dataesr/scientific-paragraphs-categorization huggingface dataset:
FRAP acquisition was started with ten images scan at low laser power. Then, the dye was bleached locally inside the ROIs at 100% laser power using a scan of 10 frames. Finally, fluorescence recovery was monitored by the acquisition of a series of 150 images at the same low laser power as the prebleach phase. Statistical Analysis: For determination of partitioning fraction between dextran-rich and PEG-rich phase of either ELP-Dex or ELP-PEG, results were displayed as mean ± standard deviations. The sample sizes (n) were provided in the figure legends. Concerning the measurements of T cp , all data of derived count rate were normalized from 0 to 1 by using the software Origin (OriginLab Corp). Copyright WILEY-VCH Verlag GmbH & Co. KGaA, 69469 Weinheim, Germany, 2018. 1 H NMR 400 MHz spectra have been taken with a Bruker Avance I (Liquid-state 400 MHz NMR spectrometer with 5 mm BBFO probe).
Mass Spectrometry Analysis (MS) To assess the colocalization of each ELP-polymer conjugate with ELP[M1V3-60], confocal microscopy images were analyzed using ImageJ/Fiji software. [2] The corrected images were analyzed using the JaCoP plugin in ImageJ/Fiji to obtain Pearson's correlation coefficient. One can first estimate the maximum numbers of colloidal assemblies (radius r = 375 nm or 115 nm) that are needed to completely cover the dextran interface (radius determined as R = 35 µm). Assuming the contact between colloids and the dextran core is point contact, to fully cover the surface of the dextran core, we can consider that colloids are aligned closely on a surface whose radius is R + r (Figure  S12b ). In addition, the area occupied by an individual colloid on the new surface is simplified to a square whose area is 4r 2 (Figure  S12c ).
Acknowledgements H. Z. and S. L. gratefully acknowledge funding from  IdEx Bordeaux  ( 10-IDEX-03-02 ). The authors thank  Bertrand Garbay  and  Guillaume Goudounet  for gene design, cloning, and production of the ELPs. The authors also wish to thank  Evonik Industries  for kindly providing products of TEGOSOFT  DEC  and  ABIL EM 90.
Method used for collecting data We originally collected 3165 answers from teachers all over France to the questionnaire we sent online, collected between May 4 and July 1, 2020; we didn't keep the 865 people who had quit the questionnaire before the end or spent less than 7 minutes on it. We also didn't consider the 313 trainee teachers in this study because their profile was different. Over the 1994 answers that constitute the base for our study, 702 came from teachers in primary school (with students from 3 to 11years old), 1034 came from French teachers in secondary school (with students from 11 to 18 years old), and 258 came from higher education teachers. Several types of questions were asked: first of all about themselves (age, gender, years in the job, place of work), about material conditions (we will not use these data here), about the tools used (multiple choices fell into 7 categories of pedagogical practices).
Some initial exploration of this method can be found in  [7] , where the approach is seen to perform well for a 2D wave-guide problem and provide robustness to heterogeneity as well as to the wave number k, albeit requiring a comparatively large coarse space. Again, in our numerical experiments we take a fixed number of eigenvectors and vary this quantity, aiming to give a relatively fair comparison of the spectral coarse space methods. 5. Implementation details. Numerical results within this paper have been obtained using FreeFEM  [30]. More precisely, ffddm-a light-weight layer in FreeFEM domain specific language that generates all domain decomposition data structureswas used on top of HPDDM  [33] , which handles the underlying computations such as matrix-vector products or preconditioner applications.
Caspase-3 activity was assessed by DEVD-AMC cleavage efficiency (A-D, right graph). Data are presented as mean G SEM of at least two independent experiments. Statistical significance was determined using two-way ANOVA followed by Tukey's post hoc test. Significance between samples is indicated as follows: *p < 0. 05; **p< 0. 01; ***p< 0. 001. (E) L929 cells were pre-treated or not with the indicated compounds or DMSO for 30 min before TNF treatment. After 6 h, cells were then lysed and immunoblotted as indicated on the left of each blot. Altogether, these data show that inhibition of RIPK1 activity did not affect early ERK1/2 phosphorylation but lowered the overall level of sustained ERK1/2 phosphorylation, likely by interfering with necroptosis initiation.
