dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis We analysed the results using a Generalized Linear Model (GLM) in R version 3. 4. 3, using package lme4. This allows us to model the main predictor along with control predictors all at once, and avoid the problems associated with running multiple analyses. In our full model we included choice of option as the dependent variable and the difference between the participant's ratings of the motives behind the two options as the main predictor, expecting that the worse a participant rated one motive relative to the other, the more likely they would be to expect the protagonist to pursue that option that satisfied that motive. We also wanted to include a control to measure whether participants were choosing the option they ranked simply as the most 'extreme' of the two. That is, if they rated one motive as -10 (toward 'bad'), and the other as +2 (toward 'good'), then we might expect they would pick the option satisfied by the motive rated at -10; but if they rated a motive as plus 10 toward good, and another at minus 2 toward bad, they would be more likely to pick the good motive.
DNA extractions from strains RH15-53 and KU-333, PCR, and Sanger sequencing were done as described previously  [33] , targeting the nuclear ribosomal small subunit (18S), the plastid-encoded rbcLS spacer (Rubisco spacer) and the genes for rbcL, psaA and psbC by using the primers listed in Table  1. Annealing temperature in the PCRs was 48-55 °C, the number of cycles 35 and the extension temperature 72 °C. The DNA sequences obtained were assembled and aligned manually with Se-Al TM v2. 0a11 (Sequencing Alignment Editor Version 2. 0 alpha 11  [34] ), checked for correctness by inspecting the chromatograms, and compared to published sequences by the Basic Local Alignment Search Tool (BLAST) housed at the United States National Center of Biotechnology Information  [35]. The newly determined sequences are deposited in the public database under accessions LC521903-LC521907 (RH15-53), MN994274 (KU-333), and MN996275 (KU-333).
0a11 (Sequencing Alignment Editor Version 2. 0 alpha 11  [34] ), checked for correctness by inspecting the chromatograms, and compared to published sequences by the Basic Local Alignment Search Tool (BLAST) housed at the United States National Center of Biotechnology Information  [35]. The newly determined sequences are deposited in the public database under accessions LC521903-LC521907 (RH15-53), MN994274 (KU-333), and MN996275 (KU-333). For present-day taxonomic and nomenclatural aspects, AlgaeBase  [43]  was consulted.
Funding: This research was funded by the  UK Natural Environment Research Council  in support to FCK (program Oceans  2025-WP 4. 5  and grants  NE/D521522/1  and  NE/J023094/1 ) and the  NERC National Facility for Scientific Diving  ( NFSD/14/02 ). We would also like to thank the  TOTAL Foundation (Project "Brown algal biodiversity and ecology in the Eastern Mediterranean Sea "). This work also received support from  AFP  from the project  IDEALG (France :  ANR-10-BTBR-04 ) and from FCK from the  Marine Alliance for Science and Technology for Scotland pooling initiative. MASTS is funded by the  Scottish Funding Council  (grant reference  HR09011 ) and contributing institutions.
15, 16 Subsequently, several immune-oncology (IO) drug combination have proven better efficacy over sunitinib in the first-line setting, with a new revolution in the treatment of mccRCC. In the phase 3 CheckMate 214 trial evaluating nivolumab plus the anti-CTLA4 ipilimumab versus sunitinib in previously untreated mRCC patients, OS and objective response rates (ORR) were significantly higher in the treatment with nivolumab plus ipilimumab than with sunitinib among IMDC (International Metastatic RCC database Consortium) intermediate-and poor-risk patients. 17, 18 Ongoing clinical trials and others recently concluded continue to study the efficacy of novel drug combinations, with special reference to the anti-PD-L1 agent avelumab or the anti-PD-1 antibody pembrolizumab with the VEGFR-inhibitor axitinib.
The analysis of PFS, defined as the time between blood sample collection and progression or death from any cause, was performed using the Kaplan-Meier method and log-rank test. Data were generated using the MedCalc software for Windows, version 18. 2. 1 (MedCalc Software, Ostend, Belgium). P values <. 05 were considered statistically significant. The optimal cut-off for sPD-1 was 2. 11 ng/ml (AUC = 1. 0, p value <. 001), 0. 66 ng/ml for sPD-L1 (AUC = 0. 88, p value <. 001), 6. 84 ng/ml for sBTN3A1, (AUC = 0. 815, p value <. 001) and 12. 73 ng/ml for sBTN3 global (AUC = 0. 741, p value <. 04); 6. 01 ng/ml for sBTN2A1 (AUC = 0. 556, p value 0. 689).
