dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 3 Drug resistance associated mutations and genomic analysis resistance to NRTIs, NNRTIs and PIs were identified and their consequences on resistance to ARV drugs interpreted according to the mutations scoring of the Stanford University genotypic resistance inter- pretation algorithm and the HIV Drug Resistance Clustal Omega algorithm. A phylogenetic reconstr- Database (https://hivdb. stanford. edu/page/release- uction by maximum likelihood from PhyML notes/). The scores are the sum of each mutation algorithm (Model GTR) with a bootstrap analyzes of penalty score for a given drug. Scores less than 10 1 000 replicates were performed on nucleic acid indicate susceptibility; between 10 and 14 a potential sequences. Phylogenetic tree was annotated with the low-level resistance; between 15 and 29 low-level available metadata on sequences.
HIV-1 IN, RT and resistance; between 30 and 59 intermediate PR sequences were deposited at the GenBank resistance. Scores of 60 or greater indicate high-level Nucleotide Archive database with the accession resistance. numbers MW373071 to MW373076. For genomic analysis, first, HIV-1 subtypes were determined using the IN gene sequences according to the genotyping tool of the NIH available on line (https://www. ncbi. nlm. nih. gov/projects/genotyping/f ormpage. cgi). From INSDC database, 1,227,800 coding sequences (CDS) belonging to taxonomic IDs 11,676 (HIV-1 M), 11,709 (HIV-2) and 12,721 (others HIV-1 O et N) were retrieved. CDS were dereplicated (884,488 unique CDS) and clustered (636 groups) with Biomanda Data to group similar CDS based on the sequence similarity and the gene annotation. CDS cluster composed of 578 229 CDS for 454,944 unique ones was selected.
Second, for strains comparisons, after the raw sequences from the 6 patients have been curated to remove sequencing errors, the nucleotide positions 2 096 to 2 551 of the genome of the reference HIV strain AF033819 was retained. It corresponds to the 195 first amino acids of the A chain of the reverse Mutations of RT and PR genes associated with transcriptase. Eighty-four unique sequences of the target region and from the gag-pol gene CDS cluster were collected from GenBank. They correspond to DOI: 10. 26502/jppch. 74050099Journal of Pediatrics, Perinatology and Child Health 159 the most frequent sequences available from AfricanCountries and other main countries (USA, France, Canada, United Kingdom, China, etc. ). Moreover, 300 sequences from CAR were included. CDS were aligned to the reference genome sequence using.
Plantar pressure measurement A dynamic pressure measurement system, FootScan® software (RScan International, Belgium), was used to record PPP (N/cm 2 ) and PTI (N/cm 2 /s). This process used 2-m-long platforms with 4 sensors/cm 2 and a 3D-Box interface synchronized with the motion capture system. Patients were instructed to walk barefoot for over three minutes before recovering the plantar pressure to accommodate the patient to normal gait and speed. After this, four registers were taken to calculate the mean of both measurements (PPP and PTI) with a two-step approach to the platform  [16]. The PPP and PTI were recorded at five locations in the forefoot: first, second, third, fourth, and fifth metatarsal heads. The region corresponding to the metatarsal head resected was not analyzed. The highest value of the four remaining metatarsals was selected  [17].
Results A total of 105 patients were included in the present study. Baseline data on demographic, foot characteristics, and pressure distribution are shown in Table  1. We did not find differences between PPP and PTI at baseline in the entire foot of patients who suffered a reulceration. Despite this, as expected, patients who suffered from a reulceration event had higher PPP and PTI in the forefoot area than those without an ulceration event (Table  2. ).
5. Statistical Analysis All experiments were perfomed in triplicate and the results were presented as mean ± standard deviations (SD). The data for TPC and TFC are presented as the mean of three replicates. Linear regression analysis was employed to determine the IC50 values of DPPH and ABTS tests. One-way analysis of variance (ANOVA) was done for all treatments to study if the differences in morphological parameters and content of secondary metabolites between vitro cultures were stimulated by the presence of elicitor. Pearson test was adopted to correlate between variables. Post hoc testing for the ANOVAs was conducted using Tukey test with p < 0. 05 being considered statistically signi cant. Our analysis was achieved using GraphPad Prism version 9 for Windows, GraphPad Software (San Diego, CA, USA).
