dataesr/scientific-paragraphs-categorization huggingface dataset:
Dose-Response Curves. Percentages of inhibition or activation at different concentrations were obtained as for screening. All measurements were carried out as 8-point dose response curves and reported as the average of at least three independent measurements. Bestatin was used as a reference inhibitor (100% inhibition at 2 mM). Data analysis was performed using Xlfit® v 5. 0 or GraphPad Prism® v 4. 0. Nonlinear curve fitting and statistical analysis was done using built-in functions.
5 minutes. The injection volume was 1 μL. MS analyses were performed under MRM detection using the parameters optimized for each peptide (capillary voltage, product ions, collision energy, desolvation temperature) (Supplementary Table  S1 ). The control of the equipment as well as the reprocessing of the analyses were carried out using MassLynx software (Waters ® ). For XSIINFEKL, 100% corresponds to the AUC of the peptide at t=0 without enzyme. Dose-response curves with compounds were performed at t= 60 min. 4. 1. 5. Docking and simulation calculations. pKa were calculated using PipelinePilot® 8. 5 from Dassault systems. Modeling and simulations were performed using non-aged X-ray crystal structure of ERAP2 PDB 5J6S. The protein was protonated then charged using AM1-BCC in Chimera 1. 13  [33].
A first challenge in the translation of oxylipins to the clinic is related to harmonization of the procedures for sampling and sample preparation. These vary widely between laboratories, as highlighted in the studies briefly described above. This point is particularly important in the field of oxylipins, as we know that depending on the type of biological sample used (i. e. , plasma, serum or urine), and the type of oxylipins one is focusing on (i. e. , free, esterified or total), the oxylipin patterns are very different and hardly comparable  [8]. This prevents the generation of large, harmonized databases, which are crucially needed to establish meaningful and consistent links between oxylipins and disease. Another challenge associated with lipids, in general, and with oxylipins, in particular, is related to their stability during storage and preparation.
Acknowledgments This publication is based upon work from  COST Action 19105 Epi-LipidNET , supported by  COST (European Cooperation in Science and Technology ; www. cost. eu). Financial support from the  French projectbased funding agency  (N •  ANR-16-HDHL-0004-01 ) within the framework of the  European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL ) as well as from the  German Federal Ministry of Education and Research (BMBF)  within the framework of the e:Med research and funding concept for SysMedOS project (to MF) as well as.
Metabolomics methods often encounter trade-offs between quantification accuracy and coverage, with truly comprehensive coverage only attainable through a multitude of complementary assays. Due to the lack of standardization and the variety of metabolomics assays, it is difficult to integrate datasets across studies or assays. To inform metabolomics platform selection, with a focus on posttraumatic stress disorder (PTSD), we review platform use and sample sizes in psychiatric metabolomics studies and then evaluate five prominent metabolomics platforms for coverage and performance, including intra-/inter-assay precision, accuracy, and linearity. We found performance was variable between metabolite classes, but comparable across targeted and untargeted approaches. Within all platforms, precision and accuracy were highly variable across classes, ranging from 0.
9-63. 2% (coefficient of variation) and 0. 6-99. 1% for accuracy to reference plasma. Several classes had high inter-assay variance, potentially impeding dissociation of a biological signal, including glycerophospholipids, organooxygen compounds, and fatty acids. Coverage was platform-specific and ranged from 16-70% of PTSD-associated metabolites. Non-overlapping coverage is challenging; however, benefits of applying multiple metabolomics technologies must be weighed against cost, biospecimen availability, platform-specific normative levels, and challenges in merging datasets. Our findings and open-access cross-platform dataset can inform platform selection and dataset integration based on platform-specific coverage breadth/overlap and metabolite-specific performance.
Targeted approaches enable absolute quantification of a defined set of metabolites, thereby facilitating longitudinal data collection; however, one inherent caveat is the narrowed scope of metabolite detection  [10]. In contrast, discovery-based approaches can provide the most comprehensive metabolome coverage; however, present coverage from any single analytical platform is only a fraction of the 19,174 metabolites detected thus far in blood  [11]. One strategy to overcome the limited coverage of any individual metabolomics assay is to combine datasets from different analytical platforms, but obstacles for merging datasets include the use of relative measurements rather than absolute quantitative measurements. For both discovery-based and targeted approaches, the size, type, and number of metabolites captured as well as measurement precision are shaped by sample preparation and analytical methods (i.
We first mapped metabolites previously implicated in PTSD with a review of prior case vs. control metabolomics studies. Understanding the breadth of metabolomics efforts in PTSD enabled us to evaluate platform-specific coverage for PTSDassociated metabolites. In parallel, we evaluated the comprehensive platform-specific performance for all metabolites reported by each vendor. Finally, we considered the performance for PTSD-associated metabolites and limitations of the scope of metabolomics approaches applied thus far in PTSD. Moving forward, the cross-platform metabolomics database generated by this study can enable optimization of platform selection for other clinical states through mapping of performance for putative metabolites and weighing these details against coverage depth.