Increased amplitude-and frequency-modulated ERK1/2 activity dynamics during cell deaths The frequency of ERK1/2 activity is a crucial determinant of cell fate decisions  (Albeck et al. , 2013; Aoki et al. , 2013 Aoki et al. , , 2017;; Ryu et al. , 2016). To resolve the distinct dynamical profiles for ERK1/2 activity between hFasL-induced apoptosis and TNF-induced necroptosis, we investigated the encoding of ERK1/2 activity dynamics. To determine whether the frequency and/or amplitude of ERK1/2 activity could be modulated between survival and programmed cell deaths in L929 cells, we quantified ERK1/2 activity pulses at the single-cell level with high temporal resolution from FRET oscillations data series using a robust FRET analysis workflow. To provide analog-to-digital conversion of the biological signal which is discrete in time and amplitude, we identified ERK1/2 activity pulses (Figure  5B ) using a custom-made Matlab-based semi-automated scoring routine (see Materials & Methods section).
Wide-field images were captured with a Nikon TiE inverted microscope with a 20X/0. 5NA objective and a DS-Qi2 CMOS camera (Nikon). Images were acquired at intervals of 2 min (biosensor reference experiment) or 4 min (biosensor imaging) with the Nikon NIS-Elements acquisition software using JOBS module (Nikon). A Lumencor Spectra X LED Light Engine (Lumencor) provided the excitation light source. Ratio imaging used a 440/30 excitation filter, a t440/ 510/575rpc multi-band dichroic mirror, and two emission filters (ET480/40M (CFP) and AT545/30M (FRET)). Lumencor provided excitation filters, and all dichroic mirrors and emission filters were obtained from Chroma Technology. An automated emission filter wheel Lambda 10-B Smart Shutter (Sutter Instrument) was used.
Image data analysis and data representation Analysis of the ERK1/2 and PKA biosensors comparison was performed with custom routines written in the IGOR Pro environment (Wavemetrics, available at https://doi. org/10. 25493/5G5V-HBC). Long time-lapse FRET biosensing measurements in the context of cell death study were analyzed using custom Matlab (MathWorks, available at github. com/ayleray/CellTraFIC) routines for segmentation (with a standard connected components algorithm), tracking (by comparing as a function of time the Euclidean distance of the coordinates of the centroids of segmented cells) and quantification of ratiometric FRET data. Based on Hoechst staining for nuclear segmentation and cell tracking, we monitored ERK1/2 activity over time. FRET intensity was calculated as a ratio of the CFP/YFP signal for each pixel on background-subtracted images.
One line corresponds to one cell, and one column corresponds to one time point. These lines are then stacked along the x-axis (time) for all the cells (y-axis). Dead cells were detected by incubating cells using propidium iodide. When red fluorescence was detected in the red channel, image analysis was stopped. The cells were ordered from top to bottom based on their living time span. The variations of ERK1/2 activity (pulses) were detected using a Matlab-based, semi-automated scoring routine. We first determined an experimental threshold (T=0,15) corresponding to the condition where little or no pulse of ERK1/2 activity should be detected, theoretically. Alternatively, results obtained with the mutated version of ERK1/2 biosensor (T>A) could also be used and processed for pulse analysis to define the threshold value.
3D modeling & prediction of FRET efficiency changes The different protein domains of the biosensor were modeled separately. A BLAST search against all Protein Data Bank (PDB) structures was performed for YPET, WW domain, ERK1/2 substrate, and eCFP. Multiple templates were selected for homology modeling with Modeller, and 10 models were generated for each protein domain. The best model was selected by comparing scores (molpdf, DOPE score, GA341 score) and Ramachandran plot assessment. Fusion Protein MODeller (FPMOD) was used to predict FRET efficiency changes by sampling conformational space through rigid body rotation using the pdb files of the modeled proteins as input and defining flexible linkers. Multiple conformations were calculated for the N-terminal part (YPET+WW domain) and the C-terminal part (ERK+eCFP) using different linkers.
