dataesr/scientific-paragraphs-categorization huggingface dataset:
Panreference annotation Transposable element identification. Transposable elements were detected using RepeatMasker (version 4. 0. 7) (43)  with the RiTE-db (version 1. 1)  (44)  and the RepBase (version 23. 11, Oryza section) (45) databases. Genes mapping. Annotation of the panreference was performed using Liftoff (version 1. 6. 1) (46) with annotated genes from the Nipponbare reference (Oryza sativa ssp. japonica cv. Nipponbare, IRGSP-1. 0-1-2021-11-11 release). Genes were considered as successfully mapped if a minimum of 50% of the Nipponbare gene was aligned to the panreference with a sequence identity higher than 50% (options -s 0. 5 -a 0. 5). Gene copies were annotated using a minimum of sequence identity threshold of 95% (options -copies -sc 0. 95). Gene ontology annotation. Genes sequences were aligned to the NCBI NR protein database (9 September 2021, Viridiplantae section) using blastx (options -e-value 1e-6).
A tool to build panreference from scratch FrangiPANe was developed as a modular and interactive application to simplify the construction of a panreference using the map-then-assemble approach (Figure  1 ). It is available as a Docker image containing (i) a Jupyter notebook centralizing codes, documentation and interactive visualization of results, (ii) python scripts and (iii) all the softwares and libraries requested for each step of the analysis. Supplementary Table  S3  presents the main list of tools required by FrangiPANe. The code, documentation, installation manual and test data are available under the GPLv3 and CC4. 0 BY-NC license at https://github. com/tranchant/frangiPANe. A dedicated virtual machine is also available on the BioSphere Cloud of the French Institute of Bioinformatics (Appliance frangiPANe, https://biosphere.
The CG14 and TOG5681 genomes We relied on an improved reference genome of the cultivar CG14 from Oryza glaberrima from the OMAP consortium (Accession GCA 000147395, https://www. ebi. ac. uk/ ena/browser/view/GCA 000147395) and on a new whole genome assembly of the cultivar TOG5681 (see below for details), both accessions being themselves part of the 248 accessions sequenced using short reads  (27). TOG5681 control genome. We obtained 509 485 ONT long reads of minimal PhredQ 7, for 6. 612 Gb of data (18x) with a N50 of 23. 8 kb. After assembly and polishing, the final dataset represents 148 contigs, for a total assembly size of 348 131 590 bases, a N50 of 15 386 152 bases (L50 of 9), and 99. 5% of the assembly being comprised in contigs larger than 50 kb. The BUSCO score for this assembly is 95%,.
However, such assembly based on short reads lead to missing regions and repeat compression. We proposed here FrangiPANe as a new solution relying on a massively parallelizable approach, based on the 'map-then-assemble' pathway. Our tool proved to be particularly accurate with 97. 7% of assembled contigs from the TOG5681 accession also present in a new long reads genome assembly of the same TOG5681 individual. FrangiPANe also provides a complete environment for panreference creation through an unique and interactive interface, without requiring huge programming skills or the installation of numerous bioinformatic softwares. Based on Docker (https://docs. docker. com/get-docker) and Jupyter (https://jupyter. org/), it streamlines the whole process involving multiple analysis steps and the data visualization in different way (e.
g. tables or plot) within a single welldocumented notebook. While long reads de novo genome assembly offers new opportunities to perform pangenome analysis  (11, 18, 19) , the vast majority of currently available datasets are from short reads Illumina technology, and are generally very large in terms of number of individuals. FrangiPANe offers opportunities to take advantage of these datasets to gain a better understanding of plant and animal genomic diversity, and also to carry out large-scale pangenomic studies to detect selection or perform association with phenotype (GWAS).
ACKNOWLEDGEMENTS The authors acknowledge  Ndomassi Tando  and the ISO 9001 certified IRD itrop HPC (member of the  South Green Platform)  at IRD Montpellier as well as the TGCC platform for providing HPC resources that have contributed to the research results reported within this paper (URL: https://bioinfo. ird. fr/-http://www. southgreen. fr). They also thank  Christophe Blanchet  and the  French Institute of bioinformatics (IFB)  to provide access to the appliance frangiPANe through the  Biosphere cloud  (https: //biosphere. france-bioinformatique. fr/cloud/).
