dataesr/scientific-paragraphs-categorization huggingface dataset:
56 Then the mean of the tumor volume with the SD of all animals per group was plotted against the number of days since the particle injection and MH1. Survival curve. Whenever an animal was sacrificed due to the humane end point (10% increase in body weight and a tumor length greater than 2 cm) or other incidences, the number of dead animals was recorded using the Kaplan-Meier survival data plot in GraphPad (noted 1 for death post humane end point and 0 for the death due to other incidence). TR-cube Injection for Biodistribution Studies. Pristine TRcubes in 100 μL of saline at concentrations of 0. 5 mg of Fe (n = 8) and 1 mg of Fe (n = 8) were injected into the tail vein intravenously. For control purposes, other animals were injected with 100 μL of just saline solution (n = 4). The animals were then monitored by weighing them periodically and checking for any physical or mental distress.
MRI images were obtained at 1 weeks, 5 months, and 9 months post MNP injection. MRI Imaging. The MRI was performed on a 4. 7 T preclinical MRI scanner (BioSpec 47/40 USR) interfaced with ParaVision software (both provided from Bruker Biospin GmbH, Rheinstetten, Germany) at the Plateforme Imageries du Vivant of Paris Descartes (PARCC-HEGP, INSERM UMR 970, University Paris Descartes, Sorbonne Paris Cite, Facultéde medecine, Paris, France). The shole body imaging protocol was performed with a volume transmission/ reception rf coil for mice with an inner diameter of 3. 5 cm (Bruker). The nanoparticle biodistribution was monitored using a gradient echo sequence (repetition time 300 ms), two different echo times (TE = 5 or 10 ms, flip angle 30°, field of view 5 × 4 cm, eight averages, and a pixel resolution of 156 × 156 μm) with coronal slices (thickness 1 mm) being positioned over the liver, spleen, kidneys, and bladder.
As suggested by the pioneer paper of Demirgüç-Kunt and  Detragiache (1998) , the quality of the legal system and of the bureaucracy signicantly reduces the probability that a banking crisis occurs. Similar results are reported by more recent studies. For instance, Boukef  Jlassi et al. (2018)  nd that the quality of domestic institutions, proxied by a number of variables taken from the International Country Risk Guide (ICRG) database, helps mitigating the adverse eects of debt liabilities on the occurrence of banking crises. Concerning trade openness, a number of recent empirical studies nd that it tends to reduce the probability of banking crises (see, e. g. ,  Caballero, 2015; Joy et al. , 2017). One would also expect that economies with a higher degree of trade openness should suer lower output decline in the aftermath of a banking crisis.
Second, we consider the annual ination rate, which is expected to increase the probability and the real costs of banking crises. 8 Indeed, as  Bordo et al. (2002)  and  Angkinand (2009)  argue, a high pre-crisis ination rate could reect poor macroeconomic policies. It is also often linked to the formation of macroeconomic and nancial imbalances (Demirgüç-Kunt and  Detragiache, 1998; Klomp, 2010). Third, we control for the size of the banking sector and the level of nancial development, proxied by the bank credit-to-GDP ratio. In line with the results obtained by  Furceri and Zdzienicka (2012) , we expect a positive link between the size of the baking sector and the expected costs of banking crises. These three variables are taken from the World Development Indicators (WDI) database. The second category of control variables aims to capture the real and nancial vulnerabilities.
The nancial vulnerability indicator that we consider is the credit-to-GDP gap. It is dened as the dierence between the credit-to-GDP ratio and its long-term trend, and it aims to capture the buildup of excessive credit in a reduced form fashion. A number of recent empirical studies nd that the credit-to-GDP gap is a strong and robust predictor of banking crises (see, e. g. ,  Giese et al. , 2014; Alessi and Detken, 2018). This explains why, in practice, it plays a preponderant role in the conduct of macroprudential policies. Indeed, the Basel Committee recommends to consider the credit-to-GDP gap, the so-called Basel gap, for calibrating the countercyclical capital buer. As suggested by the Basel Committee, we compute the trend using a one-sided Hodrick-Prescott (HP) lter. We also control for the level of sovereign debt with the public debt-to-GDP ratio taken from the database of  Abbas et al.