Materials and Methods The present study is a post-hoc analysis of the prospective phase II open-label, multicenter, randomized controlled SORAMIC trial (SORAfenib in combination with local MICro-therapy guided by gadolinium-EOB-DTPA-enhanced MRI, EudraCT 2009-012576-27, NCT0112 6645) comprising diagnostic, local ablation, and palliative study arms in HCC patients  [16] [17] [18]. The three study arms focused on the performance of Gd-EOB-DTPA (gadoxetic acid: Eovist and Primovist)-enhanced MRI versus contrast-enhanced computed tomography (CT) for stratifying patients to a local ablative or palliative therapy, on the value of radiofrequency ablation (RFA) plus sorafenib versus radiofrequency ablation, plus placebo on time to recurrence in the curative arm, and the impact of Yttrium-90 (Y90) radioembolization combined with sorafenib compared to sorafenib alone on overall survival in the palliative arm.
Study Population The SORAMIC trial consisted of a diagnostic, a curative, and a palliative study arm. In the diagnostic study arm, a total of 692 patients were included. All patients successfully screened were part of the diagnostic arm and underwent hepatobiliary contrast-enhanced liver MRI and contrast-enhanced whole-body CT. A total of 154 patients was excluded due to incomplete imaging (missing contrast-enhanced CT, gadoxetic acid-enhanced MRI, or both) or missing MRI sequences. Our post-hoc analysis assessed the data of 538 patients with complete imaging data. Among these patients, EHD was present in 74 patients according to the truth panel (see below).
Blinded Image Read and Truth Panel Assessment CT and MRI data sets were read by two blinded and independent reader groups, consisting of one board-certified radiologist (reader group 1 = RG1) and six board-certified radiologists (reader group 2 = RG2), respectively. All scans were evaluated regarding image quality, intrahepatic disease, vascular infiltration, treatment recommendation, and EHD. Each read was performed in two separate sessions. Either MRI or CT images of one patient were read in the first session. After a minimum period of 14 days, the additional images (MRI or CT) were presented to the readers, minimizing potential recall bias. All scans were evaluated by a truth panel consisting of a hepatologist managing HCC patients for more than 10 years and a radiologist with more than 10 years of experience in abdominal diagnostic imaging and interventional HCC therapy.
Statistical Analysis Statistical analysis was performed using dedicated statistics software (SAS version 9. 4, SAS Institute, Cary, NC, USA; R Project version 3. 5. 1, R Foundation for Statistical Computing). Baseline characteristics were compared according to the presence of EHD (EHDyes/EHDno) using a Pearson's chi-squared test with Yates' continuity correction. The maximum lesion diameter (median and inter-quartile range) was calculated using a Wilcoxon rank sum test with continuity correction. Odds ratios were calculated to compare the detection rates of EHD of CT vs. MRI. A McNemar's chi-squared test was used to compare the treatment recommendations of the reader groups to the truth panel decision. The level of significance was set at alpha = 0. 05.
Promoter Motif Discovery Identification of -10 and -35 regions was performed by scanning upstream sequences of the chromosomal 2261 predicted P and IP TSS for conserved motifs using MEME Differential Enrichment mode  [38]. For the -10 region, the 20 nt sequences immediately upstream of predicted TSSs were used as the positive set, and the 20 nt downstream sequences of the same length at position +100 were used as the negative set. For the -35 region, the 20 nt sequences at -15 nt upstream of predicted TSS were used as the positive set, and the 20 nt downstream sequences of the same length at position +100 were used as the negative set. For each region, we assumed that each sequence in the dataset contains exactly one occurrence of each pattern using MEME's "One occurrence per sequence (oops)" option  [38].
Data Deposition RNA-seq data were deposited in the ArrayExpress database at EMBL-EBI (www. ebi. ac. uk/arrayexpress, accessed on 21 June 2022) under accession number E-MTAB-11726. Overview of mapped reads, and coverage directly plotted on IGV genome browser are available on the MicroScope platform (https://mage. genoscope. cns. fr/microscope/mage/ index. php?), Transcriptomics tab, RNAseq project TSS_VXTYE3_Mextorquens_DM4.
Conclusions The detailed TSS of DCM-degrading strain M. extorquens DM4 map obtained here establishes a valuable complement to genome sequence and proteome data for the strain, with different nucleic acid motifs associated with gene expression, such as consensus sequences found in promoter regions, mapped out in unprecedented detail (Figure  4 ). More generally, it provides a broad genome-level foundation for future in-depth investigations in the genus Methylorubrum, not only for gene expression and organisation but also for gene discovery. This rich dataset will facilitate future studies of genes of interest as well as systems biology investigations of methylotrophic metabolism, and in particular on the utilisation of chlorinated C1 compounds and corresponding adaptative mechanisms.
Acknowledgments: Support from  LABGeM  ( CEA/IG/Genoscope  and  CNRS UMR8030 ) and  France Génomique National  infrastructure (funded as part of  Investissement d'avenir program  managed by  Agence Nationale pour la Recherche , contract  ANR-10-INBS-09 ) are acknowledged for use of the MicroScope annotation platform.
