dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Descriptive statistics were used to summarize patient characteristics. Comparison between two unpaired groups was performed using Wilcoxon-Mann-Whitney non-parametric test. Significance was considered for two-tailed P-value < 0. 05. In VICTORIA clinical trial, 8 weeks progression-free survival was used as endpoint. Thus, responders correspond to patients with no progression during the first 8 weeks of treatment and non-responders to patients who progressed during the first 8 weeks of treatment. Statistics were performed using SAS version 9. 4 (SAS Institute, Cary, NC, USA). Graphical representations were performed using GRAPHPAD PRISM version 8. 0. 1 (GraphPad Software, San Diego, CA, USA). Overall survival was performed using the web portal UALCAN, which performs Kaplan-Meier survival analysis using TCGA datasets (UCEC: Uterine Corpus Endometrial Carcinoma) and quartile as a cut-off (high: 25% of the tumours expressing the highest level of the gene of interest; low: 75% of the remaining tumours)  [14].
Detection of circulating RiBi-related peptides in blood samples To identify putative liquid biomarkers related to RiBi, we analysed autoantibodies. Indeed, detection of circulating RiBi autoantibodies has been reported in several autoimmune diseases but also in serum of cancer patients  [17, 18]. Among them, circulating autoantibodies against FBL are used to diagnose systemic scleroderma patients  [17, 19]. In a test set of four patients, no circulating FBL autoantibodies was detected using ELISA, at any of the three time points (pre-treatment, 8 weeks on-treatment or relapse) or whatever the arm (data not shown). Detection of circulating FBL peptides have then been evaluated in blood samples at pre-treatment (n = 15), 8 weeks on-treatment (n = 13) and relapse (n = 4) using ELISA (Fig. 2D, E ).
A similar increased tendency was observed for the mRNA levels of TATA-box binding protein associated factor, RNA polymerase I subunit B (TAF1B; Fig. 3E ). Altogether, these data indicate that mRNA levels of some RiBi genes might specifically predict response to V + A combination. To determine whether these three RiBi genes may have a role in determining the outcome of endometrial cancer independently of the specific therapies, we analysed association of their mRNA levels with overall survival using the web portal UALCAN (Fig. S3 )  [14]. Using a series of 543 uterine corpus endometrial carcinoma (UCEC) issued from the TCGA database, a significant association was observed for NHP2 (P = 0. 0003) and TAF1B (P = 0. 034), but not for Nop10 (P = 0. 77), À high levels of NHP2 and TAF1B being associated with the poorest outcome.
Interestingly, it has been shown that NHP2 expression level was associated with prognosis in endometrial cancer  [22]. Moreover, we also observed in a large dataset that high level of NHP2 was associated with poor outcome, in consistence with the fact that high NHP2 levels predict the lack of response to the Vistusertib + Anastrozole combination. NHP2 and NOP10 genes encode proteins that are components of the H/ACA box snoRNPs along with the pseudouridine synthase DKC1, which regulates the pseudouridylation of rRNA  [23]. Knowing the emerging concept of specialized ribosomes suggesting that change in ribosome composition, including in rRNA pseudouridylation, might affect ribosome activity to specify a particular mRNA translational program, it would be of interest in the near future to investigate the impact of mTORC1 inhibitors on ribosome composition  [24] [25] [26] [27].
Acknowledgements We would like to thank all the people involved in the VICTORIA clinical trial, including the patients and their families and investigators from the GINECO group. We thank platform' staffs that have not been referred as co-authors:  CRB Centre L eon B erard (Lyon, France , Dir:  Dr S. Tabone-Eglinger ). The VIC-TORIA clinical trial and ancillary research was founded by  INCa  ( CLIPP2 2015-153 ). This work is supported by the  SIRIC programme (INCa-DGOS-Inserm_12563 , LyRICAN). NEHM was a recipient of a fellowship from  Ligue Nationale Contre le Cancer.
The knockdown of SLC47A1 by shRNA had no effects on erastin-or RSL3-induced ACSL4 upregulation (Fig. 3a ). To determine whether basic ACSL4 expression is required for the loss of SLC47A1-induced sensitization to ferroptosis, we compared the phenotype of wild type, SLC47A1-knockout, and SLC47A1/ACSL4 double-knockout cells generated by CRISPR-Cas9 technology (Fig. 5a ). Like the knockdown of SLC47A1 by shRNA (Fig. 3a ), the knockout of SLC47A1 by CRISPR-Cas9 did not affect the basic expression of ACSL4 (Fig. 5a ). However, compared with SLC47A1-knockout cells that were sensitive to RSL3-and erastin-induced growth inhibition, cell death, and lipid peroxidation, SLC47A1/ACSL4 double-knockout cells were relatively resistant to ferroptosis induction (Fig. 5b, c  and Supplementary Fig. S5a ). These data indicate that baseline expression of ACSL4 is required for the sensitization to ferroptosis caused by SLC47A1 deficiency.
