dataesr/scientific-paragraphs-categorization huggingface dataset:
Constitutive ROS production: how much is too much? While low H2O2 levels (1-10 nM) play an important role in redox signaling, higher levels (> 100 nM) overwhelm the anti-oxidant defenses causing oxidative stress and may cause irreversible cell damage  [35]. Masoud et al. measured with the quantitative cytochrome c assay that the superoxide anion production rate elicited by Trimera is 11 nmol. min -  for 10 7 cells  [6]. Estimating that the expression elicited by Trimera started 10 h after the transfection itself (real-time observation, data not shown), we can calculate that each cell has produced 0. 9 pmol of superoxide anion within 24 h after the transfection. If we hypothesize that the superoxide anions quickly dismutate into H2O2 and that 0. 1 to 1% of H2O2 diffuse back into the cell, 0.
5 W, wavelength repeatability < 0. 1 cm -1 and a tunable repetition rate of 1-2000 kHz). We used a scan rate of 0. 5 Hz per line in the contact mode. An Au-coated silicon cantilever (Mikromasch) with a nominal tip radius < 15 nm and a spring constant between 0. 03 and 0. 2 N/m was used for all measurements (scan rate 0. 5 Hz per line, laser power 9%, pulse width 200 ns, pulse rate 766 kHz, PLL (phase locked-loop)). The AFM-IR spectra were collected with a data point spacing of 1 cm -1 , sweep rate 200 cm -1 /s, average on 4 spectra at each position (scan rate 0. 5 Hz per line, laser power 9%, pulse width 200 ns, pulse rate 766 kHz, PLL) at each wavenumber position over the spectral range 900-1900 cm -1. All experiments were performed at room temperature. The data were analyzed in Orange: baseline correction (linear), gaussian smoothing (SD 5), area normalization; and in Mountain map software: topography and IR-map analysis.
Flow cytometry measurements Transfection efficiency, pH measurements and cell viability were performed with a CyFlow flow cytometer (Partec/Sysmex) equipped with a Flomax software. For the measurement of transfection efficiency, Citrine was excited at 488 nm and detected with a 536/40 nm BP filter, mTurquoise was excited at 405 nm and detected with a 455/50 nm BP filter, and m Cherry was excited at 561 nm and detected with a 610/30 nm BP filter. For pH measurements, SNARF-1 was excited at 488 nm. The fluorescence of its protonated form was detected with a 590/50 nm BP filter and its deprotonated form with a 675/22 nm BP filter. By calculating a fluorescence intensity ratio of these two forms and using a calibration curve of SNARF-1, final intracellular pH was determined using FlowJo software.
The TCSPC fluorescence decay of all collected pixels in the selected ROIs was computed using the SymPhoTime software (PicoQuant). As donor, we used fluorescent proteins Aquamarine or mTurquoise, whose fluorescence decay can be fitted with a mono-exponential fit function: () =  '  * + , -+  (Equation  1 ) where I0 is the fluorescence intensity at t = 0, τD is the fluorescence lifetime of a donor alone, and C is the constant background. During the FRET experiment, donor and acceptors are co-expressed in cells. Some donors interact with acceptors, but some may not have any FRET partner. This situation leads to a fluorescence decay fitted with a bi-exponential function: () =  1234  '  * + , 5678 +  9:2;<  '  * + , =>6?+ +  (Equation  2 ) where long and short are the proportions of a long (τlong) and a short (τshort) lifetime components respectively (SI of  [49] ).
Introduction Recently, an unexpected outbreak of the Monkeypox virus alerted national health agencies in Europe and North America  [1]. Indeed, the number of transmission cases recorded in recent weeks greatly exceeds the usual periodic outbreaks of this virus outside Africa. In its update of 22 September 2022  [2] , the Centers for Disease Control and Prevention (CDC) recommended the use of two vaccines for the prevention of Monkeypox and smallpox viruses: JYNNEOS, a replication-deficient vaccinia virus vaccine  [3] , and ACAM2000, a cell culture-based live vaccinia smallpox vaccine  [4]. However, the effectiveness of such vaccines against a global epidemic of Monkeypox virus is not known. Indeed, in its September 2022 update, the CDC acknowledged the lack of consolidated data on the clinical efficacy or effectiveness of JYNNEOS or ACAM2000 for Monkeypox disease  [2].
