dataesr/scientific-paragraphs-categorization huggingface dataset:
True life expectancies of the three species are marked with red stars. Distribution of life expectancies across the species set of the model is shown by a histogram. Vertical dashed line marks maximum life-expectancy in our dataset. Jaculus jaculus silhouette by Maija Karala. The other animal silhouettes used to visually represent mammalian orders were downloaded from PhyloPic (http://www. phylopic. org). Extended Data Fig. 7| Association between cancer mortality risk and body mass as well as adult life expectancy across 191 mammal species. These analysis include the three species excluded in some models due to their high leverages. Plots show a-d, CMR or e-h, ICM. Occurrence of cancer in each species is plotted in function of the number of individuals with post-mortem pathological records.
Acknowledgements We are grateful to  P. Bustamante  and  T. Székely  for constructive criticism on an earlier version of the manuscript, and  R. Thompson  and  A. Teare  for invaluable explanations of ZIMS data. We are grateful to more than 1,200 zoo and aquarium members of Species360 that record data in ZIMS, making this study possible. O. V. was financed by the  János Bolyai Research Scholarship of the Hungarian Academy of Sciences  and by the  New National Excellence Programme of the Hungarian Ministry of Innovation and Technology. F. T. is supported by the  MAVA Foundation , the  ANR TRANSCAN  ( ANR-18-CE35-0009 ) and a  CNRS   International Associated Laboratory Grant. C. C. M. and A. M. B. were supported in part by  NIH  grant  U54 CA217376. C. C. M. was also in part supported by  NIH  grant  U2C CA233254 , as well as CDMRP  Breast Cancer Research Program  Award  BC132057  and the  Arizona Biomedical Research Commission  grant  ADHS18-198847.
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Cell viability was determined by measuring the absorbance with a microplate reader (EXL800, BioTek, Winooski, VT, USA). The combinations 5,6 α-EC plus 5,6 β-EC (IC 25 or IC 50 ) and 5,6-ECs with bortezomib (BTZ, CAS 179324-69-7, Calbiochem, San Diego, CA, USA) at the indicated doses were also tested. The IC 50 and IC 25 (index of cytotoxicity) that are the drug concentrations that kill 50% or 25% of the cells, respectively, after a 48 h-treatment, was calculated with the Prism software (v8. 0, GraphPad) and verified with the CompuSyn software (http: //www. combosyn. org, accessed on 21 July 2021). This software was also used to calculate the Chou-Talalay combination index (CI).
Cell Cycle Analysis To determine cell cycle distribution, cells were stained using propidium iodide (PI) and explored by flow cytometry. Following a period of culture of 24 h, JJN3 and U266 cells (2 × 10 5 cells/mL/well) were incubated with 20-80 µg/mL of 5,6 α/β-ECs for 24-72 h. Cells were next fixed in 70% EtOH at -20 • C for 1 h and incubated with 500 µL of FxCycle PI/RNase staining solution (F10797, Molecular Probes, ThermoFisher Scientific, Waltham, MA, USA) for 30 min. At least, 10 4 events were gated for each experiment using a BD FACSCanto II (BD Biosciences, Franklin Lakes, NJ, USA). Data were analyzed using the BD FACSDiva 7 software (BD Biosciences, Franklin Lakes, NJ, USA).
Annexin V-Apoptosis Detection Assay A double staining with annexin V-FITC/PI was used for evaluating the percentage of apoptotic cells induced by each treatment with the Annexin V/Dead cells apoptosis kit (BMS500-FI-300, Invitrogen, Carlsbad, CA, USA). JJN3 and U266 cells were seeded in 24-well plates at the density of 2 × 10 5 cells/mL, preincubated for 24 h period, then treated with the drugs as described before. CD138+ human myeloma cells were plated in 24-well plates and treated for 24 h with 10-40 µg/mL of 5,6 α-EC or 5,6 β-EC. The cells were then stained with annexin V-FITC/PI according to the manufacturer instructions. At least 10 4 events were analyzed for each sample using the BD FACSCanto II flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA). Data were processed with the BD FACSDiva 7 software (BD Biosciences, Franklin Lakes, NJ, USA).