PPARA is required for SLC47A1 upregulation during ferroptosis To identify the upstream transcription factor (TF) that controls the expression of SLC47A1, we took advantage of the publicly accessible databases JASPAR (http://jaspardev. genereg. net/), PROMO (http:// alggen. lsi. upc. es), and hTFtarget (http://bioinfo. life. hust. edu. cn/ hTFtarget). Using these sites for a bioinformatics analysis, we identified 25 candidate TFs that may bind to the SLC47A1 promoter (Fig. 6a ). Among them, PPARA is a key TF in the large scale rewiring of lipid homeostasis  46, 47  , which has already been reported to transactivate SLC47A1 in mouse kidneys  48. To determine whether PPARA plays a similar role in regulating the expression of SLC47A1 in ferroptosis, we suppressed PPARA expression by siRNAs in SW1990 and MIA PaCa2 cells.
b Cell viability of control and SLC47A1-knockdown cells following treatment with RSL3 (1 μM) or erastin (10 μM) for 24 h. c Indicated cells were treated with RSL3 (1 μM) or erastin (10 μM) for 6 h, and then lipid ROS were assayed. d Western blot analysis of protein expression in indicated control (Ctrl cDNA) and SLC47A1-overexpresstion (SLC47A1 OE) cells. e Cell viability of control and SLC47A1overexpresstion cells following treatment with RSL3 or erastin for 24 h. f Indicated cells were treated with RSL3 (1 μM) or erastin (10 μM) for 6 h, and then lipid ROS were assayed. a, d Each western blot data is representative of three independent experiments, data were presented as mean ± SD, statistical significance was analyzed using one-way ANOVA with Dunnett's multiple comparisons test, (a) or unpaired two-tailed T-test (d).
Methods The reagents are described in Supplementary Data 1. Cell culture and treatment SW1990 (CRL-2172), MIA PaCa2 (CRL-1420), HT-1080 (CCL-121), MCF-7(HTB-22), SKOV3 (HTB-77), and A549 (CCL-185) cell lines were obtained from the American Type Culture Collection. An NALM-6 (FH1161) cell line was obtained from Fu Heng Biology. These cell lines were grown in Dulbecco's modified Eagle's medium (Thermo Fisher Scientific, 11995073) or RPMI-1640 medium (Thermo Fisher Scientific, 22400097) with 10% fetal bovine serum (Thermo Fisher Scientific, A3840001) and 1% penicillin and streptomycin (Thermo Fisher Scientific, 15070-063) at 37 °C, 95% humidity, and 5% CO 2. All cell lines were mycoplasma-negative and characterized by short tandem repeat profiling. Dimethyl sulfoxide (DMSO) was used to prepare the stock solution of drugs.
RNAi and plasmid transfection Transfection with siRNA, shRNA, and cDNA was performed using Lipofectamine RNAiMAX (Thermo Fisher Scientific, 13778-030) or lipofectamine 3000 (Thermo Fisher Scientific, L3000-015) according to the manufacturer's instructions. We used 293FT cells (Thermo Fisher Scientific, R70007) to produce high-titer lentiviral particles, and the virus-containing medium was harvested 48 h after transfection. In addition, 2 μg/ml puromycin (YEASEN, 60210ES72) was used for the selection of transduced cells. The expression efficiency was evaluated by qPCR or western blot analysis. The sequence or order information of shRNA, and cDNA are shown in Supplementary Data 1. The sequence of a pooled siRNA library targeting phospholipid transporter genes is shown in Supplementary Data 2.
First-strand cDNA was then synthesized from 1 µg of RNA using the PrimeScript™ RT Master Mix (Takara, RR036A). Then 20 µl reactions were prepared by combining 4 µl of PrimeScript™ RT Master reaction mix, 2 µl of gene-specific enhancer solution, 1 µl of reverse transcriptase, 1 µl of gene-specific assay pool (20×, 2 µM), and 12 µl of RNA diluted in RNase-, DNase-, and genomic DNA-free water. Quantitative real-time PCR was performed qPCR using TB Green Premix Ex Taq II (Takara, RR820Q) on the C1000 Touch Thermocycler CFX96 Real-Time System (Bio-Rad). Analysis was performed using Bio-Rad CFX Manager software 2. 0 (Bio-Rad). The data were normalized to RNA GAPDH and the fold change was calculated via the 2-DDCt method. The relative concentrations of mRNA were expressed in arbitrary units based on the untreated group, which was assigned a value of 1.
