasso et al. , 2019) ), or can be only used through a web interface (e. g. (Guven-Maiorov et al. , 2020) ), thus limiting the prediction reproducibility and tool usability. In this context, and inspired by our previous work  (Zanzoni et al. , 2017) , we present mimicINT, a computational workflow for large-scale interaction inference between microbe and human proteins by detecting host-like elements and using experimentally identified interaction templates  (Mosca et al. , 2014; Kumar et al. , 2020).
Implementation mimicINT detects putative molecular mimicry elements in microbe sequences of interest that can mediate the interaction with host proteins (Figure  1 ). mimicINT is written in Python and R languages and exploits the Snakemake workflow manager for automated execution  (Köster and Rahmann, 2018). It consists of four main steps: (i) the detection of host-like elements in microbe sequences; (ii) the collection of domains on the host protein (iii); the interaction inferences between microbe and host proteins; and (iv) the functional enrichment analysis on the list of inferred host interactors. In the first step, mimicINT takes as input the FASTA-formatted sequences of microbe proteins (e. g. , viral or other pathogen proteins susceptible to be found at the pathogen-host interface) to detect host-like elements: domains and SLiMs.
The domain identification is performed by the stand-alone version of InterProScan  (Jones et al. , 2014)  using the domain signatures from the InterPro database  (Blum et al. , 2021). By default, mimicINT retains InterProScan matches with an E-value below 10 - 5 , a threshold value commonly used for detecting profile-based domain signatures in protein sequences in the context of interaction inference  (Schleker et al. , 2012). The host-like SLiMs detection exploits the motif definitions available in the ELM database  (Kumar et al. , 2020)  and is carried out by the SLiMProb tool from the SLiMSuite software package  (Edwards et al. , 2020). As SLiMs are usually located in disordered regions  (Davey et al. , 2012) , SLiMProb uses the IUPred algorithm  (Dosztányi, 2018)  to compute the disorder propensity of each amino acid in the query sequences, and generates an average disorder propensity score for every detected SLiM occurrence.
For SLiM detection, the default IUPred disorder propensity threshold is set to 0. 2, a value commonly used to limit false negatives  (Edwards and Palopoli, 2015; Edwards et al. , 2020) , and the minimum size of the predicted disorder region is set to 5, the optimal size to detect true positive SLiM occurrences  (Paulsen, 2019). Nevertheless, the user can choose all running parameters for the host-like element detection in the mimicINT configuration file. In the second step, mimicINT gathered the domain annotations of the host proteins from the InterPro database through a REST API query. In the third step, mimicINT infers the interactions between host and microbe proteins. This analysis takes as input the list of known interactions templates gathered from two resources: (i) the 3did database  (Mosca et al.
, 2014) , a collection of domaindomain interactions extracted from three-dimensional protein structures  (Rose et al. , 2013) , and (ii) the ELM database  (Kumar et al. , 2020 ) that provides a list of experimentally identified SLIM-domain interactions in Eukaryotes. The inference checks whether any of the microbe proteins contains at least one domain or SLIM for which an interaction template is available. In this case, it infers the interaction between the given protein and all the host proteins containing the cognate domain (i. e. , the interacting domain in the template). As motif-binding domains of the same group, like SH3 or PDZ, show different interaction specificities  (Gfeller et al. , 2011)  for the SLiM-domain interaction inference, we have implemented a previously proposed strategy  (Weatheritt et al.
Given the degenerate nature of SLiMs  (Davey et al. , 2012) , their detection is prone to generate false positive occurrences. For this reason, we implemented an optional sub-workflow that, using Monte-Carlo simulations, assesses the probability of a given SLiM to occur by chance in query sequences and, thus, can be used to filter out potential false positives  (Hagai et al. , 2014)  (see Supplementary Methods). To ease deployment and ensure reproducibility and scalability on high-performance computing infrastructures, mimicINT is provided as a containerized application based on Docker and Singularity  (Merkel, 2014; Kurtzer et al. , 2017). mimicINT is available at https://github. com/TAGC-NetworkBiology/mimicINT.
