dataesr/scientific-paragraphs-categorization huggingface dataset:
The scattering curves from the SANS data with the fitted flexible cylinder model and the obtained parameters is shown in Fig. S14  and Table  S2. In order to determine the internal structure of the micelles in the mixed-surfactant systems, contrast variation SANS was combined with SAXS. One of the main advantages of SANS is that the use of isotopically labelled compounds can provide detailed structural information and micelle composition through the corefinement of model fits to the scattering data from these contrasts  [32] [33] [34]. In addition to the SANS contrast presented above, protiated surfactants in D 2 O (Contrast 1), analogous samples containing h-a-C 16 G 2 and d 31 -b-C 16 G 2 in D 2 O (Contrast 2) were measured. Data from this contrast were analyzed using a similar protocol to that presented in the Experimental section and ESI, with the difference that the volume fraction of micelles of Contrast 2 was fixed to the values obtained from Contrast 1 and the SLD of the micelle calculated (SLD fit ).
Acknowledgements The authors are also thankful to  Swedish Research Council Formas  (Grant  2015-666 ) for the funding for J. L. The research in this study was performed with financial support from  Vinnova -Swedish Governmental Agency for Innovation Systems  within the  Next-BioForm Competence Centre. This work is based upon experiments performed on the KWS-3 instrument, operated by JCNS at the  Heinz Maier-Leibnitz Zentrum  (MLZ), Garching,  Germany  (Proposal No. 15556 ), and on the D11 instrument at the  Institute Laue-Langevin, Grenoble, France  (Proposal No. 9-10-1587 ). Experimental data from the D11 experiment has been made freely available at DOI: 10. 5291/ILL-DATA. 9-10-1587. The SAXS experiments were performed on beamline BM29 at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France.
Hoechst 33258 Fluorescent Staining Cells were seeded in 96-well plates at a density of 5 × 10 3 cells/well. After 12 h, cells were treated with serial concentrations of F1 and F3 for 48 h and then harvested to stain with Hoechst 33258 (Beyotime, Shanghai, China). Cells were collected by centrifugation under 500× g × 5 min, followed by fixing with 4% paraformaldehyde (Solarbio, Beijing, China) for 10 min. Then, the cells were incubated with Hoechst 33258 for 15 min at room temperature, followed by centrifugation and rinsing with phosphate buffer saline (PBS) (Beyotime, Shanghai, China) twice, resuspended in PBS and transferred to new 96-well plates. Subsequently, the images of nuclear-related apoptosis were captured with the NIS-Elements Image Software (Version 4. 0) (Nikon, Tokyo, Japan).
Cell Cycle Analysis Jurkat cells were seeded in 6-well plates at a concentration of 2 × 10 5 cells/well. After 12 h, cells were incubated with F1 or F3 for 48 h, respectively. Then, the cells were collected and washed with PBS twice, and subsequently fixed with 70% ethanol overnight at 4 • C. After fixation, cells were centrifuged and washed twice with PBS. To ensure that only DNA was being measured, the cells were resuspended in PBS containing 50 mg/mL RNase A (Sigma-Aldrich, St. Louis, MO, USA) at 37 • C for 30 min, followed by 1× PBS and 0. 25 mg/mL PI (Sigma-Aldrich, St. Louis, MO, USA) in the dark. Next, the cells were centrifuged and then resuspended in 500 µL of 1× PBS. DNA content of cells was determined by FACSCalibur flow cytometer (BD Biosciences, San Jose, CA, USA) on CellQuest Pro software (BD Biosciences, San Jose, CA, USA) with default setting.
