dataesr/scientific-paragraphs-categorization huggingface dataset:
However, for some of the statistical analyses, only data from dogs that complied most closely with the visit schedule were used (see below). At each recheck, body weight was recorded (as the primary outcome measure), and the subjective assessments of activity, QOL and food-seeking behaviour were completed. However, whilst veterinary professionals continued to assess BCS during the study, and use it to help monitor progress, it was not used as a study outcome measure given its relative insensitivity for detecting changes in body composition, (whereby a change of 10-15% body weight is typically required for a 1-unit change in BCS)  [25]. The weight loss programme was also discussed with the owner to determine if the owner had been compliant with the programme, for example whether they had measured food accurately and whether the dog had obtained additional food (e.
Data handling and statistical analysis A formal sample size calculation was not performed. Instead, enrolment of practices for the trial was a pragmatic one, whereby as many dogs as possible were recruited, if they were willing to participate and met the eligibility criteria. Within each practice, a pragmatic approach was also taken for recruitment of dogs. In signing up, no targets were set for each practice but, instead, as many dogs as possible were recruited in the time limit. The internet-based software (VET FOLLOW UP, Royal Canin) used by study investigators to determine food allocations, was also used for data recording. Data included age, sex, neuter status, breed, body weight, BCS, size, and owner-reported behaviour (activity, QOL and foodseeking). For size, dogs were assigned to one of three categories based upon TBW data (small <10 kg; medium 10-25 kg; large >25 kg).
Continuous data are reported as mean ±SD, median and interquartile range (IQR), or median and range, as indicated. The restricted maximum likelihood (RML) method was applied to linear mixed models to deal with missing data. Age data were missing for 3 dogs, information on QOL was missing from 1 dog (visit 5), and information on food-seeking behaviour was missing from 1 dog (visits 1, 2, 3 and 5). Energy allocation data were not reported for a total of 80 instances in 31 dogs, whilst body weight measurements of 16 dogs were missing (7 at Visit 2, 5 at Visit 3 and 6 at Visit 4). Data were analysed with SAS v9. 3 (SAS Institute Inc. , Cary, NC, USA), and the level of statistical significance was set at P<0. 05, for two-sided analyses. Datasets containing all study data are available in the supporting information (S1 and S2 Datasets).
Descriptive statistics on outcomes were calculated for the dogs with complete and reliable weight loss data (926 dogs; S1 Dataset). Comparative statistical analyses then performed on a subpopulation of 437 dogs that had complied best with the study visit schedule (Table  4 ,  Fig 1, S2 Dataset). Changes in rate of weight loss were assessed using linear mixed models with visit number as a fixed effect and dog as a random term. If residuals were not normally distributed, quantitative variables were rank transformed. Given that several visits were considered, posthoc analyses were adjusted with Scheffe's method to avoid alpha-risk inflation. Stepwise linear regression analyses were then performed to assess the impact of various fixed effects (age, sex, neuter status, dog size [small {<10 kg} vs.
The stability with respect to a peeling-ballooning mode (PBM) was investigated numerically with extended MHD simulation codes in JET, JT-60U and future JT-60SA plasmas. The MINERVA-DI code was used to analyze the linear stability, including the effects of rotation and ion diamagnetic drift ( * w i ), in JET-ILW and JT-60SA plasmas, and the JOREK code was used to simulate nonlinear dynamics with rotation, viscosity and resistivity in JT-60U plasmas. It was validated quantitatively that the ELM trigger condition in JET-ILW plasmas can be reasonably explained by taking into account both the rotation and * w i effects in the numerical analysis. When deuterium poloidal rotation is evaluated based on neoclassical theory, an increase in the effective charge of plasma destabilizes the PBM because of an acceleration of rotation and a decrease in * w i.
Many works have qualitatively identified their impact on both the linear stability and nonlinear dynamics of ELMs with linear and nonlinear MHD simulation codes, such as ELITE  [5] , MINERVA  [8] , MINERVA-DI  [9] , JOREK  [10] , BOUT++  [7, 11] , NIMROD  [12, 13] , M3D  [14, 15] , M3D-C1  [6] , and so on. Some quantitative analyses, including part of these effects, have been performed in parallel by investigating the stability with respect to PBM in the equilibria with the plasma profiles measured experimentally. For example, the impact of plasma rotation on the ELM stability in JT-60U was identified with MINERVA  [3] , and the ELM stability in JT-60U and JET plasmas was validated again by including not only the rotation but also the * w i effects with the linear extended MHD stability code MINERVA-DI  [16, 17].
