dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Statistical analysis was performed using SAS version 9. 3 (SAS Institute Inc. , Cary, NC, USA) and a 2-sided alpha level of 0. 05. Categorical variables were analyzed with the Chi square test or Fisher exact test. Changes in BCVA from baseline were analyzed with the Wilcoxon signed-rank test or paired t tests or were estimated from an analysis of covariance (ANCOVA) model. Other continuous variables were analyzed with the Wilcoxon rank-sum test, the Kruskal-Wallis test, or ANCOVA. Efficacy and safety analyses used observed values with no replacement for missing values in the analysis population of all enrolled patients who had BCVA data available at day 0. In sensitivity analyses, missing data for each visit were replaced with either the median value of the sample or the worst value for the patient during follow-up.
0 %) patients were treated with DEX implant and enrolled in the study. The analysis population consisted of 375 patients, because one enrolled patient had no baseline BCVA or follow-up safety data and was excluded from analysis. The 2-year study completion rate for the analysis population was 74. 4 % (279/375). The most common reason for early patient discontinuation from the study was a protocol violation: follow-up was not possible (n = 41, 10. 9 %), patient participation in another study (n = 1, 0. 3 %), or withdrawal of patient consent (n = 1, 0. 3 %). Other reasons were lack of efficacy (n = 23, 6. 1 %), patient satisfaction with the treatment (n = 10, 2. 7 %), death (n = 8, 2. 1 %), loss to follow-up (n = 9, 2. 4 %), and treatment-unrelated AE (n = 3, 0. 8 %).
The analysis of the primary endpoint used data from 323 patients, as there were 52 patients with missing BCVA data at month 6. However, results were similar in sensitivity analyses with missing values imputed. Moreover, the primary endpoint at month 6 was significant both in patients who had received only DEX implant treatment and in patients who had received additional other RVO treatment within the first 6 months of the study; the mean (SD) change from baseline BCVA at month 6 in these cohorts was 5. 5 (18. 8) letters (p < 0. 001, N = 254) and 4. 2 (19. 6) letters (p = 0. 006, N = 121), respectively. The primary endpoint was also significant in patients who received a single DEX implant and no other RVO treatment at visits up to and including the month 6 visit; the mean change in BCVA from baseline to month 6 for these patients was +5.
In patients previously treated with other RVO treatments, substantial mean increases in BCVA from baseline (8. 4 and 7. 6 letters, respectively) also were seen at months 6 and 24, suggesting that patients treated with other types of RVO therapy may benefit from a switch to DEX implant. Patients previously treated with DEX implant had better BCVA at enrollment; in these patients, the increase in BCVA from baseline at month 6 was smaller, but still significant, suggesting that BCVA improvement provided by previous DEX implant treatment was maintained after DEX implant treatment during the study. In the total analysis population, the mean increase in BCVA at 6 months (5. 1 letters) was similar to that seen in the GENEVA registration study (4. 6 to 5. 5 letters, Allergan data on file). In the DEX implant 0.
Acknowledgments Biostatem, a contract research organization in Castries, France, was responsible for safety monitoring and data management. Muriel Tounsi  and  Pascale Lewanczyk  (Biostatem) performed the statistical analysis. Medical writing and editorial assistance was provided to the authors by  Kate Ivins, PhD , of  Evidence Scientific Solutions (Philadelphia, PA, USA ) and funded by  Allergan plc. All authors met the ICMJE authorship criteria. Neither honoraria nor payments were made for authorship.
We then measured the mean lifetime of GFP-AURKA-mCherry and of the Lys162Met variant by FLIM. In contrast with GFP-AURKA-mCherry, the lifetime of the GFP-AURKA Lys162Met-mCherry variant was similar to the one of GFP-AURKA, showing lack of FRET between the EGFP-mCherry donor-acceptor pair throughout all the treatments (Fig. 2a ). This was also confirmed by the absence of pThr288 on GFP-AURKA Lys162Met-mCherry in any condition (Fig. 2b ). These data demonstrate that FRET within the AURKA biosensor shows a correlation with the autophosphorylation on Thr288 and to a catalytically active AURKA. To gain further insight on how altering the catalytic activity of AURKA perturbs the conformational changes of the AURKA biosensor, we studied the effect of the pharmacological inhibitors MLN8237 and MLN8054 on FRET efficiency.