Assessment of Apoptosis Jurkat cells were seeded in 6-well plates at a concentration of 2 × 10 5 cells/well. After 12 h, cells were treated with serial concentrations of F1 and F3. After 48 h incubation at 37 • C, cells were harvested and washed 3 times with cold PBS. Apoptosis was detected by an FITC Annexin V Apoptosis detection Kit (BD Biosciences, San Jose, CA, USA) according to the manufacturer's protocol. Cells were centrifuged and then resuspended in 500 µL of 1× binding buffer. Then, the cells were stained with 5 µL Annexin V-FITC (BD Biosciences, San Jose, CA, USA) and 5 µL PI (BD Biosciences, San Jose, CA, USA) at 37 • C for 15 min in the dark. The fluorescence of the cells was immediately determined by FACSCalibur flow cytometer (BD Biosciences, San Jose, CA, USA). The percentages of apoptotic cells were analyzed by CellQuest Pro software (BD Biosciences, San Jose, CA, USA).
ROS Detection The intracellular increase of ROS upon F1 and F3 was measured by ROS Assay Kit (Beyotime, Shanghai, China) according to the manufacturer's protocol. Cells were seeded in 24-well plates at 1 × 10 5 cells/well for 12 h, treated with F1 or F3 for 48 h at different concentrations, and then incubated with a cell-permeable fluorogenic probe 2 ,7dichlorodihydrofluorescein diacetate (DCFH-DA) (final concentration = 10 µM) at 37 • C for 15 min. Then, the cells were rinsed and resuspended with FBS free culture medium 3 times and examined by the NIS-Elements Image Software (Version 4. 0) (Nikon, Tokyo, Japan) built-in the ECLIPSE Ti fluorescence microscope (Nikon, Tokyo, Japan).
Mitochondrial Membrane Potential Analysis Mitochondrial membrane potential (MMP) of Jurkat cells after treatment with F1 or F3 at 48 h was determined using a JC-1 Mitochondrial Membrane Potential Assay Kit (Beyotime, Shanghai, China) according to the manufacturer's instructions. Briefly, Jurkat cells were seeded in 24-well plates at 1 × 10 5 cells/well and treated with F1 or F3. After 48 h, 10 µL of JC-1 dyeing working solution was added into each well and incubated at 37 • C for 30 min. Later, cells were washed with JC-1 staining buffer (1×) 3 times, and then resuspended with JC-1 staining buffer (1×). Then, the samples were examined with FACSCalibur flow cytometer (BD Biosciences, San Jose, CA, USA) and analyzed with CellQuest Pro software (BD Biosciences, San Jose, CA, USA). Excitation wavelength Ex = 488 nm and emission wavelength FL1 (Em = 579 nm); FL2 (Em = 599 nm).
The primary antibodies anti-Bcl-2, Bax, Cyto c, Cleaved Caspase-9, Cleaved Caspase-3, PARP, CDK6, Cyclin D1, Akt, p-Akt (Ser473), p-Akt (Thr308), mTOR, p-mTOR (Ser2448), p70 S6K, p-p70 S6K (Ser389) were purchased from Cell Signaling Technology (Beverly, MA, USA) and P21, P27, GSK-3β, p-GSK-3β (Ser9), β-tubulin, GAPDH were purchased from Beyotime (Shanghai, China). The membranes were washed 3 times with TBS-T (0. 01%) and incubated with an appropriate horseradish peroxidase (HRP)-conjugated secondary antibody (Beyotime, Shanghai, China) for 1 h at room temperature. The protein bands were visualized with BeyoECL Star (Beyotime, Shanghai, China) after cleaning the membrane 3 times with TBS-T. Images were captured by the gel imaging system (version 4600SF) (Tanon, Shanghai, China). Then, the proteins level was analyzed with Image J software (NIH, Bethesda, MD, USA).
Detectors Hamamatsu 10721P-210 PMTs, with > 0. 13 A W -1 cathode sensitivity at 400 nm, and up to ∼ 2 × 10 6 controllable inter-nal gain, are used to transform the optical flux into an electric current, directly digitized at 200 MHz (0. 75 m sampling along the line of sight) by three NI PXI-5124 two-channel digitizers with 50 load. The acquisition software, custommade with LabVIEW, conducts analog and photon-counting (thresholding at ∼ 3 standard deviations of the noise) accumulations in parallel during 1000 shots (50 s), every minute, which are then pre-processed and recorded (∼ 10 s downtime). Every ∼ 8 min, baselines are recorded with PMT gains set at zero. The next sub-sections describe critical points of the detectors affecting the RR and VR channel ratios.