Docking of E8L on Lipid Raft Gangliosides In the 3D model predicted by the Robetta server, the 1-245 region formed a globular domain that was docked onto a preformed GM1 ganglioside cluster using a previously described molecular modeling strategy  [30]. Several types of E8L-ganglioside complex were obtained after energy minimization with the Polak-Ribière algorithm of Hyperchem (Hypercube Inc. , Gainesville, FL, USA), using the Charmm force field. We selected the complex with the highest energy of interaction as calculated by the Molegro Molecular Viewer software (Molexus, Odder, Denmark) as previously described  [8]. This in silico strategy was developed by our group for studying the interaction of several SARS-CoV-2 variant spike proteins with host cell membranes and lipid raft gangliosides  [24, [31] [32] [33] [34].
GM1 was chosen as a representative ganglioside because it is widely expressed on human cells, especially on mucosal tissues that serve as a common portal of entry for a broad range of viruses  [10, 16, 35, 36]. The total energies of binding of the E8L-raft complexes were calculated by the Ligand Energy Inspector Tool in software Molegro Molecular Viewer  [37]. This tool allows getting detailed information about the energy interactions for each E8L-GM1 interaction (Figure  S2 ).
They display a large variety of functions, and many show carbohydrate-binding activity. They are considered as lectins due to their multivalence, but also as CBMs (e. g. , CBM13) since they sometimes occur as domains associated with glyco-active enzymes or toxins  [20, 21]. While their shape is well-conserved, their sequences do not show strong similarity, except for the presence of hydrophobic residues in the core and a QXW repeat found in most sequences  [11]. A large number of crystal structures of btrefoil lectins are available (212 from 63 different proteins), and based on their sequences they have been classified into 12 classes in the UniLectin3D database  [2]. Datamining of genomes based on the lobe sequence signature of each class resulted in the prediction of thousands of putative b-trefoil lectins spanning all kingdoms of life  [21].
RSL-MOA The original amino acid sequence of the lectin MOA from Marasmius oreades was obtained from the PDB database. The gene rsl-moa was designed as a fusion chimera with RSL at the N-terminus and the b-trefoil domain of MOA at the C-terminus via the linker PNGELLSS (Supplementary Fig. 1 ). The gene was ordered from Eurofins Genomics (Ebersberg, Germany) after codon optimization for the expression in the bacteria Escherichia coli. The restriction enzyme sites of NdeI and XhoI were added at 5 0 and 3 0 ends, respectively. The synthesized gene was delivered in plasmid pEX-A2-RSL-MOA. Subsequently, plasmid pEX-A2-RSL-MOA and the pET-25b+ were digested by NdeI and XhoI restriction enzymes to ligate rsl-moa in pET-25b+. After transformation by heat shock in the E. coli DH5a strain, a colony screening was performed, and the positive plasmids were amplified and controlled by sequencing.
Isothermal titration calorimetry (ITC) ITC experiments were performed with MicroCaliTC200 (Malvern Panalytical). Experiments were carried out at 25 °C ± 0. 1 °C. Protein and ligand samples were prepared in Buffer A. The ITC cell contained proteins in a concentration range from 0. 05 mM to 0. 2 mM. The syringe contained the ligand solutions in a concentration from 50 lM to 10 mM. 2 lL of ligands solutions were injected into the sample cell at intervals of 120 s while stirring at 750 rpm. Integrated heat effects were analyzed by nonlinear regression using one site binding model (MicroCal PEAQ-ITC Analysis software). The experimental data were fitted to a theoretical curve, which gave the dissociation constant (K d ) and the enthalpy of binding (DH).
Differential scanning calorimetry (DSC) The DSC experiments were carried out in Micro-Cal PEAQ DSC instrument (Malvern Panalytical). The protein samples were prepared in Buffer A, which could be substituted with the addition of a ligand. The sample cell contained the protein solutions in the concentration range of 9-29 mM. The reference cell contained the same buffer as present in the sample but without protein. The increase in temperature was measured from 20 to 130 °C with a scan rate of 200 °C/min. The data were analyzed by Micro-Cal PEAQ DSC software with a non-two-state and progress baseline method fitting model.
Acknowledgements This research was funded by the  European Union   Horizon 2020 Research and Innovation Program  under the  Marie Skłodowska-Curie grant agreement synBIOcarb  (No. 814029 ). AI acknowledges support from  Glyco@Alps  ( ANR-15-IDEX-02 ) and  Labex Arcane/ CBH-EUR - GS  ( ANR-17-EURE-0003 ). Moreover, WR acknowledges support by the  Ministry for Science, Research and Arts of the State of Baden-Württemberg  (Az:  33-7532. 20 ),and by the  Freiburg Institute for Advanced Studies (FRIAS). This publication is partially based upon work from  COST Action   CA18103  (INNOGLY), supported by  COST (European Cooperation in Science and Technology).