For studies of PTSD, exploratory group sizes have included 20  [17] , 34  [18] , 50  [19] , 52  [20] , and 77  [7]  cases. In parallel, cross-cohort designs have been applied for other psychopathologies leveraging data from 5283 cases  [21]  or 22,623 participants  [22]. A long-standing limitation for cross-cohort designs has been the complexity in the integration of metabolomics datasets generated through different approaches; although studies in PTSD have focused on discovery based approaches, a number of MS techniques have been utilized which suggests that the integration of currently available metabolomics data in PTSD would be challenging  [23] , but see also the integration of cross-cohort epigenetics with metabolomics in one cohort in  [24]. To date, published case-control comparisons have provided valuable insights into the diverse metabolite classes that are likely to be relevant in PTSD (Supplementary Table  S3 ).
Another key consideration is that the emergence of PTSD requires trauma exposure; therefore, case vs. control studies that do not include longitudinal measurements, including assessments preceding trauma, cannot dissociate predisposing metabolomic features from consequences of trauma and PTSD etiology  [24, 27]. Low overlap across prior studies in metabolites associated with PTSD may reflect variation in population characteristics and methodologies used, including differences in fasting state, biofluid type and processing methods, time at collection, analytical technology, and data processing. Key methodological differences can be highlighted by comparing the landmark case vs. control study by Karabatsiakis et al. [17]  with a more recent keystone metabolomics dataset used in multiple publications  [7, 25].
Karabatsiakis et al. [17]  found 6 metabolite classes affected in individuals with PTSD (55% female; n = 20) compared with controls with varying degrees of trauma exposure (44% female; n = 18). In this study, participants were instructed to have regular breakfast and peripheral blood was collected at 10 am ± 15 min. Extracted serum was then analyzed by LC coupled to quadrupole/timeof-flight MS (LC-QToF-MS). By comparison, the second dataset is comprised of male combat-exposed veterans with PTSD (0% female; n = 83) or without PTSD (0% female; n = 82)  [25]. Veterans reported to the laboratory under fasting conditions for peripheral blood collection at 8 am. Extracted plasma was then analyzed by Metabolon, Inc. , using UHPLC-MS-MS optimized for basic metabolite species and GC-MS. Additional differences that are likely to shape the two datasets include the prevalence and type of comorbidities, the severity of the PTSD population, inclusion of ethnic minorities, use of internal standards, peak annotation methods, and data processing and normalization techniques  [25, 28, 29].
Numerous metabolomics consortia and large-scale collaborative efforts have been convened to establish best practices in metabolomics including quality assurance and quality control practices  [30] , the use of standards  [31, 32] , metabolite annotation confidence  [33] , as well as data processing and statistical approaches  [34] , and data reporting  [35]. These important challenges are beyond the current scope, but it should be noted that fasting state and collection time appear to have differential effects across metabolite classes in both discoverybased and targeted approaches, and reproducibility is suggested to be lower in nonfasting samples as compared to fasting samples, such that methodological differences add complexity to the landscape of available PTSD metabolomics datasets  [36] [37] [38].
Although platform-specific normative levels can impede combining datasets across platforms, measurements of the diluted NIST SRM 1950 by Biocrates indicated linearity was preserved for these analytes suggesting that these same metabolites could be reliably measured (see Supplementary Figure  S1 ). This suggests reported values represented reliable measurements of a set of subspecies. Thus, normalization methods informed by platform-specific normative levels could inform efforts to compare or merge datasets across metabolomics approaches. In contrast to the platform-specificity in normative concentrations, the majority of metabolites across all platforms were detected with excellent linearity across the dilution curve (i. e. , coefficient of determination values near 1, suggesting that abundance is not a core obstacle in current metabolomics technologies; depicted in Supplementary Figure  S1 ).
Our findings demonstrated that metabolite coverage overlap across platforms was low, congruent with prior cross-platform comparisons  [10, 12, 13]. Percentage overlap could not be determined due to irreconcilable differences in metabolite nomenclature and vendorspecific database harmonization. Platforms pioneering annotation can provide valuable living datasets; yet merging cohorts across approaches is likely to be impeded in the near future by challenges in marrying nomenclature and platform-specificity in the subspecies represented in a metabolite measurement. At the level of metabolite classes, an average of 1. 8 vendors covered each reported class, with further vendor-specificity at the level of the metabolite. The maximum number of reported metabolites, reaching the 80% rule, was ~950, while the current estimated metabolite count in the human body is 114,100, with >19 k of these detected in blood  [11].