Gene expression analysis According to the manufacturer's protocol, RNA was extracted from L929 cells using the RNeasy Mini Kit (Qiagen, 74104) and Qiashredder (Qiagen, 79654). The DNA-freeä DNA Removal Kit (Invitrogen, AM1906) was used to remove contaminating DNA and added DNase from the RNA samples. The firststrand cDNA of L929 cells was synthesized from total RNA using SuperScriptâ III First-Strand Synthesis System for RT-PCR (Invitrogen, 18080-51). Plate-based real-time PCR amplification and detection of the synthesized cDNA was done on the Lightcycler 480 (Roche) with qPCR MasterMix Plus for SYBRâ Green I No ROX (Eurogentec, RT-SN2X-03+NR) according to the manufacturer's guidelines. mRNA levels of murine A20, IkB-a, Tnf, Mcp1, Il-6, Cxcl-1 were normalized to multiple reference genes (Actb, Cdc40, Htatsf) using qbase+ software and primers listed in Table  S2.
QUANTIFICATION AND STATISTICAL ANALYSIS Results are presented as means G SEM unless indicated otherwise in figure captions. Statistical analyses were performed using PRISM 8 software (GraphPad). One-way and two-way ANOVA tests were performed, followed by Tukey's post hoc test. Alpha was set at 0,05. For qPCR, statistical significance was determined using an unpaired t-test. Significance between samples is indicated as follows: *p < 0. 05; **p < 0. 01; ***p < 0. 001; ns, not significant.
ACKNOWLEDGMENTS We thank the  VIB BioImaging Core  for training, support and access to the instrument park. This collaborative work was encouraged by the  CNRS Groupement de recherche (GDR)  '' Imabio ''  scientific community via the biosensor workgroup initiative. FR acknowledges  Nikon BELUX  partnership. The authors would like to thank  Anne Schepers  for technical assistance with gene expression study,  Davy Gaillot , and  John Albeck  for helpful discussions and for critical reading of the manuscript. At the time of the project, FS was a postdoctoral fellow paid by  Methusalem grant  and  FWO  projects ( BOF16/MET_V/007 ,  FWO   G. 0607. 13N,  FWO   G. 0C37. 14N ) and BC held a Ph. D. fellowship from the  FWO  and was paid by  FWO  projects ( FWO G. 0C76. 18N), both are a member of the  Death Dynamics Team (DDT)  in the  Vandenabeele unit.
Research in the Vandenabeele group is supported by  EOS MODEL-IDI  ( FWO Grant   30826052 ),  FWO research grants  (G. 0E04. 16N, G. 0C76. 18N, G. 0B71. 18N, G. 0B96. 20N),  Methusalem  ( BOF16/MET_V/007 ),  iBOF20/IBF/039 ATLANTIS ,  Foundation against Cancer  ( F/2016/865 ,  F/2020/1505 ),  CRIG  and  GIGG consortia , and  VIB. FR is a full associate professor at  Lille University , a visiting research professor at  Ghent University , and DDT leader. FR acknowledges funding from  Oseo -Ministere de l'enseignement superieur et de la recherche  via the national contest 2013 d'aide ala creation d' entreprise de technologies innovantes categorie emergence in the context of the KiBioS spinoff project. This research was supported by the  Agence Nationale pour la Recherche (ANR)  via the  G2Progress program  ( ANR-13-BSV2-0016-02 ) to FR.
Data and code availability All data reported in this paper will be shared by the lead contact upon request. Custom computer code used to analyze FRET data set from long-term live-cell imaging experiments has been deposited at the APP (Agence pour la Protection des Programmes) under the following terms: Cell-TraFIC (Cellular Tracking in FRET Image aCquisition), IDDN. FR. 001. 230009. 000. S. P. 2017. 000. 21000 and on GitHub (https://github. com/ayleray/CellTraFIC). In addition, FRET data set from short-term live-cell imaging experiments has been analyzed using Ratioscope_8. 31, running on IGOR Pro environment (DOI: 10. 25493/5G5V-HBC. ) Any additional information required to reanalyze the data reported in this work paper is available from the Lead Contact upon request.