Two-way ANOVA was used for statistical analysis. ****P ≤ 0. 0001. F Migration assay performed in Boyden chambers. Representative images showing migrating cells in the different conditions. The histogram represents the quantitative determination of mean AE SD from six independent fields representative of three independent experiments, using ImageJ software. Paired Student's t-test was used for statistical analysis. *P ≤ 0. 05 and ***P ≤ 0. 001. G Western blot analysis of phenotype switch markers on lysates from M238P cells treated as in (A). H Crystal violet viability assay of M238P cells treated 72 h with the different combinations of inhibitors in the presence or not of BRAFi (vemurafenib, 3 lM). Data are represented as mean AE SD from a triplicate representative of at least three independent experiments.
Gene expression Protocol using the StepOne (Applied Biosystems): 1 lg of extracted RNA was reverse-transcribed into cDNA using the Multiscribe reverse transcriptase kit provided by Applied Biosystems. Primers were designed using PrimerBank or adopted from published studies. Gene expression levels were measured using Platinum SYBR Green qPCR Supermix (Fisher Scientific) and Step One thermocycler. Results from qPCR were normalized using the reference gene RPL32, and relative gene expression was quantified with the DDCt method. Heatmaps describing gene expression fold changes were prepared using MeV software. Protocol using the biomark HD system analysis (Fluidigm Corporation, USA) cDNAs were prepared from 100 ng of RNA using Fluidigm Reverse Transcription Master Mix (Fluidigm PN 100-647297).
Following a pre-amplification step (Fluidigm â PreAmp Master Mix and DELTAgene TM Assay kits) and exonuclease I treatment, samples diluted in Eva-Green â Supermix with Low ROX were loaded with primer reaction mixes in 96. 96 Dynamic Array TM IFCs. Gene expression was then assessed on a Fluidigm BioMark HD instrument. Data were analyzed with real-time PCR analysis software (Fluidigm Corporation) and presented as relative gene expression according to the DDC t method. Heatmaps depicting fold changes of gene expression were prepared using MeV software. miRNAs expression 20 ng of extracted RNA was reverse-transcribed into cDNA using the miRCURY LNA RT Kit (Qiagen). Mature miRNA expression levels were measured using the miRCURY LNA SYBR Green PCR Kit (Qiagen). Results from qPCR were normalized using miR-16-5p and relative gene expression was quantified with the DDCt method.
Fibrillar collagen imaging Collagen in paraffin-embedded tumors was stained with picrosirius red using standard protocols. Tumor sections were analyzed by polarized light microscopy as described in  (Girard et al (2020). Images were acquired under polarized illumination using a light transmission microscope (Zeiss PALM, at 10× magnification). Fiber thickness was analyzed by the change in polarization color. Birefringence hue and amount were quantified as a percent of total tissue area using ImageJ software.
Migration and invasion assays Migration properties of melanoma cells were tested using Boyden chambers containing polycarbonate membranes (8 lm pores transwell from Corning). After overnight starvation, 1 × 10 4 cells were seeded on the upper side of the chambers placed on 24-well plates containing 10% FBS medium for 24 h, unless otherwise stated, at 37°C in 5% CO 2. At the end of the experiment, cells migrated on the lower side of the chambers were fixed in 4% paraformaldehyde, stained for 15 min with Hoechst, and imaged at the microscope (five random fields per well). Nuclei counting was performed using the ImageJ software. To assess invasion properties of melanoma cells, transwells were coated with Matrigel (1 mg/ml) and cell solution was added on the top of the matrigel coating to simulate invasion through the extracellular matrix.
Reads were processed with the FLAIR pipeline (https://doi. org/10. 1038/s41467-020-15171-6). Raw reads were aligned to hg38 with minimap2 (version 2. 17-r941). Misaligned splice sites were corrected according to the GENCODE v. 35 annotations. High-confidence isoforms were defined after grouping corrected reads of all samples sharing same unique splice junctions, by selecting for each group a representative isoform with confident TSS/TES and supported by more than three reads. Selected isoforms were quantified using minimap2 in each sample. Differential isoform expression and alternative splicing event significance were tested without replicates using ad-hoc scripts provided on the Brook's lab GitHub (https://github. com/BrooksLabUCSC/FLAIR). Statistical analysis and biological theme analysis: Microarray data analyses were performed using R (http://www.
