dataesr/scientific-paragraphs-categorization huggingface dataset:
The downstream targets of cytoplasmic cyclin D1/CDK4 dimers regulating migration/invasion remains unknown, but our proteomic data identify the characterized cytoskeleton-associated proteins as possible effectors. (E) JVM2 cells were transfected with either pEGFP, pD1a-EGFP or pD1b-EGFP expression plasmids using the Nucleofector II device (solution V, program A-023). After a 48 h-recovery period, the transfection efficiencies were estimated using the NucleoCounter NC-3000. Transfected cells were assayed for migration as described above. The experiment was performed twice with quadruplicate samples. The means ± s. d. are indicated on the graph, ***p < 0. 001 in t-tests. KPT-185 and KPT-286, two XPO1 inhibitors, block the proliferation and induce the apoptosis of MCL cell lines and primary cells in vitro  32, 33.
75 NA objective and DPManager software v2. 1. 1 (Olympus). For analysis of the effects of KPT-330 on cell engraftment, SCID mice (n = 10) received i. v. injections of JeKo1-mCherry-Luc cells as described above. On the day of the injection, the mice were assigned to two groups receiving either orally a 10 mg/kg dose KPT-330 or i. p. an equal volume of vehicle twice weekly. Mice underwent BLI once per week, beginning one week after cell injection. When the mice were killed, their infiltrated organs were removed and processed for flow cytometry and IHC analyses. Statistical analyses. The Student's t-test was used to determine the significance of differences between two experimental groups. Data were analysed in two-tailed tests, with p < 0. 05 (*) considered to be significant. Fisher's exact test was used to evaluate the distribution of the various histological and clinical parameters between MCL cases as a function of cyclin D1 immunostaining pattern.
Acknowledgements We thank  Anne Barbaras  ( Université de Caen Normandie, Caen, France ) for help with cell culture,  Dr Aránzazu Chamorro-Jorganes  ( IDIBAPS, Barcelona, Spain ) for help in the establishment of GFP-expressing cell lines,  Dr Antonio Postigo  ( IDIBAPS ) for the gift of the pMSCV-Luc2-PKG-Neo-IRES-GFP plasmid,  Dr David A. Solomon  ( UCSF, San Francisco, CA ) for the gift of pD1a-GFP and pD1b-GFP plasmids, and  Dr Benoît Haelewyn  ( CURB, Université de Caen Normandie ) for advice concerning mice. We thank  ICORE and Cyceron  ( Université de Caen Normandie ) for microscopy, cytometry, and imaging facilities. The work was supported by grants from  ITMO Biologie cellulaire , développement et évolution (to BS),  Ligue contre le Cancer -Comité de l'Orne  (to BS), and  Fondo de Investigación Sanitaria   PI12/01847  and  PI15/00102 ,  European Regional Development Fund (ERDF)  " Una manera de hacer Europa " (to GR).
Statistical analysis Statistical analyses were performed using StatView, SPSS and/or GraphPad Prism software. The levels of mRNA expression for SHP-1, SHP-2 and SOCS-1 in plasma cells were compared between the different study groups using the non-parametric Mann-Whitney U test. Correlations between the gene expression of SHP-1, SHP-2 and SOCS-1 and either the bone marrow infiltration, the response to treatment or STAT3 signaling activation were analyzed using Spearman's rank correlation. The χ 2 test was used to compare the frequency of patients exhibiting a hypoexpression of SOCS-1, SHP-1 and SHP-2 in the different sub-groups identified. The statistical significance was assigned to p values < 0. 05.
Medical and pharmaceutical admission medication reconciliation and treatment review Medication reconciliation (MedRec) at admission will be performed by a pharmacist or a pharmacy resident within 24 h after patient admission. On Saturdays and Sundays, admission MedRec will take place the following Monday morning, and for patients admitted on holidays, the MedRec will be performed the day after. The admission MedRec first involves completion of the medication history, with research into information about patient treatment. Data from several sources should be gathered; previous medical and pharmaceutical records before admission or information from the patient him-or herself and/or the family or Fig. 2  ConcReHosp study -flow of the intervention caregivers, the community pharmacist and physician should be gathered by phone.