These data suggested that RNF113A E3 activation may be linked to a more stable association with the spliceosome when it encounters damaged RNA. We reasoned that if we inhibit spliceosome activity at a step prior to RNF113A binding, MMS-induced activation of this E3 would be mitigated. Addition of a small molecule inhibitor PB, which binds to SF3B and inhibits early spliceosome activation  (Cretu et al. , 2018)  reduced MMS-mediated RNF113A autoubiquitination while significantly increasing pH2A. X signaling (Figure  5D ). This potentially links spliceosomal function, RNF113A activation, and DNA damage signaling. To complement these studies, we performed RNA immunoprecipitation followed by mass spectrometry (RIP-MS) of nucleotides, resulting from RNase treatment, from cells expressing FLAG-RNF113A.
Materials availability All reagents generated in this study are available from the Lead Contact without restriction. Data and code availability RNA-seq data have been deposited at GEO and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. Original western blot and microscopy image data have been deposited at Mendeley and are publicly available as of the date of publication. The DOI is listed in the key resources table. This paper does not report original code. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
For RNA nucleosides, the run started with a 3 min gradient of 2%-8% methanol, followed by a sharp increase to 98% methanol which was maintained for 4 min. Mass spectrometric detection was performed using an Agilent 6470 RNA sequencing and analysis RNA was purified using the QIAGEN RNeasy Mini Kit (#217004). Samples were prepared according to library kit manufacturer's protocol, indexed, pooled, and sequenced on an Illumina HiSeq. Basecalls and demultiplexing were performed with Illumina's bcl2fastq software and a custom python demultiplexing program with a maximum of one mismatch in the indexing read. RNA-seq reads were then aligned to the Ensembl release 76 top-level assembly with STAR version 2. 0. 4b (Dobin et al. , 2013). Gene counts were derived from the number of uniquely aligned unambiguous reads by Subread:featureCount version 1.
, 2009)  to test for changes in expression of the reported log 2 fold-changes reported by Limma in each term versus the background log 2 fold-changes of all genes found outside the respective term. Heatmaps were generated using the Heatmapper online tool (heatmapper. ca). All RNA-Seq primary data and metadata have been deposited in the Gene Expression Omnibus repository (https://www. ncbi. nlm. nih. gov/geo) under accession number GSE158090. qRT-PCR and nascent RNA analysis For the MS2 reporter analysis, RNA was extracted using the QIAGEN RNeasy Mini Kit (#217004). Reverse transcription was performed on the purified RNA with the ProtoScript First Strand cDNA Synthesis Kit (NEB E6300) using poly(dT) primers. SYBR Green JumpStart Taq Ready Mix (Sigma S9194) with used with qPCR using the QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems).
For nascent RNA analysis, cells were incubated with 0. 2 mM 5-ethynyl uridine (EU) at 37 C for 1 hour, then harvested for total RNA extraction at indicated time points. 5 mg total RNA was subjected to nascent transcript isolation by using Click-iT Nascent accumulation, and all cytoplasmic and nucleoli staining was removed before analysis. We subtracted cytoplasmic and nucleolar staining, as determined by co-staining with nucleolin. Images were analyzed using an ImageJ macro that sequentially subtracted nucleolin staining, detected and outlined nuclei based on DAPI staining, and measured mean integrated density of s9. 6 staining in nuclei to avoid bias. At least 100 cells were counted for each individual replicate. His-ubiquitin pulldown assays His-tagged ubiquitin was immunoprecipitated after denaturation as described previously  (Gajjar et al.