Raman spectra were recorded using 633 nm laser line (laser intensity was 0. 4 mW) using a 50x objective and an acquisition times of 5 s. A minimum of five replicate samples were measured to ensure that representative spectra are obtained. The structure of the arrays was characterised using a Bruker Dektak XT Stylus Profilometer and a Bruker Dimension 3100 AFM equipped with a NanoScope IIIa controller using a silicon nitride tip with a tip diameter of 20 nm operating in contact mode. The topology of the nanocavity array structures were also investigated using a Hitachi S5500 Field Emission Scanning Electron Microscope. SEM image analysis was carried out using Image J software.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Grant  Agreement No. 813439. This publication has emanated from research supported in part by a research grant from  Science Foundation Ireland (SFI)  under Grant Number  16/RC/3948  and co-funded under the  European Regional Development Fund and by FutureNeuro industry partners. The financial support of the  Environmental Protection Agency (Ireland ,  Award 2019-W-MS-41 ) and the  Agence Nationale de la Recherche  ( France ,  ANR-18-WTW7-0006-05 ), is gratefully acknowledged.
Ultra-high-pressure Liquid Chromatography-Mass Spectrometry metabolomics and Lipidomics Analyses were performed using a Vanquish UHPLC coupled online to a Q Exactive mass spectrometer (Thermo Fisher, Bremen, Germany). Samples were analyzed using a 5 min gradient as described  (Nemkov et al. , 2017; Nemkov et al. , 2019; Reisz et al. , 2019). Solvents were supplemented with 0. 1% formic acid for positive mode runs and 1 mM ammonium acetate for negative mode runs. MS acquisition, data analysis and elaboration was performed as described  (Nemkov et al. , 2017; Nemkov et al. , 2019; Reisz et al. , 2019).
Statistical Analyses Graphs and statistical analyses (Mann-Whitney non-parametric U-test or ANOVA) were prepared with GraphPad Prism 5. 0 (GraphPad Software, Inc, La Jolla, CA) and MetaboAnalyst 5. 0  (Pang et al. , 2021). Spearman correlation analyses and related p-values were calculated with MetaboAnalyst 5. 0. Experimental design and pathway illustrations were generated using Biorender (www. biorender. com).
Results The FYVE domain bearing RUFY3 isoform is preferentially expressed in activated immune cells. We identified RUN and FYVE domain-containing 3 and 4 (RUFY3 and RUFY4), as two proteins highly expressed in bone marrow-derived dendritic cells (bmDCs), likely involved in the regulation of endosomal and autophagosomes dynamics in response to Interleukin-4   (IL-4) exposure or MAMPs detection  27, 28. Genomic databases interrogation indicates that the rufy3 mRNA is submitted to tissue dependent alternative splicing, with two main transcripts respectively predicted to code for proteins of 53 kDa (nRUFY3, NP_001346137. 1) and of 75 kDa (iRUFY3/RUFY3XL, NP_001276703. 1) containing a Cterminal extension of 150 amino acids with a putative FYVE domain  22  (Fig. 1a ). According to the ImmGen expression atlas  29  and qPCR monitoring, mouse nRUFY3 and iRUFY3 mRNA expression is mutually exclusive with nRUFY3 restricted to brain and iRUFY3 mostly detected in immune cells (Fig.
Quantitative PCR Total mRNA from cells or tissues was purified using the RNeasy Mini Kit (Qiagen). 100 ng to 1 µg of total RNA were subjected to reverse transcription using SuperScript II. Each gene transcript was quantified by SYBR Green method with 7500Fast (Applied Biosystems). The relative amount of each transcript was determined by normalizing to internal housekeeping gene expression (gapdh). A complete list of primers can be found in the Extended Data Table  2.
Immunodetection All antibodies are listed in Extended Data Table  1. For immunoblotting of tissue extracts, organs were taken from euthanized mice and put in 3 ml RPMI supplied with 5% FCS and 1% gentamycin. Tissues were dissociated with gentleMACS™ Octo Dissociator (Miltenyi), followed by liberase (5mg/ml) and DNase I (150µg/ml) digestion for 30 min at 37°C. Cell suspensions were centrifuged and pellets were lysed in 1% triton 100X, 50mM Tris pH7,4, 150mM NaCl, 5mM MgCl2, complemented with protease inhibitors cocktail (Roche). Lysates were centrifuged (16. 000g, 30min, 4°C), prior running 20-30 µg of soluble proteins in sample buffer on 3-15% gradient or 12% SDS-PAGE. Transfer was performed on PVDF membranes, which were incubated in blocking solution (TBS1X + BSA 5%) prior antibody binding and chemiluminescence detection (Pierce).