Acknowledgements. The authors thank  Andreas Behrendt  and  Diego Lange  for sharing component specifications and for useful discussion. Financial support. This work was funded by the  Centre National d'Etudes Spatiales (CNES) , the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA) , and the  Agence Nationale de la Recherche (ANR) WaLiNeAs  Grant (grant no. ANR-20-CE04-0001 ). Review statement. This paper was edited by  Robert Sica  and reviewed by two anonymous referees.
Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of falsepositive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases (KEGG, Reactome, and BioCyc), led to vastly different results in both the number and function of significantly enriched pathways.
Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.
First, metabolomics datasets tend to cover a much lower proportion of the total metabolome than transcriptomic datasets do of the genome. Hence, metabolomics datasets tend to contain far fewer metabolites than transcripts found in transcriptomic datasets. Second, mapping compounds to pathways is not as straightforward as the equivalent mapping with genes and proteins, and there is often a significant level of uncertainty surrounding metabolite identification, both with respect to structures and database identifiers in any metabolomics dataset. There are several methods for PA, which can be classed into three broad categories: overrepresentation analysis (ORA), functional class scoring (FCS), and topology-based methods  [5]. In this paper, we focus on ORA, one of the most mature and widely used methods of PA both within the metabolomics  [6, 7]  and transcriptomics  [8]  communities.
ORA (referred to by some authors as metabolite enrichment analysis) has found widespread use in the identification of significantly impacted pathways in numerous metabolomics studies  [9] [10] [11] [12] [13]. It works by identifying pathways or metabolite sets that have a higher overlap with a set of molecules of interest than expected by chance. The approach typically uses Fisher's exact test to examine the null hypothesis that there is no association between the compounds in the pathway and the outcome of interest  [14]. To perform ORA, three essential inputs are required: a collection of pathways (or custom metabolite sets), a list of metabolites of interest, and a background or reference set of compounds. Pathway sets can be obtained from several databases, for example, the Kyoto Encyclopaedia of Genes and Genomes (KEGG)  [15] , Reactome  [16] , and BioCyc  [17]  databases, or commercial counterparts such as the Ingenuity Pathway Analysis (IPA) database  [18].
The list of metabolites of interest is generated by the user, most commonly obtained from experimental data and by using a statistical test to find metabolites whose levels are associated with an outcome (e. g. , disease vs. control), and selecting a threshold (e. g. , on the p-values) to filter the list. The background set contains all molecules which can be detected in the experiment. For example, in transcriptomic studies, this consists of all genes or transcripts which can be quantified. In targeted metabolomics, the background set would contain all compounds assayed; in untargeted metabolomics, all annotatable metabolites (i. e. , all the features in a dataset that can be annotated to a compound name or ID). P-values for each pathway are calculated using a right-tailed Fisher's exact test based on the hypergeometric distribution.
Varying ORA inputs can result in large changes to outputs, which raises the question of how such parameters should be chosen in order to obtain the most reliable results. Moreover, as ORA was initially developed for use with transcriptomic data and later adapted for use on metabolomic data, there are certain considerations particularly important to metabolomics that may affect ORA results, such as the level of compound identification. Our aim here, therefore, is to investigate the robustness of ORA in typical metabolomics analysis, by examining the impact of varying the input data and parameters. The factors examined are: the background set, selection of differential metabolites, pathway database choice, organism-specific pathway sets, metabolite misidentification, and chemical bias of the assay.
Using five experimental datasets, we vary the inputs, each time comparing to the original or standard settings, thus demonstrating the effect of these choices on the output lists of significant pathways. Based on our approach, we offer a set of recommendations for ORA applied to metabolomics data, as well as a set of minimal reporting recommendations which we hope can help contribute to future best-practice guidelines. It is hoped that this research will promote a deeper understanding of the use ORA in metabolomics, allowing researchers to better interpret their data in a pathway context.