DATA AVAILABILITY frangiPANe is freely available in the GitHub repository https://github. com/tranchant/frangiPANe, under the double licence CeCiLL-C (http://www. cecill. info/licences/ Licence CeCILL-C V1-en. html) and GNU GPLv3. A virtual machine is also available at the BioSphere service of the French Institute of Bioinformatics (Appliance frangiPANe, https://biosphere. france-bioinformatique. fr/catalogue/appliance/201/). The sequences (fasta file) and their placement on the reference genome (csv file) have been deposited in the IRD dataverse: Tranchant, Christine; Chenal, Clothilde; Blaison, Mathieu; Albar, Laurence; Klein, Valentin; Mariac, Cédric; Wing, Rod; Vigouroux, Yves; Sabot, Francois, 2022, 'Supporting data for the African Rice Panreference produced by the frangiPANe software', DataSuds, V1, https://doi.
In addition, our detailed screening yielded a few existing review articles found within the reliable and valid dataset in which we searched. Some notable ones, for example, were: a literature-based typology of missionoriented innovation  (Jütting, 2020) ; a review of the grand challenges pertaining to higher education  (Nowell et al. , 2020) ; and a review of the existing technologies in the food-energy-water ecosystem  (Miao & Khanna, 2020). Whereas the latter two reviews (i. e. ,  Miao & Khanna, 2020; Nowell et al. , 2020)  took a traditional approach, Jütting (2020) employed a bibliometric technique of literature review in pursuance of developing a literaturebased typology of mission-oriented innovation. This reiterates the relevance of reviewing the extant innovation and grand challenges research by using structured and quantitative review techniques.
| Data retrieval process and data The data for our analysis were retrieved from the Sco-pus© database by following the conventional wisdom pertaining to the data retrieval process for systematic reviews  (Tranfield et al. , 2003). Our data retrieval steps included; first, a clear and pre-planned identification of our aims and objectives; second, inclusion and exclusion criteria and methods; and, third, the manual screening and assessment of the selected data. The units of analysis for our review were research documents (i. e. , journal articles, review papers, book chapters, books, editorials, conference papers, etc. ) published within the selected research field. Hence, we retrieved the bibliometric data of all eligible and relevant research documents from the Scopus database. As a strategic choice, we chose Scopus over Web of Science as our primary and key source of our data as it is the largest multidisciplinary database of peer-reviewed literature within the social sciences domain  (Bartol et al.
| Performance analysis of the innovation and grand challenges research This section presents the general trends and a description of the research domain of innovation aimed at tackling grand challenges. We followed a protocol and retrieved the bibliometric metadata of 66 research documents. These documents included 58 research articles, 4 book chapters, and 4 review papers published in 50 sources (i. e. , journals and books). The publication timeline for these research documents was between 2008 and 2021. The citation average for these research documents was found to be 12. 88 each. More details on the dataset are shown in Table  1. Figure  2  (see online appendix) presents the annual publication trends in regard to innovation aimed at managing grand challenges in a for-profit context. It shows how the research stock in this field has grown with an annual rate of 27.
We would like to reiterate that an understanding of the most productive authors in a research field is essential as it helps in knowing how actively authors have engaged in a research field and their respective contributions to it. Moreover, such information is crucial in order to Source: Authors analysis of bibliometric metadata of relevant papers retrieved from SCOPUS (October 2021). understand the suitable outlets for research on innovation aimed at tackling grand challenges in a for-profit context. Hence, we identified the top 10 such publication sources. These include the Academy of Management Journal (four publications); Journal of Business Ethics (four publications), Business and Society (three publications), and Research Policy (three publications). Thereafter, the list includes the Academy of Management Perspective, Australian Journal of Management, Journal of Cleaner Production, Journal of Industry Competitions and Trade, Local Economy, and Journal of Management Studies, all with two publications each.