Results We sought to evaluate the ability of mimicINT to correctly infer SLiM-domain interactions, as this inference can generate many false positives  (Weatheritt et al. , 2012) , using the default parameters for SLiM detection (see Implementation). To do so, we used as controls two datasets of established motif-mediated interactions (MDI) from the ELM database  (Kumar et al. , 2020)  (see Supplementary Methods): (i) 103 interactions between 87 viral and 44 human proteins (vMDI); (ii) 31 interactions between 16 bacterial and 23 human proteins (bMDI). We were able to correctly infer the majority of these interactions (91 vMDI, true positive rate = 88. 3%; 21 bMDI, true positive rate = 67. 7%). As the availability of negative SLiM-mediated interaction datasets is very limited  (Weatheritt et al.
0 database  (Blohm et al. , 2014). Only two virushuman interactions (5. 4%) were inferred as motif-mediated by mimicINT. In the light of these results, we used mimicINT to infer the interactions between human proteins and the Marburg virus (MARV), an emerging infectious agent for which experimental protein interaction data is scarce (23 interactions for VP24 protein in IMEx interaction databases  (Orchard et al. , 2012) ). We downloaded MARV protein sequences (7 proteins, Proteome ID: UP000180448) from UniprotKB in FASTA format. For domain detection, we considered only InterProScan matches in MARV sequences and ran mimicINT with default parameters. In total, we inferred 11,431 interactions between 7 MARV and 2757 human proteins (see Supplementary Data). The vast majority of the inferred interactions, namely 10,101, are motif-domain interactions (MDI, 7 MARV and 2324 human proteins), and the remaining 1,339 are domain-domain interactions (DDI, 5 MARV and 479 human proteins).
Acknowledgments The authors thank  Paul de Boissier  for helping in the early development of the workflow. The authors are also grateful to the members of the DIME project for fruitful scientific discussions and advices. Centre de Calcul Intensif d'Aix-Marseille  is acknowledged for granting access to its high performance computing resources. 0001] and from Excellence Initiative of Aix-Marseille University -A*MIDEX; and the  European Union 's  Horizon 2020 Research and Innovation Programme  [Project  ID 101003633 ,  RiPCoN ]. SAC received funding from the " Espoirs de la recherche" program  managed by the  French Fondation pour la Recherche Médicale  ( FDT202106013072 ).
4. 2. All the numerical solutions are obtained using direct numerical methods, which consist in solving a finite-dimensional optimization problem obtained by discretizing the optimal control problem; we use the bocop  [21]  software that efficiently automatizes this procedure. In bocop, both the state and control are discretized on fixed time grids. Depending on the numerical scheme chosen to approximate the dynamics (Crank-Nicolson, e. g. ), these grids may coincide or not. Enforcing the pointwise state and control constraints at each grid point, one obtains a finite-dimensional optimization problem. This transformation of the original optimal control problem (described in C++ and text files) into a nonlinear mathematical program is performed by bocop. The solver then relies on the very efficient code ipopt 1 to solve the optimization problem.
Numerical solutions of the reduced problem obtained using direct method implemented in Bocop software show that, for different parameters, initial data and final times, the obtained solutions are always with at most 3 arcs (see Sect. 3. 1 for example in case E M = k 1 = K 1 = K = 1). Moreover, the solutions with exactly 3 arcs are the most frequent solutions and occur when final time is large. For such solutions, the observed structure is Bang-Singular-Bang (B-S-B). We will take advantage of this observation and assume in following that solutions of the reduced problem have at most 3 arcs and the sequence of the arcs is a sub-sequence of B-S-B. With this assumption, we will construct a new algorithm to approximate the singular arc by a trajectory with the constant control r = r , from the solution of the static problem  (9).