dataesr/scientific-paragraphs-categorization huggingface dataset:
An ImageJ macro was programmed to determine automatically the colors of the individual cells (macro code can be found in the Supporting information). Briefly, the macro combines the intensities from the three NP channels in a single gray scale image. This image is then used to detect the locations of the individual cells and so to create a mask, which allows measuring the intensities of the three channels for each individual cell. The intensities are then normalized to 1 in order to obtain the RGB barcode of each cell. Tracking cancer cells in zebrafish embryos: Wild-type Golden zebrafish embryos (48 hours postfertilization (hpf)) were anesthetized in Danieau containing 650 µM tricain and mounted in 0. 8% low melting point (LMP) agarose. Meanwhile, unlabeled D2A1 cells (murine mammary carcinoma cells) were cultured in 6-well plates and incubated for 3h at 37°/5% CO 2 with NPs (2 mg L -1 ) of different color and their one-to-one mixtures (red: DiD NPs, green: DiI NPs, blue: DiO NPs, cyan: DiO NPs and DiI NPs, magenta: DiD NPs and DiO NPs, yellow: DiD NPs and DiI NPs) before being trypsinized.
Acknowledgements. This work was supported by the  European Research Council   ERC Consolidator grant  BrightSens  648528. BA was supported by  LabEx Chimie des Systèmes Complexes. We thank  Y. Mely  and  P. Didier  for providing access to wide-filed fluorescence microscopy and  C. Ruhlmann  for help with electron microscopy. We thank  Romain Vauchelles  for help with image analysis using the ImageJ software and  Claudine Ebel  for help with flow cytometry. We are very much grateful to  Francesca PERI  (EMBL) and  Kerstin RICHTER  ( EMBL ) for providing zebrafish embryos. We thank the  BioEmergences platform  and  FBI ANR-10-INBS-04  for injections and 3D+time imaging. Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)).
Pathway enrichment analysis confirmed our initial findings and showed induction of genes in lipid metabolism (Fig. 4D, E ). Additional interrogation of our datasets revealed that in addition to increased expression of enzymes involved in fatty acid synthesis (Srebf1, Fas, Fig. 1F ), S196A expression increased the hepatic levels of enzymes involved in fatty acid elongation (Elovl3, Elovl5) and fatty acid oxidation, with a trend towards increased levels of fatty acid desaturation enzyme Fads1 (Fig. 4F ). These changes likely contribute to the distinct hepatic fatty acid profile present in S196A mice (Fig. 1H ). Interestingly, expression of most of these enzymes is severely repressed by the HFHC diet (Fig. S4a ) further highlighting the modulatory role exerted by LXRα-S196. Also corroborating previous analyses, the phospho-mutant mice showed a robust decrease in the levels of wound healing and fibrotic mediators including several collagen genes and enzymes responsible for collagen stabilisation (Fig.
Histopathological analysis Formalin-fixed, paraffin-embedded mouse livers were cut and stained with hematoxylin and eosin (H&E) or Picrosirius Red (Abcam) dyes. Liver histology was blindly scored by an independent histopathologist based on three semiquantitative items: steatosis (0-3), lobular inflammation (0-3) and hepatocellular ballooning (0-2) (not shown)  (56). Stained sections were scanned with NanoZoomer Digital slide scanner (Hamamatsu) and quantification of Picrosirius red-stained areas was performed using Image J on three independent areas per section. Data is represented as the average positively-stained percent of area of interest. Apoptosis was detected in liver tissue sections using a terminal deoxynucleotidyl transferase dUTP nick end-labeling (TUNEL) assay (R&D Systems). Sections were imaged using the Axio Imager.
Lipid droplet identification Identification and quantification of lipid droplets were made with the help of Eli (Easy Lipids) v1. 0, an in-house software developed between the Multiscale Cardiovascular Engineering (MUSE) and Dr Pineda-Torra's groups at UCL. This software uses a method based on the Hough Transform  (57)  for the identification of the droplets estimating the centres and radii of each of them.