Effector Caspase 3/7 Activity To evaluate the effect of 5,6 α/β-ECs on caspase 3/7 activity, the cleavage of the Ac-DEVD substrate was measured using the CellEvent caspase 3/7 green flow cytometry assay kit (C10740, Molecular Probes, Eugene, OR, USA)  [20]. JJN3 and U266 cells were seeded at a density of 2 × 10 5 cells/mL/well in 24-well culture plates for 24 h and treated with 20-80 µg/mL of 5,6 α/β-ECs or with 500 µM H 2 O 2 as positive control for 24-72 h. Next, cells were processed according to the manufacturer instructions. At least 10 4 cells per sample were acquired with BD FACSCanto II and data were analyzed with the BD FACSDiva 7 software (BD Biosciences, Franklin Lakes, NJ, USA).
Measurement of Intracellular ROS and Effect of ROS Production on MM Cell Death The overproduction of intracellular ROS was measured with dihydroethidium (DHE) staining (D11347, ThermoFisher Scientific, Waltham, MA, USA). JJN3 and U266 cells were seeded at a density of 2 × 10 5 cells/mL/well into 24-well plates and incubated for 24 h before treatments with 20-80 µg/mL of 5,6 α/β-ECs or with 250 or 500 µM H 2 O 2 as positive controls. Cells were further incubated with 2 µM DHE for 15 min at 37 • C. The production of O 2-was monitored by oxidized DHE that exhibits an orange/red fluorescence  [11]. The signal was collected from a minimum of 10 4 events for each sample using the BD FACSCanto II flow cytometer (BD Biosciences) and data were processed with the BD FACSDiva 7 software (BD Biosciences).
JJN3 or U266 cells were plated for 24 h in 24-well plates (2 × 10 5 cells/well), treated with 5,6 α-EC or 5,6 β-EC (20-80 µg/mL) alone or after a 2 h-pretreatment with 400 µM vitamin E (Vit E, #258024, Sigma-Aldrich, Saint Louis, MO, USA). After 24 or 48 h of treatment, cells were stained with 1 µg/mL PI (#P4170, Sigma-Aldrich, Saint Louis, MO, USA). At least, 10 4 events per sample were acquired and analyzed by flow cytometry BD FACSCanto II. Data were processed using BD FACSDiva 7 software (BD Biosciences, Franklin Lakes, NJ, USA).
Effects of Autophagy Inhibition or Triggering on MM Cell Death and Apoptosis U266 cells were seeded in 24-well plates (2 × 10 5 cells/well) for 24 h, treated with 5,6 α-EC or 5,6 β-EC (20-80 µg/mL) alone or in combination with 5 µM rapamycin (#R0395) or 10 µM of 3-methyladenine (3-MA) (#M9281), as autophagy inducer and inhibitor, respectively, all from Sigma-Aldrich (Saint Louis, MO, USA). After 24 h of treatment, cells were stained with 1 µg/mL PI for cell death assessment. At least, 10 4 events per sample were acquired and analyzed by flow cytometry BD FACSCanto II. Data were processed using BD FACSDiva 7 software (BD Biosciences, Franklin Lakes, NJ, USA). U266 cells were seeded in 24-well plates (2 × 10 5 cells/well) for 24 h, pre-treated (or not for control) with 50 nM bafilomycin A1 (BafA1, 19-148, Merck, Darmstadt, Germany) then treated with 5,6 β-EC (20 or 40 µg/mL).
Statistical Analyses In vitro experiments were performed three times with samples in triplicate (n = 3). Representative results are shown as means ± standard deviation (SD). Statistical analyses were carried out with the two-tailed unpaired Student's t-test using the Excel software. The differences between two experimental groups were considered to be statistically significant when the obtained p-value was <0. 05. To avoid unnecessarily loading the figures, the statistical data are gathered in additional tables in the Supplementary Information file.
