dataesr/scientific-paragraphs-categorization huggingface dataset:
Instead, integrating and analyzing together all those different levels of information is key to improve the predictive models and to provide a more accurate mechanistic view of the system under study. Genome-scale metabolic networks (GSMN) are suitable computational models for the integration of these multiple levels of knowledge. These models are automatically built and manually curated networks that encode all reactions with their stoichiometric coefficients, metabolites, enzymes, gene annotations and biochemical constraints that are known for an organism. GSMNs are generic models of an organism, independent of the type of tissue, cell or condition. In order to generate more accurate models for specific tissues or conditions, experimental data such as gene or protein expression can be integrated on top of GSMNs using context-specific network reconstruction methods.
That is why the concept of diversity in metabolic networks must be carefully analyzed with synthetic and real data that allow observing the behavior and quality of the solutions. As a response to the current limitations, here we formalize the problem of enumerating optimal context-specific metabolic networks from a computational perspective and we develop DEXOM, a collection of MILP-based methods for diversity-based enumeration of optimal metabolic networks. We implemented in total four different techniques in DEXOM, namely: Reaction-enum, Icut-enum, Maxdist-enum and Diversity-enum. The objective of this paper is threefold. First, to analyze and formalize, from a computational perspective, the enumeration problem of optimal solutions and propose different practical techniques that can be used to obtain not one but many, equally good and diverse contextspecific metabolic networks.
Essential genes in Yeast 6  [46]  were curated using most updated information from YDPM (http://www-deletion. stanford. edu/YDPM) database and the SGD (https://www. yeastgenome. org) project  [47]  (S1 File). Genes that are essential due to mechanisms not directly related to metabolism were excluded from the set, as they cannot be predicted using FBA. In total, 188 genes out of the 900 in Yeast 6 are considered to be essential under aerobic conditions. A maximum of 2,000 optimal networks were enumerated for each combination of threshold and method, using a time limit of 8h per threshold/method, and 5 min. timeout for each MILP problem. The lower bound of the biomass reaction was constrained to carry a small positive flux, to ensure that all initial sub-networks will allow biomass production and therefore could be used to simulate the effects of gene knockout on the biomass production using FBA.
Diversity-enum achieves the best TPR for all thresholds, with the best overall TPR of 0. 7713 for the threshold [0. 25, 0. 90], which corresponds to the correct classification of 145 genes out of the 188 essential genes in the dataset. These results are followed by the Reaction-enum method, which achieves the same TPR as Diversityenum in 8 out of 16 tests, with a slightly lower FPR in 6 out of those 8 tests. In contrast, Maxdist-enum and Icut-enum ensembles are not very well positioned in terms of TPR, although both methods achieve the lowest rates of false positives for some ensembles. Concretely, the Icut-enum method obtained the lowest FPR in 9 out of the 16 tests. Differences between ensembles can be better assessed by placing each ensemble in a ROC space (  Fig 7) , in which each point is an ensemble represented by its TPR and FPR.
S1 File. Essential genes in Yeast. Dataset used for the evaluation of in silico prediction of essential genes in Yeast. We used annotations from SGD  [47] , YDP (https://www. yeastgenome. org/) and Yeast 6  [46]  to classify each gene as essential or not under aerobic conditions. (XLSX) S1 Appendix. Supplementary information. PDF including: 1) computational time of the different enumeration techniques; 2) distribution of predicted essential genes among methods; and 3) variability in the detection of enriched pathways between the different methods. (PDF).
This work was supported by grants from the  Institut National contre le Cancer (INCa-PLBIO)  and the  Labex EpiGenMed  (" Investissements d'avenir" program , reference  ANR-10-LABX-12-01 ). This work has also received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement GOLIATH No. 825489 , and from the  Association pour la Recherche contre le Cancer (ARC) Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The need for QA procedure regards all the steps of NTA/SS workflow, as presented in Figure  1. This harmonization task is extremely challenging considering the need to optimize and adapt all the steps required to perform NTA depending on the developed method, including the generation of data, the use of bioinformatics tools such as MZmine2  [7]  or XCMS  [8]  to treat complex HRMS raw datasets or the annotation process  [9]. There are some tools available to perform this workflow up to pre-annotations (with manual curation still required), such as Compound Discoverer  [10] , XCMS  [8] , or MS-DIAL  [11]. These tools must still be optimized by choosing the appropriate steps and parameter values depending on the application (e. g. , choosing to add or remove blank subtraction or area normalization, setting the noise level depending on the matrix complexity and potential contamination levels, etc.