Furthermore, the impact of the * w i , resistivity and viscosity on the ELM stability in JET-ILW has been investigated with the nonlinear extended MHD stability code JOREK  [18]. The results showed that these physics effects contribute to determining the ELM trigger conditions in such large tokamak experiments. In this paper, based on these understandings, we consider the impact of plasma rotation on the stability of PBM when additional physics effects are taken into account simultaneously. The numerical analyses were performed with the extended MHD codes MINERVA-DI and JOREK. In section 2, the basic equations used in MINERVA-DI and JOREK are briefly introduced. Section 3 shows the results of the quantitative analysis of the linear PBM stability in JET-ILW, including the plasma rotation and * w i effects with MINERVA-DI.
Basic equations In this section, the basic equations of the linear and nonlinear extended MHD stability codes, MINERVA-DI and JOREK, are introduced briefly; the details are written in  [9]  and  [18] , respectively. The MINERVA-DI code solves the extended Frieman-Rotenberg equation, which is the linearized equation of motion of the diamagnetic MHD model * * x x x r r r ¶ ¶ +  ¶ ¶ +  ¶ ¶ = + ( • ) ( • ) ( ) V V F F t t t 2 , 1 0 2 2 0 0,MHD 0 0, i MHD i x  = • ( ) 0, 2 with the definition of velocity vectors as = +  | | ( ) V V B B V , 3 E MHD = ´( ) V E B B , 4 E 2 * = ´ ¯( ) V B eZNB p 1 , 5 i 2 i where F MHD and * F i are the force operators coming from the ideal MHD and ion diamagnetic correction parts; the third term on the left hand side of (1) and * F i appears by including the lowest order ion diamagnetic correction terms into the ideal MHD model  [9].
Here ρ is the mass density, x is the Lagrangian displacement vector defined with the linearized Eulerian velocity as x x x º ¶ ¶ +  -  ( • ) ( • ) ( ) V V V t , 6 1,MHD 0,MHD 0,MHD the subscript 0 (1) indicates the equilibrium (perturbed) quantity, B is the magnetic field, E is the electric field, e is the quantum of electricity, Z is the ion mean charge satisfying = N ZN e , N e (N) is the electron (ion) number density, and p i is the ion pressure. It should be noted that the incompressible assumption (2) and the flute approximation x   ( • ) B 1 0 , which are sometimes imposed in the extended MHD models (e. g. [20] ), are introduced for deriving (1). The original diamagnetic MHD model was developed to investigate the impact of the * w i effect on ideal MHD stability in rotating plasmas; hence, MINERVA-DI allows us to analyze the linear stability of PBM with the rotation and * w i effects, where * * w º • V k i 0 , i , and k is the wave number vector.
The pedestal height is usually determined by analyzing the linear stability of PBM with a simple model of diamagnetic stabilization as * g w > 0. 5 p MHD i , where g MHD is the growth rate of the ideal MHD mode in static plasmas, and * w pi is the half maximum value of the ion diamagnetic frequency in the pedestal. In this study, we have updated the PBM constraint on the pedestal height by using the MINERVA-DI code which can analyze the * w i effect more self-consistently in rotating plasmas. The rotation profile is predicted with the TOPICS code by solving the momentum balance equation with pinch, diffusion, NBI torque and neoclassical toroidal viscosity (NTV, see e. g. [30] ) terms  [31] [32] [33] , where the NTV due to the nonaxisymmetric perturbed magnetic field caused by toroidal field coils is calculated by coupling TOPICS with the threedimensional (3D) equilibrium code VMEC  [34]  and the 3D non-local neoclassical transport simulation code FORTEC-3D  [35, 36] ; the details of the procedure can be found in  [32].