15 In the present paper, we are going further by comparing simulated and experimental images of the beam produced in a gridded electron gun. Our simulations are performed with the CST Studio Suite  16  and, more precisely, with its tracking solver, which is much faster than PIC solvers. The beam micro-focusing effect due to the grid is observed both in the simulations and in the experiment, and the emittance growth is therefore estimated. Section II describes the details of the LEETCHI experimental setup. Section III is dedicated to numerical simulations of beam dynamics. First, we present a comparison between CST tracking solver predictions with the more accurate PIC results performed with the code MAGIC. 17 Then, we have carefully modeled with CST the 3D gun geometry, which includes a grid whose effects are reported.
III. NUMERICAL SIMULATIONS As already mentioned, the LEETCHI gun was originally designed by performing 2D simulations with both the PIC-code MAGIC and the ray-tracing code EGUN. 25  Grid effects, which are purely 3D, were not taken into account in these preliminary studies. In addition, the gun geometry was optimized for low-emittance operation at the previous current specification of 8 A. This required dga distances less than 45 mm, and above this length, the current is space charge limited. In this case, both codes predicted a low rms geometrical emittance of the order of 15 mm mrad. Since then, the DB gun current specification was reduced from 8 A to 4. 5 A. This value is sufficiently high to obtain the right bunch charge when the DB exits the combiner rings. 26 Additional simulations have recently been performed taking into account this new current value.
Moreover, we precisely simulated the electron source with the 3D code CST, which has several interesting capabilities. First, its tracking solver, much faster than full PIC simulations, is particularly useful for extensive beam transport optimization in a magnetic system. For example, an accurate tracking simulation for the LEETCHI geometry lasts approximately one day, whereas at least one week is needed for the corresponding PIC calculation. Second, importing 3D mechanical drawing packages is easy with CST. We would like to mention that simulations of gridded thermionic cathodes have already been performed by using the PIC code MICHELLE, which is largely used for gun-collector designs in the US,  15  especially for Free-Electron-Lasers (FELs). First, we decided to benchmark the 3D-CST results (PIC and tracking) with the 2D-MAGIC PIC code.
14 ). The emittance specification for the CLIC-DB drive beam injector at 50 MeV, as defined by Eq. (  1 ), is 100 mm mrad. The design study showed that the emittance doubled from the gun to the end of the accelerator. 26 Therefore, we are aiming for an emittance of the order of 50 mm mrad at the gun exit, and the results presented in this section give us confidence that this goal can be achieved. Theoretically, the beam emittance could be evaluated from the data of a solenoid scan, as shown in Figs. 10 and 13 using envelope equations. Given the fact that the initial conditions for such an approach are not well known, the result is deemed to be inaccurate. On the other hand, a direct emittance measurement using a pepperpot or multi-slit setup is possible  31  and will be attempted as soon as the experimental setup is available.
V. CONCLUSION We have presented the first experimental measurements with the CLIC-DB electron source prototype LEETCHI. We have performed simulations with the CST tracking code by taking into account precisely the mechanical geometry of the cathode-grid assembly. Beam images, as acquired by an OTR station, were well reproduced by simulations. This allowed us to estimate the beam emittance with confidence. Although the thick grid is only used to maintain the cathode-thin grid assembly, it is responsible for beam micro-focusing and emittance increase. This effect was observed both in experiments and simulations. Simulations have also indicated that the beam emittance could be significantly reduced by modifying the cathode-grid topology. Finally, according to the experimental results and corresponding simulations, we showed that the thermionic electron source of the DB prototype should be capable of fulfilling the CLIC specifications.
arborea and H. molleri in Western France using a multilocus dataset. Our comparative analysis supports a trend that contacts zones resulting from post-glacial expansions and human translocations feature more extensive introgression than those established within refugial areas. Integrating the biogeographic history of incipient species, i. e. their age since first contact together with their genetic divergence, thus appears timely to draw sound evolutionary and taxonomic inferences from patterns of introgression across hybrid zones.