Lipid data processing analysis Lipid species were identified using the LipidSearch software version 4. 2 (Thermo Scientific) to process the raw data and for peak alignment, retention time correction, and extraction peak area. Adducts of +H, + NH4 were selected for positive mode searches, whereas -H, + CH3COO were selected for negative mode searches because ammonium acetate was used in the mobile phases. For data extracted from LipidSearch, ion peaks with >50% missing values in the group were removed. The internal standards for different lipid species identification are provided in Supplementary Data 5. After normalization and integration using the Perato scaling method, the processed data were imported into SIMPCA-P 16. 1 (Umetrics, Umea, Sweden) for multivariate statistical analysis, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA).
Lipids with significant differences were identified based on a combination of statistically significant thresholds of variable influence on projection (VIP > 1) values obtained from the OPLS-DA model (mutidimensional statistical analysis) and two-tailed student's t-test (P < 0. 05) on the raw data (unidimensional statistical analysis) by PASS 16 (https://www. ncss. com/software/pass/) before experiments. Mediannormalized lipidomic datasets are presented in Supplementary Data 6.
Fatty acid data processing analysis The MSD Chem Station software was used to extract chromatographic peak area and retention time. The content of medium and long chain fatty acids in the sample was calculated by plotting the curve. The quality control samples were processed together with the biological samples. Detected metabolites in pooled samples with coefficient of variation (CV) less than 30 % were denoted as reproducible measurements. To find different expressed metabolites, statistical analyses between two sample groups were performed by calculating the Student't test p values of metabolites. Metabolites with P values <0. 05 were marked as the significantly changed metabolites between sample groups. Median-normalized fatty acid datasets are presented in Supplementary Data 7.
Molecular docking The molecular docking modeling was employed to study the interaction of free fatty acids (DHA and DPA) and SLC47A1 (based on PDB ID: 5YCK)  78. The small molecules-fatty acid was docked to the proteins using online tools (http://www. yinfotek. com/). The fatty acids and the protein structures were converted from pdb into pdbqt format using online tools (http://www. yinfotek. com/). The best model was selected, in which the small molecule was bound at the catalytic site of SLC47A1 with the highest binding affinity (the lowest binding energy).
Bioinformatics analysis Three open-access databases, including JASPAR (https://jaspar. genereg. net/), PROMO (http://alggen. lsi. upc. es/cgi-bin/promo_v3/ promo/promoinit. cgi?dirDB=TF_8. 3), and hTFtarget (http://bioinfo. life. hust. edu. cn/hTFtarget/#!/), were used to predict TF binding sites. The relative profile score threshold was set at 80%. To filter the predictions, in JASPAR, the sequence-matching score was above 10; in PROMO, dissimilarity was under 15%; and in hTFtarget, confidence scores were above 10.
Acknowledgements We thank  Dave Primm  ( Department of Surgery, University of Texas Southwestern Medical Center ) for his critical reading of the manuscript. M. Y. was supported by the  National Natural Science Foundation of China  ( 81974000  and  82270185 ). J. L. was supported by the  National Natural Science Foundation of China  ( 32200594 ). G. K. is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR) -Projets blancs; AMMICa  US23/CNRS UMS3655 ;  Association pour la recherche sur le cancer (ARC) ;  Association "Ruban Rose" ;  Cancéropôle Ile-de-France ;  Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  Equipex Onco-Pheno-Screen ;  European Joint Programme on Rare Diseases (EJPRD) ;  Gustave Roussy Odyssea , the  European Union   Horizon 2020 Projects Oncobiome and Crimson ;  Fondation Carrefour ;  Institut National du Cancer (INCa) ;  Inserm (HTE) ;  Institut Universitaire de France ;  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ); the  Leducq Foundation ; a  Cancer Research ASPIRE Award  from the  Mark Foundation ; the  RHU Torino Lumière; Seerave Foundation ;  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and  SIRIC Cancer Research and Personalized Medicine (CARPEM).