However, this is not surprising given that Metabolon's technologies have been applied to PTSD populations  [7, 20, 25] , while platforms with distinct coverage, such as HMT, Lipotype, and Biocrates, have yet to be applied in large-scale case vs. control cohorts in PTSD, and could implicate novel metabolites based on their non-overlapping coverage. Given that researchers must weigh the benefits of applying multiple metabolomics technologies with cost, biospecimen availability, and challenges in merging datasets across approaches, there is great utility in vendors linking their internal libraries to prominent metabolite databases actively updated by the academic community. For example, HMDB links metabolites to published literature and key available information, including normative data, biological properties, and known associated diseases.
In the future, harmonizing metabolite nomenclature is likely to increase in importance as those at the frontier expand annotation of the human metabolome. Comparisons of precision and accuracy across absolute and relative quantitative metabolomics approaches must consider trade-offs in coverage and performance inherent in current technologies  [8] , As expected, the lowest coefficients of variation were yielded by NMR, with a trade-off in coverage breadth  [15, 46]. Lower precision was found in lipid classes across platforms, reflecting prior reports that quantification of lipids is difficult due to lipid solubility, incomplete separation during chromatography, and a variety of other challenges  [47]. Overall, precision in the final normalized dataset was similar between targeted and untargeted MS approaches.
). Thus, longitudinal designs would benefit from the inclusion of blinded reference plasma within each assay run to anchor study-specific normalization, as well as maximizing the number of samples within each run (and minimizing batches) to the degree possible. Although prior findings have emphasized greater measurement drift for discovery approaches compared with targeted approaches, this gap appears to be attenuated in the current dataset, likely as a result of the rapidly advancing normalization and data processing procedures conducted by the vendors  [10]. For certain metabolite classes, vendor reported measurements were consistently lower compared with concentrations in the NIST COA or values reported by other vendors (for example, Figure  4C ). There are many possible explanations for systematically lower values in a metabolite or metabolite class: subspecies or isomers may not be captured by a specific approach, additionally fragmentation, matrix effects, and ion suppression can result in random or systematic errors  [15].
These latter challenges may be exacerbated for direct infusion techniques which do not have elution order to aid in metabolite annotation  [14, 49]. Systematic differences in the reported concentration of a metabolite or metabolite class may be especially important in light of increasing efforts to combine cohorts to achieve the statistical power necessary for biomarker discovery in complex, heterogenous disease states  [21, 22]. Our findings and publicly available dataset provide a novel ability to address platform-specific "normative" levels. To enable the community to leverage the current cross-platform datasets for clinical states beyond PTSD, a platform exploration tool is being developed that will allow users to select specific metabolites of interest and explore coverage and technical variation between platforms.
Cross-Platform Comparison Design: Clinical, Control, and Pooled Reference Plasma Samples Each metabolomics vendor received two identical, blinded plasma shipments containing clinical samples, control samples, and pooled reference plasma (National Institute of Standards & Technology (NIST) Standard Reference Material (SRM) 1950). Each shipment contained 23 samples, identical across shipments, for a total of 46 samples. Metabolomics data from the control and PTSD samples were evaluated separately to account for potential population-specific performance. Given that identifying metabolites affected by PTSD was not the goal of the current study, and the current sample size is far from the scale necessary for the discovery of a disease signature, control and PTSD samples were not directly compared.
A complete list of plasma samples included is provided in Supplementary Table  S1. To assess inter-assay variation, identical technical replicates were sent in two shipments; the first shipment was analyzed and data were received prior to sending the second shipment. Shipments were separated by 7-20 weeks pending vendor analysis timelines, with an average of 14 weeks between the two shipments. The sample run order was block randomized within 5 × 5 sample shipment boxes and was consistent across all platforms and shipments. All plasma technical replicates were aliquoted and prepared in parallel and shipped by the Indiana University Genetics Biobank (IUGB, Indianapolis, IN, USA). IUGB also generated a dilution curve of the NIST SRM 1950 diluted with physiological saline to 80%, 60%, and 40% of the starting concentration (  [14] ; additional detail in Supplementary Methods).
A comprehensive search of the Google Scholar database was conducted for each disease state of interest using the following criteria for inclusion: (i) conducted in humans in the disease state of interest; (ii) published in a peer reviewed journal; (iii) assessed in a blood-based biofluid; (iv) an original report of empirical metabolomics data; and (v) published in or after 2015. Details extracted included: (1) number of clinical participants; (2) number of healthy control individuals (separated by discovery vs. test sets if applicable); (3) metabolomics analytical technology; and (4) vendor (if applicable). For PTSD, a search was conducted on 21 July 2020 using the following keywords: ("PTSD" OR "posttraumatic stress disorder") AND "metabolomics". A total of 100 studies were reviewed for inclusion, with a final total of 7 studies selected for inclusion.