| Knowledge structure of the field To understand the status and boundaries of the research base in the field of innovation aimed at tackling grand challenges in a for-profit context, we constructed the related knowledge structure. This details the ontology and taxonomy of a particular and relevant knowledge base, defined as the knowledge of how the concepts in a particular and relevant domain or field are interrelated  (Jonassen & Wang, 1993). We performed an analysis of the bibliographic data, constructing a strategic map of innovation aimed at tackling grand challenges in a forprofit context using the Bibliometrix R package  (Aria & Cuccurullo, 2017). Figure  1  shows the historiography or historical direct citation network of the top 30 cited research documents.
g. ,  Calzetti et al. 2007 ). In addition, the high spatial resolution of MIRI (FWHM = 0 3 at 10 μm, corresponding to 2. 4 kpc at z ∼ 1) enables studies of resolved morphological structures in distant galaxies for the first time. In this paper, we use the MIRI data taken as part of the Cosmic Evolution Early Release Science (CEERS; S. Finkelstein et al. 2023, in preparation; G. Yang et al. 2023, in preparation)  survey to measure the morphology of galaxies in rest-frame MIR to trace the obscured star formation region for galaxies down to stellar mass of M * ∼ 10 9 M e in the redshift range of 0. 2 < z < 2. 5. We compare these data to data from the HST covering the rest-frame NUV for these galaxies from the UltraViolet imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) survey, which traces their unobscured star formation.
70, and Ω M = 0. 30. (S. Finkelstein et al. 2023, in preparation)  is an Early Release Science (ERS) program (Proposal ID #1345) that will cover a total ∼100 sq. arcmin of the Extended Groth Strip (EGS;  Davis et al. 2007 ) field. This field is one of the five legacy fields of CANDELS  (Grogin et al. 2011; Koekemoer et al. 2011 ) and partially covered by the 3D HST Treasury Survey  (Skelton et al. 2014; Momcheva et al. 2016 ). CEERS will obtain observations in several different modes with the JWST, including NIRCam imaging  (Bagley et al. 2022) , MIRI imaging (G. Yang et al. 2023, in preparation) , and NIRSpec multiobject spectroscopic observations (see P. A. Haro et al. 2023, in preparation).
CIGALE SED Fitting We employed the SED fitting code CIGALE  (Boquien et al. 2019; Yang et al. 2020)  in order to constrain possible AGN contribution to the IR luminosity of our samples and to estimate the IR and FUV luminosities in a self-consistent framework that considers the energy balance between the UV/ optical and IR. In detail, we adopted a delayed exponential SFH allowing the τ and stellar age varying from 0. 1-10 Gyr and 0. 1-10 Gyr, respectively. We assumed a Chabrier (2003) IMF and the stellar population synthesis models presented by  Bruzual & Charlot (2003)  with solar (Z e ) metallicity. The dust attenuation follows  Calzetti et al. (2000) ʼs extinction law allowing color excess E s (B -V ) to vary from 0 to 0. 4. The amplitude of the absorption UV bump feature produced by dust at 2175 Å and the slope of the power law are allowed to vary from 0-3 and -0.
5-0, respectively. For the dust emission module, we adopted the dust templates of  Draine et al. (2014). This module models the dust emission with two components, the diffused emission and the PDR emission associated with star formation. We allow the mass fraction of PAH to vary between four different values and the minimum radiation field (U min ) to vary between 0. 1, 1. 0, 10, 30. For the AGN module, we adopt the SKIRTOR template. We retain the default parameters in the AGN module, other than setting the viewing angle i to 30°and 70°for type 1 and type 2 AGNs, respectively, and a full range of AGN fraction ( f AGN ) from 0 to 0. 9, a fraction that denotes the contribution from the AGN to the total IR luminosity. More details of the parameter setting are shown in Table  2. We adopt the "PDF analysis" method in CIGALE to compute the likelihood (χ 2 ) for all the possible combinations of parameters and generate the marginalized probability distribution function for each parameter and each galaxy.
We acknowledge the hard work of our colleagues in the CEERS and UVCANDELS collaboration and everyone involved in the JWST mission. This work benefited from support from the  George P. and  Cynthia   Woods Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University. C. P. thanks  Marsha  and  Ralph Schilling  for generous support of this research. R. A. W. acknowledges support from  NASA   JWST Interdisciplinary Scientist  grants  NAG5-12460 ,  NNX14AN10G  and  80NSSC18K0200  from  GSFC. This work acknowledges support from the  NASA/ ESA/CSA  James Webb Space Telescope through the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-03127. Support for program No. JWST-ERS01345  was provided through a grant from the  STScI  under  NASA  contract  NAS5-03127.