). Among all steps, the annotation/identification process (i. e. , providing a putative/confirmed identity to a signal present in HRMS datasets) is the current bottleneck of SS and NTA. There is at present no tool available to fully automatize this step (i. e. , manual curation still required), which means that a large portion of HRMS is returned partially annotated. This annotation process is complex and tedious, and requires providing solid information such as isotopic pattern, retention time and MS/MS fragmentation pattern in order to provide confidence in the annotation. As a final step and when possible, injection of reference standard compounds allows unambiguous identification of features (a feature is defined as retention time x m/z associated with an abundance (area/intensity))  [12].
Materials and Methods In order to check the compatibility of the ISO/IEC 17025 with the specificities of NTA methodologies applied to organic contaminants, a detail study of the guideline was developed, analyzing each ISO requirement. Although this analysis was guided towards assessing exposure to organic compounds, similar reasoning can be applied for inorganic chemicals such as are rare earth elements, metals, etc. From this step, a scale was established defining each item as "Compatible", "Adaptable" or "Not applicable". After the definition of the scale, we have found that, with adaptations, every item of ISO/IEC 17025 framework was applicable to NTA/SS studies and we have therefore eliminated the "Not applicable" level. Each requirement identified as "adaptable" was classified in five "discussion topics": measurement uncertainties, validity of the method, validity of the results, report, software and an adaptation strategy to comply with the framework's requirement was proposed for each of them.
Measurement Uncertainties • "Measurement uncertainties" in "7. 2 Selection and verification of methods" The normative requirements of ISO/IEC 17025 regarding measurement uncertainties are focused on quantitative results, which is coherent in the case of targeted analyses, as these results are usually the main pursued objective. However, NTA techniques lead to the production of results that rely on both qualitative (e. g. , annotation of unknown features in HRMS datasets) and quantitative (e. g. , inter-individual relative comparison) aspects. To fulfill the normative requirements of ISO/IEC 17025, we consider that measurement uncertainty must include both the qualitative and quantitative aspects. The qualitative aspect (i. e. , the annotation process of HRMS datasets) is currently one of the bottlenecks of NTA.
It is usually considered than less than 10% of HRMS dataset can be annotated  [19]. Identification relies mainly on structural information such as MS/MS fragmentation pattern but also on other criteria such as the m/z accuracy, retention time or isotopic pattern. Confidence levels from 1 to 5 based on information generated during the analytical process (m/z, retention time, isotopic pattern, MS/MS data) have already been proposed for xenobiotic annotation by Schymanski et al. [6]. Hence, the reporting of qualitative criteria should include all the relevant information and provide a confidence level based on these recommendations or other initiatives (e. g. , Metabolomics Standard Initiative  [20] [21] [22]. It is also important to mention that in order to help the annotation process, the strategy to find relevant information to annotate unknown features can be adapted to the research topics, the matrix, and of course the analytical technique.
Specific databases exist for environmental applications (e. g. , MassBank  [23] ) and human applications (e. g. , HMDB  [24] , Drugbank  [25] , Exposome explorer  [26] ) depending on the matrices such as Serum Metabolome  [27]  or Urine Metabolome  [28]  for human serum or urine in the case of biological matrices, or FOR-IDENT  [29]  for water in the case of environmental matrices. Quantitative criteria are also challenging to define for NTA/SS. These methodologies only provide semi-quantitative data (i. e. , comparison of fold changes of normalized area between individuals) as opposed to targeted analyses, which can provide quantitative data using internal standards to correct analytical variabilities. So far, quantitative criteria for NTA/SS mainly rely on area/intensity integration. Uncertainty of area quantification can be estimated using different normalization strategies based on batch correction using quality controls (i.
This diagram represents the measurement process and the elements that influence the outcome. According to LAB GTA 86, if certain factors have an influence considered insignificant, the laboratory should specify the elements that prove it. Several factors are particularly or exclusively influent in the case of NTA/SS approaches to investigate exposure assessment. These influence factors are presented in Figure  2  and can be retained in six main categories: injected, identity and concentrations of internal standards). It must also be noted that are integration is also conditioned by the chosen peak picking software and parameters use which should also be reported. • "Measurement uncertainties" in "7. 6 Evaluation of measurement uncertainty" Evaluating measurement of uncertainty must also include a step of identification influence factors.
Several factors are particularly or exclusively influent in the case of NTA/SS ap proaches to investigate exposure assessment. These influence factors are presented in Fi ure 2 and can be retained in six main categories: Materials can refer to samples, reference or standard solutions, or software tools use for acquisition and/or data treatment. Storage of samples is a critical point as temperatu must be controlled to avoid analyte loss or degradation. Since compound-specific freezin study would be a limited option for a NTA study, all precautions must be taken to prote the sample from degradation as a whole. A storing temperature of -80°C is usually pr ferred and recommended, particularly for biological matrices. Calibration solutions a also a point to consider carefully, as NTA/SS methods using HRMS rely on accurate mas Frequency of calibration can vary based on the used instrument: time-of-flight (TOF) i struments usually offer real-time calibration (up to a calibration for each sample), where Orbitrap instruments may be calibrated weekly.
