dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-seq data analysis -case study To illustrate how GIANT can facilitate the analysis of RNA-seq data, we present here an example of GIANT-based analysis of RNA-seq data designed to identify new biomarkers in a rat dietary NASH model (GEO:GSE134715)  24. This dataset is composed of 48 samples with the 2 diet conditions, CSAA (choline-supplemented L-amino acid-defined control diet) and CDAA (choline-deficient L-amino acid-defined NASH diet) and 3 timepoints (4, 8 and 12 weeks) with 8 animals per group. For this analysis, the read count matrix available on the GEO public repository was used. First, differential analysis was performed on read count matrix using existing Galaxy tools. As the input of GIANT data mining tools can be any tabulated file without specific order of columns, results of most popular differential methods such as DESeq2, edgeR and Limma-voom can be mined with GIANT.
Then, the Quality Check tool was used to assess sample quality and to evaluate factor influence from the normalized counts file. Among generated plots, the 3D PCA (Figure  8a ) highlights a strong influence of the Diet factor in all samples and a Time factor effect specific to the CDAA diet samples in which the 4 weeks samples were clustered away from the 8 and 12 weeks samples. Interpretation of PCA was facilitated by the interactivity of the plot which permitted to dynamically rotate the graph and to display sample information when the mouse hovers over the dots. Furthermore, customization of dots color and shape based on diet and time factor values improved readability of the plot allowing factor values identification at first glance without need of unnecessary text fields. Finally, the GIANT tools allowed to mine the limma-voom results and to prepare data for enrichment analyses.
Funding This work was supported by grants from  Agence Nationale pour la Recherche  ( ANR-16-RHUS-0006-PreciNASH  and  ANR-10-LBEX-46 ) and  Fondation pour la Recherche Médicale  (Equipe labellisée,  DEQ20150331724 ). BS is a recipient of an  Advanced ERC Grant  ( 694717 ).
Data availability Availability and versioning The GIANT source code is freely available on GitHub (https://github. com/juliechevalier/GIANT) under GNU General Public Licence version 3. The Galaxy tool suite is available on the Galaxy Main Tool Shed ( https://toolshed. g2. bx. psu. edu; name:suite_giant; owner:vandelj) and can be installed on any Galaxy instance. GIANT tools have been installed and tested on Galaxy releases v18. 09 and v19. 09. Tools are versioned according to tool functionalities and intput/output formats. As the Galaxy platform allows the independent selection of different version for each installed tool, compatibility issues may occur if compatibility rules are not respected. Version compatibilities are summarized in the "README. rst" file, available on the GIANT GitHub repository.
Availability of supporting data and materials The GIANT documentation is available on the GIANT GitHub repository. This documentation contains a troubleshooting section and a step-by-step tutorial. Microarray raw data and RNA-seq read count matrix used in the Application section are available at NCBI (www. ncbi. nlm. nih. gov) GEO:GSE46495 and GEO:GSE134715 respectively. Screenshots of GIANT tool parameters, required input and output files for each step of presented microarray and RNA-seq analyses are available as Supplementary data 1. Several microarray configuration files required by the APT-Normalization tool are provided in the GIANT zenodo page https://doi. org/10. 5281/zenodo. 3908285. This repository contains pgf, clf, bgp, mps, cdf and formatted annotations files for MOE430A 1. 0, MOE430B 1.
With the most recent releases of MD-JEEP, new relevant features have been included to our software tool. MD-JEEP solves instances of the class of Discretizable Distance Geometry Problems (DDGPs), which ask to find possible realizations, in a Euclidean space, of a simple weighted undirected graph for which distance constraints between vertices are given, and for which a discretization of the search space can be supplied. Since its version 0. 3. 0, MD-JEEP is able to deal with instances containing interval data. We focus in this short paper on the most recent release MD-JEEP 0. 3. 2: among the new implemented features, we will focus our attention on three features: (i) an improved procedure for the generation and update of the boxes used in the coarse-grained representation (necessary to deal with instances containing interval data); (ii) a new procedure for the selection of the so-called discretization vertices (necessary to perform the discretization of the search space); (iii) the implementation of a general parser which allows the user to easily load DDGP instances in a given specified format.