Cell death was evaluated in HMCLs by PI staining after a 24 h-treatment with 40 or 80 µg/mL 5,6 α-EC or 5,6 β-EC in the presence or the absence of Vit E. Results showed that Vit E remarkably reduced 5,6ECs-induced cell death (Figure  4B , Figure  S5 , Table  S8 ). For example, the percentage of PI+ U266 cells significantly decreased from 33. 5% with 40 µg/mL 5,6 α-EC and 40. 1% with 40 µg/mL 5,6 β-EC, to 12. 8% and 9. 8% in combination with Vit E (400 µM), respectively. Flow cytometry data showed similar results for JJN3 cells supporting that Vit E prevented the production of ROS and the induction of an oxidative stress leading to cell death. Finally, we inhibited ROS generation with a NAC pre-treatment (1 mM for 12 h) and treated U266 cells with 20 or 40 µg/mL 5,6 β-EC for 24 h. Apoptotic cells (annexin V+) were analyzed by flow cytometry.
Protein levels were estimated by densitometry and collected data from three independent experiments were presented in histograms (means ± SD). (D). The modification of autophagic flux was evaluated by flow cytometry. U266 cells were seeded into 24-well plates for 24 h. The cells were treated with 5,6 α-EC or 5,6 β-EC alone or in combination with 5 µM rapamycin or 10 µM 3-MA for 24 h. The cells were stained with PI and PI positive cells were recorded. At least 10 4 events were gated. The experiment has been done once with triplicate samples. (E) U266 cells were pre-treated with BafA1 (50 nM) for 4 h and then treated with 5,6 β-EC (40 µg/mL). The cells were stained with annexin V/IP as described before and sorted. Cytometry profiles were presented together with the percentage of cells within each quadrant.
The trend was also maintained for higher doses (IC 50 ). Moreover, the Chou-Talalay combination index (CI) calculated with the CompuSyn software, indicated a strong (notable) synergistic effect. In fact, the CI value was less than 1 with both IC  25  and IC 50 treatment doses, supporting favorably a synergistic interaction between 5,6 α-EC and 5,6 β-EC on myeloma cells (Table  2 ). Finally, we analyzed a potential synergic effect of both 5,6-ECs with the gold standard drug in MM: bortezomib. We compared anti-tumor effect of the single treatments with 5,6-ECs and the combined treatments 5,6 α-EC/BTZ or 5,6 β-EC/BTZ with an MTT assay. The concentrations of the drugs that were used are indicated in the Table  3 , as well as the Chou-Talalay CI. The dual BTZ/5,6-ECs treatment revealed a strong synergistic effect reinforcing the interest of oxysterol compounds for anti-myeloma therapy.
Protein levels were estimated by densitometry and collected data from three independent experiments were presented in histograms (means ± SD). (D). The modification of autophagic flux was evaluated by flow cytometry. U266 cells were seeded into 24-well plates for 24 h. The cells were treated with 5,6 α-EC or 5,6 β-EC alone or in combination with 5 µM rapamycin or 10 µM 3-MA for 24 h. The cells were stained with PI and PI positive cells were recorded. At least 10 4 events were gated. The experiment has been done once with triplicate samples. (E) U266 cells were pre-treated with BafA1 (50 nM) for 4 h and then treated with 5,6β-EC (40 µg/mL). The cells were stained with annexin V/IP as described before and sorted. Cytometry profiles were presented together with the percentage of cells within each quadrant.
Acknowledgments: We thank  Gérard Lizard  from  Team 'Biochemistry of the Peroxisome, Inflammation and Lipid Metabolism' EA 7270/Inserm, 21000 Dijon  for providing us the 5,6 Epoxycholesterol isomers used in the experimental section. We are grateful to the research platform of Medical Sciences and Technology of the  Faculty of Medicine of Tunis (University of Tunis El Manar ). We also thank the  Structure Fédérative de Recherche ICORE  ( Université de Caen Normandie ) for cytometry facility.
While resistance against thrips is noticeably different between accessions (Fig. 1b ; Additional file: Figure  S2 ) this may involve trichome-independent metabolites such as (glyco)alkaloids, as was recently suggested for thrips feeding behaviour  [81]. Alternatively, it might still be that volatile metabolites measured here, may be involved but were not selected because of sparsity in the dataset. This is supported by the observation that a relatively large portion of the metabolites were exclusively detected in a single accession (Fig. 3 ). The metabolic diversity in the set of tomatoes used here makes statistical analysis challenging, especially since the number of resistant accessions with overlapping metabolites is small, particularly in case of the volatiles. As noted above, it might mean that a uniquely appearing metabolite, despite having biological activity, was not selected only due to a lack of statistical power.