Follow-up and data collection The evaluation will be performed at three different time points: baseline (T1), discharge at the hospital (T2) and 30 days after discharge from the hospital (T3). The study procedure and date collection are detailed in Table  3  and have been established as per the SPIRIT guidelines. A clinical research associate will collect the data with the clinical pharmacist. They will be obtained from the analysis of institutional documents of the medication reconciliation between admission and discharge and telephone follow-up documents 30 days after discharge.
Statistical considerations Sample size, power and statistical methods The calculation of the required number of subjects was made from the primary endpoint, i. e. , the rate of rehospitalization and/or death. Based on the literature, mainly a recent metaanalysis including the RCTs working on a pharmacistled medication review  [11] , and data provided by the medical information service, the expected proportion of re-hospitalization and/or death in the standard group is 20% (worst case scenario). To detect a difference of at least 6% between the two groups (i. e. , 14% re-hospitalization of the experimental group) given a two-sided significance level of 5% and 80% power, 1300 patients are needed (650 in each group). To prevent 10% dropout, a total of 1400 subjects should be included. The recruitment will be open for 18 months, and we expect to recruit about 75 patients per month.
Data analysis The major principles of the analysis are reported below. However, a specific protocol of the analysis will be provided a second time before the initialization of the study (validated by the investigator coordinator, the person responsible for the analysis, and the biostatistician). The data will be analyzed using SPSS version 17. 0 software. Statistical significance is defined as p < 0. 05. The methodology is based on the Consolidated Standards of Reporting Trials Statement (CONSORT, http:// www. consort-statement. org/consort-statement/) [23]. The full analysis population (including all subjects who will be randomized and at least evaluated at the baseline) will be used in the primary analysis, and the per protocol population (including all subjects who will be randomized and will not have major protocol deviations) will be used in the secondary analysis to assess the robustness of the results.
No interim analysis is planned. Demographic and baseline characteristics will be summarized for the two groups ('standard' and 'experimental' groups). Missing data will not be replaced. Primary endpoint. The proportions of readmission and/or death at day 30 will be compared between the two groups (chi 2 test or Fisher's exact test). Proportions will be presented with 95% confidence intervals. Logistic regression will be performed to adjust potential confounding factors; variables relevant to the models will be selected based on their clinical interest and/or a threshold p-value ≤ 0. 1 during univariate analysis. The center will be automatically entered into the model. The final models expressed the odd ratios and their 95% confidence intervals. The unadjusted analysis will be the primary analysis, and the adjusted analysis will be the complementary analysis.
Ethical aspects, laws and regulations This study is sponsored by the French Ministry of Health (PREPS 2014 no. 14-0330). The Assistance Publique-Hôpitaux de Marseille is the promoter and is in charge of all the administrative measures. As required by French law, all patients or their relatives will provide written informed consent to participate  [24]. The French committees for data handling (CIL) approved the study. The study has been registered at ClinicalTrials. gov since March 2016 under the number NCT02734017.
D. dadantii infection causes tissue maceration visible 24-48 h post infection (24-48 h p. i. ). The maceration spreads starting from the hole. Symptoms are scored during the 1st days (here 3 days) using the 0-5 DSI scale (Figure  1A ). To compare these two procedures, photographs were taken at different time points after inoculation, areas of maceration were measured by computer-assisted analysis of the images. In order to compare the two methods, we chose Arabidopsis accessions displaying different sensitivities to infection based on symptom scoring (data not shown). These accessions are Bur-0 displaying a reduced sensitivity and Cvi-0 displaying a high sensitivity. The progression curves of LS and DSI indicated that the relative susceptibilities of the two accessions were the same for both symptom-scoring methods, Bur-0 being the most tolerant, Cvi-0 being the most susceptible.
dadantii. To determine whether the quantification of disease based on symptom severity can be used to discriminate between Arabidopsis genotypes, we studied D. dadantii infection on a panel of eight accessions chosen for their different origins (data not shown). For this purpose, bacterial infection was performed on the accessions Bur-0, Can-0, Col-0, Cvi-0, Edi-0, Ge-0, and Oy-0. Disease severity estimated by LS at 48 h p. i. indicates that three statistically distinct groups could be defined. According to the LS criterion, the most tolerant accessions are Oy-0, Edi-0, and Bur-0. The most susceptible ones are Ge-0, Sakata, and Can-0 (Figure  2 ). These data indicate that natural accessions of Arabidopsis display different levels of tolerance to D. dadantii suggesting that tolerance associated loci might be found in this plant species.
