dataesr/scientific-paragraphs-categorization huggingface dataset:
More field campaigns and more in situ observations would not only provide an observational benchmark dataset to understand and improve modeling capability in the region but would be also useful for the validation and calibration of satellite products. Here we use Taylor score to quantify model-satellite discrepancies. Tay-lor score  (Taylor, 2001 ) is defined by S = 4(1 + R) σf + 1/ σf 2 (1 + R 0 ) , where σf is the ratio of σ f (standard deviation of the model) and σ r (standard deviation of observations), R is the correlation between the model and the observations, and R 0 is the maximum potentially realizable correlation (= 1 in this study). Taylor score ranges from 0 to 1, and a higher Taylor score indicates better satellite-model agreement. To identify potential locations, we separate the African continent into 5 • × 5 • (latitude × longitude) pixels, as shown in Fig.
Overall, the performance of MUSICAv0 is comparable to WRF-Chem. The underestimation of CO is a common issue in atmospheric chemistry models such as MUSICAv0 and WRF-Chem. The overestimation of O 3 in MUSICAv0 is likely driven by too strong stratosphere-to-troposphere fluxes of O 3 and perhaps an overestimate of lightning NO emissions; however, future studies are needed to confirm and solve this issue. The significant underestimation in surface PM 2. 5 at two sites in East Africa and the overall overestimation in AOD in Africa compared to MODIS imply missing local sources and an overestimation of dust emissions and require further study. In addition, lack of data could also contribute to disagreement in model and in situ observations as one site in a city is not representative of the full city.
Acknowledgements. This paper is based upon work partially supported by the  National Aeronautics and Space Administration  under grant no. 80NSSC23K0181  issued through the  NASA   Applied Sciences SERVIR program. We thank the  ATom, WDCGG, IAGOS , and  NASA/GSFC SHADOZ  teams and the  U. S. State Department and U. S. EPA  for providing in situ observations. We thank  Anne Thompson  and  Gonzague Romanens  for providing a detailed explanation of the SHADOZ Ozonesonde data format. We thank the MOPITT, MODIS AOD, OMI NO 2 , and  OMI HCHO  teams for the satellite products. The NCAR MOPITT project is supported by https://doi. org/10. 5194/gmd-16-6001-2023 Geosci. Model Dev. ,  16 ,  6001-6028 , 2023 W. Tang et al. : Application of MUSICAv0 on air quality in Africa the  National Aeronautics and Space Administration (NASA)   Earth Observing System (EOS ) program.
We thank the  QA4ECV  project. We thank  Sabine Darras  for CAMSv5. 1 emissions. We would like to acknowledge high-performance computing support from Cheyenne (https://doi. org/10. 5065/D6RX99HX, Computational and Information  Systems Laboratory , 2019) provided by  NCAR's Computational and Information Systems Laboratory , sponsored by the  National Science Foundation. This material is based upon work supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  National Science Foundation  under cooperative agreement no. 1852977. We thank  James Hannigan ,  Ivan Ortega ,  Siyuan Wang , and all the attendees of  ACOM CAMchem/MUSICA  weekly meeting for providing helpful discussions. Financial support. This material is based upon work supported by the  National Science Foundation  under grant no.
Code and data availability. The model code used here can be accessed through https://doi. org/10. 5281/zenodo. 8051435 (Tang,  2023a). The data produced by this study can be accessed through https://doi. org/10. 5281/zenodo. 8051443 (Tang, 2023b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-16-6001-2023-supplement.
The human microbiome and its metabolic output play an important role in shaping human health and disease. The host-microbe crosstalk is often characterised using metabolomics and metagenomics, yet a large proportion of microbial metabolites and the molecular functions of gut microbes remain unknown. This review summarises the strategies for annotation and discovery of the novel metabolites from the human gut microbiome, either using metabolomics alone or in combination with metagenomics, and presents data analysis methods for combining these two types of data to obtain biological insights. Applications of the gut microbiome research in biomarker screening, precision medicine, microbiome medicine, and drug discovery are also discussed, along with the perspectives, challenges and limitations of this research field.
