dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, the "KnownClusterBlast" option refers to a curated database containing experimentally characterized clusters, while the "Cluster-Blast" option uses an extended database composed mostly of only hypothetical clusters  (Weber et al. , 2015a). The only database fully dedicated to NRP compounds, Norine, currently comprises 1190 peptides, including 346 NRPs of fungal origin distributed in 46 families. Norine offers different search modes: (1) an "annotation search" engine, which includes queries with all annotations (e. g. , biological activity, molecular weight, monomer count, or producing organism), and (2) a "structure mode" allowing searches based on monomer composition fingerprints, sequence similarity or whole structures. The analysis of predicted peptides permits searching for related analogous compounds already characterized, or to check if some amino acid patterns involved in some biological activities can be spotted  (Pupin et al.
When pioneering predictive models for the substrate specificity of adenylation domains were developed a decade ago  (Minowa et al. , 2007; Rausch et al. , 2005) , very few fungal data was available. Therefore, these models provide predictions based mainly on monomers identified as a substrate for the few well-characterized bacterial NRPSs at that time. Consequently, these bioinformatics tools cannot predict with high accuracy the substrate specificity of most of the Adomains of eukaryotic NRPSs. Furthermore, based on the Norine database, it appears that NRPSs of fungal and bacterial origin display only a moderate range of overlapping substrate and incorporate a different set of monomers into the NRPs (Fig. 3 ). For example, Aib (2-Fig. 3. Analysis of the 50 most frequent monomers constituting the nonribosomal peptides classified in the Norine database.
, 2008) , amino alcohols in NRPs result from a modification of the amino precursors during synthesis of the peptide  (Du and Lou, 2010; Manavalan et al. , 2010). Determining if the monomer is loaded as such or modified during the synthesis is a complex task as no generalized rules can be applied. For example, D -amino acids are generally the result of an epimerization domain activity on L -amino acids, but, in some cases, D -Ala was reported to be directly loaded  (McErlean et al. , 2019). Even if a few software packages, such as NRPSpredictor2  (Röttig et al. , 2011) , have previously developed models to predict the substrate specificity of adenylation domains, there is still an important need for devising tools enabling to predict more accurately which monomers are incorporated in fungal NRPs.
Heterologous production of NRPS in fungal hosts The number of available fungal genomic sequences in databases is continuously expanding thanks to individual or large-scale collaborative efforts, such as the "1000 fungal genomes project"  (Araujo and Sampaio-Maia, 2018; Stajich, 2017). These available fungal genomes represent a gold mine for novel NRP compounds discovery. Unfortunately, in many cases, the newly sequenced fungi do not (or only poorly) produce these natural products under usual laboratory conditions in synthetic media, which are not always suitable to develop the filamentous fungi or to produce NRPs  (Sung et al. , 2017). One option is to activate or enhance the homologous production in the native host through promoter exchange for instance  (Bode et al. , 2015; Soukup et al.
Concluding remarks The recent development of high-throughput sequencing technologies combined with substantial drop in sequencing costs have driven an impressive increase in the number of available fungal genomes. This factor coupled with the improvement of genome annotating methodologies has partially unlocked the SM production potential of fungi. Hence, efficient bioinformatics tools now enable a more accurate analysis in terms of nonribosomal pathway identification and substrate specificity predictions. Nonetheless, the prediction robustness is lowered because already elucidated fungal NRPs and NRPSs remain underrepresented in dedicated databases as compared to bacterial ones. Consequently, the predictions based on genomic sequences need to be validated by structural elucidation of the NRPs which implies its efficient production.
Acknowledgments This work was supported by the  CPER Alibiotech  project; the  INTERREG V France-Wallonie-Vlaanderen Project SmartBioControl/ BioScreen ; the  Fonds pour la Formation à la Recherche dans l'Industrie et l'Agriculture (FRIA) ; and the  international laboratory Microbial Secondary Metabolites MOM (University of Lille/Gembloux Agro-Bio Tech University of Liege ). Grant PhD co-funding of the  University of Liege  ( Welcome Grant Philippe Jacques ) and the  University of Lille.