Eight loci did not amplify in H. meridionalis, in line with the older divergence time. In addition, we re-analyzed microsatellite reads from reference populations of H. arborea (from Switzerland, the Netherlands, and France, loc. 1-3, n = 80), H. molleri (from Spain, loc. 30, n = 16) and H. meridionalis (from southern France, loc. 31, n = 16) from our previous work  [24] [25] [26]. The new and published genotypes were generated by the same genetic analyzer (an ABI3130) and (re)scored together in GenMapper 4. 0 with a single set of bins and panels, to ensure the correspondence of alleles. The total microsatellite dataset (Western France + reference populations) included 363 individuals. We first performed Bayesian clustering with STRUCTURE 27 by running 10 chains of 100'000 iterations, after a burnin of 10'000, for each value of K from 1 to 11.
The most likely number of clusters (K) was estimated with STRUCTURE HARVESTER  28  , concatenated over replicates with CLUMPP  29  , and graphically displayed with Distruct  30. Second, we performed a Principal Component Analysis (PCA) of individual genotypes with the R packages ade4 and adegenet  31. Third, we computed pairwise genetic distance (F st ) for populations with n ≥ 5 using the R package hierfstat  32. Mitochondrial DNA from 230 hybrid zone samples was profiled by enzyme restriction with MseI, which differentially cuts the three mitotypes (methods in  33  ). In addition, we included the published mtDNA barcoding data from two additional populations located close to our loc. 15-16 (n = 9 H. arborea/molleri and 3 H. meridionalis,;  22  see File S1). Finally, we fitted sigmoid clines to mitochondrial frequency and nuclear admixture coefficients (STRUCTURE's Q) along a north-south transect running along the Atlantic coast (loc.
A total of 6,363 localities were used, comprising our own records and previously published data (File S3), filtered to avoid spatial autocorrelation and duplication by NicheToolBox  56. We selected localities of each species to obtain the maximum number of localities that were at least 10 km (0. 093°) apart, which resulted in 3,685 presence-only locations for analyses. Altitude and 19 bioclimatic layers (30 arc seconds and 2. 5 arc minutes spatial resolutions) representative of the climatic data over ~1950-2000 were extracted from the WorldClim 1. 4 database (http://www. worldclim. org). An additional three layers were considered: the aridity index (Global Aridity and Potential Evapo-Transpiration; http://www. cgiar-csi. org/data/global-aridity-and-pet-database), spatial homogeneity of global habitat (EarthEnv; http://www.
earthenv. org/texture. html), and the global percent of tree coverage (https://github. com/globalmaps/gm_ve_v1). To consider topography in the model, four landscape layers were calculated with QGIS: aspect, exposition, slope, and terrain roughness index. For the LGM predictions, we used the same 19 bioclimatic layers (2. 5 arc minutes spatial resolution) and applied two general atmospheric circulation models: the Community Climate System Model (CCSM; http://www2. cesm. ucar. edu/) and the Model for Interdisciplinary Research on Climate (MIROC  57  ). Analyses were conducted with species-specific masks covering to the area of occurrence and adjacent regions. To eliminate predictor collinearity before generating the model, we calculated Pearsons's correlation coefficients for all pairs of bioclimatic variables using ENMTools  58.
4  and 5 ). Several limitations prevent a more quantitative assessment of the relationship between the time since contact and the patterns of introgression across the radiation, which would strengthen our results. First, the heterogeneity of the hybrid zone datasets published on Hyla (uneven sampling, diverse types of molecular markers) precludes comparable and spatially-explicit genetic reconstructions of the demographic history of hybridizing populations (e. g. 68 ), which could have provided estimates of the ages since first contact. Genomic phylogeographic data (e. g. RAD-seq) and transect sampling for all hybrid systems would have also allowed to compare admixture patterns with more accuracy. Second, our categorization of the relative ages of contact, taken from phylogeographic data and SDM predictions, assumes that species niches did not change since the LGM, and that old hybrid zones remained static through time.
Acknowledgements We thank  J Jacquiéry  and  O Drillon  for providing samples, as well as  M Stöck  and  G Dufresnes  for their help on the field. V Gvoždík  and two anonymous reviewers provided useful comments on this manuscript. This study was funded by a grant from the  Swiss National Science Foundation  (n°  31003A_166323 ) to NP and a PhD fellowship from the  Faculty of Biology and Medicine of the University of Lausanne  to CD; SLN was supported by a grant from the  Russian Foundation for Basic Research  (n° RFBR. 20-04-00918 ).