To obtain leaves with a known bacterial titer, Arabidopsis leaves were inoculated with different known amounts of bacteria, then were immediately frozen. Total DNA was extracted as described, and two bacterial genes were amplified (pelA and rpoB) to avoid artifacts related to primer sequence. The pelA gene encodes a pectate lyase present in pectinolytic bacteria  (Hugouvieux-Cotte-Pattat et al. , 2014). The rpoB gene encodes an RNA polymerase which is a powerful tool used for bacterial identification  (Mollet et al. , 1997). These primers were used in a previous study (Nam  Phuong et al. , 2012) , and their specificity was checked using as a template DNA of the closely related D. solani  (Pedron et al. , 2014). Amplification efficiencies with D. solani genomic DNA were out of acceptable range thus confirming the specificity of our primers (data not shown).
Sequence alignments of pelA and rpoB primers with the corresponding genes from D. solani show several mismatches (Supplementary Figure  1 ). To quantify D. dadantii DNA, a standard curve was obtained using a serial dilution of genomic bacterial DNA from D. dadantii 3937. Data on Figure  3  show that bacterial DNA estimated by qPCR is in agreement with the expected DNA in the inoculum (Supplementary Table  2 ) based on the fact that a D. dadantii bacterial suspension of 0D 600 = 0. 1 contains approximately 5 × 10 8 CFU/mL  (Antunez-Lamas et al. , 2009)  and that a bacterial cell contains approximately 5 fg of DNA  (Hutchison and Venter, 2006). Bacterial DNA amounts estimated via pelA and rpoB amplification were similar indicating that DNA quantification does not depend on the primers' sequence.
, 2008; Antunez-Lamas et al. , 2009). The second method is based on qPCR quantification of bacterial DNA in inoculated plants. We therefore compared these two methods, using plants at the extremes of the gradient of sensitivity of our panel of differentially susceptible accessions to investigate whether symptom severity is related to bacterial populations when disease progression has occurred (24 and 48 h p. i. ). Leaves of Cvi-0 and Bur-0 plants were inoculated with 5 µL of a bacterial suspension at 5 × 10 8 CFU/mL. Leaves displaying different intensities of DSI and of LS were harvested individually. As expected, bacterial populations at 0 h p. i. , quantified by both methods corresponded to the inoculum, i. e. , 25,000 bacteria/leaf (data not shown). Then, at 24 and 48 h. p. i. , the bacterial titers in each inoculated leaf were determined, using either the DNA quantification method or the serial dilution method.
Taken together, our data show that bacterial populations are positively correlated to disease symptoms. However, bacterial populations in planta show, an important variability at the early stages of the infection, which may explain why it is quite complex in some cases to evaluate disease severity with this bacterium  (Czajkowski et al. , 2011). We note that the estimated bacterial populations by qPCR were in general higher than the bacterial populations estimated by counting of CFU although immediately after the inoculations, the initial inocula were identical. One possible explanation for this difference is that qPCR-based estimation of bacterial cells includes a part of the bacterial populations that died during the infection process and thereby over-estimates the amount of bacteria. Conversely, grinding the infected tissue then diluting and counting growing colonies as CFU probably kills a proportion of the bacterial cells and from the plant defense system.
Nanopore whole genome sequencing DNA quality of fresh-frozen tumor tissue was determined using NanoDrop (Thermo Fisher Scientific) and samples were quantified using a QuantiFluor dsDNA assay (Promega, Madison, WI, USA). For whole genome sequencing, libraries were prepared using Rapid 1D Sequencing Kit (SQK-RAD001, SQK-RAD002, or SQK-RBK001, Oxford Nanopore Technologies, UK) following the manufacturer's instructions. Briefly, 200 ng of tumor DNA was fragmented using a transposase and subjected to adapter ligation. Sequencing was performed using R9 or R9. 4 flow cells on a MinION Mk 1B device (Oxford Nanopore) with the Min-KNOW software (versions 1. 0. 5-1. 5. 12), respectively. For samples run with R9. 4 sequencing chemistry, basecalling was performed using Albacore 1. 1. 0 (Oxford Nanopore).