Acknowledgments The authors would like to thank  S Ide  and  G Giruzzi  for providing an opportunity for the collaborative study between the  EU and Japan. The authors are grateful to  Drs Y Kamada ,  M Yagi ,  N Oyama ,  C F Maggi ,  E de la Luna  and  J Garcia  for their beneficial comments and suggestions. Some of the authors (NA and MH) gratefully acknowledge contributions from  Drs S Satake  and  Y Suzuki  in the NTV calculations. This work was partially supported by  JSPS KAKENHI  Grant Number  15K06656 , and has been partly carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
Impact assessment protocols typically consider one to three broad categories of impacts (such as environmental, economic and social impacts), each of which addresses a number of different impact mechanisms (e. g. predation, human health) formulated in questions. The Generic Impact Scoring System (GISS) is a generic protocol that measures the environmental and economic impacts of invasive alien species in 12 impact categories, and the protocol has already been widely used to compare IAS impacts in different regions and across taxonomic groups  (Kumschick & Nentwig, 2010; Nentwig et al. , 2010 Nentwig et al. , , 2016;; Kumschick et al. , 2011; Vaes-Petignat & Nentwig, 2014; Laverty et al. , 2015; van der Veer & Nentwig, 2015). The Environmental Impact Classification for Alien Taxa (EICAT) protocol was recently derived from GISS, but modified to classify species according to the magnitude of their detrimental environmental impacts, using a broad range of impact categories that correspond to the ones used by the IUCN Global Invasive Species Database  (Blackburn et al.
This included an overview document and more than 40 relevant publications (both peer-reviewed papers and grey literature such as NGO/governmental reports) as suggested preliminary evidence base. This database was not meant to be exhaustive, and experts were encouraged to complement and share any additional evidence they were aware of. Additionally, we leveraged the ParrotNet (http://www. ke nt. ac. uk/parrotnet/) network to ask a member of each EU country with established parakeet populations to survey the literature on their impacts in their national language and to send a summary of any evidence found to the workshop coordinators. A sixth step is then the actual 'elicitation of expert judgement'. This is the two-day workshop that took place in March 2015, and which started with a presentation of the scope and purpose of the meeting, informing the experts about what would be expected from them during the 2 days.
g. the method can remain effective when applied to a variety of conditions, such as different taxonomic groups, data quality, scopes). Narrative summaries were provided for each RACER category, and respondents allocated a numerical score on a scale of 1 (criterion not fulfilled) to 5 (criterion fully fulfilled) to each question, supporting the visual presentation of the results in an easily readable overview summary table. The full questionnaire can be accessed via a link provided at the end of the acknowledgements section.
Statistical analyses We rescaled data for each question between 0 and 1 to compare impact estimates across protocols. This was done by calculating (V À V min )/(V max À V min ), where V represents the impact score assigned to a question in the original dataset and min and max refer to the minimum and maximum scores attainable. For each of the four protocols, impact estimates were then aggregated into impact categories (total impact; environmental, economic and social impacts) in two ways, that is by taking the arithmetic mean (~additive impacts) and the maximum impacts (~impact defined on the basis of the most severe impact only). To test whether impact estimates varied among protocols and impact categories, we included protocol and impact category and their interaction as fixed effects in a linear mixed model, with 'rater' (i.
ACKNOWLEDGEMENTS This study is a joint effort from a workshop organized under  COST European Cooperation in Science and Technology Actions Parrotnet  and  Alien Challenge  ( TD1209 ). We would also like to thank two anonymous referees for helpful comments on an earlier version of this manuscript. D. S is currently funded by a  Marie Skłodowska-Curie Action  under the  Horizon 2020 call  ( H2020-MSCA-IF-2015 , grant number  706318 ) and acknowledges the  Danish National Research Foundation  for support to the  Center for Macroecology, Evolution and Climate  (grant number  DNRF96 ). J. C. S. was supported by funds from the  Ministry of Economy and Competitivity, Spanish Research Council  ( CGL-2016-79568-C3-3-P ).
Induction of dextran sulfate sodium colitis Eighteen days after beginning of the experiment, half of the animals both from the CS (n = 10) and sham treatment group (n = 10) were treated with 3. 5% of dextran sulfate sodium (DSS, molecular weight 40,000, TdB, Uppsala, Sweden) dissolved in drinking water for 10 days to induce colon inflammation resembling human UC (Suppl. Fig. 1 ). The dose and duration of DSS treatment were based on literature data  [15]. Consumption of water and water with DSS was determined on daily basis for each cage. Body weight of each mouse was measured on the first and the last day of experiment. Animals were monitored daily, and the activity of DSS-induced colitis was determined with the use of previously described disease activity index (DAI), which included the following variables: weight loss, stool consistency, and rectal bleeding  [16, 17].