FUCCI Cell Cycle Sensor (BacMam 2. 0) was purchased from Thermo Fisher Scientific and GFP-AURKA or GFP-AURKA mCherry cells were transduced according to the manufacturer's instructions. Widefield and confocal microscopy. FUCCI transduction efficiency and cells displaying spindle poles abnormalities were scored with a DMRXA2 microscope (Leica), equipped with a Â 63 oil immersion objective (numerical aperture (NA) 1. 32) and driven by the MetaVue software (Molecular Devices). Multicolour images were taken with a Leica SP8 inverted confocal microscope (Leica) and a Â 63 oil immersion objective (NA 1. 4), driven by the LAS software. CETN1-iRFP670 spots were quantified with the Analyse Particles plugin of the ImageJ software. FLIM microscopy. FLIM analyses were performed with a time-gated custom-built system attached to a Leica DMI6000 microscope (Leica) with a CSU-X1 spinning disk module (Yokogawa) and a Â 63 oil immersion objective (NA 1.
Lifetime measurements and calculations were performed with a custom-built programme running inside the MetaMorph software (Molecular Devices). Lifetime was calculated only when pixel-by-pixel fluorescence intensity in the first gate was above 3,000 grey levels. Fluorescence recovery after photobleaching. FRAP analyses were performed on a Leica SP8 inverted confocal microscope with a Â 63 oil immersion objective (NA 1. 4) and using the Leica Acquisition Suite (LAS) software. A region of interest was photobleached with a 488 nm argon laser for 20 ms at maximum power. Fluorescence intensity images were taken before and after photobleaching with the same excitation wavelength; fluorescence recovery in the region of interest was followed until it reached a plateau (75 s) and a total of 40 frames were recorded every 500 ms (15 frames), 1 s (15 frames) and 5 s (10 frames).
The ImageJ software (NIH) was used to measure fluorescence intensities. At least 30 images per condition were acquired and normalized as previously described  70. Microtubule regrowth assays. Images of HeLa GFP-TUBA1A cells were taken with a Nikon Ti-E inverted confocal spinning disk microscope (CSU-X1 spinning disk scan head), a Â 60 oil immersion objective (NA 1. 4) and a sCMOS ORCA Flash 4. 0 camera (Hamamatsu). Images of microtubules were acquired with a 491 nm laser and a 500-550 nm band-pass emission filter every 5 s, using the MetaMorph software. The quantification of the maximal length of microtubules at each time point and within each cell was performed applying and an automatic threshold over the microtubule area and using the Skeletonize plugin of the ImageJ software. Flow cytometry.
Acknowledgements We thank  S. Huet  and  T. Lebeaupin  for helpful advice on FRAP experiments and their analysis,  N. Mohamed  for technical assistance,  C. Benaud  for providing HeLa GFP-TUBA1A cells together with  X. Pinson ,  S. Le Bras ,  G. Rabut ,  E. Watrin  and  R. Le Borgne  for helpful discussions. We are grateful to  S. Prigent  for valuable help in the analysis of microtubule regrowth and in macro designing, all the members of the  Microscopy-Rennes Imaging Center (Biologie, Sante, Innovation Technologique, BIOSIT, Rennes, France) , and  Gersende Lacombe  and  Laurent Deleurme  from the Flow cytometry and Cell Sorting platform ( BIOSIT, Rennes, France ) for assistance. This work was supported by  ComiteNationale de la Recherche Scientifique , the  Agence Nationale de la Recherche  ( ANR-11-BSV5-0023 KinBioFRET  to C.
9 mm, d=10 mm. We then used the CST Microwave studio commercial package to generate the dispersion diagram associated with the spoof plasmon polaritons in these cases. To do this, we consider Floquet-Bloch conditions in the xy-plane: E(x+d, y+d, z)=E(x, y, z,)exp(i(kx+ky)d) (and similarly for the magnetic field) with k=(kx, ky) the Bloch vector that describes the first Brillouin zone ΓXM in the reciprocal space (Γ=(0, 0), X(π/d, 0), M(π/d, π/d)), where d is the pitch of the array in the physical space. We consider a small volume of vacuum above and below the structured metal, and periodic conditions in the z-direction, since CST does not allow for a combination of Floquet-Bloch conditions and perfectly matched layers (this seems to be mainly due to the fact that this would involve search of complex eigenfrequencies associated with so-called quasi-modes  [33] , which cannot be done in CST).