For R9 chemistries, online EPI2ME basecalling (Metrichor Ltd, Oxford, UK) was performed. Template reads were exported as FASTA using nanopolish or poretools version 0. 6  [18]  and aligned to the hg19 human reference genome using BWA MEM 0. 7. 12 with the "-x ont2d" option  [17]. Due to compatibility issues of data generated with R9 chemistries, only samples with R9. 4 flow cells were used for copy number analysis and methylation-based classification.
Copy number analysis For copy number analysis, the QDNAseq package version 1. 8. 0  [33]  and R/Bioconductor, version 3. 3, were used. Reads with a minimum mapping quality of 20 were sorted into 1000 kbp bins. Bins with missing reference sequence were excluded from analysis. To account for region-and technology-specific artifacts, public nanopore WGS data for the NA12878 human reference genome were processed identically and subtracted from the normalized tumor sample bin counts. Circular binary segmentation was performed as implemented in the DNAcopy package requiring an alpha value <0. 05 to accept change points. Arm-level copy number calls were made by calculating the segment length weighted mean log ratio per chromosome arm.
Structural variant detection For detection of structural variants in amplified regions, we aligned nanopore FASTQ files from sample 3427T to the human reference genome, build GRCh37, using LAST (version 744) with settings: -Q 0. The last-train function was used with 1000 nanopore reads (~10 million bases) as input to adapt the alignment scoring parameters (-p) for error-prone nanopore reads. LAST alignment files (MAF) were converted to BAM files using the maf-convert function. BAM files were used as input for NanoSV  [36]  (available at https://github. com/mroosmalen/nanosv) with default settings.
Microarray methylation profiling Samples for Illumina Infinium BeadChip 450K profiling were prepared as described before  [14]. Briefly, 500 ng of DNA was subjected to bisulfite conversion. Hybridization and imaging were performed by IntegraGen (Evry, France). Raw IDAT files were preprocessed using the GenomeStudio software (Illumina, San Diego, CA, USA). Processed methylation data from previously characterized samples  [14]  were retrieved via ArrayExpress (accession E-MTAB-3903). Beta values were used for all the subsequent analysis steps.
Data and material availability Raw sequencing data are available via the European Genome-phenome Archive (accession EGAS00001002213). Microarray-based methylome data are available at Array-Express (E-MTAB-5797). TCGA data were retrieved from the UCSC Cancer Browser  [11]  or the TCGA FireBrowse website (http://www. firebrowse. org). Pipelines, scripts, and supplementary data to reproduce all results presented in this work are available at https://gitlab. com/pesk/glioma. nano-seq.
Copy number profiling We then used WGS data to generate CN profiles. Reads were counted in 1000 kb windows, normalized and subjected to circular binary segmentation (Fig. 1c ). No correction of GC bias or mappability is necessary for nanopore reads; however, the long reads cause alignment artifacts with current reference genomes in regions with repetitive sequence such as centromeres. Still, the resulting CN profiles closely resembled matched SNP array-based profiles (Fig. 1d ). Importantly, codeletion of chromosome 1p/19q as a diagnostic criterion for oligodendrogliomas implemented in the 2016 WHO classification of CNS tumors was detected in three out of four affected samples (Fig. S1 ). The remaining sample did not yield sufficient read depth (<0. 01) due to low input DNA quality (Table  S1 ).
Methylation profiling A major advantage of nanopore sequencing is the ability to detect base modifications, especially 5-methylation of cytosines, in native DNA without need for bisulfite conversion. Epigenomic changes are functionally important in cancer, but also aid in delineating cancer entities. For example, IDH mutations cause a global hypermethylation of CpG islands  [25] , a phenotype of utmost prognostic importance in neuro-oncology. We thus aimed to detect the G-CIMP phenotype from nanopore reads. First, we compared methylation events in CpG sites identified by nanopore sequencing to matched methylome microarrays. Good correlation was observed between single read methylation status of a given CpG site and its corresponding beta value in microarray data (Fig. 2a ). Next, we applied random forest (RF) classification to predict IDH mutation.