ACKNOWLEDGMENTS We wish to thank  Yang Shi ,  Hani Zaher , and the  Structural Cell Biology of DNA Repair Machines  (SBDR) members for their advice on this paper. We acknowledge the  Extreme Science and Engineering Discovery Environment  (XSEDE; Pittsburgh Supercomputing Center [PSC] allocations  TG-BIO160040  and  TG-MCB170053 ), which is supported by  National Science Foundation (NSF)  grant  ACI-1548562. J. A. T. acknowledges support as a  Cancer Prevention and Research Institute of Texas (CPRIT) Scholar in Cancer Research and Robert A. Welch Distinguished Chair in Chemistry. This work was supported by  Agence Nationale de la Recherche (ANR)  ( ANR-16-CE11-0018  to N. R. );  Institut National Du Cancer (INCa)  ( PLBIO19-021  to N. R. ); the  NIH   Structural Cell Biology of DNA Repair Machines (SBDR) program  project ( P01 CA092584  to J.
X (Mouse) PB (Pladienolide B) Abcam Cayman Chemical Company ab26350; RRID: AB_470861 16538 pH2A. X (Rabbit) Puromycin Active Motif Sigma 39117; RRID: AB_2793161 P8833 RNF113A (Rabbit) Hydroxyurea Sigma Sigma HPA000160; RRID: AB_1079821 H8627 RNF8 (Rabbit) Bleomycin Abcam Millipore-Sigma ab105362; RRID: AB_10711502 203401 RNF168 (Rabbit) Shield-1 Gift of D. Durocher (U of Toronto) Takara Bio N/A 632189 Ubiquitin (Mouse) Doxycycline Santa Cruz Sigma sc-8017; RRID: AB_2762364 D3447 b-actin HRP Methyl methanesulfonate (MMS) Sigma Sigma A3854; RRID: AB_262011 129925 BRR2 (Rabbit) Dimethyl sulfate (DMS) Bethyl Sigma A303-454A; RRID: AB_10953802 D2438 SRRM2 (Rabbit) Camptothecin (CPT) Biorbyt Selleckchem orb337685 S1288 S9. 6 Critical commercial assays Kerafast ENH001; RRID: AB_2687463 Anti-rabbit Alexa Fluor 488 Anti-mouse Alexa Fluor 546 MTS assay Anti-mouse Alexa Fluor 594 Click-iT Nascent RNA Capture Kit Next Small RNA Library Prep Set for Illumina Thermo Fisher Thermo Fisher Promega Thermo Fisher Thermo Fisher NEB A11034; RRID: AB_2576217 G3580 A21123; RRID: AB_141592 C10365 A32742; RRID: AB_2762825 7300 Chemicals, peptides, recombinant proteins HiScribe T7 In Vitro Transcription Kit NEB E2050 His-BsV-AlkB (bacterial) RNA Clean & Concentrator-5 Kit This study Zymo Research N/A R1013 His-BsV-AlkB (H156A; bacterial) RNasey Mini Kit This study QIAGEN N/A 217004 His-hALKBH3 Comet Assay This study Trevigen N/A 4250-050-K Flag-METTL8-NLS (HEK293T) ProtoScript First Strand cDNA Synthesis Kit This study NEB N/A E6300 Flag-METTL8-NLS (D230A; HEK239T) DNeasy Blood & Tissue Kit This study QIAGEN N/A 69506 Flag-METTL8-NLS (G204A/G206A; HEK239T) Nucleoside Digestion Mix This study NEB N/A M0649S Flag-RNF113A-WT (HeLa-S) Deposited Data This study N/A UBE1 UBch5c Raw sequencing data Original data Boston Biochem Boston Biochem This study This study E-305 GEO: GSE158090 E2-627 Mendeley Data: https://data.