(2011). In line with the existing literature, we expect that both variables increase the expected costs of banking crises. As discussed in the previous section, the nancial and trade openness of an economy is an important determinant of the occurrence and costs of banking crises. As is usual in the literature, we measure the degree of trade openness by the trade-to-GDP ratio. This ratio corresponds to the sum of exports and imports of goods and services measured as a share of GDP. This variable is taken from the WDI database. In line with  Caballero (2015)  and  Joy et al. (2017) , we expect a negative sign for the coecient associated with this variable. Next, following  Davis et al. (2016) , we measure the degree of nancial openness using the KAOPEN index developed by  Chinn and Ito (2006).
This is a de jure measure of nancial openness that aims to traduce the degree of restrictions on cross-border Then, to control for the fact that twin crises tend to be more severe and more costly than individual banking or currency crises (see, e. g. ,  Hutchison and Noy, 2005) , we include a dummy variable which takes the value of 1 when a domestic currency crisis occurred in time t, and 0 otherwise, in the set of control variables. Following  Reinhart and Rogo (2009) , we consider that a currency crisis occurred when the annual nominal depreciation of the national currency against the US dollar exceeded 15%. Data on nominal exchange rates are taken from the International Financial Statistics (IFS) database. We expect a positive sign for the coecient associated with this dummy variable. Finally, the last set of control variables concerns the scal and monetary responses that are intended to sustain economic recovery in the aftermath of a crisis.
According to a vast literature, scal policy rules are restrictions that enhance discipline. 11 This may reduce the risk of a sovereign debt crisis and the risk of twin sovereign-banking crises. Moreover, rules are a way for policymakers to forge their credibility, which is important for the eciency and success of economic policies. However, all these advantages may be oset by a lack of exibility and by possible pro-cyclicality in the event of a crisis, even if rules can oer policy space for a response to shocks  (Klomp, 2010; Romer and Romer, 2018). Tying the hands of policymakers may make the crisis more costly. To test the global impact of scal rules on the expected cost of banking crises, we use the database provided by  Schaechter et al. (2012). 12 We focus specically on budget balance rules, for three main reasons.
Papers that investigate this issue empirically usually nd that banking regulation and supervision are negatively linked to the real cost of banking crises (see, e. g. ,  Hoggarth et al. , 2005; Angkinand, 2009; Fernández et al. , 2013). While banking regulation may be an important determinant of the unconditional cost of banking crises, it has been neglected thus far for sample size reasons. Indeed, information on national banking regulation is less extensive than the usual macroeconomic data are. We collected information from the Database of Regulation and Supervision of Banks around the World, detailed in  Barth et al. (2013) , which is a survey that was rst published in 1999. 18 This means it excludes the banking crises that occurred from 1970 to the early 1990s. Nonetheless, considering a smaller sample can also serve as an additional robustness check.
This may increase the likelihood and the conditional cost of banking crises. Overall, empirical ndings generally suggest that the rst eect dominates, and as a safety net preventing bank runs, deposit insurance coverage is negatively related to the real costs of banking crises (see, e. g. ,  Hoggarth et al. , 2005; Angkinand, 2009; Fernández et al. , 2013). To check the robustness of our results once the existence of a deposit insurance is considered, we dene a dummy variable equal to 1 if there is such a scheme in country i at time t and 0 otherwise. The information comes from the WDI database, and the results are reported in Table  8. As can be seen, our previous results are robust to the inclusion of this additional control variable. Next, we focus on the globalisation and the economic integration, which can explain dierences in the expected cost of banking crises across countries.
These variables are taken from the International Country Risk Guide (ICRG) database and are available from 1984. In line with  Claessens et al. (2005) , we consider these two variables alternatively in each of our specications. The results are reported in Table  10. As can be seen, we still nd that the policy frameworks are key drivers of the unconditional cost of banking crises. Their impact is distinct from the inuence of institutional quality. Considering simultaneously the dierent policy frameworks. Finally, it may be possible that each variable related to a given policy framework accounts for common, and possibly unobserved, characteristics. To check this, we simultaneously include the variables capturing the frameworks for monetary policy, scal policy and the exchange rate in the same regression.
2 per unit length was used in all simulations presented here. Topoisomerases then increment the SC level of each domain by k topo t() dt. In absence of topoisomerases, we checked that the genomeaveraged SC level is constant throughout the simulation. Simulations, as well as all data analyses, were carried in Python with the NumPy numerical package. Note that the effective initiation rate of a gene in a simulation depends not only on its basal rate, but also on the number of RNAPs in the simulation, SC levels, and initiation rates of the other promoters. In all simulations of in vivo systems, we used a concentration of 0. 25 M for gyrase, and 0. 025 M for topoisomerase I  (22). Our unidimensional description of the chromosome disregards the 3D organisation of topological domains  (25) , especially plectonemes, loops, etc.