Levels of co-localization were quantified using JACoP plugin  60  and manual definition of the Regions of Interest (ROI). Final r Pearson coefficient values corresponds to the mean of all stacks of the ROI. All images were acquired in Z-stack mode prior assembly and reconstitution in IMARIS 9. 9 software to obtain 3D rendering. For some images, drift was corrected by applying images stabilizer plugin from Image J software. For each 3D images, frame was put and X, Y, Z axis were represented at the top left corner. For each image, surfaces were added on nucleus. Cell borders were manually added in white. For PLA spots quantification indicating a proximity of targets < 40nm, all images were modeled in 3D using IMARIS 9. 9, spots were counted and distance to the nearest neighbor measured.
Clustering index To obtain the Clustering index, a centroid based method was used with dedicated macro on ImageJ (https://github. com/Imagimm-CIML/Determining-cell-polarisation, Macro_polarisation_polar_dispersion. ijm). Masks on LAMP1, ARL8b and nucleus staining were applied individually. The coordinates of the centroid and the distance between LAMP1/nucleus or ARL8b/nucleus centroid were calculated and normalized on cell radius (calculated from Feret diameter) to obtain a score between 0 to 1 (Clustering index). Polarized cells are defined with a unique perinuclear site and a score close to 0. 5. Conversely, non-polarized cells with limited accumulation at a unique site will have a shorter centroid between nucleus and organelle mask with a score close to 0.
360-ASOD method 360-ASOD (360° Angular Scanning monitoring Organelle Distribution) has been filed on Github (Imagimm-CIML/360-Angular-Scanning-monitoring-Organelle-Distribution (github. com). A mask on nucleus was generated to identify the cell centroid. From this centroid, an image with polar coordinates is created from the ImageJ plugin "Polar transformer". From this radial image, a dedicated macro (available in the github link) was used to extract the angular profile of the organelles signal summing the signal in the radial direction. This profile is generated by counting the number of pixels in the radial direction of the nucleus centroid above an intensity threshold for each angle (from 0 to 360 degrees). Each profile was then normalized on the sum of the signal to transform the angular profile in probability.
Sequences alignment Rufy3 sequences from human and mouse were obtained from ImmGen and Ensembl databases. Alignments were performed with Sea View analysis software  61. For all alignments, amino acids are colored according to their biophysical properties. The accession numbers of the proteins are as follows: Human EEA1 (NP_003557. Where A t=0 is the initial wound area, At=n is the wound area after n hours of the initial scratch, both in µm². Velocity of CD11c Cre and CD11c Cre -rufy3 loxp/loxp GM-CSF BMDCs was performed as previously  62. Briefly, DCs were taken at day 10 of culture and put (iDCs) in microfluidic channels (5x5µm) or challenged with a 30 min pulse of 10ng/mL LPS (LPS-DCs) prior washing and channel introduction. Hoestch dye was added to stain nuclei and follow cells. Images were acquired every 2 min for over 16h at 37°C in 5% CO2, with a 20X objective video microscope (Zeiss, Axio-observer ).
Data are shown as the median of velocity. In vitro Salmonella infection S. enterica infection was performed as described  36. Briefly, Control and RAW rufy3 -/-cells were seeded in six well plates with 1. 10 6 cells per well. S. enterica strains were treated 30min on ice with mouse serum and added at MOI 20. After phagocytosis, gentamicin was added at 100 µg/ml during one hour and 5 µg/ml for the rest of the experiment. Infected cells were lysed 2h and 16h after infection by adding 0,1% triton to harvest bacteria. The lysate was diluted in cascade 4 times with a factor of 10 prior plating on agarose. The drop was dried at room temperature and the number of colonies were counted for each dilution factor after 24 hours. The final replication factor was expressed as ratio of colonies numbers at 16h p.
Acknowledgments RC received a fellowship from the  Fondation de la Recherche Médicale" (FRM). The laboratory is Equipe FRM sponsored by the grant  DEQ20180339212  and the  Institut National du Cancer (INCA)  ( PLBIO17-187 ). The project was supported by grants from l' Agence Nationale de la Recherche (ANR) «DCBIOL Labex   ANR-11-LABEX-0043  », «  INFORM  Labex  ANR-11-LABEX-0054  » funded by the " Investissements d'Avenir" French government program. This work was also supported by the project  PTDC/BIA-CEL/28791/2017  and  POCI-01-0145-FEDER-028791 , as well as  POCI-01-0145-FEDER-030882  and  PTDC/BIA-MOL/30882/2017 , funded by  FEDER , through  COMPETE2020 -Programa Operacional Competitividade e Internacionalização (POCI) , and by national funds (OE), through  FCT/MCTES. "We thank the  Maratona da Saúde  for its support.