ACKNOWLEDGEMENTS We are grateful to  Prof Edward Fisher  ( NYU School of Medicine ) and  Dr James Thorne  ( University of Leeds ) for insightful discussions. We also thank  Dr Ruth Lovering  ( University College London ) for her help with the bioinformatics analysis of the RNAseq datasets. This work was supported by a  Medical Research Council New Investigator  Grant  G0801278  (IPT),  British Heart Foundation  Project Grant  PG/13/10/30000  (IPT),  UCL   Grand Challenges PhD Studentship  (NB, IPT),  Swedish Research Council  ( VR2016-01743 ) (ET),  Center for Innovative Medicine at Karolinska Institute (ET)  and  University of Oslo DIATECH@UiO initiative  (HRL). and analysed data. B. P. and O. M. P. helped establish mouse colonies. T. V. L scored liver sections. S. G. , N. L. and E. T. provided unpublished ChIPseq data.
Indeed, this excitation is composed of a turbulent component and an acoustic one (3; 7; 8) and both components contribute to the interior noise. Another solution may consist in performing a numerical simulation of similar flow configurations. Thus, to reproduce a flow configuration approaching the A-pillar vortex, a Forward Facing Step (FFS) configuration is considered. This simplified geometry allows the generation of an aeroacoustic wall pressure field impacting the upper part of the step corresponding to a vibrating structure. Even though forward facing step has been intensively studied in the past in incompressible subsonic flows (see for instance  (9; 10; 11)  and references therein), the analysis of compressible FFS flow has been much less investigated. Based on previous experimental and numerical database, it has been shown that the main region containing acoustic sources is located in the recirculating flow close to the step corner.
These modal analyses are ones of the most popular flow decomposition techniques used for the analysis of the experimental and/or numerical space-time database. POD and Fourier analysis both allow a space-time description of the flow dynamics as a set of statistical quantities referred as eigenfunctions. For the POD technique, eigenfunctions are determined based on the energy content  (25). Conversely, for Fourier decomposition, the modes are related to the wavenumber and/or frequency content of the available database. These flow decompositions have demonstrated in the past their efficiency in identifying the flow structures present in turbulent flow. More precisely, POD technique allows the detection and the characterization of the large scale energetic coherent structures  (25)  while Fourier analysis extracts periodic flow structures.
5% of the computational domain for each of them). Note that, in the following figures, flow variables will only be represented in the physical domain. Such a flow configuration is very sensitive to initial conditions  (23). To initialize it, the initial state (set of pressure, density and velocities in the whole domain) is computed by solving the incompressible RANS equations with the open source CFD software OpenFoam  (31; 32). It provides a statistically well converged mean flow field related to the FFS flow configuration. A uniform and cartesian mesh of (1000 × 750) points is used to discretize the (x, y) domain of the FFS configuration. A laminar boundary layer is imposed in inflow. The model for turbulence is the classical k-model. This method allows us to save an important time comparatively with a naive initialization of the code with, for instance, a uniform state in the whole domain.
2 and to 4. 4 based on the determination of the friction velocity when the TBL flow is developed. These values are obtained at x/h ≈ -3. 5. Such a grid resolution is sufficient to accurately predict the aeroacoustic flow variables. More than 3. 6 10 6 time steps have been computed (about 108 hours on 64 CPUs). Once the initial condition is entirely evacuated, the flow variables are stored every 500 time steps, therefore the sampling frequency is 33630 Hz. We consider that the data are significant after 2000 time steps. Indeed, beyond that number of time steps, the different statistical quantities studied in the next paragraphs are converged. Hence, the database containing the flow variables is made of 5000 samples for each point of the mesh grid. That leads to an analyzable time duration of 0.
1488s which corresponds approximately to ten times the time travel of a fluid particle advected through the complete flow domain at the uniform speed of the flow. Note that the time duration of the simulation may place some constraints on the following stochastic data analysis. However, when performing Fourier, the frequency domain covers [0 : 16815]Hz with a frequency step of 6. 7Hz. These values seem quite sufficient for the present investigation. Indeed, the flow structure passage is largely inferior to 10000Hz and the frequency resolution provides a sufficient accuracy for the characterization of the present aerodynamic events. The wall pressure is extracted from this database. The spatial coordinate of the vibrating structure will be referred to as to x vib indicating that x is varying from 0 to 14h and y = 0.