Statistics and data analysis Sequenced reads from D. dadantii were mapped on the reference genome (NCBI NC 014500. 1) with Bowtie2, counted with htseq-count (33) against the reference annotation, and gene differential expression was analysed with DESeq2  (34). A threshold of 0. 05 on the adjusted P-value was chosen to define differentially expressed genes (between 1250 and 1750 for novobiocin treatment). The normalised dataset from E. coli (10 208 probes per microarray) was cured to only retain data from Affymetrix probes that entirely match within one coding sequence of the REL606 reference genome (NCBI NC 012967. 1) and with the highest score of sequence homology in case several probes target the same gene. The cured dataset (3839 probes per assay) was obtained for all three strains (six replicates each) and log 2 transformed.
Differential expression was analysed with the optimal discovery procedure (35) implemented in the EDGE v1. 1. 290 software  (36) , and based on the false-discovery rate. Differentially expressed genes were first defined using a q-value threshold of 0. 1; for the orientation analysis which required larger datasets (see below), a looser threshold of 0. 25 on the P-value was used instead. All processed data are available in a Supplementary File. The relation between gene response and local orientation was carried with a homemade Python code. All error bars shown are 95% confidence intervals. Proportions were compared with the 2 test. 1C ) is calibrated from pelE expression levels measured on purified plasmids prepared at different SC levels (4). Due to the absence of topological barriers in the plasmid, transcription-induced supercoils do not accumulate and SC levels remain constant.
To analyse the latter effect, we therefore generated new datasets of differentially expressed genes, in numbers similar to those obtained in relaxation shock experiments, by reducing the threshold of statistical significance (Figure  4 , P-value < 0. 25). This operation implies that these larger datasets likely contain many false positives which might blur out the investigated effects of TSC, but the latter may still emerge as a statistical feature. We therefore looked for a statistical relationship between gene orientation and response (Figure  5C, D ). Both evolved clones with increased SC level indeed exhibit a higher rate of activated divergent genes, as expected from our previous analysis. The confidence level is high for the 2K strain where there are few other mutations (P = 1. 6 × 10 -3 ), and also significant for the 20K strain (P = 0.
FUNDING  INSA Lyon  [ BQR 2016  to S. M. ]; IXXI;  Agence Nationale de la Recherche  [ ANR-18-CE45-0006-01  to S. M. ,  ANR-18-CE35-0005-04  to D. S. ];  European Community [EU-FET  grant  EvoEvo ICT-610427  to D. S. ];  Centre National de la Recherche Scientifique  [CNRS grants to D. S. and T. H. , to S. R. , W. N. and S. M. ];  Université Grenoble Alpes  [ UGA  to D. S. and T. H. ];  Université de Lyon  [UL grant to S. R. , W. N. and S. M. ]. Funding for open access charge:  Agence Nationale de la Recherche  [ ANR-18-CE45-0006-01 ].
The DNA was purified using High Pure PCR Cleanup Micro Kit (Roche). DNA libraries were prepared using TruSeq ChIP Sample Preparation Kit (Illumina) according to manufacturer's instructions. The libraries were sequenced using an Illumina HiSeq 2500 and the reads were mapped to the sacCer3 genome assembly using HTSStation (shift = 150 bp, extension = 50 bp;  David et al. , 2014). To compare depleted versus non-depleted cells, we divided the signal from the + auxin and/or rapamycin and/or cycloheximide samples by the signal from the -auxin and/or rapamycin and/or cycloheximide (vehicle) samples and log2 transformed this value. All data from publicly available databases were mapped using HTS Station (http://htsstation. epfl. ch; David et al. , 2014).
8, 500 mM NaCl, 5 mM EDTA, 2% NP-40, 1 mM PMSF, and protease inhibitor cocktail), and centrifuged at 16,000 g for 20 min at 4˚C. Insoluble proteins were washed with Wash II buffer (20 mM Na-phosphate pH 6. 8, ice-cold), pelleted and sonicated (2x for 10 s) in 40 ml of Wash II buffer. For analysis by SDS-PAGE (4-12% acrylamide) and subsequent western blotting, proteins were first boiled in Laemmli buffer. 1x of the total cell lysate and 25x of the insoluble pellet fraction were separated and analyzed by Coomassie Blue staining or immunoblotting. Proteins were identified by shotgun mass spectrometry analysis at the Functional Genomics Center Zurich (ETH, Zurich) following TCA precipitation (20%) and acetone washing, according to posted procedures. Database searches were performed by using the Mascot (SwissProt, all species; SwissProt, yeast) search program, using very stringent settings in Scaffold (1% protein FDR, a minimum of 2 peptides per protein, 0.