RF classification is a commonly used machine-learning algorithm based on randomly generated (weak) decision trees  [3]. Majority votes then integrate decisions from the entire forest to provide robust classification. The challenge with low-pass WGS data is that it is not known beforehand which CpG sites will be sequenced and the classifier can be built upon. Therefore, we generated random forests ad hoc. With increasing numbers of probed CpG sites, we expect the classifier's error rate to decrease. To test the feasibility of this approach, we simulated multiple random forests for a given number of CpG sites using the low-grade glioma cohort  [5]  from The Cancer Genome Atlas (TCGA) and determined misclassification rate for this "random taiga" (Fig. 2b ). The simulations show that the mean class error rate to predict IDH and 1p/19q status does not improve for more than approximately 500 CpG sites.
While WGS using rapid, transposase-based library preparation works very well with high molecular weight DNA, some of the clinical routine fresh-frozen tumor DNA samples were highly fragmented and yielded insufficient results. Quality of input DNA thus seems to be pivotal. For use of FFPE material, changes to the protocol and further optimization are needed. Methylation data can directly be obtained from the same WGS data set which makes time-consuming bisulfite conversion and specialized methylation assays (sequencing or hybridization-based) expendable. Very recently, it has been shown in the context of meningioma that classification of tumors using methylome data alone is sufficient or superior to make a correct diagnosis  [32]. With low genome coverage, we obtained sparse random sampling of CpG sites.
We show that this information is sufficient to subtype gliomas into IDH-mutant vs. wild-type samples and that cancer entities from different tissue origins can be distinguished in a few hours. This may aid in the differential diagnosis of primary brain tumors vs. brain metastases and greatly facilitate staging and the search for unknown primary tumors  [22]. However, as diagnosis is inferred from relatively sparse data, it precludes inter-patient comparison and reuse of data with currently obtainable coverage in the (relatively short) time frame of 6 h of sequencing. Finally, we used PCR-based amplicon generation followed by nanopore sequencing to identify point mutations. Using a small, but diagnostically relevant gene panel (covering target regions with a total of 12 kb), high read depth could be routinely obtained in less than 30 min of sequencing when using real-time depth monitoring.
We are indebted to the Nanopore Human Genome Sequencing consortium for early release of data at https://github. com/nanopore-wgs-consortium/ NA12878. The results published here are in part based upon data generated by The Cancer Genome Atlas (TCGA) project established by the NCI and NHGRI. Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at http://cancergenome. nih. gov/.
Compliance with ethical standards Funding This work has been supported by Deutsche Forschungsgemeinschaft (EU 162/1-1 to PE), the program "Investissements d'avenir" (ANR-10-IAIHU-06 to AI), Institut Universitaire de Cancérologie (to AI), Ligue Nationale Contre le Cancer (to AI), Institut Carnot (to KL), and Fondation ARC pour la recherche sur le cancer (n°PJA 20151203562 to FB).
Modelling of GaPO4 Piezoelectric Transducer The piezoelectric device module of Comsol Multiphysics software package is used to model the electromechanical behavior of the GaPO4 transducer. The coupling between the structural and electrical domains can be expressed as a relation between the material stress and its permittivity at constant stress (Stress-charge form, Equation (  1 )) or as a relation between the material strain and its permittivity at a constant strain (Strain-charge form, Equation (  2 )). (1) (2) The finite element analysis carried out in this study uses strain-charge relation in Equation (  2 ). Whereby S is the strain, T is the stress, E is the electric field and D is the electric displacement field. The material parameters , d and are material compliance, coupling property and relative permittivity at constant stress; and are tensors of rank 4, 3 and 2 respectively and are represented using abbreviated subscript Voigt notation.
In situ Data Field data were acquired during two bio-optical cruises in Bourgneuf Bay from 08 to 12 April 2013 and from 12 to 13 April 2016 in the frame of the ANR GIGASSAT and FP7 HIGHROC projects, respectively. During both cruises, water sampling and radiometric measurements were performed following the same protocol. Sampling stations were located nearshore, mostly within the intertidal zone and in the vicinity of farming sites (Figure  1 ). Sampling took place at different times of the tidal cycle in order to acquire reflectance spectra over a wide range of SPM and chl a concentration. The same flat-bottomed barge was used during both cruises. This kind of vessel makes it possible to navigate throughout the shallow intertidal waters, even during low tide. Some stations were visited when the water depth was as low as 0.