Simplified workflow for novel NRP discovery by genome mining with an overview of the main bioinformatics tools available (accessible through hyperlinks). Second Generation Sequencing, Third Generation Sequencing, Hybrid methods SPADes & Canu Genomic data Genomic annotation Biosynthetic Gene clusters identification Annotation of genes included in BGC NP. Searcher, ClustScan, antiSMASH Potential NRPS gene cluster e v i t a t u P NRPS backbone elucidation Structural annotation of NRPS genes (Prediction of the modular organization in domains) PKS/NRPS Analysis, InterProScan, antiSMASH Putative NRPS structure n o i t c i d e r P of related NRPs Analysis of A domain substrate specificity Identification of C domain sub-classes and prediction of associated NRP modifications NRPSsp, NRPSpredictor2, SEQL-NRPS, PrediCAT (SANDPUMA), Nrps-linker, NaPDoS Databases NCBI ClusterMine360 Query on databases MultiGeneBlast Predicted NRPs Putative novel natural products antiSMASH Database Fig.
The mean angular velocity and the total duration of probing plus antennating contributed mostly to PC2 (see Figure  A4  and Table  A1  in the Appendix), so the matching activity score 2, hereafter "exploitation activity", represented the exploitation intensity of a host-mimicking patch, a high score corresponding to a high level of patch exploitation activity. The two activity scores followed a Gaussian distribution, so we fitted a linear mixed model (LMM) for each score with the female identity as a random effect (Female), the day of measure and the kairomone solution used to mimic a host patch (because we had to prepare some additional solution during experiments; factor S1 vs. S2) as fixed effects (Day and Solution) and their interaction (Day:Solution) in the models. We estimated the repeatabilities of exploration, movement activity and patch exploitation activity with the package rptR  (Stoffel, Nakagawa, & Schielzeth, 2017).
We have developed this tool suite to analyse microarray data from Gene Expression Omnibus (GEO) database or custom data from affymetrix c microarrays. These data are then combined to carry out meta-analysis using metaMA package. SMAGEXP also offers to combine raw read counts from Next Generation Sequencing (NGS) experiments using DESeq2 and metaR-NASeq package. In both cases, key values, independent from the technology type, are reported to judge the quality of the meta-analysis. These tools are available on the Galaxy main tool shed. Source code, help and installation instructions are available on github. Conclusion: The use of Galaxy offers an easy-to-use gene expression meta-analysis tool suite based on the metaMA and metaRNASeq packages.
Background Meta-analyses are widely used in medicine and health policy to increase statistical power in studies suffering from small sample sizes. Gene expression experiments are a typical example of such designs. The R packages metaMA and metaRNASeq are dedicated to gene expression microarray and NGS meta-analysis, respectively. While metaMA and metaRNASeq are open source and available on CRAN, they require coding skills in R to perform meta-analysis. Thus, to facilitate the use and the dissemination of these packages, we developed Galaxy wrappers. Galaxy  [1, 2, 3]  is an open, web-based platform for data intensive biomedical research. It keeps tracks of history and all analyses can be rerun. Galaxy community is very active and a lot of bioinformatics tools are included in Galaxy thanks to a modular system based on XML wrappers.
Methods Overview of R packages integrated into Galaxy metaMA Gene expression microarray data meta-analysis can be performed thanks to the metaMA [4] R package. It proposes methods to combine either p-values or moderated effect sizes from different studies to find differentially expressed genes. In our pipeline we only keep the inverse normal method  [5]  to combine the p-values calculated by limma  [6]  for each single study. metaRNAseq RNA-seq data meta-analysis can be performed thanks to the metaRNASeq  [7]  R package. It implements two p-value combination techniques : the inverse normal and Fisher methods  [8]. Single study p-values are comptuted with DESeq2  [9].
Microarray data meta-analysis GEOQuery tool GEOQuery tool fetches microarray data directly from GEO database, based on the GEOQuery [10] R package. Given a GSE accession ID, it returns an Rdata object containing the data and a text file (. cond file) summarizing the conditions of the experiment. The. cond file is a text file containing one line per sample in the experiment. Each line is made of 3 columns: Column names are optional and only the columns order matters. As the GEO dataset should already have been normalized, the GEOQuery tool does not perform any normalization method, apart from an optional log2 transformation. • Sample ID QCNormalization tool It is possible to analyze. CEL files from affymetrix gene expression microarray. The QCnormalization tool offers to ensure the quality of the data and to normalize them.