Metabolite Coverage and the Nomenclature across Platforms To enable the assessment of the coverage overlap across different platforms, vendor reported metabolite nomenclature was collaboratively harmonized to Human Metabolome Database (HMDB) IDs for the identified metabolites, where possible  [11]. In some cases, nomenclature could not be matched across vendors, particularly for some lipid classes and reported "unknowns". Levels for unknown metabolites were provided by HMT and Metabolon; unknowns represent analytes in a vendor's internal library which can be consistently measured and may be identified in the future, but currently do not have known identities. Unknowns are beyond the scope of the current effort; however, reported levels for unknowns are provided in the Supplementary Materials.
Some vendors provided a list of subspecies and structural isomers represented in their metabolite measurements where possible/applicable. This is highly beneficial as platform-specific capturing of metabolite subspecies can result in platform-specific normative levels and can pose challenges for integrating datasets across approaches (Biocrates, Metabolon, HMT). Metabolite coverage for a platform can change over time as annotation algorithms are updated. Nightingale was the only vendor to report a change in their algorithms between shipments, reflecting an expansion of their internal dataset (~July, 2020). The Nightingale data reported here represents their most up-to-date algorithm (as of the date of publication); for reference, shipment 1 Nightingale data, analyzed with the current and prior algorithm, are provided in the Supplementary Materials.
Measurement Accuracy: Comparison to Known Values in the National Institute of Standards and Technology (NIST) Reference Plasma For the subset of metabolites which have concentrations reported in the NIST Certificate of Analysis (COA, revised June 2020), percent accuracy was determined for metabolites reported by each vendor in the NIST SRM 1950 samples ((reported value-NIST COA value)/NIST COA value) × 100; using shipment 1 data). For the dilution curve of the NIST reference plasma, containing samples at 100%, 80%, 60%, and 40% of the initial concentration, linearity was assessed by fitting a zero-intercept linear regression to the shipment 1 data in R version 4. 0. 4. The coefficient of determination (R 2 ) was calculated as a goodness of fit parameter for each metabolite reported by each vendor, using all points in the dilution curve.
Data Analysis and Visualization Missing data in metabolomics can result from analytical, computational, and biological factors  [52, 53]. Here, vendor-applied methods for missing data were not modified (i. e. , if a vendor imputed missing data, the imputed data were used; if a vendor reported a datum as below threshold or missing it was omitted not imputed or substituted). For vendors that imputed data, a pre-imputation version of the dataset is included in the Supplementary Materials where possible. Substitution was not used here because the source of missingness likely differed between analytical platforms and therefore substitution could have introduced biases, e. g. , distortion in data distribution or underestimation of the standard deviation of a variable or group  [54]. However, it is important to note that missing values are often imputed in an experimental context with a strategy reflecting the probable source(s) of missingness in the metabolomics platform used  [54, 55].
Conclusions Our findings emphasize that comprehensive coverage is not yet possible for the metabolome and large-scale metabolomics platforms yield distinct coverage, with classspecific performance and measurement variability within and across all platforms. Therefore, researchers selecting a platform must weigh (i) the breadth and depth of coverage previously applied to their clinical state of interest, (ii) the strength of evidence for putative metabolites, and (iii) class-specific measurement robustness, which can be interrogated for metabolites of interest using the publicly available dataset generated by the current study. For diseases in nascent stages of metabolomics characterization, researchers may be able to leverage information on metabolites associated with genes or proteins of interest (e.
g. ,  [57]  or metabolites associated with biological pathways (e. g. ,  [58]. Numerous publicly available databases have been developed to support these efforts (e. g. , Reactome, MetaCyc). Further efforts are underway to define species-specific reference values across biospecimen types, as well as the impact of clinical variables on population-level normative values including sex and age  [11]. Beyond species-specific considerations, effects of technical variation-including variation from metabolite stability over time or preanalytical variables such as freeze-thaw cycles-could be minimized through the inclusion of blinded technical plasma in every assay run to enable study-specific normalization. Similarly, distributing groups across runs in block randomization could minimize the impacts of batch variation where possible.
For example, in an innovative recent metabolomics study in PTSD conducted by Konjevod et al. [26] , discovery and test cohorts "were analyzed under the same conditions, one year apart". Technical variation across assays has the capacity to obfuscate biological differences and may have limited the metabolites validated in the test cohort to 2 metabolites out of the 33 initially implicated metabolites  [26]. Studies combining cohorts to increase statistical power are promising but can necessitate integrating metabolomics results across approaches, which is hindered by (i) vendorspecific nomenclature, (ii) platform-specificity in the metabolite subspecies represented in a metabolite measurement, and (iii) platform-specific "normative" levels. The former two challenges may be ameliorated by harmonizing to a publicly available database where possible (e.