IV. CONCLUSIONS We have presented three of the most important features introduced in the latest release of MD-JEEP. They represent another step ahead in the development of a general tool for solving DDGP instances. As for example, the implemented heuristic for expanding the boxes B v used in the coarsegrained representation in the BP algorithm may be replaced, in the near future, with a more efficient deterministic procedure. We expect then to provide more formal descriptions, as well as pseudo-codes, for these MD-JEEP features. Moreover, a longer term project consists in extending MD-JEEP to instances consisting of only interval distances, which represents another important step for the generalization of the software tool. As for the general parser that was introduced in the current MD-JEEP release, we intend to extend it in the next releases so that it will be able to load some more complex data formats, such as the ones that are generally employed by the structural biology community.
ACKNOWLEDGMENTS AM, LL and JHL are supported by the international project  MultiBioStruct  funded by  ANR in France  ( ANR-19-CE45-0019 ) and by the  Ministry of Science and Technology of Taiwan  ( MoST 109-2923-M-001 -004 -MY3 ). DG and CL are supported by  CNPq. DG is also supported by  CAPES/Print Process 88881. 310538/2018-01 ,and CL is also thankful to FAPESP. NM thanks the  COPPETEC Foundation.
Shape fabrics were analyzed by applying the SURFOR method  (Panozzo, 1984; Heilbronner and Barrett, 2014)  on samples from the Meluzina eclogites. Phase maps were obtained for garnet, omphacite and quartz by thresholding and grey level slicing of BSE contrast SEM images using ImageJ. Grain maps were obtained by further manual segmentation. On x-z sections (parallel to lineation), the outlines of ~360-540 garnet and ~1700 to 2500 omphacite grains were digitized, and on y-z sections (normal to lineation), those of ~40-100 garnet and ~660 to 760 omphacite grains. Grain size distributions (diameters of area equivalent circles) of garnet and omphacite were determined using the Analyze menu of ImageJ. The stripstar program was used to convert to volume weighted distributions of 3D grain size (diameters of volume equivalent spheres).
Acknowledgements We thank  Rüdiger Kilian  for his help with MTEX calculations. Discussions with  Pierre Lanari ,  Claudia Trepmann ,  Sumit Chakraborti ,  John Wheeler ,  Patrick Cordier , J€ org Renner, and  Alfons Berger  have helped to clarify some ideas. James Mackenzie  has made the initial important observations in the Meluzina eclogites that have initiated this study. We are also indebted to careful reviews by  Luca Menegon  and an anonymous reviewer.
Third, the potential of synergy processing of the groundbased Sun-photometric and lidar observations, with the in situ backscatter sonde measurements was explored using the data from KAUST. 15 and KAUST. 16 field campaigns held at King Abdullah University of Science and Technology (KAUST) in the August of 2015 and 2016. The inclusion of radiosonde data has been demonstrated to provide significant additional constraints to validate and improve the accuracy and scope of aerosol profiling. The results of all retrieval setups used for processing both synergy and stand-alone observation data sets are discussed and intercompared.
Lidars emit a series of electromagnetic pulses and register the returned responses from the atmosphere. These responses registered as a function of the time delay are sensitive to the amount and properties of the aerosol at different atmospheric layers. At the same time, compared to passive observations, lidars have notably lower information content with respect to the detailed properties of aerosols such as particle sizes and composition. The most popular lidar systems measure attenuated elastic backscattering registered at the same wavelengths as emitted radiation. Such measurements are affected by variation of all aerosol properties including concentration, size and shape distributions as well as particle composition. Quantitative interpretation of such data is challenging and requires significant a priori information about the aerosol properties (see, e.
sonet. ac. cn/index. php, last access: 30 March 2021) still in an active deployment phase. The lidar systems are more complex in development and substantially more demanding in operation, correspondingly, lidar networks, as a rule, have a significantly smaller number of sites; e. g. MPLNET has 70 sites globally (although only 20 are active at the moment) and EARLINET has about 27 active sites in Europe. The complementarity of photometric and lidar data is well recognized by the research community and the creation of joint observations sites where both the photometric and lidar observations are available is highly encouraged, often by upgrading a photometric site with lidar instrumentation. For example, MPLNET sites are always collocated with AERONET sites. In these regards, European ACTRIS (Aerosols, Clouds, and Trace gases Research Infrastructure Network) infrastructure (https://www.