Microarray hybridization RNA was amplified, labeled, and hybridized onto Single Color SurePrint G3 Mouse GE 8 × 60K Microarrays (AMADID:046066; Agilent Technologies) at Oxford Gene Technology, Oxford, UK, following the instructions of the manufacturer. Following hybridization, the arrays were scanned to derive the array images. Feature extraction software v10. 7. 3. 1 was used to generate the array data from the images.
Microarray Data analysis Raw data in standard format from the microarray experiment have been deposited in the Gene Expression Omnibus (GEO) database of NCBI 1 under accession n. GSE79397. Raw data were normalized and analyzed using GeneSpring software (Agilent Technologies). Transcript expression between the experimental groups was compared by Student's t-test done on the log2 of the gProcessed signal. Fold change in the expression represents the ratio between the averages of the gProcessed signals of the various groups and is expressed as log2. FigUre  1  | GSH + GSSG levels and ROS production in LPS-treated cells with or without BSO. Cells were pretreated with 120 µM BSO for 24 h, followed by 10 ng/ml LPS for 2 h. Total GSH + GSSG and GSSG levels measured in the cell lysates are the mean ± SD of six biological replicates from three independent experiments and are expressed as nmoles/mg protein.
0 for Mac OS X). Hierarchical cluster analysis was performed using Genesis software version 1. 8. 1 for Windows  (19). Functional annotation and biological term enrichment to identify the overrepresented gene ontology biological processes (GO:BP) categories and KEGG pathways was done using DAVID software version 6. 8. 2  DAVID calculates a modified Fisher's exact P-value to demonstrate enrichment. Categories with P < 0. 05 were considered significantly enriched.
Microarray Data Validation Reverse transcription (RT) and qPCR were carried out as reported  (20)  on total RNA from quadruplicate samples. PCR reaction were run on the MX3000 PCR machine (Stratagene/ Agilent), using Taqman ® gene expression assays (Applied Biosystems/Life Technologies) and Brilliant III qPCR master mix (Stratagene/Agilent Technologies). Gene expression was quantified using the comparative threshold cycle method, according to Applied Biosystems' guidelines. Results were normalized to HPRT1 expression (reference gene) and expressed as relative expression (fold change) vs one of the control samples at 2 or 6 h (as indicated), chosen as the calibrator.
5 and a significance level of P < 0. 05; the numbers of upregulated genes are in red, and those downregulated are in green. Lipopolysaccharide affected about 5,000 transcripts at each time point, with an almost identical number of upregulated and downregulated ones. Of the transcripts affected by LPS, we selected those up-or downregulated by BSO (significantly different by fold change of 1. 5 and a significance level of P < 0. 05 when comparing BSO + LPS vs LPS alone). At both time points, we could identify four groups of transcripts: (1) upregulated by LPS and increased further by BSO; (2) upregulated by LPS and decreased by BSO; (3) downregulated  The dataset was filtered to select transcripts that were affected by LPS (LPS vs control, first step), then by BSO (BSO + LPS vs LPS alone, second step).
Transcription Factor (TF) analysis To identify possible common molecular mechanisms responsible for the differential regulation by BSO of the LPS-induced genes in Groups 1 and 2, we performed an unbiased analysis for the overrepresented TF-binding sites using oPOSSUM software  (23). All categories identified by DAVID for Groups 1-4 are reported. For Group 2, DAVID returned 51 categories and only the top 15 (ordered by EASE score, a modified Fisher's exact test) are reported. BSO, buthionine sulfoximine; GO:BP, gene ontology biological process; LPS, lipopolysaccharide. In Group 1 (Figure  5A ), the TF results in the highest Fisher score and a high number of target genes was NFE2L2 (nrf2), whose main function is the response to oxidative stress, thus confirming the results obtained with DAVID.
In Group 2 (Figure  5B ), the TF that had the highest score was NF-kB with its various subunits. We thus manually searched our dataset for the expression of known NF-kB target genes. From the list of 364 genes available at https://www. bu. edu/nf-kb/gene-resources/target-genes/, we could identify, in our dataset and with a cutoff of 1. 5-fold change and P < 0. 05 significance level, 87 NF-kB target genes induced by LPS at 2 h and 107 at 6 h (File S2 in Supplementary Material). However, only 8 out of 87 at 2 h and 4 out of 107 at 6 h were downregulated by BSO. Thus, because only a very small percentage of NF-kB target genes induced by LPS are in Group 2 (downregulated by BSO), we could rule out that BSO acts simply by downregulating NF-kB.