Another example worth discussion is th Laboratory information management system (LIMS), which has to be dimensioned fo Materials can refer to samples, reference or standard solutions, or software tools used for acquisition and/or data treatment. Storage of samples is a critical point as temperature must be controlled to avoid analyte loss or degradation. Since compound-specific freezing study would be a limited option for a NTA study, all precautions must be taken to protect the sample from degradation as a whole. A storing temperature of -80 • C is usually preferred and recommended, particularly for biological matrices. Calibration solutions are also a point to consider carefully, as NTA/SS methods using HRMS rely on accurate mass. Frequency of calibration can vary based on the used instrument: time-of-flight (TOF) instruments usually offer real-time calibration (up to a calibration for each sample), whereas Orbitrap instruments may be calibrated weekly.
This ties with issues of sensitivity, which is another critical instrument characteristic, and requires rigorous schedules and procedures for cleaning to avoid response decay. Lastly, data produced by instruments will have a particular format (RAW, wiff, etc. ). This format can be a constraint when using certain data processing software tools, as each tool accepts a limited number of input formats. Data can be converted to open formats such as mzML or mzXML, which are accepted by most open source NTA data processing tools. The conversion process should however be investigated to ensure that no data is lost or altered. Methods and procedures should be evaluated with the specifics of NTA/SS in mind regarding compound coverage, selectivity, and sensitivity. Since these approaches aim at creating an analytical fingerprint of samples, it should be possible to investigate a posteriori exposure assessments months or years after the analysis took place.
Samples should therefore be registered with an unequivocal name that allows tracing back to its characteristics (origin, date, operator, etc. ) as well as analytical techniques used for the analysis. Regarding these techniques, recommendations have already been made for NTA research for quality assurance/quality control, which heavily depend on the type of method used  [31, 32]. Indeed, choosing liquid or gas chromatography, ionization polarity, chromatographic conditions, sample preparation technique, and data processing software are critical points when using NTA since it can widely influence the results generated  [33]. Methodological aspects should be thoroughly investigated to characterize their advantages and limitations, which is critical for result interpretation. To this end, reference materials can be used to determine a method's observable chemical space, such as what was performed for EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) using the National Institute of Standards and Technology (NIST)'s Standard Reference Material (SRM) 2585 and human serum (NIST SRM 1957)  [34].
The data processing software is also a critical point when using NTA since it influences the returned data. Such tools should be thoroughly investigated to determine the optimal parameters for each intended application. Similarly, tools and libraries used in the identification process will affect the obtained annotations, and should therefore be investigated, or at least reported. Environment encompasses all that surrounds the analysis process and is less specific to NTA/SS, as regular blank measurements  [5] , temperature  [35]  and humidity controls are always recommended. Manpower underlines the need for operators to be trained to handle samples intended for NTA and the resulting data. For instance, quality control procedures such as repeated blank or reference materials measurements are needed to ensure reproducibility.
Validity of the Method Although method validation is well defined for targeted approaches, it is not the case for NTA/SS because these methodologies deal with a tremendous amount of unknown features (up to several thousand within one dataset) as opposed to a limited number of chemicals for multi-residue methods. It is often recommended for NTA to use minimal sample preparation to avoid the loss of compounds of interest. However, using more selective sample preparation is often necessary for exposure assessment purposes to decrease analytical interferences and detect very low-abundant xenobiotics in complex matrices, in particular with UHPLC-ESI-HRMS platforms which suffer from ion suppression  [36]. Hence, method validation for NTA methods is a critical point to be discussed, as it is intrinsically different from targeted methods.
Ideally, the identity of features specific to a sample preparation method should be reported to better characterize its coverage. Statistical tools such as principal components analysis (PCA) or t-tests can be used to detect abnormalities or drifts within batches, and potentially identify some causes  [35]. This is especially the case when a composite quality control sample is used, since its signal should be stable. Another specificity of NTA methods is software optimization. Indeed, establishing the list of features for each sample using raw data processing software is a critical step to avoid false negatives. To answer the standard's requirements regarding software validation, a list of application-specific criteria (technical, financial, etc. ) should be defined to choose and then evaluate possible data treatment solutions.
The technical criteria can be based on a list of molecules as for targeted methods, and can include ease of implementation, data processing time, false negative rate, etc. To attain these objectives, as discussed earlier, it is possible to build the raw data processing workflow (peak picking and deconvolution algorithms, normalization, blank subtraction, etc. ) and optimize the related parameters (noise threshold, m/z and retention time tolerance, etc. ). Similarly, the identification process is specific to these approaches, and can be performed by a software tool that should be optimized for the intended application. During this process, a list of parameters will be compared between features found in the sample and compounds issued from a database. This list of parameter includes m/z, retention time (in the case of SS), isotopic pattern or ratio, fragmentation pattern or ratio, etc.