Results To facilitate the modeling of the Sigmoidal Mixed Model, and Piecewise Mixed Model with abrupt or smooth random change, we have created an R package called nlive. All needed pieces such as functions, covariance matrices, and initials generation were programmed. The package was implemented with recent developments such as the polynomial smooth transition of the piecewise mixed model with improved properties over Bacon-Watts, and the stochastic approximation expectation-maximization (SAEM) for efficient estimation. It was designed to help interpretation of the output by providing features such as annotated output, warnings, and graphs. Functionality, including time and convergence, was tested using simulations. We provided a data example to illustrate the package use and output features and interpretation.
Background Continuous longitudinal data may have a trajectory that is not linear. This is the case of cognitive aging and other processes in fields such as agriculture  [1] , pharmacology  [2] , and marketing  [3]. Although some less parsimonious models have been proposed to model such longitudinal data, the use of models with a specific functional form such as the Sigmoidal Mixed Model (SMM)  [4]  and the Piecewise Mixed Model (PMM)  [5]  with abrupt or smooth change allow the interpretation of the defined parameters. The SMM is currently implemented in SAS using PROC NLMIXED  [4] , which maximizes the marginal likelihood by using an adaptive Gaussian quadrature  [6]  or other approximation methods, such as the first-order method  [7]. In R, most of these packages focus on dose-response optimization and curve-fitting  [8]  such as qpcR  [9] , grofit  [10] , FlexParamCurve  [11] , drfit  [12] , and MCPMod  [13]  or aim to automate fitting and classify multiple curves  [8].
However, none of these packages can fit the SMM allowing the inclusion of covariates for all 4 parameters. The PMM is commonly fitted using Bayesian inference and implemented in OpenBugs or WinBUGS, but is also commonly fit in R using the lme4  [14] , which maximizes the marginal likelihood by using a Laplace approximation. A recently developed Stochastic Approximation Expectation Maximization (SAEM) algorithm was shown to be more successful  [15]  and faster  [16]  to identify the maximum likelihood estimators of non-linear mixed models. This can be implemented directly using the package saemix  [17]  (version 3. 0). However, one downside of having such flexible packages as the lme4 and saemix is that they require more analytical skills to code. It is worth noting that some simple-to-use packages in R can fit the abrupt PMM, including segmented  [18]  and rcpm  [19].
However, these packages also do not use SAEM and with them, it is not possible to (i) include covariates for all 4 parameters, (ii) consider a smooth polynomial transition, and/or (iii) estimate directly the last level (e. g. level close to death). In this work, we present the the version 0. 2. 0 of the nlive package implemented within R software. The main objective of the package is to facilitate and broaden the application and interpretation of the SMM and PMM for longitudinal data. All needed elements to fit the models have been programmed, including the computation of the structural model and the automatic generation of initials for the main parameters. As such, less experienced R users only need to specify the model to fit via a single intuitive line of code, with only five mandatory arguments.
The package was implemented with the most recent and efficient algorithms for non-linear models. Implementation was also performed with the most interpretable parameterization and was based on the most recent developments in each type of model. For example, for the smooth PMM, instead of using the Bacon-Watts  [20]  which can create an artificial increase in the trajectory right after the changepoint  [21] , we considered the most recently developed polynomial smooth transition  [22]. In this article, we reintroduce these models, describe the implementation of the package, and provide a simulation study to demonstrate the performance of the package. We also demonstrate the use of the model and interpretation of the output using a made-up dataset with trajectories similar to those found in the cognitive aging of individuals followed until death  [23].