The licences specifically state that: ViiV shall provide any Sublicensee with NCE Exclusivity or other regulatory exclusivity waivers to the extent required by the applicable regulatory authorities in order to manufacture or sell Product in the Territory in accordance with the terms of the Sublicence. ViiV shall further provide to any Sublicensee such consents which it has the legal capacity to give as are necessary to enable such Sublicensee to perform its obligations  [31]. As indicated in the patent database Medspal  [32] , the formulations of DTG 50mg and ABC/DTG/3TC 600/50/ 300 mg are protected by test data exclusivity in Guatemala until 11 November 2020 and 29 November 2021 respectively. However, MPP licensees will nevertheless be able to register and market generic versions of these formulations in Guatemala before the expiration of these rights, based on the waiver included in the MPP licence agreements.
Files containing expected error estimations were also dereplicated to retain only the lowest expected error for each unique sequence. Clustering was performed with swarm v2. 1. 8 [23] , using a local threshold of one difference and the fastidious option. Molecular operational taxonomic unit (OTU) representative sequences were then searched for chimeras with vsearch's command uchime_denovo  [24]. In parallel, representative sequences received taxonomical assignments using the stampa pipeline (https://github. com/fredericmahe/stampa) and a custom version of the fungal reference database UNITE v7 (https://unite. ut. ee/;  [25] ). In brief, the stampa pipeline requires the reference sequences to be trimmed with cutadapt, using the same primers as those used for the amplification of the environmental sequences.
Using vsearch's exact global pairwise comparisons, each environmental sequence is compared to all reference sequences and is assigned to the closest hit. In case of a tie, the environmental sequence is assigned to the last-common ancestor of the co-best hits. The abbreviation "cf. " is used throughout the text for interpretation at the species level because of potential taxonomic biases relative to length and variability of the amplified region (ITS1 region). As the UNITE database contains only fungal sequences, 20 sequences were added to our custom reference database to identify the plants also amplified by our ITS primers (see S1 File). Clustering results, expected error values, taxonomic assignments and chimera detection results were used to build a raw OTU table (script available in S1 File).
Up to that point, reads that could not be merged, reads without tags or primers, reads shorter than 32 nucleotides and reads with uncalled bases ("N") were eliminated. To create the "cleaned" OTU table, additional filters were applied to retain: non-chimeric OTUs, OTUs with an expected error divided by length below 0. 0002, OTUs containing more than 3 reads or seen in 2 samples, OTUs assigned to plant or fungal taxa with at least 80% similarity or containing more than 10,000 reads. All codes and representative sequences of OTUs can be found in HTML format (S1 File) and raw data are available under the BioPproject ID PRJNA378471 (https://www. ncbi. nlm. nih. gov/ bioproject).
Statistics Diversity (Shannon, inverse Simpson [1/D]), richness (number of MOTUs, Chao1) and evenness (Pielou) indexes were estimated using R  [26]  and the R package vegan version 2. 4-3  [27] , and differences among cork oak habitats were assessed by non-parametric permutational multivariate analysis of variance (PERMANOVA), as implemented in the perm. anova() function from the R package RVAideMemoire version 0. 9-65  [28]. Fungal community membership was assessed using venn diagram analysis with the R package VennDiagram version 1. 6. 17 [29]. The differences in fungal community structure among the three habitats were displayed with nonmetric multi-dimensional scaling (NMDS) implemented in the metaMDS() function. Significance in fungal community structure variation was also assessed using PERMANOVA in the adonis() function.
Multivariate dispersion was estimated using the betadisper() function and permutest() as it can affect PERMANOVA results. Soil parameters were fitted to the NMDS using the envfit() function (9,999 permutations). Correlation among soil parameters was assessed using the Pearson correlation coefficient, as implemented in the cor. test() function. All functions are available in the R package vegan. Table transformations in R were performed with the tidyverse packages version 1. 1. 1 [30] , and plots were visualized with the packages ggplot2 version 2. 2. 1  [31]  for NMDS and ggtern version 2. 2. 1  [32]  for ternary plots. The presence of fungal indicator species of a specific type of habitat (Maamora, Benslimane, Chefchaoun) was determined using the corrected Pearson's phi coefficient of association (r.
g; 9,999 permutations) implemented in the multipatt() function from the R package indicspecies  [33]. Fungal indicator species with respect to soil properties (pH, C:N ratio, and available P) were determined using the indicator value (IndVal) index, as implemented in indicspecies' multipatt(). Two different probabilities were calculated, i. e. A (specificity), representing the probability of a site to be defined by a given soil property, given that the species have been detected, and B (sensibility) representing the probability of finding the species in different sites characterized by a given soil property. We considered as valid indicators the OTUs showing both A (specificity) and B (fidelity) superior to 0. 8 and 0. 6 respectively, as recommended in  [18].