These results evidence the potential of investigating the specialised metabolites in gut microbiome metabolomics studies to enhance our understanding of the metabolic capabilities of the gut microbiome, its interactions with the host, and its implications for health and disease. The integration of metabolomics and metagenomics data is often challenging due to the biological complexity of the gut microbiome, the heterogeneity of these data, and the lack of standard pipelines dedicated to this integration. Regarding the latter, common strategies to integrate the metagenomics and the metabolomics data range from classical bivariate statistics (e. g. , calculation of correlations) to more sophisticated machine learning and pathway analysis methods. An overview of these methods will be given in section 4.
Study and identification of microbial metabolites in health and disease Microbiota-derived metabolites studies have been focused on a few chemical classes (secondary bile acids, short-chain fatty acids, branchedchain amino acids, trimethylamines and aromatic amino acid derivatives due to their pathophysiological relevance in metabolic disorders  [1]. Currently, the trend is to profile a larger number of metabolites (typically between 500 and 1500) by LC-MS. If then a metabolite is found to be associated with a pathology, different methodologies are applied to determine whether the metabolite is of microbial origin or not. While it is possible to establish the nature of some of these metabolites by a simple database search (since databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG)  [43]  and Chemical Entities of Biological Interest (ChEBI)  [44]  compile this information), for some other metabolites this information is not available.
e. , the Fecal Metabolite Library from MetaSci, the Microbiome Metabolite Library of Standards from IROA Technologies) to facilitate the building of in-house reference libraries. Putative annotations (Level 2) are based on the data from public databases or on the physicochemical properties when reference standards are lacking. Despite their usefulness, these chemical libraries are incomplete as the number of detected metabolites is smaller than that found for the largest comprehensive studies  [58]. Regarding MS/MS, a key advantage is that fragmentation of molecules using MS is highly consistent when repeated MS/MS acquisitions are performed under same or similar experimental conditions including the instrument technology, type of mass analyser, ionization method, collision method, and other parameters to be considered.
This facilitates the annotation and identification of metabolites based on their fragmentation mass spectra using compound databases and pure standards, respectively. Harmonisation of metabolite identifiers. In gut microbiome cohort studies, just like any other epidemiological study, biological findings are typically replicated in an independent cohort. Besides the complexity of the logistics for organising a replication study, comparing the metabolomics data from the two cohorts analysed in two different laboratories is not straightforward since metabolite annotations between studies need to be harmonised first. To facilitate the metabolite name harmonisation process, it is recommended to provide curated identifiers such as database identifiers (HMDB) or structural identifiers (e. g. , the IUPAC International Chemical Identifier (InChI), SMILES) in addition to the trivial names as these are often non-standardized (i.
To address this, metabolites can be arranged in smaller sets and explored separately. Metabolites can be grouped according to the biological pathways they belong (as defined in BioCyc  [61]  or KEGG  [43]  databases) or to the chemical ontology, the latter inferred directly from the MS/MS data (Qemistree  [62] , CANOPUS  [63] ) or from their structural identifiers (ClassyFire  [64] ). ClassyFire chemical ontologies present a hierarchical classification (Kingdom, SuperClass, Class, and SubClass) format that resembles that of taxonomic data such as for metagenomics. Public databases. General public databases contain MS/MS spectra of metabolites from different origins (e. g. , MassBank  [65] , METLIN  [66] , HMDB  [67] ) and can be consulted for metabolite annotation. However, public databases for microbial metabolite annotation, such as the Human Microbial Metabolome Database (MiMeDB)  [68]  and the Natural Products Atlas  [69] , are scarcer.