actris. eu/, last access: 29 March 2021) can be mentioned as one of the best examples of networks emphasizing the acquisition of diverse complementary observations at each site. Specifically, all AC-TRIS observational supersites possess, not only both photometric and complex multi-wavelength lidar systems but also additional in situ data of different kinds. It should be noted that due to the complexity of lidar systems, especially of advanced multi-wavelength systems, the unification of both lidar observations and their processing is very challenging. For example, EARLINET includes very different lidar systems with different data processing and different customized aerosol retrieval approaches. In these regards, there is significant progress in unification of lidar data processing within ACTRIS lidar network, even though de facto lidar systems remain different.
g. Dubovik and King, 2000; Dubovik et al. , 2000 Dubovik et al. , , 2006;; Torres et al. , 2014; Sinyuk et al. , 2020; Nakajima et al. , 2020). While it was shown that addition of polarized skyscanning observations could provide some improvements in the retrieval accuracy of aerosol fine particles size distribution and refractive index (e. g. see  Li et al. , 2018; Fedarenka et al. , 2016) , due to the high complexity of polarimetric observations, such measurements are employed operationally only in SONET  (Li et al. , 2018; Dubovik et al. , 2019). Correspondingly, one of the main challenges of implementing synergy retrievals based on coincident radiometric and lidar data is achieving a sufficient flexibility of the retrieval in using different lidar observations and assuring their adequate and consistent fusion with the passive measurements.
One of rather successful examples of such a retrieval tool is the GARRLiC algorithm developed by  Lopatin et al. (2013)  integrated into the generalized approach by  Dubovik et al. (2011)  that is now named GRASP  (Dubovik et al. , 2014). GAR-RLiC/GRASP inverts both the photometric and lidar ob-servations and is currently being employed for operational processing of such combined data within the framework of the European ACTRIS infrastructure (https://www. actris. eu/, last access: 29 March 2021). However, the original GAR-RLiC algorithm was developed for the application to the specific observational set of multi-wavelength elastic scattering lidar together with AERONET-like Sun-sky-radiometer observations and did not include the possibilities of utilizing other types of lidar observations (e.
g. depolarization and nonelastic scattering). This paper discusses the evolution of GARRLiC/GRASP approach and demonstrates a wide spectrum of the possibilities for realizing the processing ground-based observations. Specifically, the current version of GRASP is useful not only for a synergetic retrieval using diverse radiometric and lidar observations but also for a stand-alone instrument processing. To be precise, the present version of GRASP allows new possibilities for inversion of lidar-only observations. The inversion of radiometer-only data is an inherent feature of GRASP (e. g. see  Lopatin et al. , 2013; Fedarenka et al. , 2016; Torres et al. , 2017)  since it has evolved from AERONET retrieval developments  (Dubovik et al. , 2011 (Dubovik et al. , , 2014)). Moreover, the GRASP approach allows for combining the remote sensing data with coincident in situ observations.
For example, this paper demonstrates the potential of synergy processing of the ground-based remote sensing observations together with advanced lidar or backscatter sonde data (that can be considered as a certain in situ analogue of lidar backscattering measurements). Specifically, the data from SHADOW-1/SHADOW-2 and KAUST. 15/KAUST. 16 field campaigns held at Institut de Recherche pour le Développement (IRD) in Dakar and King Abdullah University of Science and Technology (KAUST) in 2015-2016 were comprehensively analysed using GRASP approach. Finally, the paper shows benefits of using the multi-pixel approach that has been introduced in GRASP for improving reliability of satellite data processing  (Dubovik et al. , 2011). This approach uses a priori knowledge of limited time or spatial variability of the parameters retrieved from coordinated but not fully coincident and/or simultaneous observations.