Regeneration index was calculated by multiplying the tadpole numbers showing excellent, good, partial or none regeneration phenotypes by 3, 2, 1 or 0 points, respectively. Then, this value was divided by the total number of tadpoles. Quantitative assessment of regeneration was carried out by fixing tadpoles at 7 dpa and imaging individual tadpoles with a stereomicroscope. The regenerated tail length to the body length ratio was measured by Fiji and was used as an indication of the extent of regeneration. Regenerated tail lacks somite segmentation, and this information was used to locate the amputation site. For tadpoles that were not able to regrow any tail, the stump length was measured. Investigators were not blinded during data acquisition or analysis. Any sample with fewer than three tadpoles in a replicate were omitted from further analysis.
Acknowledgements The transgenic testes used in this study and pTransgenesis vectors were obtained from the  European Xenopus Resource Centre , curated with funding from the  Wellcome Trust/BBSRC , and maintained by the  University of Portsmouth, School of Biological Sciences. The CFP-NTR construct was provided by  J. Mumm  and  M. Zuber. We thank  H. Ma  for use of their stereoscopes. We thank  R. Jones-Green  for excellent animal care. We thank  E. Rawlins ,  B. Simons  and  B. Steventon  for their critical reading of the manuscript.
Funding C. A. is funded by  University of Cambridge and Cambridge Trust. J. J. and J. B. G. are funded by a grant from the  Wellcome Trust  ( 101050/Z/13/Z ). T. W. H is funded as part of a  Wellcome Trust   Strategic Award  to study cell fate decisions ( 105031/D/14/Z ) and  EMBO Long-Term Fellowship  ( ALTF 606-2018 ). This work is funded by a grant from the  Wellcome Trust  ( 101050/Z/13/Z ) and supported by  Gurdon Institute  core grants from  Cancer Research UK  ( C6946/A14492 ) and the  Wellcome Trust  ( 092096/Z/10/Z ). Open access funding provided by the  University of Cambridge. Deposited in PMC for immediate release.
Data collection, data analysis and framework validation We conducted semi-structured interviews, as is common in case research  (Yin 2014) , where semistructured interviews are used to identify the important factors and to collect information on the companies' DSD. Three test interviews with potential respondents were used to improve and complete the questionnaire. The 33 sectoral respondents included 12, 12 and 9 interviewees from fashion, consumer electronics and online retail, respectively. All 33 were logistics managers or directors of logistics. The 18 logistics experts included 5 academic experts and 13 industry experts. The interview protocol was designed as follows. The first part focused on the general characteristics of the company, and mainly included fixed response questions designed to simplify the analysis of the cases.
The interviews were transcribed and filed to create a trail of case evidence. The transcripts were sent to the respondents for corrections and approval and then coded using NVivo software. We grouped answers based on code and case to identify differences and similarities across industry sectors  (Voss, Tsikriktsis, and Frohlich 2002). The coding protocol was based on the factors included in the generic conceptual framework. Additionally, open coding was applied, to allow us to extract new factors from the interviews. To check the intercoder reliability, 6 of the 51 interviews were double coded by a second independent researcher familiar with the research topic. Diverging codes and case results were discussed within the research team to reach a consensus. We synthesised the results of the three cases on a within-case and across-case basis.
Quantitative reverse transcription PCR (RT-qPCR) Total RNA was extracted using TRIZOL reagent (Invitrogen) following manufacturer's instructions. cDNA was then synthesized using the Trans Script First-Strand cDNA Synthesis Super Mix Kit (Trans Gen Biotech, Beijing, China). qPCR was performed in a CFXTM real-time thermal cycler (Bio-Rad, Hercules, CA, USA) using a Trans Start Top Green qPCR Supper Mix kit (Trans Gen Biotech). Data analysis was performed with the comparative ΔCt method using GAPDH as internal control  [13]. The sequences of the primers used in this study are listed in Table  S3.
Transwell assay Transwell assay was performed as already described with minor modifications  [14]. Briefly 2x10 5 cells were resuspended in RMPI-1640 medium supplemented with 1% FBS and seeded in the upper chamber of a 24-well transwell plate with 8 μm pore size. The lower chamber was filled with RMPI-1640 supplemented with 10% FBS. 6 hours after seeding, the transmigrated cells were fixed with 4% paraformaldehyde and stained with crystal violet. Pictures were acquired using an Olympus BX51 microscope and the number of cells was counted using imaging J software.