Data were collected from two independent experimental batches of three replicate groups for each combination of parasitoid species and Btk concentration. WUDWLRQ DSSOLHG WR WKH KRVW LQ LQWHUDFWLRQ ZLWK WKH À\ VWUDLQ and parasitoid species (Table  S2 ). For L. boulardi S(5 LQ ³&DQWRQ6´ À\ ODUYDH H[SRVHG WR î 5 CFU/g decreased by FRPSDUHG WR WKH FRQWURO S(5 GHFUHDVHG DOVR DW WKH WZR other Btk FRQFHQWUDWLRQV EXW QRW VLJQL¿FDQWO\ UHODWLYH WR WKH FRQWURO 7DEOH 6 7XNH\ SDLUZLVH FRPSDULVRQV RQ Fig. 2A ). 7KH S(5 RI L. boulardi reared in "Sefra" host decreased with increasing Btk FRQFHQWUDWLRQ FRPSDUHG WR WKH FRQWURO EXW RQO\ VLJQL¿FDQWO\ E\ DW î 7 CFU/g of Btk 7DEOH 6 Tukey pairwise comparisons on Fig. 2A ).
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This Research Topic aimed to cover the recent findings on the role of gut microbiota during cardiovascular and metabolic diseases. The metabolic risk factors, including diabetes, hypertension, and hypercholesterolemia in combination with genetics and lifestyle factors contribute to the etiology of cardiovascular diseases. Guo et al. conduct a systematic review of human observational studies interlinking gut microbiota and hypertension. The database search, as per PRISMA guidelines, resulted in 17 studies with 9,085 participants. The comprehensive analysis of all studies shows gut microbial dysbiosis with decreased diversity and changes in microbial metabolites, specifically short-chain fatty acids, during hypertension. Yet another major risk factor for cardiovascular diseases is diabetes, which is characterized by an increase in blood glucose.
Computational Tools for CRISPR Identification Computational protein prediction from sequences combined with experimental work has resulted in a vast and continuously increasing list of CRISPR-Cas systems. Many online tools help identifying new CRISPRs, spacers, and Cas proteins using sequence analysis. Several Cas system databases have been compiled. For example, Tang et al. constructed the Cas Protein Data Bank that catalogs Cas proteins from bacteria and archaea  [34]. CRISPRminer attempts to describe the full biological picture of CRISPR genes, classification, spacers, and targets in several thousand bacterial and archaeal species  [35]. CRISPRCasFinder  [36]  identifies CRISPR arrays, DRs, spacer sequences, and Cas genes along with their type and subtype in user-provided sequences. The associated CRISPRCasdb database catalogs this information for all known bacterial and archaeal genomes  [37].
Target recognition may depend on divalent ions, such as Mg 2+ and Mn 2+ , which is an important consideration for the experimental design (i. e. , EsCas13a  [64] ). Even if the gRNA architecture is well understood, the selection of the actual target-specific spacer sequence may still substantially affect the functioning of the detection system. Several bioinformatics tools aim to help select spacer sequences for Cas9 systems targeting DNA  [65]. Tools for non-Cas9 systems have also been developed and often consider the secondary structure of both the gRNA and target RNA  [66] [67] [68]. For example, Wessels et al. created an online tool that predicts Cas13d gRNAs for all protein-coding transcripts in the human genome (cas13design. nygenome. org) [62]. If not already considered by the primary tool, secondary structure predictors, such as RNAfold  [69]  and RNAxs  [70] , may be employed as a secondary filter.
However, apparent dissociation constants (K d s) are sometimes reported for the binding of the Cas-gRNA complex to its targets  [26]. The availability of these data was the final criterion that promoted SauCas9 and LwaCas13a to the list of top notable candidates. Moreover, specific applications or experiments (e. g. , control reactions) may require catalytically dead Cas proteins. We prioritized Cas effectors for which (i) target binding had been reported with both active and catalytically dead versions and (ii) the full description and (if possible) structural analysis of these inactivating mutations were available  [17].
Therefore, we prioritized the Cas effectors for which structures were published. Additionally, we kept LwaCas13a, where a detailed record of biochemical characterization compensates for the lack of a structure. Where quantitative comparisons were available, we preferred Cas orthologs with the higher catalytic activity (e. g. , LwaCas13a rather than LshCas13a)  [44]. Although relevant, we found that the affinity of the interaction between the Cas effector and its gRNA is generally lacking. However, apparent dissociation constants (Kds) are sometimes reported for the binding of the Cas-gRNA complex to its targets  [26]. The availability of these data was the final criterion that promoted SauCas9 and LwaCas13a to the list of top notable candidates. Moreover, specific applications or experiments (e.