Acknowledgements R A, S E, and S G are thankful for a  PEPS INSIS/CNRS  funding through project  CLOAK EXTERNE. S G acknowledges funding from  ERC  through the project  ANA-MORPHISM. Research conducted within the context of the  International Associated Laboratory ALPhFA: Associated Laboratory for Photonics between France and Australia. This work has been carried out thanks to the support of the A *  MIDEX  project (no. ANR-11-IDEX-0001-02 ) funded by the  Investissements d'Avenir French Government program , managed by the  French National Research Agency (ANR).
Samples were then purified with an RNeasy mini elute kit (Qiagen, Hilden, Germany). The hybridization reactions were performed for 17 h in Agilent's SureHyb hybridization chambers containing 600 ng of Cy3-labeled cRNA per hybridization using Agilent's Gene Expression Hybridization kit. Slides were disassembled, washed according to manufacturer's instructions, and scanned at 3 μm/pixel resolution using the Agilent DNA Microarray Scanner G2505C, and images were analyzed with Agilent Feature Extraction Software (version 10. 7. 3. 1) using the GE1_107_Sep09 extraction protocol. All microarray data have been deposited in NCBI's Gene Expression Omnibus  [15]  and are accessible through GEO Subserie accession number GSE70838 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE70838).
Microarray data analysis All analyses were performed using the R software version 3. 0. 2 [16]. Raw spot intensities were first submitted to quality filtration based on four criteria: background intensity value, diameter, saturation and uniformity of the spot. Positives and negative controls probes were those available in the Agilent-026440 microarray. Intensities of filtered spots were log 2 transformed and median-centered. Altogether, 37,113 spots were finally retained for statistical analyses. Data were submitted to an analysis of variance considering the fixed effects of line, diet and their interaction. Data were then submitted to Benjamini and Hochberg (BH) multiple testing correction procedure. For the diet effect, no genes were found to be differentially expressed (DE) after application of the BH correction.
Therefore, DE probes with a fold change (FC) cutoff higher than |1. 1| between compared groups, and an uncorrected P-value below 0. 01 after statistical analysis  [17]  were then selected for further functional analysis. The lists of DE probes were investigated by an enrichment analysis of specific Gene Ontology (GO) terms for Biological Processes (BP), using the functional annotation clustering of Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics resources (http://david. abcc. ncifcrf. gov) [18, 19]. Due to the low number of genes found to be DE between the two dietary treatments, the functional annotation clustering was performed only for genes DE between RFI lines. The list of DE genes in low RFI versus high RFI pigs was divided into two lists according to their over-or under-expression.
t006 the response to genetics and nutrition strategies. To date, the majority of studies including a microarray analysis from whole blood or PBMC have been performed in adults exhibiting several disorders  [23]. Therefore, the current study provides novel information on the response of blood transcriptome during growth. This study further supports the interest to use RNA extracted from whole blood which is easier to obtain compared with RNA extracted from PBMC which requires supplementary steps for cell isolation. In the current study, there was no significant difference in the numbers of lymphocytes, monocytes and neutrophils between the Comparison of microarray and qPCR data for eight genes in pigs divergently selected for low or high residual feed intake (RFI). Transcriptomic differences between the two lines were validated by qPCR at 132 and 74 days of age (onset of the growing period).
Acknowledgments The authors thank  Y. Billon  and  A. Priet  ( INRA, UE1372 GenESI, Le Magneraud, F-17700 Surgères ) for line selection, B. Carrisant,  H. Demay  and  J. Liger  ( INRA, UMR1348 Pegase ) for blood sampling, and  G. Guillemois  ( INRA, UMR1348 Pegase ) for diet preparation. They are also grateful to  E. Baéza  ( INRA, UR83 Recherches Avicoles, F-37380 Nouzilly ) for her critical reading of the manuscript.
Lake sampling All lakes were sampled by raft for basic physicochemical data and profiled with a Hydrolab DataSonde 5a (OTT Hydromet, Loveland, CO) to measure water temperature, dissolved  oxygen, pH, specific conductance, and location of the thermocline. In stratified lakes, water was taken from the epilimnion, metalimnion, and hypolimnion. In those lakes that were not stratified, we sampled shallow, middle, and bottom waters. For sampling all lakes, we anchored at Z max or at a depth > 1% PAR, based on previous surveys. Whole water grabs were taken from each lake and each depth using a horizontal Van Dorn bottle. Water samples for ions, DOC, and nutrients were filtered through Whatman GF/F 0. 7μm filters and chilled for return and analysis at the University of Maine. Water for chlorophyll a (Chl a) analysis was also filtered onto GF/F filters and frozen for later analysis.