r-project. org/). Quality control of expression arrays was performed using the Bioconductor package arrayQualityMetrics and custom R scripts. Additional analyses of expression arrays were performed using the Bioconductor package limma. Briefly, data were normalized using the quantile method. No background subtraction was performed. Replicated probes were averaged after normalization and control probes removed. Statistical significance was assessed using the limma moderated t-statistic. Quality control of RNA-seq count data was assessed using in-house R scripts. Normalization and statistical analysis were performed using Bioconductor package DESeq2. All P-values were adjusted for multiple testing using the Benjamini-Hochberg procedure, which controls the false discovery rate (FDR). Differentially expressed genes were selected based on an adjusted P-value below 0.
miRNA target analysis MiRonTop is an online java web tool (http://www. genomique. info/) (Le  Brigand et al, 2010)  integrating whole-transcriptome expression data to investigate whether specific miRNAs are involved in a specific biological system. MiRonTop classifies transcripts into two categories ("Upregulated" and "Downregulated"), based on thresholds for expression level, differential expression, and statistical significance. It then analyzes the number of predicted targets for each miRNA, according to the prediction software selected (Targetscan, exact seed search, TarBase).
D-E M238P cells were transduced with a miR-143/-145 construct and selected for stable expression of the cluster or transduced with a control vector, followed by RNAseq analysis (dataset 2, n = 2). (D) Heatmap highlighting the common predicted upstream regulators altered in cells stably expressing the miR-143/-145 cluster and M238R cells compared to control M238P cells. A subset of common regulators (out of the top 50 scores) corresponding to transcription factors (TFs), cytokines and growth factors (GFs), transmembrane receptors, kinases, and phosphatases is shown. Red arrows indicate annotations related to the TGF-b pathway. (E) Venn diagram summarizing the comparison of the best-predicted targets following the two gain-of-function approaches. Subsets of miR-143-3p and miR-145-5p predicted targets downregulated by both mimics and stable lentivirus expression are shown (red boxes).
Acknowledgements This work was supported by funds from  Institut National de la Sant e et de la Recherche M edicale (Inserm) ,  Centre National de la Recherche Scientifique" (CNRS) ,  Ligue Contre le Cancer ,  Fondation ARC, Institut National du Cancer (INCA_12673) ,  ITMO Cancer Aviesan (Alliance Nationale pour les Sciences de la Vie et de la Sant e, National Alliance for Life Science and Health)  within the framework of the  Cancer Plan (Plan Cancer   2018  "ARN non-codants en canc erologie: du fondamental au translationnel" n°18CN045), and the  French Government (National Research Agency, ANR)  through the  "Investments for the Future" LABEX SIGNALIFE: program  reference #  ANR-11-LABX-0028-01  and  ANR-PRCI-18-CE92-0009-01 FIBROMIR ). The financial contribution of the Conseil G en eral  06 ,  Canceropôle PACA , and  Region Provence Alpes Côte d'Azur  to the C3M and IPMC is also acknowledged.
S. D. was a recipient of doctoral fellowships from the  LABEX SIGNALIFE and Fondation pour la Recherche M edicale. I. B. and A. C. were recipients of a doctoral fellowship from  La Ligue Contre le Cancer. The PDX work was supported by  FWO  (# G. 0929. 16N )and  KULeuven  ( C1 grant ) to J-C. M. We thank  Roger Lo  for M229P/R, M238P/R, and M249P/R melanoma cells,  Richard Neubig  for UACC62 parental and resistant cell lines,  Markus Bosenberg  for YUMM1. 7 cells,  Ghanem Ghanem  for shortterm cultured melanoma cells, and  Shensi Shen  for the A375DR melanoma cells. Melanoma PDX models are from the  PDX facility TRACE (KULeuven, Belgium). We thank  Patrick Brest  for helpful discussions. We also thank the C3M animal room facility and the  C3M imaging facility , as well as the  UCA GenomiX platform  and the  PVM Vectorology Platform.
Data availability Expression datasets that support the findings of this study have been deposited in the Gene Expression Omnibus SuperSerie record GSE171883 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE171883) containing 3 distinct datasets under the following accession codes: -Dataset 1: GSE171880 (http://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE171880). Effect of miR-143-3p or miR-145-5p mimics overexpression in M238P cells (microarrays). -Dataset 2: GSE171881 (http://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE171881). RNA-Seq analysis of M238P stably expressing miR-143/-145 cluster. -Dataset 3: GSE171882 (http://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE171882). Transcriptome analysis of M238R versus M238P using nanopore long-read sequencing.