Extended Data table 2: Primers list Mouse: raw264. 7 cells ATCC SCRC-1008 Mouse: Raw264. 7 cells rufy3 -/-Name GRIS lab, Shanghai Forward Primer (5' => 3') Reverse Primer (5' => 3') N/A Mouse: Raw264. 7 cells rufy3 -/-+ irufy3 Mouse rufy3 all variant CAGTTGGCAGAGGAAGAGC This paper N/A ACATTCAGACCCACCAGGAG Mouse: Raw264. 7 cells rufy3 -/-+ nrufy3 Mouse rufy3 long variant GCTCTCTGAAAAGCCACAGG This paper N/A CTTGACGGAAGAGGCAGTTC S. enterica WT GFP Mouserufy3short variant JPGlab TATGCCAACCCCCACCAC Strain 12023G TCTGTCAAATCCTCCCAAGAG S. enterica WT GFP + PiPB2-HA Mouse fyco1 JPGlab TTCCTTTTCCCTCTCAAGCA Strain AAG020G GCCACATGGATCGTGAATTA S. enterica DsifA Mouse rufy1 JPGlab CTCTGTCTGAAGGACGCACA Strain DH215K TCGTCTTTTCCAAGCCATCT Deposited Data Mouse rufy2 TCCACTCATTCCACCTTGCC GGATACGGGTCTGTTGGGAG Mouse rufy4 GCATAGGCTGCAACAAGGTC Github : CATCAGGGTTGGGAAGAGAA Mouse H-2 Iadα 360-ASOD : 360° Angular Scanning monitoring GTCAACCGTGACCATTCCTT This paper Organelle Distribution Mouse H-2 Iadβ ACCCAGCCAAGATCAAAGTG https://github.
A new approach to the verification of the predictions of the earlier developed model of puffing/microexplosion is suggested, based on the implementation in the numerical code of the analytical solution to the heat transfer equation within a composite droplet. Verification is based upon a comparison between the predictions of the numerical code with this model and those of a fully numerical solution to the equation (the heat transfer module available from COMSOL software). The agreement between the predictions of both codes supports both approaches to the problem. The model is generalised to consider the shifting of the water subdroplet away from the centre of the fuel droplet. This generalisation is based on the numerical solution to the heat transfer equation in the composite droplet taking into account this shift.
This extended model proved to be effective in many applications including our recent investigations of puffing/micro-explosion in two and three droplets in a raw, one behind the other  [15, 16]. At the same time, it has several important weaknesses. Firstly, the verification of the model was limited by the development of two separate numerical codes, using Wolfram Mathematica v 12. 1 and Matlab R2020a, in which the analytical solution to the heat transfer equation in a fuel-water droplet was implemented. Although these codes predicted the same results, both results could be wrong if there is something wrong with the analytical solution.
The verification of the centre model The verification of the Centre Model (CM) was performed by comparing its predictions with those of the modified version of this model (Modified Centre Model, MCM) in which the analytical solution to the heat transfer equation in a fuel-water droplet is replaced with its numerical solution. The latter was obtained using the heat transfer module of COMSOL Multiphysics which contains numerical tools for the simulation of heat conduction and convection based on the finite element methods  [15, 16, 19, 20]. The description of the heat transfer inside a composite fuel-water droplet relied on several simplifications. One of these is that the shear force due to the ambient air flow and the internal circulation it can induce in the droplet were not considered. This approximation allowed us to solve a problem of pure heat conduction inside a composite droplet.
When deriving  (9)  , droplets were assumed stationary, which implies that the Spalding numbers B T and B M are related as: Nu = 2 ln ( 1 + B T ) B T , ( 9 ) B T = c pv ( T g -T s ) L ( T s ) -( ˙ q d / ˙ m d ) B T = ( 1 + B M ) ϕ -1 , ( 11 ) where ϕ = c pv c pg 1 Le , ( 12 ) Le = k g / c pg ρ total D v is the Lewis number. In the CM  [12]  , the analytical solution to the above problem was expressed as an infinite series. This was evaluated at each time step using numerical code to obtain the temperature field distribution inside the droplet. In the MCM, the finite element method was applied using COMSOL Multiphysics to resolve the same problem numerically. An unstructured mesh of about 500 triangular elements was generated for the discretisation of the space. Both CM and MCM were used to analyse heating and evaporation of a composite n-dodecane/water droplet leading to its puffing/micro-explosion.