In cases where data did not meet normality or equal variance assumptions, they were natural log (ln) transformed for analysis. We used backwards stepwise regression to generate a predictive model for methane concentrations across our lake basins in Greenland. Inputs to the model included physicochemical lake factors (water temperature, dissolved oxygen, Chl a, and DOC) and ions (SO 4 2-, Na + , Mg 2+ , Ca 2+ ) known to be correlated with methane in this region. All data were ln-transformed to meet conditions of normality and autocorrelation among independent variables was assessed prior to model generation. We tested the model predictions using data collected across southwest Greenland during the summer of 2013. Of the 12 lakes from 2013, half of them (n = 6) were not re-sampled in 2014 for the development of the model due to logistical constraints.
Acknowledgments We thank  Steve Whalen  for methane analysis,  Mike Handley  and  Sharon Sneed  for ion analysis, and  Chris Osburn  for DOC analysis. Additionally, we thank  Kathryn Warner ,  Benjamin Burpee ,  Hamish Greig ,  Kristin Strock , and  Rachel Fowler  for field assistance and  CH2M-Hill Polar Services  for logistical support. We appreciate the help of  Johanna Cairns  in creating the map for this paper. The fieldwork and chemical analyses (except the methane) were conducted as part of another funded project on which JES is the principal investigator ( US National Science Foundation  grant #  1203434 ).
Finally, soil coring, root washing and scanning have been successful in describing root system architecture traits of adult plants in the field and in controlled environment conditions and have been widely used as a standard technique to compare new methods against  (Metzner et al. 2015). The measurement of the root system architecture traits from images is carried out using appropriate software. The most commonly used are the commercial WinRHIZO (Regent Instruments, Quebec, Canada) and the public domain IMAGEJ  (Schneider et al. 2012). The development of methods that measure changes in the root DNA concentration in soil could eliminate the need for separation of roots from soil and permit large-scale phenotyping of root genotypes and responses to environmental stresses in the field  (Huang et al.
Chromosome number and GLT capacity As part of the IMPC programme  [4] , we microinjected large numbers of imported ES cell clones, many of them derived from the JM8 parental line  [17] , into blastocysts under standardised conditions. The aim of these microinjections is to create chimeric founders that will be mated to obtain GLT of the mutation targeted in the ES cells. Figure  1  presents the percentage of successful GLT per ES clone in relation to the percentage of euploid cells present. The micro-injected clones represent a large population, unbiased for gene families or pathways targeted, thus supporting the systematic analysis of the relationship between GLT potential and chromosome number. The dataset showed that clones with lower percentage of euploid cells have significantly diminished ability to contribute to the germline.
Screening strategy We found that assays along each chromosome could be employed interchangeably, provided that the required annealing temperatures were compatible with that of the calibration gene assay (Additional file 8: Figure  S7 , Additional file 5: Table  S2 ). We validated the ddPCR method by comparing karyotyped chromosome metaphase spreads and ddPCR data obtained from 16 clones (some euploid and some aneuploid). This showed excellent correlation between the outcomes of Although some clones trisomic for Chr 8 also carry other chromosomal abnormalities (0. 8 % clones analysed have aberrations on Chr 8 and 1, 4. 1 % have trisomies on both Chr 8 and 11 and aneuploidy on Chr 8 coupled with Chr Y is detected at 4. 0 %), the frequencies of such compound event suggest that these aneuploidies arise independently (See Additional file 5: Table  S3 ).
Interestingly, double trisomy of Chr 8 and 11, found at high frequencies in 2 unstable cell lines by Gaztelumendi & Nogue  [10] , was observed at much lower frequency in JM8 subclones which suggests that these compound rearrangements are specific to the cell lines or dependent on cell culture conditions. These data show that for JM8 cells, determining the copy number of Chr 8 and Y represents the most accelerated screen, which should detect the majority of the aneuploidy events and enable identification of mouse colonies where female chimeras should be bred instead of males. Therefore, reducing the panel of assays to two chromosomes (i. e. 8 and Y) can be sufficient. One hundred and thirty JM8 clones with normal karyotype by ddPCR for Chr 8 and Y were subsequently analysed by metaphase spread-based karyotyping.