Live-cell imaging Live-cell imaging for cell migration analysis was performed using AS MDW live-cell imaging system (Leica Microsystems) controlled by the Image Pro software (version 6. 3; Media Cybernetics). Cells were cultured at 37 °C in a humidified 5% CO 2 atmosphere. Bright-field images were acquired every 5 min over 4. 5 h. Cell migration was tracked manually using the ImageJ plugin MTrackJ and plotted with the origin of migration superimposed at 0. 0.
CD82 is glycosylated in ovarian cancer cells Numerous studies demonstrated that CD82 plays a pivotal role as inhibitor of tumor metastasis in various cancers  [10, 17, 18]. However, the role of CD82 in ovarian cancer metastasis has not been investigated. We explored CD82 expression using The Cancer Genome Atlas database (TCGA). No significant changes of CD82 expression were found at different stages of ovarian cancer (Figure  1A ). Moreover, no association between CD82 expression and lymphovascular invasion was observed (Figure  1B ). The expression of CD82 in human biopsies from primary and metastatic ovarian cancers was analyzed using IHC. We did not observe any significant change in CD82 expression in ovarian metastatic and paired primary tumors (Figure  1C, S1A ). Interestingly, western blotting data indicate that CD82 mainly detected at around 55 kDa in primary tumors, while a reduction of 55 kDa and a significant increase of smaller between 35 and 55 kDa detected in metastatic tissues (Figure  1D, S1B ).
Glycosylation of CD82 at Asn157 impairs ovarian cancer cells migration in vitro We next investigated whether the glycosylation of CD82 has any impact on ovarian cancer cells migration. A transwell migration assay demonstrated that CD82, N129Q and N198Q mutants significantly reduced migration of ES2 cells. In contrast, the N157Q mutant, the double mutant N157/198Q and the triple mutant 3NQ did not exert any significant effect on migration of ES2 cells (Figure  2A ). Similar results were observed in scratch assay (Figure  2B ). Consistent results were also obtained in SKOV3 stable cell lines (Figure  S2D-E ). Taken together these data strongly imply that the glycosylation of CD82 at N157 is sufficient for CD82-mediated inhibition of ovarian cancer cells migration. To provide a more exact assessment of the migratory potential, we analyzed the migration distance of control, CD82 and N157Q mutant cells using time-lapse assay.
Multidimensional datapoint clouds representing large datasets are frequently characterized by non-trivial low-dimensional geometry and topology which can be recovered by unsupervised machine learning approaches, in particular, by principal graphs. Principal graphs approximate the multivariate data by a graph injected into the data space with some constraints imposed on the node mapping. Here we present ElPiGraph, a scalable and robust method for constructing principal graphs. ElPiGraph exploits and further develops the concept of elastic energy, the topological graph grammar approach, and a gradient descent-like optimization of the graph topology. The method is able to withstand high levels of noise and is capable of approximating data point clouds via principal graph ensembles. This strategy can be used to estimate the statistical significance of complex data features and to summarize them into a single consensus principal graph.
Introduction One of the major trends in modern machine learning is the increasing use of ideas borrowed from multi-dimensional and information geometry. Thus, geometric data analysis (GDA) approaches treats datasets of various nature (multi-modal measurements, images, graph embeddings) as clouds of points in multi-dimensional space equipped with appropriate distance or similarity measures  [1]. Many classical methods of data analysis starting from principal component analysis, correspondence Introducing a penalty for excessive branching of elastic principal graphs (α parameter) Principal graph ensemble approach This publication Estimating confidence of branching point positions, constructing consensus principal graphs Reducing the computational complexity of elastic principal graphs This publication Accelerated procedures and heuristics in order to enable constructing large principal graphs Use of GPUs for constructing elastic principal graphs This publication Scalable Python and R implementations of elastic principal graphs (ElPiGraph), improved scalability and introducing various plotting functions List of used abbreviations: GDA-geometrical data analysis; TDA-topological data analysis; ElPiGraph-elastic principal graphs; GPU-graphical processing unit; CPU-central processing unit; kNN-k neareast neighbors; SOM-self-organizing maps; LLE-local linear embedding; UMAP-Uniform Manifold Approximation and Projection for Dimension Reduction.