Nevertheless, the RF method chosen here resulted in the selection of specialised metabolites from complex mixtures of acylsugars and volatiles in tomato glandular trichomes that could discriminate resistant from susceptible genotypes. Regression-based classification-methods like PLS-DA, widely applied in metabolomics and plantinsect interaction studies in particular  [83] [84] [85] [86] , could not be applied here, as a consequence of the sparsity, or zero-valued elements, and the high feature to sample ratio  [87, 88]. Even sparse PLS-DA  [89]  led to impaired model convergence. The RF approach is regarded to be a powerful classification method, especially in case of sparse data  [50, 90]  and a low sample-to-feature ratio, common in multi-omics analyses  [91]. In addition, the comparison of the RF results to a permuted dataset allowed statistical discrimination of the importance of individual metabolites.
Despite its promise only a few examples of the use of RF for metabolite selection in plant science are available, including metabolite selection for potato-tuber discolouration  [92] , fingerprinting of primary metabolism in Arabidopsis mutants  [93]  and sex-related volatiles emitted by the cones of Ficus species  [94]. Here we pose that RF can predict specific metabolites with potential in defence from a challenging dataset, making it a promising tool in future identification of specialised metabolites with potential biological significance from complex mixtures often found in plants. Breeding for metabolite-based resistance is challenging and requires the introgression of several wild-species QTLs to obtain effective concentrations of herbivorespecific metabolites. Advances towards insect resistance in tomato have been made by creating pre-breeding material, introgression lines containing metabolite-QTLs that can be combined for insect-specific resistance, especially for acylsugars  [28, 30, 36, 37, 74, 95].
1902). Whitefly survival (%) was first averaged over the two clip cages to obtain an average survival per plant before calculating the accession's average survival and standard error. To create two phenotypic groups as input for the RF modelling, a generalised linear model was fitted using the glm and the glmer function of R package lme4 [107, 108] taking the variable "accession" as a fixed effect and using S. lycopersicum accession Moneymaker, functioning as a sensitive standard, as the intercept. Whitefly survival data from S. pennellii LA0716 was excluded from this analysis; as the survival was 0%, the coefficient could not be calculated. Accessions with statistically significant (p < 0. 05) coefficients were labelled as "resistant" and others as "susceptible". For the thrips assays we assayed larval performance, as thrips become virus vectors at this life stage, hence establishing the median survival time as survival parameter.
Data acquisition and identification Volatile analysis was done using an Agilent 7890A gas chromatograph coupled to a 7200 accurate mass time-of-flight (TOF) mass spectrometer. One μL of extract was injected, immediately heated to 275 °C and separated on a HP-5 ms column (30 m × 250 μm; 0. 25 μm film thickness; Agilent) using helium as a carrier gas (7. 0699 psi; flow rate: 1 mL/min). The column was heated for 3 min at 40 °C thereafter the temperature increased with 5 °C/min to 140 °C, then 10 °C/min to 250 °C which was held for 5 min. Samples were ionised at 70 eV under vacuum using EI. After a solvent delay of 4. 1 min, ions in the range of 30-350 mu were detected at 50 scans/second. Peak detection was done using Mas-sHunter Qualitative Analysis software package (Agilent). After chromatogram deconvolution, peaks were picked with 50 ppm accuracy when having a minimum ion count of 1% compared to the highest peak in the chromatogram and a S/N ratio > 10.
One μL of extract was injected and separated on an Agilent ZORBAX RRHD Eclipse Plus C18 column at 50 °C with a mobile-phase flow-rate of 0. 3 mL/min. The mobile phase consisted of water + 0. 1% formic acid (A) and acetonitrile + 0. 1% formic acid (B) in the following A:B gradient; from 60:40 to 45:55 in 6 min to 10:90 in 8 min to 60:40 in 3 min. Molecules were ionised at 325 eV in positive mode and ions were detected in the range of 50-1500 mu at 1 spectrum/sec. Acylsugars were identified using MassHunter Quantitative Analysis software (Agilent) by calculating the molecular formulas of the parent ion constituting the chromatographic peaks. Here, molecular formulas were constrained by allowing carbon, hydrogen and oxygen atoms to reconstruct the parent ion, in combination with H + , Na + and K + and formate adducts to appear, plus a double bond equivalent (DBE) range of 1-10.