Model specifications As a prelude to the introduction and demonstration of the new nlive package, we first describe the general formulation of the nonlinear mixed models implemented in the package. The simplified general form of nonlinear mixed models can be written in terms of a known nonlinear function f given by: where y ij denotes the longitudinal outcome value of subject i (i = 1,. , N ) collected at the observation time t ij (j = 1,. , n i ) ; ψ i is a vector of normally distributed person-specific parameters function of fixed effects and individual random effects; and ǫ ij are random error, with ǫ ij ∼ N (0, σ 2 ǫ ). Motivated by the application on late-life cognitive decline, the nlive package implements two main classes of nonlinear mixed models: the Sigmoidal Mixed Model (SMM)  [4, 24]  with four parameters and the Piecewise Linear Mixed Model (PMM)  [5]  with two linear phases and a single changepoint.
The piecewise linear mixed model with a random changepoint The PMM model  [5]  assumes that the stochastic process of the longitudinal outcome is characterized by two or more different phases. Under this class of models, the nlive package implements two PMM models with an abrupt change (PMM-abrupt)  [25]  and a smooth polynomial transition (PMM-smooth)  [22]  between the two linear phases. These models provide an appealing statistical approach to detecting the time when the onset of accelerated decline occurs.
Software To facilitate the application and interpretation of the SMM, PMM-abrupt, and PMM-smooth models for a broader audience, who is not necessarily familiar with statistical programming, we developed a user-friendly R package called "nlive" (non-linear mixed models with initial values estimated) with R version 4. 0. 3. All needed elements to fit the models, including the definition of the structural model and the generation of initials for the four main parameters, have been programmed so that the user only needs to specify a single intuitive line of code to fit the model. A variety of options can also be specified. Along with the functions to fit individual models, the package also provides a function that displays the longitudinal data. A made-up data frame, under the name dataCog, is provided with the package.
This data represents cognitive patterns previously observed in cognitive aging when participants are followed until death. Real data on cognitive aging with annual follow-up until death from the Religious Order Study and the Memory and Aging Project (ROSMAP) from the Rush Alzheimer's Disease Center can be obtained under request at https:// www. radc. rush. edu. These cohorts are described elsewhere  [23]. The version 0. 2. 0 of the nlive package is freely available via the Comprehensive R Archive Network (CRAN) at https:// CRAN. R-proje ct. org/ packa ge= nlive.
Estimation The SMM, PMM-abrupt, and PMM-smooth previously described were all estimated using the saemix package (version 3. 0) developed by Comets and colleagues  [17]. The saemix package, among other things, requires the definition of the structural model; thus, first, nlive includes the structures of the models. For SMM, the nlive algorithm relied on the SSlo-gis5() function of the nlraa package  [27]  (version 1. 2), which initially defines a 5-parameter logistic curve but can be reduced to a 4-parameter logistic when the 5 th parameter is fixed to 1. For PMM-abrupt and PMM-smooth, the structure of the models are explicitly coded in the nlive algorithm. In addition to the output information provided by saemix, the nlive (16) ∂ ∂t ij g transition (ψ 4i + v) = ψ 2i package also provides p-values for the main terms  [28]  that would not be available otherwise.
The SAEM algorithm The computational technique for maximum likelihood estimation implemented in saemix is the Stochastic Approximation Expectation Maximization (SAEM) algorithm, which is a stochastic approximation version of the standard EM algorithm proposed by Khuhn and Lavielle  [29]. The SAEM algorithm showed to be efficient in the context of non-linear mixed models, converging quickly to the maximum likelihood estimators  [16]  and achieving better performance than linearization-based algorithms  [15]. In preliminary testing during the algorithm coding process, in line with the literature, saemix showed convergence to the adequate solution more often than two main competing software package  [17] : nlme  [6]  and lme4  [14]. Note that in saemix, the likelihood can be computed by linearisation or by importance sampling.
Initial values One of the great advantages of the nlive package is that it has an embedded algorithm that examines the data and automatically provides informative initials. Here we briefly describe this algorithm. For SMM, the four main parameters are the last level, first level, midpoint, and Hill slope. An initial for each parameter needs to be provided. For that, we build upon an algorithm previously developed in SAS by Capuano and colleagues  [4]  (algorithm hosted and accessible at github. com/AWCapuano/sigmoidal). In this work, we expand this algorithm for different data structures (e. g. , different time scales). Briefly, first, the algorithm segments the time into the initial (5th percentile) and final (95th percentile). At these periods the initial and final mean levels of the outcome are ascertained.