Correspondingly, only K concentrations c k v drive the modelling of columnar properties of aerosol. This approach allows a significant reduction in the number of the retrieved parameters, which is especially fruitful for the observations with limited sensitivity to the size, shape and refractive index of the aerosol particles. Such multi-component external mixture approach has already been proven to be efficient for MERIS (https://www. grasp-open. com/products/ meris-data-release/, last access: 29 March 2021) and POLDER applications  (Chen et al. , 2020). In those applications, the vertical aerosol distribution was assumed the same for all the components and modelled as an exponential function of only one scale height parameter. In contrast, below in Sect. 3. 2, this multi-component model will be considered for application to the lidar and radiosonde data where components are characterized by a separate detailed vertical distribution.
g. Karol et al. , 2013)  and the vertical backscatter profile could be provided by radiosonde observations. Specifically, in this study, the data from the Compact Optical Backscatter Aerosol Detector (COBALD; see https://iac. ethz. ch/group/ atmospheric-chemistry/research/ballon-soundings. html, last access: 29 March 2021) are used. Another example is the airborne nephelometer that can provide the measurements of aerosol total extinction, absorption and scattering, together with angular scattering and degree of linear polarization at different layers  (Espinosa et al. , 2017 (Espinosa et al. , , 2019;; Schuster et al. , 2019). Finally, the elastic lidars emitting polarized signals can detect profiles of the signal depolarization that is a function of the elements P 22 and P 11 of the scattering matrix.
In most of practical lidar applications, the particle depolarization is derived directly from observations and considered as one of principal characteristics for further analysis. At the same time, the derivation of particle depolarization from observed volume depolarization profiles relies on rather scrupulous data selection and requires the knowledge of the backscattering ratio. In these regards, direct inversion of the volume depolarization is an interesting alternative because a comprehensive forward model utilized in the retrieval simulates depolarization using almost no or only very general assumptions. The examples of using volume depolarization in GRASP retrieval can be found in the earlier paper by  Hu et al. (2019). It should be emphasized that the observation of depolarization is a very powerful tool for detecting the presence of the non-spherical or irregular-shaped particles such as desert dust aerosols or crystal clouds.
11, 17 and 18)  and therefore to calibrate the profiles. This approach was adapted from earlier synergy retrieval LiRIC by  Chaikovsky et al. (2016)  that also processed combined radiometric and lidar data. However, LiRIC used the results of inversions from groundbased radiometers to constrain stand-alone lidar retrievals. In these regards, GARRLiC/GRASP proposed a more profound synergy approach by inverting joint lidar and photometric data set and simultaneously retrieving both columnar and vertical aerosol properties. This concept of the joint fitting allows for using an approach for addressing calibration uncertainties denoted by the constant A(λ) in the lidar equations that is simpler and more straightforward compared to the conventional procedures used by  Lopatin et al. (2013)  and  Chaikovsky et al.
Acknowledgements. We are thankful to the  KAUST Core Lab  and IT department for supporting the data collection and archiving. The authors also pay tribute to the exhausting and, sometimes, sleepless labour of the teams that make the operation field campaigns possible, notably the teams of AERONET, MPL and  IRD (Institut de Recherche pour le Développement ) in Dakar and  LOA (Laboratoire d'Optique Atmosphérique) in Lille  for SHADOW campaigns and  Jean-Paul Vernier  for the operation of radiosonde flights during KAUST campaigns. Financial support. The research reported in this publication was partially supported by funding from  King Abdullah University of Science and Technology (KAUST)  through the  Competitive Research grant  no. URF/1/2180-01-01  " Combined Radiative and Air Quality Effects of Anthropogenic Air Pollution and Dust over the Arabian Peninsula ".
Funding: This research was funded by  INSTITUTO POLITÉCNICO NACIONAL , grant number  SIP-20202083. Acknowledgments: M. A. -V. is grateful to  Universidad de Las Palmas de Gran Canaria, Spain  for the support to research stay in CICIMAR-IPN. I. S. L. is grateful to  CONACYT  and  BEIFI A200802  for the support. C. J. H. -G. and E. T. Q. acknowledge  Instituto Politécnico Nacional (IPN)  for the support " Beca de exclusividad (COFAA) " and " Estímulo al Desempeño de los Investigadores" (EDI) ". C. H. acknowledges  Biogenouest  for the support. Thanks for the support in molecular analysis go to  Dra. María Andrea Murillo Gallo.