Software Regarding software requirements, ISO/IEC 17025 states that information management systems should be validated for functionality, including the proper functioning of interfaces within the laboratory information management systems. The guideline also considers that all used software and libraries should be validated using a set of criteria determined by the user to ensure that all used software accurately perform the intended tasks. Off-theshelf software in general use within its designed application range can be considered to be sufficiently validated. In NTA methodologies, there are many "ready-to-use" and open source software available but optimization must be performed. Furthermore, even though there is no universal global software tool to perform all data treatment required for NTA/SS, i.
e. , from the raw data processing to the finalized identification, it is worth mentioning that very promising workflows are emerging to harmonize the whole process, e. g. , Workflow4Metabolomics  [39]. In NTA screening, chromatograms are screened for features using suitable peak finding software. To validate these results, ISO/IEC 17025 demands several verifications, e. g. , software's lock, to guarantee the correspondence between mass and element. Other validation criteria for the software, such as tolerance for false negatives, integration repeatability, or speed of implementation, are up to the user to determine, as there are no further recommendations from the framework. Furthermore, according to LAB REF 02-Exigences pour l'accréditation des laboratoires selon la norme NF EN ISO/IEC 17025  [40] , the laboratory must ensure the accuracy of any external data that it uses, including databases, when they are used to develop the laboratory's activities.
Digital Archives HRMS-based methods generate "full scan" fingerprints which offer the possibility of "coming back in time" many years later to check a posteriori the potential presence in these fingerprints of a specific environmental contaminants suspected to be associated with a studied health outcome. Hence, appropriate data storage is critical for NTA methodologies. To fulfill this purpose, it is necessary to store a Chemical Archive within a data repository to complement the storage of biological samples. Such repositories also allow data exchange within the scientific community, leading to increased efficiency, robustness, and confidence in findings. They also allow the storage of metadata (sample preparation, analytical methods, processing software, etc. ), which is critical for result interpretation.
File Conversion to Open Formats Since users have little power over the duration of vendor software support, vendor formats may become obsolete at any given time. To avoid issues of unreadable formats, and therefore loss of data, data produced in NTA/SS methodologies must be made available in open formats. All data generated should therefore be duplicated in an open format, using readily available software such as ProteoWizard's MSConvert  [44]. Reproducibility and readability throughout the entire archiving period must be assured.
Conclusions In the context of an accredited laboratory already relying on this framework for targeted analyses, or of a team or a scientist's individual will, the ISO/IEC 17025 framework can be a useful tool to formalize quality assurance and quality control procedures in NTA/SS studies. Although there are no items in the framework that are incompatible with these new methodologies, some adaptations must be made regarding the evaluation of method and result validity, measurement uncertainties, and processing software. Moreover, several critical aspects of NTA/SS approaches are not covered by this framework, such as the need for well-maintained chemical archives. Through the application of an adapted version of an already available quality management standard, regardless of individual methodological choices, results of exposure assessment research using NTA/SS approaches will gain in robustness, replicability, and transparency.
OAS1 exhibits multiple characteristics expected from an anti-SARS-CoV-2 effector To identify effectors present at the sites of SARS-CoV-2 infection, we examined the IFN responsivity of the six candidate effectors using published studies from the interferome version 2. 01 database (Fig. 2D )  (21). We also determined their basal expression in res-piratory and gastrointestinal tracts (GTEx) (Fig. 2E ) and assessed their transcript abundance in postmortem lung tissue from COVID-19 patients (Fig. 2F  and fig. S2A ). In addition, we examined the genomic locus of each candidate effector for single nucleotide polymorphisms (SNPs) within alleles that may be associated with increased susceptibility to infection and/or severe disease (Fig. 2G  and fig. S2B ). Consistent with a potential role in influencing susceptibility to SARS-CoV-2 infection  (22) , OAS1 was frequently detected in nasal epithelium [which is a potential site of initial infection  (23) ] sampled from healthy individuals (three of six individuals) (fig.
S3 )  (38). To further understand OAS1-mediated sensing, we assessed the transcriptome-wide binding of OAS1. The number of host transcripts associated with OAS1 was substantially higher in infected cells compared with mock controls (Fig. 3, J  and K ). This suggests that OAS1 RNA-binding activity may be enhanced by SARS-CoV-2 infection. OAS1 interacted primarily with cellular RNAs that are highly structured, including small nucleolar RNAs, long noncoding RNAs, and the intronic regions of mRNAs (Fig. 3J ). These data are compatible with the notion that OAS1 senses short stretches of dsRNA that are found in stem loops. We also 3 of 18   observed that OAS1 interacted with several mitochondrial RNAs (fig. S3 ), which is consistent with the presence of dsRNA in the mitochondria that can induce innate immune responses  (39).