Nonspecific background sets result in erroneously high levels of enriched pathways First, we examined several factors which are common to all ORA applications, beginning with the background set. Five publicly available metabolomics datasets have been used throughout this work (Table  1 , see Methods). These datasets, obtained using untargeted mass-spectrometry (MS), were selected to encompass a diverse range of organisms, sample sources, and experimental conditions. The term background set (of size N, see Eq 1) is used to describe all the compounds identifiable using a particular assay. For example, for a targeted approach, this corresponds to the compounds assayed; for an untargeted approach, this corresponds to all annotatable compounds. Despite being a key parameter of ORA, specifying the background set is an often-  overlooked step.
The use of a generic, non-assay-specific background set implies that nonobserved compounds are considered in the Fisher's exact test formula, which, by definition, will always be absent from the list of metabolites of interest (of size n, Eq 1). We investigated the effect of using a nonspecific background set, consisting of all unique compounds present in the KEGG organism-specific pathway set, compared to an assay-specific background set, consisting only of compounds identified and present in the abundance matrix of each dataset. The nonspecific KEGG human background set contained considerably more compounds (3373) than any of the example datasets. A clear discrepancy was observed in many of the pathway p-values when using the nonspecific vs. specific background set (Fig 2A ). A greater proportion of pathways had lower p-values when using the nonspecific background set than the specific counterpart.
Interestingly, some pathways were significant at p � 0. 1 when using one background set but were not significant using the other, as evident in the upper right and lower left quadrants of Fig 2A. We also investigated the number of significantly enriched pathways (SEP) before and after multiple testing correction (using Benjamini-Hochberg False Discovery Rate (BH FDR)) when using the two different background sets (Fig  2B ). When using the specific background set, there were far fewer SEPs at p � 0. 1 (solid bars) and q � 0. 1 (hatched bars) than there were using the nonspecific background set. Surprisingly, when using the specific background set (lighter coloured bars), two datasets contained no pathways which remained significant after multiple-testing correction (no hashed bars). Since our further analyses require several pathways to be enriched in the original datasets, we decided to use a significance threshold corresponding to an uncorrected p-value of � 0.
1. While we do not recommend this threshold in practice as it is relatively liberal, this approach allowed us to demonstrate the characteristic behaviour of ORA across a wide range of datasets. A key difference between the specific and nonspecific background sets used in the simulations in Fig 2  is  the number of compounds they each contain. For the human datasets (Yachida, Stevens, and Quiros) for example, the nonspecific background set contained a total of 3373 unique compounds, whereas the specified background sets for these datasets ranged in size from 286 to 1110 compounds. It is therefore reasonable to ask whether the changes seen in Fig  2A  and 2B could be due to the size of the background sets. Accordingly, we investigated how the size of the background set affects ORA results. In Fig  2C , we simulated a reduction in the number of compounds identified in the experiment and identify differentially abundant (DA) metabolites based on the compounds in the reduced background set.
Reduction of the background set without removal of the original DA metabolites resulted in a much more variable decline in the number of significant pathways (Fig  2D ). Datasets that had larger background sets to begin with, such as Fuhrer et al. , appeared to be the least affected by the background set reduction. This is likely attributed to the fact that even when the reduced background set contained just 10% of the original compounds, it still contained over 240 metabolites. The trends observed in Fig 2D also imply that a.
ORA results are influenced by pathway database choice, organismspecificity, and database updates An important consideration when conducting any type of pathway analysis is the nature of the pathway sets used. Pathway sets can differ between databases in many ways, including the number of pathways present, the size of pathways, how pathways are curated (either manually or computationally, or a combination of both), pathway boundaries, and the organisms supported. We compared several properties of three pathway databases: KEGG, Reactome, and BioCyc. As this work focuses on metabolomics, only pathways which contain at least three metabolites were considered for the purposes of this paper, and genes and proteins were excluded from the pathway definition. Using human pathways as an example, as of December 2020, Reactome contained the highest number of pathways (1631), followed by HumanCyc We next investigated the similarity of metabolite composition for KEGG and Reactome pathways.