FigUre2| Effect of GSH depletion on LPS-induced changes in gene expression profile. The dataset was filtered to select transcripts that were affected by LPS (LPS vs control, first step), then by BSO (BSO + LPS vs LPS alone, second step). Cutoff for selection was 1. 5-fold change and P < 0. 05, using the Student's t-test. Significantly different expressed genes were further selected by using one-way ANOVA, followed by a correction for multiple comparisons by controlling the false discovery rate with the two-stage step-up method of Benjamini, Krieger, and Yekutieli. The number of transcripts resulting from filtering is indicated and color coded (red, increased; green, decreased). ANOVA, analysis of variance; BSO, buthionine sulfoximine; GSH, glutathione; LPS, lipopolysaccharide.
, 2006) , a Structure from Motion system applied on unordered image collections. This system takes as input a set of images taken around the plant and provides a sparse point cloud. Then a dense point cloud is provided from the CMVS  (Furukawa et al. , 2010)  and PMVS2  (Furukawa and Ponce, 2010)  software, a multi-view stereo software (MVS) that takes as input the spare point cloud produced by Bundler. Moreover, during the acquisition process, a chessboard pattern is placed on the ground in order to retrieve the scale of the cloud. Outliers are manually removed with Meshlab and the cloud is then scaled with CloudCompare. An example of a 3D point cloud acquired on a sunflower with Bundler+CMVS/PMVS after the filtering and scaling steps, is given in figure  2. As visible in this figure, the 3D point cloud gives a faithful reconstruction of the sunflower.
This algorithm can achieve the leaves segmentation without specifying the number of clusters. In general, this algorithm meets the same problem as K-means for the main stem and the top. Furthermore, the parameters required by this algorithm are quite a bit difficult to tune for a multitude of varieties. An example of result produced by this algorithm is shown in figure  4. Another approach consists in working with the 3D mesh built from the point cloud, like  (Paproki et al. , 2012). To obtain such a mesh of a plant, they use 3DSOM, a commercial 3D digitization software. They first apply a coarse segmentation with a constrained region-growing algorithm which allows to identity the main stem and leaves. Then, a tubular shape fitting provides a precise stem segmentation, the petioles, their inter-nodes and finally they proceed to the leaf segmentation.
Material and Methods The National Cancer Database (NCDB) was queried to identify women with vulvar cancer that had one or more positive lymph nodes diagnosed between 2004 and 2012. All patients were surgically staged. Chi-square tests and multivariate logistic regression were performed to analyze factors associated with receipt of radiation. Survival analysis was performed using overall survival confidence intervals (CI), log-rank test, Kaplan-Meier estimates, and Cox proportional hazards regression.
Conclusion In a large national cancer database, receipt of EBRT was associated with improved survival in woman with vulvar cancer and two or more positive inguinal nodes. In patients with one positive node, a survival advantage was seen using Kaplan-Meier estimates and Cox survival model, but lost statistical significance in a multivariate regression model. These data lend support to the role of EBRT in the management of node positive vulvar cancer. Further research with more detail of other known prognostic factors are needed to validate these findings. PO-0718 18-FDG PET/CT parameters to predict survival and recurrence in cervical cancer N. Scher 1 , F. Herrera 1 , A. Depeursinge 2 , T. Breuneval 1 , J. Bourhis 1 , J. Prior 1 , M. Ozsahin 1 , J. Castelli 1 1 Centre Hospitalier Universitaire Vaudois, Radiation Oncology department, Lausanne Vaud, Switzerland 2 Ecole Polytechnique Fédérale, imaging, Lausanne Vaud, Switzerland.
Nine Del(CpG114) F0 founders were obtained that were homozygous for the deficiency, and none of them suffered from growth retardation. Clean stocks for both the deficiency and the inversion were further established, and RNAs were isolated from pairs of homozygous and wild-type control siblings at E9 for RNA-sequencing (RNAseq). Browser Extensible Data (BED) graphs of mapped reads on both the forward and the reverse strands of the HoxD cluster indicated that in wild-type mice Hoxd1, Hoxd3, and Hoxd4 were all transcribed as expected from the Hox forward strand (Fig. 6C ). On the reverse strand, reads mapping to the Haglr locus were clearly recovered, even though the fragments per kilobase of transcript per million mapped reads (FPKM) value was five-to 10-fold reduced compared with Hox genes transcripts.