▸Figure 4. 4 Figure 4. Identification of gene targets and cellular pathways functionally associated with the miR-143/-145 cluster-mediated undifferentiated/ mesenchymal-like melanoma cell phenotype. A-C M238P cells were transfected separately with miR-143-3p, miR-145-5p or a negative control (miR-neg) mimics, and RNA content was analyzed using wholegenome microarrays (dataset 1, n = 2). (A) Heatmap showing the genes differentially expressed after individual miRNA mimic overexpression. (B) Overrepresentation of miRNA predicted targets in the set of downregulated transcripts following miR-143-3p and miR-145-5p mimics transfection using miRonTop webtool. Each arrow indicates the corresponding overexpressed miRNA. (C) Venn diagram showing the selection of the best target candidates (red boxes) using miR-143-3p and miR-145-5p mimics transfection, as well as comparison of M238R and M238P transcriptomic profiles.
Acknowledgements The authors acknowledge funding and support from the  Wellcome Trust  (C. Z. :  UNS43859 ),  CSC  (fellowship to W. W. & J. W. ),  DFG  (D. M. T:  442507947 ;  Gottfried-Wilhelm-Leibniz  award to L. A. ), the  Medical Research Council  ( MR/R01566X/1 ), and an  ERC Consolidator Grant  ( DYNAFLUORS ,  771443 ). This research was funded in part by the  Wellcome Trust  [Grant number  UNS43859 ]. For the purpose of open access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The authors acknowledge BioRender. com for assistance with figure creation. Keywords: probes • fluorescence • Candida • BODIPY • amino acids.
Study site and data collection Tasmanian devils were captured at West Pencil Pine (WPP), a subalpine region in northwest Tasmania (41 • 31 ′ S, 145 • 46 ′ E) (Fig. 1 ). The trapping site is a 25km 2 area situated on private production forestry land that includes native and non-native eucalypts for commercial harvesting, dominated by shining gum (Eucalyptus nitens)  (Fraik et al. , 2019). Rainfall is highest in July and lowest in February (decile 5 (median) rainfall: 247. 4 mm and 84. 6 mm respectively) and mean minimum temperature is lowest in July and highest in February (0. 8 • C and 7 • C respectively) in the area (recorded at the closest weather station: Waratah (Mount Road), site number: 097014, latitude: 41 • 44 ′ S, longitude: 145 • 53 ′ E, Elevation: 609 m, www. bom. gov. au). The devil population at this site has been regularly monitored since DFTD was first detected in 2006.
An estimation of tick load on the host was determined by tallying the number of ticks recorded on the head, ears, tail, limbs and groin (with most of them being located on the ears and tail). As our study used a database collected over 13 years by several researchers, recording the life stages and exact species of individual ticks was beyond the scope of the study, and thus were not determined. Thirty-five animals for which tick number, weight, head width or tumour volume had not been recorded were excluded from the analysis. Individuals younger than two years old (the usual age of sexual maturity  (Jones et al. , 2008) ) were classified as juveniles, the others were considered as adults. Tumour volume (V), expressed in mm 3 , was calculated using the ellipsoid volume formula: V = 4Π 3 × ( length 2 ) × ( width 2 ) × ( depth 2 ).
Statistical analysis Since individuals were captured several times, the number of ticks per individual were not independent, therefore, a series of generalized linear mixed models (GLMMs) was used  (Zuur et al. , 2009a). These models contain both fixed effects and random effects, and the individuals (recognised via their unique microchip identifier) are considered as a random intercept effect: the model fits a different intercept for each individual, while the calculated slopes are the same for all individuals. The variables initially included in the analyses were the capture month, population size, host age group, sex, weight, residual body mass, host disease status and time since appearance of DFTD. However, preliminary exploration of the data indicated that population size (computed using the POPAN model) and time since DFTD appearance were strongly correlated (Spearman's rho rs = 0.
Normality of residuals was interpreted from quantile-quantile plots and from residual histograms. In addition, effect sizes were visualized by computing the marginal effects of each variable included in the final model. Marginal effects measure the effect that a change in a given explanatory variable has on the predicted number of ticks, when all the other covariates are held constant. All model assumptions were fulfilled. The package glmmTMB  (Brooks et al. , 2017)  was used to implement the zero-inflated Poisson GLMMs, and MuMIn  (Bartoń, 2020)  and bbmle  (Bolker et al. , 2020)  were used to compute AIC scores and Akaike weights. Since the computation of p-values is not reliable for mixed effect models (see  Halsey et al. (2015) ;  Zuur and Ieno (2016) ), effect sizes are considered significant when the 95% confidence intervals do not overlap.