Composition of cork oak root-associated fungal community A filtered dataset of 792,931 sequences was obtained from a raw dataset of 1,129,145 trimmedand paired-sequences (see material and methods for more details). Sixty percent of sequences were affiliated to ITS sequences from plants (Streptophyta). Overall, a dataset of 315,597 fungal sequences (27 samples) was rarefied down to 3,447 sequences per sample to improve the robustness of fungal community comparison among the three habitats  [34]. Analysis of taxonomic fungal community composition in the Moroccan cork oak forest detected 1,768 OTUs belonging to 4 known fungal phyla, 39 orders, 78 families, and 127 genera (S1 Table ). Two percent of OTUs were uncharacterized at the phylum level (about 0. 2% of sequences), and 43% at the genus level (about 30% of sequences).
Acknowledgments This study was supported by the " Programme de Recherche Agronomique pour le Developpement " ( PRAD 04/10 , Gestion et valorisation de la strate arbustive ectotrophe pour la conservation et la productivitedurable des suberaies) and the  Cirad PhD program  " Doctorant du Sud ". We thank  Morris Traduction  (http://andrewmorris. fr/) for English language editing.
Background: Recently, everolimus was shown to improve median progression-free survival (PFS) by 7. 1 months in patients with advanced, progressive, well-differentiated, nonfunctional neuroendocrine tumors (NET) of lung or gastrointestinal (GI) tract compared with placebo (HR, 0. 48; 95% CI, 0. 35-0. 67; P,0. 00001) in the Phase III, RADIANT-4 study. This post hoc analysis evaluates the impact of prior therapies (somatostatin analogs [SSA], chemotherapy, and radiotherapy) on everolimus activity. Trial registration: ClinicalTrials. gov identifier: NCT01524783. Patients and methods: Patients were randomized (2:1) to everolimus 10 mg/day or placebo, both with best supportive care. Subgroups of patients who received prior SSA, chemotherapy, or radiotherapy (including peptide receptor radionuclide therapy) were analyzed and reported.
Patients and methods study design The RADIANT-4 trial was a prospective, double-blind, randomized, parallel-group, placebo-controlled, international, multicenter, Phase III study (ClinicalTrials. gov number NCT01524783). The detailed study design has been reported previously. 21 Patients were randomly assigned, in a 2:1 ratio, to receive everolimus 10 mg/day or placebo in combination with best supportive care. Treatment was continued until documented radiologic disease progression, development of an unacceptable adverse event (AE), initiation of new cancer therapy, or withdrawal of consent. Patients were prospectively stratified according to status with respect to prior SSA treatment (defined as continuous SSA for $12 weeks; receipt vs no receipt), tumor origin (based on prognostic level, grouped into 2 strata: 1) stratum A [better prognosis]: appendix, cecum, jejunum, ileum, duodenum, or NET of unknown primary; 2) stratum B [worse prognosis]: lung, stomach, colon [other than cecum] or rectum), and World Health Organization (WHO) performance status (0 vs 1) at baseline.
Acknowledgments We thank all the investigators and their patients for their participation in the study. We thank  Rohit Kachhadiya ,  M. Pharm , ( Novartis Healthcare Pvt. Ltd. )  for providing medical editorial assistance with this manuscript. This study was funded by  Novartis Pharmaceuticals Corporation. This study was presented in part at the  European Neuroendocrine Tumor Society (ENETS) conference, Barcelona , Spain; March 9 to 11, 2016.
Construction of recombinant vectors Binary vectors were generated by PCR using high fidelity Taq Platinum DNA polymerase amplification and recombinant plasmids harboring the HP15, HP16, and HP17 haplotypes of CcDREB1D promoters, as templates. The PCR reactions used the forward primers S-DREB, M-DREB, and L-DREB carrying the HindIII restriction site to amplify the short (S: +182/-608), medium (M: +182/-966), and long (L: +182/-1. 308) fragments of the CcDREB1D promoter haplotypes, and the reverse primer R-DREB carrying the BglII restriction site (Table  1 ). The position of the R-DREB primer made it possible to include the entire 5′-UTR region of the CcDREB1D gene in the final constructs. PCR reactions were carried out using 10 ng of plasmids, Taq Platinum DNA polymerase, and primers as previously described, except that the first ten amplification cycles were carried out at an annealing temperature of 60 °C to ensure primer mismatch (calculated with the PrimerQuest SM ® software, Integrated DNA Technologies).