Random forest As input for the RF classification algorithm, metabolite levels of the accessions were averaged across biological replicates and the accessions' classification (i. e. "resistant" or "susceptible") was taken from the regression analysis. Every RF model was run by a six-fold cross validation scheme to build training-and test sets using 1000 decision trees, yielding a feature importance for every metabolite. The model was run 5 times with random initialisation parameters producing an average feature importance and standard deviation for each metabolite (Fig. 4 , step 1). Next, the significance of each metabolite's average feature importance was evaluated by comparison to feature-importance distributions obtained from 100, 250 and 500 RF models using randomly-permutated datasets (Fig.
4 , step 2). A metabolite was regarded significantly contributing to phenotypic classification when the calculated average feature importance was higher than in 95% of the 100 permuted RF models (p < 0. 01; Fig. 4 , step 3). The modelling was performed using the Python Machine Learning library scikit-learn implementation  [116]  in the Python Pandas data structure  [117]. The full script is available as Jupyter notebook in the companion GitHub repository (http://www. github. com/BleekerLab). Methods and plant material described above complied with relevant institutional, national and international guidelines and legislation.
Background Identifying peptides carrying modifications by mass spectrometry Most of the spectra generated by mass spectrometry proteomic experiments remain unidentified after their analysis by an identification software. One of the main reasons for this poor identification rate is that most of these spectra correspond to the fragmentation of peptides carrying modifications. Conventional methods try to pair imperfect experimental spectra to ideal reference spectra -called theoretical spectra -generated from a peptide database. However, in order to avoid excessive computations, these methods limit the mass difference ∆m between any experimental spectrum and the theoretical models that will be tentatively paired to it -usually, ∆m is limited to the mass spectrometer tolerance. As a result, many potential Peptide Spectrum Matches (PSMs) involving modifications cannot be detected, since these modifications may imply a larger ∆m.
To go further, we also developed a method that processes hitModified in order to build an amino acids sequence. This allows us to show that hitModified strings carry enough information to interpret most modifications, i. e. to unambiguously transform them into amino acids sequences, providing all or part of the bait sequences. Then, we analyse in depth the strengths and weaknesses of SpecGlob, and conclude that SpecGlob behaves very well on the tested dataset. Finally, we discuss its use in an experimental context.
Evaluation of SpecGlob efficiency In order to test SpecGlob, we ran SpecOMS  [9]  on a set of 1,148,608 theoretical spectra generated from the target-decoy human proteome database (see Methods). All of  Each row corresponds to a PSM (bait, hit) provided by SpecOMS, as well as its output hitModified by SpecGlob and its interpretation. For readability, spectra (bait and hit) are represented by their corresponding peptides: although bait plays the role of an unknown spectrum, its peptide sequence can be subsequently used to validate our results. The hitModified column shows the strings returned by SpecGlob, which provides an optimal alignment of hit to bait. Then, information contained in hitModified is transformed by a series of editing operations (deletions, insertions or substitutions) described in the fifth column, whenever this is possible.
Thus, at first glance, using the FDR measure in our context may seem surprising. The idea behind its use is that if a population of pairs of spectra were sufficiently similar to each other -a non-random similarity for the population of PSMs being determined by the FDR threshold -, then it would be possible to recover baits sequences from hits. Based on this assumption, we have reported in Figure  8  the PSMs colours, depending on whether hit is a target or a decoy peptide. Among the 132,137 green PSMs, it is interesting to note that 38,350 of the hits (29%) are decoys. We thus conclude that a large number of PSMs are interpreted thanks to decoy hits, which can be considered as an issue, since the decoy database should reflect randomness. Moreover, we have shown that at 1% FDR (SPC = 21), only 57,784 PSMs (12.