The same calculation was performed considering only genes in equivalent KEGG and Reactome pathways. 55 pathways were comparable, and while the OC values were larger than those derived from comparison of metabolite-only pathways (median = 0. 64, interquartile range = 0. 42-0. 81), these also suggest moderate differences the gene composition of pathways from different databases. To explore whether similar biological functions could be inferred from an ORA using different databases, we compared the SEPs obtained using the Yachida et al. dataset based on KEGG, Reactome, and HumanCyc pathways (Table A in S1 Supporting Information). By manual inspection of pathway names, there appeared to be low concordance between the results of the three databases in terms of biological function. Similar observations were also made in the other datasets.
To quantify this effect, we pooled all metabolites from the significant pathways (p � 0. 1) detected using KEGG and Reactome and calculated the OC between the two sets of compounds for each dataset. OC values ranged from 0. 23 (Stevens dataset) to 0. 62 (Labbedataset) (Fig B in S1 Supporting Information), indicating low to medium consensus between ORA results derived using different pathway databases. In addition to selecting a pathway database, many pathway databases offer both reference and organism-specific pathway sets. Reference pathway sets are not associated with any organism and can be useful when the organism under study does not have an associated pathway set. We compared basic properties of the KEGG human and KEGG reference pathways sets. The KEGG reference pathway set contained both more (377 vs.
261 pathways) and larger pathways (mean pathway size 45 vs. 30 compounds). The two pathway sets had a median OC of 0. 92 (IQR = 0. 83-0. 97) for pathways with a common ID (e. g. , Glycolysis: HSA00010/ MAP00010), indicating a high level of similarity between the pairs but that analogous pathways are not identical. We performed ORA for each example dataset using both the organism-specific and reference pathway sets and compared the SEPs obtained (Table  2 ). While there was a large overlap, many more pathways were significantly enriched in the reference pathway set alone as opposed to in the organism-specific pathway set alone. This is likely due to the fact that the reference set contains more pathways, although not all of these may be of biological relevance to the organism in question. A final consideration when selecting a pathway database is the version of the database one will use.
Not all ORA tools will use the latest version of a certain pathway database available. The vast majority of pathway databases will undergo at least yearly updates, with some such as Reactome providing four major releases per year. To investigate how much impact pathway database updates can have on ORA results, we obtained four years' worth of Reactome pathway sets spanning the period from June 2017 to December 2020. We compared three aspects of the Reactome human pathway sets (R-HSA) between each release: the number of pathways, the number of unique compounds in the database, and the mean pathway size (Fig  4B ). As expected, the number of new pathways increased gradually from release to release, alongside the number of unique compounds. From 2017 to 2020, over 200 new pathways were added as well as almost 500 new compounds.
Metabolite misidentification results in both gain and loss of truly significant pathways Next, we investigated some factors which are specific to metabolomics data, such as metabolite misidentification and assay chemical bias. A major bottleneck in untargeted metabolomics is the identification of compounds. In untargeted metabolomics, it is commonplace to putatively identify ("annotate") metabolites based on their physicochemical properties (e. g. , m/z ratio, polarity) and similarity to compounds in spectral databases, and then confirm the identities of compounds of interest using chemical reference standards. Consequently, a large proportion of compounds in untargeted metabolomics assays are expected to have a degree of uncertainty in their identification, ranging from Metabolomics Standards Initiative (MSI) confidence levels 2-4  [20].
For both approaches, we calculated the pathway loss and gain rate for each dataset at 4% simulated misidentification which, although there are few published estimates of misidentification rates in metabolomics studies  [21] , endeavours to simulate a representative scenario (  Fig 5). All the example datasets had nonzero pathway loss and gain rates at 4% simulated misidentification either by molecular weight or formula. Such findings suggest that even at a misidentification rate as low as 4%, it is likely that some pathways are significant simply as an effect of misidentification, and other pathways are not detected as significantly enriched due to the noise in the data caused by the misidentification. The similarity between the two modes of misidentification may reflect the fact that most of the uncertainty in metabolite identification lies in associating a structure with a formula, rather than linking a formula to a mass.