We found that only 3. 8 % (5/130) would have been eliminated by metaphase spread-based karyotyping. In 3 out of these 5 clones, the anomaly was a chromosome translocation, which is not expected to be readily detected with a method aimed at copy counting, as it is unlikely to affect the overall copy numbers of genes assayed. Translocations without accompanying chromosome number abnormalities represent such a rare occurrence (3/130) within our large dataset, that we elected not to aim to identify them as part of our screen. Noticeably, the switch to ddPCR to identify and discard aneuploid clones from the process of ES cell to mouse conversion was not associated with any loss of GLT efficiency in the production pipeline of either centre. However, when we applied the same approach to clones obtained from two other C57BL/6N parental lines isolated at ICS (S3 and TB1 ES, unpublished data, ICS), we found 13.
Karyotype analysis of ES cells Cells grown without feeders were treated with 0. 02 μg/ml colcemide for 2 h and then treated as in  [19]  to obtain mitotic chromosome spreads on glass microscope slides for karyotyping. For identification of chromosomes, either of the following methods was used: (a) Images of DAPI stained mitotic chromosome spreads were captured using a fluorescence microscope with Smartcapture 2 software (Digital Scientific, Cambridge, UK). The DAPI staining on the chromosomes was reversed, and enhanced by the software to produce reversed DAPI banding (or pseudo G-bands) that are equivalent to Giemsa stained G-bands. This allowed comparison with the standard ideogram of the mouse  [15]  to precisely determine the origin of any aneuploidy or other chromosome anomalies identified in each mitotic spread.
Examples of data obtained are shown in Additional file 10: Figure  S9 , panels C and D; or (b) Gbanded  [19]  chromosome mitotic spreads from approximately half of the ES cell clones were analysed using a standard laboratory microscope for aneuploidy or other chromosome anomalies. Initially 50 metaphase spreads were analysed from each ES cell line. This was later reduced to 30 cells per cell line with no change in the accuracy of results.
Analysis of ddPCR data The QX200 Droplet Reader recorded the number of accepted droplets in which fluorescence within the droplet is detected (positive droplets) by either one or both optical channels, whilst also counting the number of droplets in which fluorescence is absent (negative droplets). A minimum of 10,000 accepted droplets per sample were used for analysis. Optimised amplification of each of the two assays produced a 2D plot of amplitude showing four distinct groups of data points corresponding to FAM ™ negative/VIC ® negative, FAM ™ negative/ VIC ® positive, FAM ™ positive/VIC ® negative and FAM ™ positive/VIC ® positive droplets (Fig. 2a ). The number of droplets recording fluorescence for the chromosome-specific assay was compared to the count obtained for the reference-specific assay, known to occur as two copies per genome.
Final copy numbers were calculated employing the manufacturer's Quanta-Soft Software (Bio-Rad, CA, USA) by applying Poisson statistics to the fraction of end-point positive reactions  (18). For further confirmation of copy number calls, we also ran a known trisomic and known normal control. For multiplex reactions, the 2D plot now displays eight distinct groups of data points, which correspond to the different combinations of each assays being positive or negative (d escribed in details in Fig. 4 ). As the fluorescence profile i. e. level of amplitude recorded is largely unchanged for each assay when multiplexed, it is possible to determine which groups are associated with each assay and as such it is possible to assign results to each assay individually compared to the reference, whilst running in multiplex.
Acknowledgements We thank  Wendy Gardiner ,  Denise Lynch ,  Deb Bogani ,  Valérie Erbs ,  Thomas Ding  and the  MRC  and  ICS ES cell culture  staff for cell culture support. We especially thank  Bill Skarnes  and  Wendy Bushell  ( Wellcome Trust Sanger Institute, Hinxton, UK ) for facilitating access to ES cell resources. We thank  Steve Thomas  ( MRC Harwell ) for expert support with figure preparation. Finally, we thank  Hugh Morgan  ( MRC Harwell ) for expert and friendly advice on statistical analysis. The work was supported by the  Medical Research Council  ( Strategic award ), the  National Institute of Health  (Grant  U42OD011174 ), the  programme Investissements d'Avenir, "Agence Nationale de la Recherche ' ( ANR-10-INBS-07 PHENOMIN ) and the  FRM foundation "Fondation pour la Recherche Médicale " ( DGE20121125624 ).