MiMeDB contains information from the taxonomy of 1,904 microbes of the human microbiome as well as the metabolites they produce. The Natural Products Atlas, as opposed to MiMeDB, is oriented to the specialised metabolites produced by fungi and bacteria. Additional databases of microbial metabolites, either public or not, can be consulted in the review of van Santen et al. [70]. Open spectral libraries are not only used for consulting metabolite annotation, but they are also a source of machine learning data for training and method validation of novels methods for compound identification in metabolomics. Some computational advances worth mentioning are MS2LDA  [71] , MESSAR  [72] , Spec2Vec  [73] , MS2DeepScore  [74]  and CFM-ID  [75]  which represent the next generation of metabolomics analyses, however these will need to be validated in follow-up studies as the spectral databases keep growing in richness and diversity with high-quality data  [17, 76].
Open-source software. MS-DIAL  [77] , MZmine  [78]  and the MetaboAnnotation R-package  [79]  allow the processing of MS data including metabolite annotation. Other free tools have been reviewed by Misra, B  [80]. Compound Discoverer (Thermo Scientific TM ) and Mass-Hunter Workstation (Agilent© Technologies) have been developed as alternative commercial software solutions. In addition, the MASST interface can be used to search MS/MS data on public repositories (such as MassIVE  [81] , MetabolomicsWorkbench  [82]  and MetaboLights  [83] ), similarly to the Basic Local Alignment Search Tool (BLAST) method for investigating genes and protein sequences  [84]. Metabolomics reports. One decade after the MSI guidelines were initially proposed, some updates have been made to better fit the needs that have been emerging across the community including the "MEtabolomics standaRds Initiative in Toxicology" (MERIT) that provides the most relevant practical guidelines for metabolomics specifically in regulatory toxicology  [85].
GNPS molecular networks can be interrogated with the Structural similarity Network Annotation Platform for Mass Spectrometry (SNAP-MS) tool to identify candidate metabolite classes of microbial origin from the Natural Products Atlas  [89]. Similarly, the MS2LDA tool from the GNPS environment can be used to identify molecular substructures from MS/MS spectra  [90]. Alternatively, if the expected reference spectra of the compounds are unavailable, these can be generated with software tools for in silico fragmentation such as SIRIUS  [91]  and MetFrag  [92] , among others. Another important development within the GNPS environment is the ion identity molecular networking (IIMN) which addresses the bottleneck of unconnected ion adducts given that one compound can generate multiple ion species and each precursor can exhibit a different fragmentation behaviour  [93].
Computational identification of thousands of BGCs in microbial genome sequences is possible thanks to the development of automated bioinformatic software. One of the first tools developed in the early 2000s for automated cluster mining was the Decipher® pipeline and database  [112]. Their strategy consisted of first, scanning microbial genomes coding for known chemically-diverse metabolites to build a database of gene clusters and then, to purify and confirm the metabolite structure through a genomics-guided discovery platform. Since then, different open source software and pipelines have emerged: • BAGEL  [113]  • CLUster SEquence ANalyzer (CLUSEAN)  [114]  • NP. searcher  [115]  • Secondary Metabolite Unknown Regions Finder (SMURF)  [116]  • PRediction Informatics for Secondary Metabolomes (PRISM)  [117]  • antibiotics & secondary metabolite analysis shell (antiSMASH)  [118]  AntiSMASH was introduced and refined over the last 10 years, being currently the most widely used and the gold standard tool for mining bacterial, fungal (fungiSMASH) and plant (plantiSMASH) genomes for specialised metabolite BGCs  [101] , including automated identification of primary metabolic gene clusters from the gut microbiota (gutSMASH)  [119].
Briefly, different types of sequence files (including FASTA files) or GenBank accession numbers can be provided to the web server (https ://antismash. secondarymetabolites. org) or to the desktop downloadable version. From this information, genes encoding proteins are searched and a library of models specific for each type of BGCs is constructed with high accuracy (71 BGCs types for antiSMASH version 6)  [101]  along with the prediction of the chemical structures of metabolites produced by the BGCs. The outputs can be visualised as interactive and user-friendly maps, images and hyperlinks. Despite the development of similar tools, antiSMASH provides a comprehensive analysis by integrating multiple algorithms and databases for studying microbial genomes and secondary metabolite biosynthesis covering a wide range of metabolites.