Results Reference genome and resequencing data. We generated a new sfC genome assembly using PacBio reads, displaying 27. 5X coverage. The resulting assemblies are 384 Mb in size, and N50 is 900kb. Super-scaffolding of this assembly was performed using Hi-C 22 data and HiRise software  23. The final assembly size is 384. 46 Mb, which is close to the expected genome size estimated from flow cytometry (396 ± 3 Mb)  10. The number of sequences is 125, and N50 is 13. 15 Mb. The number of the longest superscaffolds explaining 90% of genome assembly (L90) is 27. The scaffold lengths show a bimodal distribution, and a mode corresponding to the longest sequences (>5 Mb) contains 31 scaffolds (Supplementary fig. 1 ). As the fall armyworm has 31 chromosomes in the haploid genome, this pattern demonstrates chromosome-sized scaffolds in the assembly.
Our new assembly has a higher proportion of complete BUSCO genes (Table  1 ) and a lower proportion of duplicated BUSCO genes (28/1658 = 1. 69%) than the assemblies by Liu et al. 25  (134/1658 = 8. 08% for male assembly, 97/1658 = 5. 86% for female assembly). From this BUSCO analysis, we concluded that our assembly has higher accuracy and better contiguity, because of a much smaller number of contigs and a much smaller L90 value than the assemblies from Liu et al. 25  (Table  1 ). Resequencing data were obtained from S. frugiperda populations from Mississippi (MS) and Puerto Rico (PR) using Illumina sequencing technology. We used mitochondrial genomes  27  to identify host strains for each individual (Supplementary fig. 2 ). Resequencing data of MS contained nine sfC and eight sfR individuals, while data from PR contained 11 sfC and four sfR individuals.
According to population genetics theory, when purifying selection is weak, the proportion of rare alleles is increased because slightly deleterious variants still remain unpurged as rare variants in a population. To test the second possibility, we compared the proportion of singleton variants between CNVs and SNVs using the unfiltered CNV dataset. We observed that CNVs have a much lower proportion of singleton polymorphisms than SNVs (0. 29% for CNV, 94. 0% for SNV, p < 2. 2 × 10 -16 ), suggesting that weaker purifying selection on CNVs is not supported. Instead, stronger positive selection on CNV is a more likely scenario. The folded site frequency spectrum is very different between CNV and SNV (Fig. 4a ). Neutral or slightly deleterious effects are not likely to cause the observed pattern of folded site frequency spectrum of CNV, because, in this case, the proportion of singleton variants is expected to be highest among all site frequencies even in the presence of a bottleneck  48.
llumina paired-end reads and mate-pair reads  10  were mapped the genome assemblies using bwa  63  , and scaffolding was performed using BESST  64. Pooled high molecular weight genomic DNA was extracted from 10 pupae of sfC, reared at the same insectarium as above. Dovetail™ Hi-C library was constructed from this strain, followed by Illumina Hiseq-X sequencing. Hi-Rise™ software  23  was used to perform scaffolding from the PacBio genome assembly. The correctness of the genome assembly was assessed using BUSCO  26. The gene annotation was performed using maker  65. Resequencing data. The paired-end resequencing data from S. frugiperda samples from Mississippi (MS) was obtained from NCBI SRA (PRJNA494340), which was generated for the fall armyworm genome project  10  and a speciation study  66.
For the S. frugiperda sample from Puerto Rico (PR), we collected larvae samples at Santa Isabel (Puerto Rico, see  42  for more detail). The larvae were raised to adults, and gDNA was extracted from thorax using the Promega Wizard ® Genomic DNA Purification Kit. Then, 150 bp pair-end Hiseq-4000 sequencing was performed from each individual. Resequencing data had approximately 28. 9X and 23. 4X coverage per individual for MS and PR, respectively. Adapter sequences were removed using adapterremoval  67. To identify host strains, we mapped the Illumina reads against mitochondrial genomes (NCBI: KM362176) using bowtie2  68  , followed by performing variant calling together with individuals from MS using samtools  69. The host strain of individuals from MS was already identified from mitochondrial COX1 markers in our previous study  10.