Staphylococcus aureus (SauCas9) The single-effector Cas SauCas9 belongs to Class 2 Type II. With a molecular weight of 124 kDa, SauCas9 is markedly shorter than other Cas9 effectors (typically ~160 kDa)  [78, 79]. It possesses two nuclease domains (RuvC and HNH) and can cleave both dsDNA and RNA, but not simultaneously  [26, 78]. The RNA-cleavage activity has a strong preference for nonstructured/ssRNA targets. Unlike other RNA-targeting effectors, SauCas9 does not have collateral ribonuclease activity. It requires a relatively long PAM sequence (5 -NNGRRT-3 ) for DNA targeting, but neither PAM nor PFS are required for RNA cleavage  [26]. The literature on DNA targeting is extensive [78,  [80] [81] [82] [83]  and includes crystal structures with DNA targets (Protein Data Bank (PDB): 5AXW and 5CZZ)  [78].
Leptotrichia buccalis (LbuCas13a) This Cas system is a single-effector protein of about 140 kDa. Similar to other effectors in Class 2 Type VI, it targets ssRNA and possesses both cis and trans cleavage activity in the presence of Mg 2+  [16]. LbuCas13a can process its pre-crRNA, but the processing of the crRNA is not required to activate the effector protein  [16, 41, 84, 85]. Its specificity landscape has been studied in detail, concluding that this protein can distinguish between fully complementary and mismatched RNA transcripts (even those containing only a single mismatched nucleotide) before activating the RNP complex  [58]. LbuCas13a is a sensitive RNA detector with observable reporter cleavage in the presence of only 10 fM of the activator target. Its structure has been determined (PDB:5XWP and 5XWY  [84] ), revealing conformational changes both after binding to the gRNA and after forming the triple complex with the target  [84].
Eubacterium siraeum (EsCas13d) Derived from a gut-resident bacterium, EsCas13d (~105 kDa) was among the two first characterized single-effector Cas of Class 2, Type VI, Subtype d  [71, 74]. EsCas13d is an ssRNA-only nuclease  [71]. It exhibits cis and trans cleavage activity, with trans less efficient than cis  [71]. Structures for both apoprotein and binary (Cas:gRNA) or ternary (Cas:gRNA: target) complexes have been determined (Electron Microscopy Data Bank (EMDB): 9015, 9013, and 9014 and PDB: 6E9E and 6E9F [74]). Although EsCas13d exhibited minimal activity in human cells, in vitro experiments demonstrated more robust nuclease activity than Cas13a effectors  [71]. EsCas13d does not require a PFS sequence. However, Mg 2+ is necessary for target cleavage and can improve the efficiency of crRNA maturation.
s -1 , that correspond to slopes at 20 • -70 • from the horizontal axis ; -a 2D centered Fast Fourier transform (FFT) is then applied to the timefrequency image, that gathers the energy of all parallel drifting features in a single inclined line at 90 • from the average drift ; -we further enhance the contrast of this 2D FFT by dividing it by its integrated value along each axis ; this has also the effect to remove any residual RFI that would appear as a vertical or horizontal line crossing the origin ; -then we compute the centered Radon transform of the resulting image, i. e. its integration over a rotating diameter ; the result is divided by the Radon transform of a uniform 425 × 425 array in order to eliminate the influence of the square shape of the dynamic spectrum ; we obtain thus a 1D curve of the Radon amplitude versus the slope angle ; -finally, we measure the peak amplitude of this Radon transform outside the angle intervals 0 • ± 15 • and 90 • ± 15 • that may be polluted by residual RFI ; the curve is then shifted in order to center its main peak, that is fitted by a Gaussian distribution, the SNR of which is measured by dividing the peak amplitude by the standard deviation of the fluctuations outside a ±2σ window around the peak ; all parameters pertaining to each analysed dynamic spectrum are stored in a database.
With the above pipeline, we have detected fast drifting fine structures in 24% of the RH dynamic spectra (corresponding to emissions from Jupiter's northern hemisphere) and 18% of the LH ones (southern hemisphere). These high percentages result from the pre-selection, by the NDA operator at the end of each JunoN observation, of data intervals potentially containing fine structures. Relative to the total observation time of Jupiter, these percentages are of order of 2-3%, consistent with previous estimates  [30]. 1  Occurrence of detected S-bursts as a function of the CML (Central Meridian Longitude = sub-observer's longitude) and Io (resp. Ganymede) phase on the left (resp. right) panel. The colored symbols refer to S-bursts of Io-induced (within the white boxes in the left panel), Ganymede-induced (within the white boxes in the right panel) and auroral origin (unrelated to these moons, outside of the boxes in both panels).