This is supported by the accurate prediction by combining methods such as Hidden Markov Models (HMMs) considered as "the Lego of computational sequence analysis"  [120]  with other bioinformatic techniques. Besides this, the extensive database of antiSMASH is regularly updated, facilitating the annotation and prediction of specific compounds and therefore providing insights about their potential biological activities. As developed for the GNPS, antiSMASH has also provided community support mainly in the field of natural product discovery but also applied in microbiome research and microbial-related disciplines. It is also worth mentioning that antiSMASH is constantly updated and enriched by the multiple complementary emerging tools used to improve the genome mining techniques that researchers may simultaneously employ.
These constant improvements position this tool as one of the most widely used. Expansion of genome mining. The advances achieved regarding the creation of the 'Minimum information about a Biosynthetic Gene cluster' (MIBiG) allowed the standardised data annotation and storage of experimentally validated BGCs of known functions  [121]  but also the cross-links that are provided between the Natural Products Atlas  [69] , GNPS  [88]  and PubChem  [122]. Latest updates include the addition of structural identifiers in SMILES format directly from cross-linked databases, avoiding conflicts caused by SMILES formatting. Moreover, more compounds were linked with their bioactivities for bioactivity predictions from structure and finally, the molecular targets of more compounds were annotated  [123]. As the analysis of genomes is expanding to large-scale pan-genomic mining such as those of the microbiomes, the 'Biosynthetic Gene Similarity Clustering And Prospecting Engine' (BiG-SCAPE) has been developed to facilitate the exploration of BGCs similarity networks at multiple hierarchical levels  [124].
The 'core analysis of syntenic orthologs to prioritise natural product gene clusters' (CORASON) software tool, where syntenic refers to the genes present on the same chromosome of a given species, helps the elucidation of the evolutionary relationships between BGC phylogenies and gene cluster families (GCFs, groups of BGCs coding for chemically similar metabolites)  [124]. The streamlined computational workflow using BiG-SCAPE and CORASON allows the integration of gene cluster identification and empirical biosynthetic data comparison including antiSMASH and MIBiG  [124]. Despite recent advances in the previously mentioned software tools, pairwise BGCs comparisons used to build similarity networks and perform clustering analysis leads to a quadratic time complexity and therefore, to an impractical time of analysis.
Functional mapping of metagenomic gene abundances Metagenomic datasets from gut microbiomes studies contain millions of genes. While the major sequencing consortia efforts between 2010 and 2016 reported that human gut microbiome is comprised of 10 million non-redundant genes  [126] [127] [128] , the latest efforts have put this number to be close to 22 million  [129]. In addition to their massive sizes, metagenomics datasets also present an intrinsically large data dimensionality (e. g. , genes are annotated at the same time at the taxonomic and functional levels, genes from different species can encode orthologous proteins). In order to cope with this high dimensionality, the taxonomic (or ecological) and functional variations of the microbiome are often evaluated separately. Gene abundances in shotgun metagenomics data are affected by multiple external sources of sample variability (e.
g. , differences in sequence depth, systematic variability introduced during the DNA extraction, sample-specific differences)  [130]. Hence, metagenomics datasets are routinely transformed in order to account for this variability and be able to pinpoint the intrinsic differences between study samples. Examples of such transformation methods include total count, cumulative sum scaling and rarefying, among many others  [130]. Regarding these transformations, two considerations must be made. First, it is advised to use samples with a similar total cell count as transforming metagenomics data from samples with very different cell counts can lead to misleading interpretations. For example, after transformation, low abundance species may appear to be absent in samples with low cell count. Second, metagenomics variables describe relative proportions rather than absolute abundances due to the aforementioned external sources of variability.