Acknowledgements We thank  Mylene Docquier  and the  Genomics Platform of iGE3 at the University of Geneva  (https:// ige3. genomics. unige. ch/)for high-throughput sequencing services, the  Functional Genomics Center Zurich  ( ETH, Zurich ) for mass spectrometry analysis,  Prof. Helmut Bergler  ( Karl-Franzens-Universita ¨t, Graz, Austria ) for his generous gift of diazaborine,  Lyudmil Raykov  and the  Bioimaging Center at the Faculty of Sciences, University of Geneva  (http://bioimaging. unige. ch/)for help with confocal microscopy,  Nicolas Roggli  for expert assistance with data presentation and artwork,  Philipp Milkereit  and  Robbie Loewith  for enlightening discussions and all members of the  Shore lab  for comments and discussions throughout the course of this work. BA acknowledges support from a long-term EMBO postdoctoral fellowship in the early phases of this work.
Acknowledgements This work was supported by the  CNRS/INSU/EC2CO program  through the  PHYTOBOL  project (P. I. ; G. Sarret), and by  Labex   OSUG@2020 (Investissements d' Avenir - ANR10 LABX56 ) through the PERIPHYTOBOL (P. I. ;  G. Sarret ) and TRACISOMER (P. I. ;  S. Guedron ) projects. This work was performed within the young research associate team " JEAI TITICACA " (P. I. ;  D. Acha ) supported by the  French Institute for sustainable Development (IRD). We also thank the  IRD  for its technical and logistic support, and the  ESRF  for the provision of beamtime. We also thank  Stephane Perrot-Minnot  for his help during campaign PB2 and the analyses,  P. Rozynek  and  M. Leroux  for their help in the field sampling, and the technical staff of FAME beamline for their help during data collection.
Transcriptome analysis For RNAseq analysis total RNA was isolated from cells using the Absolutely RNA Miniprep Kit (Agilent) and RNA quality was verified using an Agilent 2100 Bioanalyzer. Subsequent cDNA library preparation, sequencing and bioinformatics analysis, including differential gene expression analyses, were performed by the Edinburgh Genomics facility. Library preparation was performed using the TruSeq Stranded mRNA Library Prep Kit (Illumina) and libraries were sequenced on the Illumina HiSeq4000. Reads were mapped to the mouse genome GRCm38 from Ensembl and were aligned to the reference genome using STAR (version 2. 5. 2b). Differential gene analysis was carried out with edgeR (version 3. 16. 5). Gene ontology analysis was performed on genes upregulated at 24h using the STRING database  (Szklarczyk et al, 2017).
For microarray analysis RNA was prepared using Absolutely RNA Purification Kits cRNA was synthesized using an Illumina TotalPrep RNA Amplification Kit. Labelled RNA was submitted to the WTCRF MRC Human Genetics Unit (University of Edinburgh) for further processing. cRNA quality was checked using an Agilent 2100 Bioanalyser and hybridization was performed on a MouseWG-6 v2 BeadChip (Illumina). Raw data was processed in R using the beadarray  (Dunning et al, 2007)  and limma  (Smyth et al, 2005)  packages from the Bioconductor suite  (Gentleman et al, 2004). RNAseq data is available in GEO with accession number GSE132003 and microarray data is available in GEO with accession number GSE42539.
Image analysis / cell polarity analysis Nuclei in 3D confocal images were detected using either Nessys  (Blin et al, 2019)  for LaminB1 stained embryos or Farsight  (Al-Kofahi et al. , 2010)  for propidium iodide stained pluripotent stem cells. The result of automated segmentations were then manually corrected using the Nessys editor tool. Images were further analysed using PickCells (https://pickcellslab. frama. io/docs/). In order to detect centrosomes we used the difference of Gaussian method  (Marr & Hildreth, 1980)  implementation of ImgLib2  (Pietzsch et al, 2012). and manually corrected the result of the automated step using the PickCells 3D spot editor module. To create a polarity vector for each individual cell, centrosomes were assigned to their respective nuclei using the nucleus to centrosome assignment module in PickCells (documentation available at https://pickcellslab.
conditions (FGF+Activin) (Fig 3A). Upregulated genes include read-outs of FGF signalling (Spry4, Dusp6) as expected, reflecting early responses to the change in extrinsic signals. Strikingly, Itga3 was the third most strongly upregulated gene at 12h (Fig 3A) in this genome-wide dataset. Upregulation of Itga3 occurs before cells have initiated expression of 'primed' markers Fgf5 or Pou3f1 (Fig 3B). Itga3 heterodimerises with Itgb1 to form the laminin receptor: in contrast to Itga3, transcripts for Itgb1 are readily detectable in naive cells and remain constant during the transition (Fig 3C).