Several normalization methods are available : • rma normalization • quantile normalization + log2 • background correction + log2 • log2 only This tool generates several quality figures : microarray images, boxplots and MA plots. It also outputs an Rdata object containing the normalized data for further analysis with the limma analysis tool. Import custom data tool This tool imports data stored in a tabular text file. Column titles (chip IDs) must match the IDs of the. cond file. A few normalization methods are proposed, but it is possible to skip the normalization step, by choosing "none" in the normalization methods options. Therefore this tool is of special interest when the input dataset has been previously normalized. This tool also generates boxplots and MA plots and outputs an Rdata object containing the data for further analysis with the limma analysis tool.
Limma analysis tool The Limma analysis tool performs single analysis either of data previously retrieved from GEO database or normalized affymetrix. CEL files data. Given a. cond file, it runs a standard limma differential expression analysis. The user choose two conditions extracted from the. cond file (see Figure  3 ). It generates boxplots for rough quality control of normalization, p-value histograms to ensure that statistical hypotheses are not violated and a volcano plot to quickly identify the most-meaningful changes. This tool also outputs a table summarizing the differentially expressed genes and their annotations. Genes are sorted by ascending Benjamini-Hochberg adjusted p-value, and annotations are retrieved via GEO database. This list of genes can be exported to excel or to csv format.
Microarray data meta-analysis tool The meta-analysis relies on the metaMA R package. Prior to the meta-analysis itself, a pre-processing is made in order to ensure compatiblity between several sources of data. In fact, data could come from different types of microarrays. First, we list the Entrez gene ID corresponding to each probe of each dataset. Next, we keep the probes corresponding to the genes which are shared by all the experiments of the meta-analysis. Then, for each dataset, we merge the microarray probes originating from the same Entrez gene ID by computing their mean. Note that the merging of different technologies induces a loss of information and might generate several conflicts as probes do not necessary reflect the same biological reality. Finally, the p-value combination method of metaMA is run on the merged dataset.
It generates a Venn Diagram summarizing the results of the meta-analysis, and a list of indicators to evaluate the quality of the performance of the meta-analysis : • DE : Number of differentially expressed genes • IDD (Integration Driven discoveries) : number of genes that are declared differentially expressed in the meta-analysis that were not identified in any of the single studies alone • Loss : Number of genes that are identified differentially expressed in single studies but not in meta-analysis • IDR (Integration-driven Discovery Rate) : corresponding proportion of IDD • IRR (Integration-driven Revision) : corresponding proportion of Loss It also outputs a fully sortable and requestable table, with gene annotations and hypertext links to NCBI gene database.
Application Microarray meta-analysis example SMAGEXP was applied to two GEO datasets identified with the following IDs : GSE3524 and GSE13601. These two datasets contain human oral squamous cell carcinoma (SCC) data. See Figure  4  for an overview of the worfklow of this analysis. First, we fetch data from the GSE3524 using the GEOQuery tool (with parameter "log2 transformation" = auto). Then we launch the limma analysis, using the output from the GEOquery tool. It generates an Rdata output, which will be usefull for the meta-analysis. Results can be seen on Figure  5  and Figure  6  Figure  5 : limma analysis tool output plots. Figure  6 : limma analysis tool : table of top 10 genes for GSE3524 dataset. Secondly, the same kind of analysis is run from raw. CEL files. We choose to keep six.
Conclusion We developed SMAGEXP, a toolsuite dedicated to gene-expression data meta-analysis. This toolsuite proposes quality controls, single analyses and meta-analyses of microarray and RNAseq data, suggesting appropriate pipelines for each type of data. It delivers fully annnotated results of differentially expressed genes, exportable in several usual formats. Integrated into Galaxy, SMAGEXP is easy to use for biologists and life scientists. R packages metaMA and metaRNAseq thus inherit reproductibility and accessibility support from Galaxy. SMAGEXP is available on the Galaxy main toolshed  [11]. Source code is available on github at : https://github. com/sblanck/smagexp. Furthermore, thanks to Docker, we made these Galaxy tools and their dependencies easy to deploy. A fully dockerized instance of Galaxy containing SMAGEXP is available at : https://hub.