Real-time RT-PCR assays PCR experiments were performed as previously described by  Marraccini et al. (2012). Primers (   (Ramakers et al. , 2003). Expression levels were calculated by applying the formula (1+E) -ΔΔCt where ΔC t,target =C t,targetgene -C t,CaGAPDH and ΔΔC t =ΔC t,target -ΔC t,references ample , with the T 0 samples being used as references for each construct. Expression levels were normalized with the expression of the CaGAPDH gene (GB accession number GW445811 using primer pair GAPDH-F/R) as the endogenous control  (Cruz et al. , 2009).
Acknowledgements This work was carried out under the  Embrapa-CIRAD scientific cooperation project entitled 'Genetic determinism of drought tolerance in coffee '. The authors acknowledge financial support from the  Brazilian Consortium on Coffee R&D ,  Instituto Nacional de Ciência e Tecnologia-Café  ( Conselho Nacional de Desenvolvimento Cientifico e Tecnologico/Fundação de Amparo à Pesquisa do Estado de Minas Gerais)  and  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior/Comité Français d'Évaluation de la Coopération Universitaire et Scientifique avec le Brésil  (Project  407-2012 /University of Lavras and Montpellier SupAgro ). The authors would like to thank  Pr. Murilo Sversut Dias  ( University of Brasilia-Department of Ecology ) for his kind contribution of statistical and imaging analysis.
The resulting sequence data was analyzed for single-nucleotide variants, insertion/deletions (indels), structural variants and copy number changes. We identify MAPK activating mutations in combination with β-catenin activating mutations as a characteristic feature of DPN. We demonstrate that CTNNB1 mutations arise after BRAF mutation in DPN arising from common acquired nevi and confer the characteristic phenotypic features of DPN. In DPN-like melanomas, we identify the presence of additional genetic alterations indicating that DPN are genetically intermediate between benign nevi and DPN-like melanomas.
Three versions of the custom-designed bait libraries were used, and the genes targeted and their overlap between versions are detailed in Supplementary Data 1-6. The target intervals cover mostly exonic but also some intronic and untranslated regions of 293 (version 1), 538 (version 2), and 480 (version 3) target genes. The target genes were curated to comprise common cancer genes with particular relevance to melanoma. Captured libraries were sequenced as paired-end 100 bp reads on a HiSeq 2000 or HiSeq 2500 instrument (Illumina). Sequence reads were mapped to the reference human genome (hg19) using the Burrows-Wheeler aligner (BWA)  34. Recalibration of reads was performed using the Genome Analysis Toolkit (GATK)  35. Mutation calling was performed with FreeBayes  36. Coverage and sequencing statistics were determined using Picard CalculateHsMetrics and Picard CollectInsertSizeMetrics 37 (Supplementary Table  5 ).
Variant annotation was performed with Annovar  38  and variants with frequency in the 1000 genomes or exome sequencing project datasets of > 0. 001 were excluded from further analysis. For fusion detection, read pairs with one or more reads unaligned, insert sizes greater than 1000 bp, or with soft clipping of at least one read were extracted and re-aligned using BWA-SW  39  and used as input to CREST  40. Structural variants predicted by CREST were reviewed by visual inspection in the Integrative Genomics Viewer  41. We predicted the resulting fusion transcript by joining the exon directly upstream from the genomic breakpoint with the exon directly downstream. Predicted protein sequences were then determined from the predicted transcripts. Copy number analysis was performed using CNVkit  42.
Methods The specimen was examined with a stereomicroscope equipped with a camera lucida drawing arm. The interpretative drawing was produced with Adobe Illustrator CS6 from the camera lucida drawings and from photographs. Measurements were taken with a compass or with the software ImageJ 1. 5 from radiographs; angles were also measured with ImageJ. The method for estimating the degree of projection of the crest is modified from  Craig, Hastings & Pondella (2004) : it is based on the angle between the straight line from the tip of the crest to the proximal end of its anterior margin (instead of the tip of the upper jaw, due to varying jaw positions in fossils) and the vertical line drawn perpendicular to the main axis of the parasphenoid (instead of the vertebral column, not preserved in MNHN.