Acknowledgements We are grateful to the staff at the  SCRM FACS facility ,  Tissue Culture  facility, transgenic unit, and imaging facility for help and guidance. We thank  Raphael Pantier ,  Dougie Colby ,  Darren Wisniewski Karolina Punovuori  and  Sam Wu  and  Daina Sadurska  for help and advice and members of the lab for useful discussions. SL is funded by a  Wellcome Trust   Senior Fellowship   WT103789AIA  and GB was funded by a  Wellcome Trust   Sir Henry Wellcome Fellowship   WT100133.
LC-MS experiments The mouse tissue, colon contents, cereal, and in vitro fermented samples were analyzed by ultra-highperformance liquid chromatography quadrupole timeof-flight mass spectrometry (UHPLC-qTOF-MS) system (Agilent Technologies), consisting of a 1290 LC system, a Jetstream electrospray ionization (ESI) source, and a 6540 UHD accurate-mass qTOF spectrometer. The chromatographic separation was performed with hydrophilic interaction chromatography (HILIC). The sample tray was kept at + 4 °C during the analysis. The data acquisition software was the MassHunter Acquisition B. 04. 00 (Agilent Technologies). During the liquid chromatography, 3 μL of the sample solution was injected onto the column (Acquity UPLC BEH Amide column, 2. 1 × 100 mm, 1. 7 μm; Waters Corporation) and maintained at 45 °C.
Data were collected in the centroid mode at the acquisition rate of 2. 5 spectra/s (i. e. 400 ms/spectrum) with an abundance threshold of 150 counts. QC samples were used for the automatic data-dependent MS/MS analyses. From every precursor scan cycle, 4 most abundant ions were selected for fragmentation. These ions were excluded after 2 product ion spectra and released again for fragmentation after a 0. 25-min hold. The precursor scan time was based on ion intensity, ending at 20,000 counts or after 300 ms. The product ion scan time was 300 ms. The collision energies were 10, 20, and 40 V in subsequent assays. The continuous mass axis calibration was performed by monitoring two reference ions (m/z 121. 050873 and m/z 922. 009798) from an infusion solution throughout the assays. Microbial DNA extraction, 16S ribosomal RNA gene sequencing, and microbial composition analysis Total bacterial DNA was extracted using the Max-well® 16 Total RNA system (Promega) with Stool Transport and Recovery Buffer (STAR; Roche Diagnostics Corporation, Indianapolis, IN).
Data analysis For the betaine metabolites, data processing was done using Profinder (Agilent Technologies), which enabled manually picking up peaks associated with the amino acid-derived betaines from the mass spectrometry data. Betaines were identified by comparison of retention times and use of targeted MS/MS spectra, which were compared to spectra of commercial and synthetized chemical standards (4-aminobutyric acid betaine, 5-AVAB, alanine betaine, glycine betaine, phenylalanine betaine, pipecolic acid betaine, proline betaine, trigonelline, tryptophan betaine, valine betaine) or published spectra (other betaines). For the statistical analysis, we used parametric oneway ANOVA for comparison between all the diet groups for the betaine levels, Mann-Whitney U test for comparison between germ-free and conventional mice for the organ-specific betaine levels and for pairwise comparisons of the relative bacterial abundances between the diet groups, and Kruskal-Wallis one-way ANOVA for comparison between relative bacterial abundances between all diet groups.
The α level was set at 0. 05 for all statistical tests. For the microbial composition analysis, data processing and analysis was carried out using in-house developed scripts  [65]. The samples were rarefied at 3900 reads prior to the alpha diversity analyses (Shannon's index and Simpson diversity index), which were carried out in QIIME  [66]. To determine the difference between the impact of control and treatment diets on the relative microbial abundance, we performed ANOSIM analyses in QIIME using weighted and unweighted UniFrac distances. The redundancy analyses (RDA) were performed in Canoco5  [67]. In RDA, the variance between the samples (genus-level relative bacterial abundance) is explained by environmental variables (in this case diet and betainized metabolites), which were fitted to corresponding matrices in the resulting illustration.