In this research, we established a System Dynamics Model named "E&I-SD" to study the development of the energy structure and industrial structure in China from 2000 to 2030 using Vensim Simulation Software based on energy economy theory, system science theory and coordinated development theory. We used Direct Structure Test, Structure-oriented Behavior Test, and Behavior Pattern Test to ensure the optimal operation of the system. The model's results showed that the indicators of total energy consumption, total added value of GDP after regulation, energy consumption per capita, and GDP per capita were on the rise in China, but emissions per unit of energy showed a downward trend. Separately, the model predicted average annual growth rates in China through 2030. Based on these findings, we proposed important policies for China's sustainable development.
The E&I-SD Model Description and Validation Test Using the E&I-SD model, we simulated the coordinated development of China's energy structure and industrial structure from 2000 to 2030. We set the running time of the simulation at thirty years, with a time step of one year, beginning with the year 2000. For this work, VENSIM (VENSIM is an industrial strength simulation tool for improving the performance of real systems. Its rich feature set emphasizes model quality, connections to data, flexible distribution, and advanced algorithms. ) was employed to write the simulation programs, and EVIEWS software was used to regress parts of the equations. There were 115 system dynamics equations and parameters established through repeated modification and improvement on the model, parts of typical variables and equations are shown in Appendix A Table  A1.
Hypotheses and Boundaries of the E&I-SD Model The consensus in the literature holds that building a model entails understanding the essential features of a system through abstraction and induction. An excellent model should be as simple as possible based on a true reflection of the research questions. Accordingly, this study simplified the system while ensuring the integrity of the model. We made the following hypotheses and set the boundaries of the E&I-SD model as described: (1) Since the data of each variable and parameter were known only through 2016, we chose to project the 2016 standard values as constant from 2016 to 2030. The variables for the three economic sectors (primary, secondary, and tertiary) included variables such as the following: • The proportion of GDP; (2) The data concerning environmental pollution were too extensive to include in its entirety.
MUSE observations and data reduction MUSE integral field spectroscopy of the six DLA fields was acquired in service mode between June 2015 and April 2016 under ESO programmes 095. A-0051 and 096. A-0022 (PI Fumagalli). Observations were split into sets of 1480s exposures. While four fields were completed with 6×1480s exposures, J0851+2332 and J0818+2631 were only partially completed with 4×1480s and 2×1480s respectively. The observations were taken in dark time with seeing ranging from 0. 7 arcsec to 0. 9 arcsec using the Nominal Wide Field Mode, with clear conditions. In each sub-exposure the quasar was centred in the field of view, and small dithers combined with instrument rotations in increments of 90 • were made to improve the quality of the final data product. The initial reduction of the data is carried out using the ESO MUSE pipeline  (Weilbacher et al.
Metallicities In order to calculate the metallicities of the DLAs, we adopt the metal ion column densities compiled in  Fumagalli et al. (2014). These values are based on the apparent optical depth 1 A Lyman Limit System (LLS) is an absorption line with 10 17. 5 < (N H I / cm -2 ) < 10 20. 3 , such that the system is optically thick to ionising radiation below the Lyman limit (i. e. λ <912 Å). LLSs are thus characterised by breaks in quasar spectra below the Lyman limit. method using unsaturated transitions, or are bracketed by upper and lower limits. Table  1  indicates the ions used to estimate the metallicity of the DLAs in the sample. Fumagalli et al. (2017b)  showed that in the case of J0255+0048 the Si II column density estimated with Voigt profile decomposition of HIRES and X-Shooter data was consistent with the value obtained with ESI and the apparent optical depth method.
The eagle simulations Evolution and Assembly of GaLaxies and their Environments (eagle,  Schaye et al. 2015 ) is a suite of cosmological hydrodynamical simulations performed used the GADGET-3 Tree/SPH code  (Springel 2005)  with modifications to the hydrodynamics solver described by  Schaller et al. (2015). The simulations incorporate the dominant cooling and heating processes of gas in the presence of the uniform but timevarying UV/X-ray background of  Haardt & Madau (2001)  as described by  Wiersma et al. (2009). Physics below the resolution scale, such as star and black hole formation and their feedback effects, are incorporated as 'subgrid modules' with parameters calibrated against observations at redshift z = 0 of the galaxy stellar mass function, the relation between galaxy mass and size, and the relation between galaxy mass and black hole mass  (Crain et al.