KEGG provides two systems, KEGG PATHWAYS and KEGG MODULES, that group KOs (identifiers relative to functional orthologs defined from experimentally characterised genes and proteins) by the function units of the enzymes they encode or by the chemical reactions these enzymes catalyse, respectively. Aiming at the gut microbiome, a new KEGG-based classification has been recently made available that groups KOs into Gut Metabolic Modules (GMMs)  [133]. Each GMM, manually curated, represents one cellular enzymatic process present in the gut, such as the anaerobic catabolism of carbohydrates, amino acids and lipids, cross-feeding interactions, the production of fermentation end products, archaeal methanogenesis and enzymatic processes related in mucus degradation  [133]. Due to the high-dimensionality of the metagenomics data, a common practice in gut microbiome studies is also to use these data for determining descriptors that can be more easily visualised and interpreted than the raw metagenomics data.
These descriptors can be numeric (such as the classical ecological descriptors) or categorical (such as by defining enterotypes). Both approaches are presented in the following paragraphs. Alternatively, the metagenomics data can also be examined with multivariate statistical tools capable of reducing the large number of variables into just a few components that are a linear combination of the former, increasing the dataset interpretability and enabling their visualization with relatively simple plots. The most frequent multivariate statistical methods used in metagenomics studies will be presented in section 4. 2. Ecological descriptors. Alpha-and beta-diversity indices are calculated to study intra-and inter-individual differences in the microbiome structure, respectively. Examples of alpha-diversity metrics are the number of gene-based units, Chao1, Simpson's and Shannon's indices.
This discretized information can be applied to stratify the cohort dataset into subgroups  [139]  or to add it as an independent variable in multivariable logistic regression models  [140]. Besides being representative of a particular microbiome composition, enterotypes can also be linked to specific health conditions. For instance, Bact2 enterotype (marked by a high level of Bacteroides) is associated with severe obesity and inflammation  [131, 139].
However, at least 90% of the microbiome-derived metabolome is not characterised and many of these unknowns are highly likely to represent the missing link between the human-microbiome interactions  [151]. In this regard, the Paired Omics Data Platform (PoDP) is a community-curated platform that links metabolomic and genomic or metagenomic datasets, together with the experimental details on culture, extraction, and instrumentation settings  [152]. The main objective of this platform is to facilitate the accessibility of meticulously curated paired data sets, deposited in various public repositories, in the aim of aiding identification of natural product biosynthetic origins and metabolite structures  [152]. As of March 2023, the PoPD dataset contained 76 projects comprising 4,932 unique metabolome samples and 1, 306 metagenomes (among them, 481 metagenomes from human gut microbiomes), and provided empirical evidence for 115 BGCs and MS/MS spectra  [152].
Metabolome-metagenome integrative analysis The analysis of metagenomics data for integrative purposes is challenging since data from all genes and metabolites are used, as opposed to the (meta)genome-metabolome mining approach where analyses are focused on BGCs and on a few metabolites. The approaches used for integrating these two types of data can be classified in either data-driven or knowledge-based approaches  [154]. Data-driven approaches use statistical and machine learning algorithms to identify patterns and relationships between the data, while knowledge-based approaches are founded on external information that provides the link between the two sets of -omics data (e. g. , biological databases, scientific literature)  [154]. Data-driven integration approaches reveal data relationships by statistical and machine learning means.
Most of these approaches can be grouped into statistical analysis, exploratory analysis, clustering analysis, regression analysis, discriminant analysis, multi-block analysis. The most used knowledge-based methods consist of pathway visualization, over-representation, and enrichment analysis and metabolic modelling. In order to integrate metabolomics and metagenomics data in the context of human microbiota studies, epidemiological data analysis recommendations relative to the use of human cohort samples must be put into practice. In cross-sectional cohort studies, differences between samples can be driven by the patient interindividual differences (e. g. , demographics, medication) also known as covariates. In this aim, three strategies are normally implemented to cope with these covariates either via: 1) stratification, or splitting the data into groups according to the covariates and to study each group separately  [40] ; 2) matching, or Fig.