mendeley. RNase A Invitrogen 12091-039 com/datasets/fmzn5hdbmw/1 RNase H Experimental models: Cell lines NEB M0297 Nuclease S1 293T Sigma ATCC N5661 N/A FastAP alkaline phosphatase HeLa Thermo Fisher ATCC EF0651 N/A SUPERasedIn RNase inhibitor HeLa-S Thermo Fisher ATCC AM2694 N/A RNaseOut recombinant ribonuclease inhibitor U2OS Thermo Fisher ATCC 10777019 N/A RNase T1 and RNase inhibitor/NEB/M0314 U2OS FUCCI Thermo Fisher Byrum et al. , 2019 EN0542 N/A Polynucleotide Kinase U2OS ASCC2 KO NEB Brickner et al. , 2017 B0201S N/A Protesase & phosphatase inhibitor cocktail U2OS ASCC3 KO Thermo Fisher Brickner et al. , 2017 A32961 N/A Flag peptide U2OS 2-6-3 Sigma Janicki et al. , 2004 F3290 N/A Benzonase U2OS 2-6-5 Sigma Shanbhag et al. , 2010 E1014 N/A Anti-Flag M2 agarose beads Sigma A2220 (Continued on next page) (Continued on next page).
This was addressed by setting small bounds for the parameters ˇ1-2. Finally, the twelve data sets for values after acid addition were used to identify the parameters of Eq. (4). Parameter identification have been performed by a global gradient free method called pattern search in MATLAB searching to minimize the sum of the squared errors of the experimental data with respect to the model. Sensitivity analysis has been performed to assess the influence of each parameter on the measured output (pH) as well as the possible interaction (correlation) between these effects. The local sensitivity was studied according to the first-order sensitivities for Eq. (  5 ) as: where f (H + ) denotes the right hand side of Eq. (  5 ), P the 12 parameters of the model, and S the sensitivity matrix. As pH, or in this case H + is the only output, the sensitivities for H + with respect to the parameters P were calculated as Ṡ = ∂f (H + ) ∂H + S + ∂f (H + ) ∂P (7) ∂H + ∂P = S (8) The sensitivity functions are evaluated at the identified parameter values P and used to compute the Fisher information matrix (FIM) which provides information about the confidence intervals and the correlation of the parameters.
Partitioning of the protein Hofland  et al. (1999)  reported steady state data for the yield of protein precipitation as shown in Fig. 7. The protein yield was calculated from the residual protein concentration in the whey assuming that the whey proteins did not co-precipitate and the final value was considered as a complete precipitation. Therefore, the final values are always 1. Nonetheless, the aim in this paper is to propose a model to describe the dynamics of the proteins (Pr) depending on pH and temperature. To this end, let us consider a regression of the data following a logistic function as: Y Pr = 1 e ( Pr /T) e ( Pr /T) + e (-2 H + + 3 ) (9) where Y Pr is the protein yield, T represents the actual temperature, Pr holds for a constant to deal with temperature changes (K) and H + are the hydrogen protons.
Model extension for yoghurt acidification In order to extend the model developed in Section 2. 3 to yoghurt acidification, which is a notably slower process, we have used two data sets from literature. The first set is taken from the work of  Aguirre-Ezkauriatza et al. (2008) , where they have studied the effect of mixing in yoghurt fermentation in two types of reactors. In this case, the selected data comprises controlled experiments performed under constant temperature whose samples were taken at three different locations. The reader is referred to the paper of  Aguirre-Ezkauriatza et al. (2008)  for more details. In these experiments the pH was recorded for 5 h until the pH reached a value of 4. 5. The dynamics of pH are displayed in Fig. 11. The second data set was taken from  Soukoulis et al.
(2007)  where the authors monitored viscosity and pH in the industrial yoghurt manufacture. The data set used here corresponds to skimmed milk (to be in accordance with the model presented in Section 2. 3), where the acidification of yoghurt was recorded for 6. 5 h, at which point it had reached plateau pH of 4. 5-4. 6. The dynamics of pH are shown in Fig. 11. The pH profiles for yoghurt acidification from both experimental data sets are described by three stages: (i) lag phase (slow decrease of pH), (ii) logarithmic stage (rapid decrease of pH), and (iii) a slowdown stage where pH settles into an equilibrium point. On the other hand, the experiments of milk acidification, which have shown only two stages: (1) acid addition and (2) diffusion. In the case of yoghurt, however, acid is produced in situ by the lactic acid bacteria, which are distributed homogeneously throughout the product.