Identifying LAEs in eagle Gravitationally bound substructures in eagle are identified combining the friends-of-friends,  (Davis 1985)  and subfind  (Springel 2005; Dolag et al. 2009 ) algorithms. Physical properties of these 'galaxies' such as their centre of mass position and velocity, stellar mass and star formation rate, are computed and stored in a database  (McAlpine et al. 2016). In eagle the star-formation rate of a gas particle is a function of its pressure, it is zero below a metallicity-dependent density threshold and above a pressure-dependent temperature threshold  (Schaye et al. 2015). SFRs and stellar masses of subhalos are computed by summing over the gas and star particles (respectively) within a given subhalo identified with subfind. Lacking sufficient resolution to resolve the ISM, we cannot predict from first principles the Lyα properties of the simulated galaxies.
A halo prescription for DLAs As well as exploiting the hydrodynamics of the eagle simulations to predict the position and properties of DLAs, we employ a second model by assigning DLAs to the dark matter halos from the simulations via a simple halo "painting" model. This is similar to the model put forward in Font-Ribera et al. (  2012  In this model, the relation between DLA cross section and halo mass is described by Eq. 3, where Σ(M h ) is the DLA cross-section for a halo of mass M h above some minimum halo mass M min with a power-law slope of α and a zero-point Σ 0. Σ(M h ) = Σ 0 (M h /10 10 M ) α (M h > M min ) (3) The halo catalogues used in this work are obtained from the eagle database  (McAlpine et al. 2016)  Friendsof-Friends table for the 25 Mpc and 100 Mpc eagle boxes. While the 100 Mpc box suffers from resolution effects at low halo masses (M 200 10 9 M ), the higher-resolution 25 Mpc box suffers from a limited volume that contains few massive halos (above M 200 = 10 13 M ).
The completeness estimated for the MUSE data has been applied to the simulations rather than as a correction to the data. At this lower luminosity, the observations now agree more closely with the eagle/urchin prediction, but this result is more uncertain due to difficulties in estimating the LAE completeness. constant cross-section (i. e. α=0). As before, the constant Σ 0 is calibrated in the 100 Mpc simulation to match the (X) predicted from the urchin calculation. In the lowest mass bin, the halos simply do not contain enough LAEs or cluster with them strongly. In the 10 11 < M h <10 11. 5 M bin the profile reproduces the MUSE data well. Above 10 12 M the profile peak declines, because the DLA cross-sections have become so large that some chosen DLAs can be very far from the halo center.
We do emphasise, however, that our sample is still rather small, and therefore any current inference is likely to be subject to sample variance (see next section). Fig. 14  also shows a divergence in model predictions with luminosity. At high luminosity (L Lyα > 10 42 erg s -1 ) both the painted eagle and galics profiles are in very close agreement for the same model parameters. However, at the lower luminosity, the predictions start diverging despite being derived with the same DLA prescription. Future datasets will require more precise modelling of LAE properties, andconversely -deeper observations will be able to discriminate more among different models. If some significant fraction of the DLA population are indeed hosted in massive halos, one can ask why bright LBGs are not typically detected in past photometric searches for DLA host galaxies.
Using the eagle 100 Mpc simulation with the painting method described above, we calculate the fraction of the total DLA cross-section which is hosted by halos which also contain a UV bright galaxy (M FUV < -20). The FUV magnitudes are taken from the eagle database and are calculated based on the GALEX FUV filter in the rest-frame, including dust attenuation  (Camps et al. 2018). Using the scheme parameters defined in Table  3 , we find fractions of 0. 03, 0. 11 and 0. 22 for α =1. 1, 0. 75 and 0. 0 respectively. This is only including LBGs in the same halo, i. e. the one halo term not accounting for LBGs which may be clustered on larger scales. We conclude that the low rate of LBG detections around DLAs is consistent with the models motivated by clustering measurements. As discussed in Sect.