dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, the expected proportion of marker successes can be determined based on the distribution of marker successes. The proportion corresponding to the mode of the distribution can be used as the expected proportion of successes. This strategy can be established over the dataset to analyze or over a larger compendium of cytometry data. In our approach, cell clusters can be matched to multiple other cell clusters depending on the consistency of clustering results. While circular graph representations provide explicit ways to represent these results, this could lead to results difficult to interpret in some situations. A solution consists of representing for each cluster only the best matching cell cluster. When generating circular graphs, users used this feature to make the representations easier to interpret.
Additionally, the parallel coordinates and parallel heatmap representations that we propose in CytoCompare extends the regular parallel coordinates representations that we originally used in the SPADEVizR package  [4]. MDS and circular graph representations generated by CytoCompare are interactive through HTML files and the D3. js library. These interactive features are essential to interpret comparison results from large cytometry datasets in an easy and explicit way. Additionally, cell clusters can be manually grouped in circular graphs to better visualize the phenotypic associations between the different cell populations and subpopulations.
Conclusion CytoCompare is a flexible R package for automatically comparing the phenotypes of cell clusters identified by automatic gating algorithms in high-dimensional cytometry data. These phenotypic comparisons of cell clusters are critical to benchmark results from different clustering approaches and are met by system biologists in many analytical situations. For each comparison of two cell clusters, CytoCompare computes a distance measure and a p-value to determine whether the cell clusters are different or not. Our approach can quantify the contribution of each marker in the phenotype comparisons, which is a critical point for the interpretation of cell populations. Multidimensional scaling and circular graph representations can be generated to make these comparison results easily interpretable and explicit.
Data and Methods We apply a transformation technique on T and S profiles coined functional principal component analysis (fPCA) in the multivariate case  (Berrendero et al. , 2011; Ramsay & Silverman, 2005). The source code used for the analysis is available as an R package via Github (https://github. com/EPauthenet/fda. oce). In this section we briefly describe the data sets and methods used and we refer the reader to  Pauthenet et al. (2017)  for further details on applying fPCA to a collection of T and S profiles.
In Situ Data The PF detection is validated by projecting in-situ MEOP  (Roquet et al. , 2018)  and ARGO  (Argo, 2000)  profiles on the fPCA basis. Since 2004, elephant seals have been equipped with high-accuracy conductivity-temperature-depth satellite-relayed data loggers providing T and S profiles along their tracks  (Treasure et al. , 2017). Here we use the profiles that have been interpolated on a regular vertical grid (1-dbar spacing) from the MEOP-conductivity-temperature-depth database  (version 2017-11-11). We found 54,667 MEOP profiles extending from 5 to at least 300 m deep in our study area over the 2007-2017 period. One key advantage of this data set is the availability of finely resolved sections across the PF at different times of the year  (Roquet et al. , 2009)  that will be used to assess the robustness of our criterion.
Acknowledgments We are grateful for helpful comments and discussions to  Giovanni Dosi ,  Jean-Luc Gaffard ,  Nobuyuki Hanaki ,  Francesco Lamperti ,  Matteo Sostero ,  Marcelo Pereira  and  Murat Yildizoglu  as well as to the participants to ISCEF 2016 conference in Paris, the 4th Workshop on  Complex Evolving Systems Approach in Economics in Sophia-Antipolis , the EMAEE 2015 in Maastrich, the WEHIA 2015 conference in Sophia-Antipolis. All usual disclaimers apply. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements No. 649186 -ISIGrowth  and No. 640772 -DOLFINS.
LIDAR surveys (as with most remote sensing techniques) needs to be ground-truthed to detect any errors in data acquisition or processing deficiencies  (Gutierrez et al. , 2001). This can be done by checking the elevations using traditional surveys or Real Time Kinematic (RTK) GPS. Traditionally air photographs, satellite images, and topographic profiles were used to assess coastal systems and plan fieldwork. The advent of platforms like Google Maps, Google Earth, NASA Worldview and NASA Word Wind provide free imagery collected over time, bolstered the amount of data available (Figure  2 ). LiDAR penetrates the vegetation that often obscure details of the morphology in aerial imagery and removes this obstruction during processing. Digital terrain models created form LiDAR data refine the morphology detecting subtle dune topography.
2014 ). The classic prograded barrier system located near Moruya, Australia, offers an example of the detail and lateral extent mapped in LiDAR (Figure  1a ) as compared to the original two-dimensional topographic profile (Figure  1b ). This LiDAR captures the uniform shoreline progradation represented by the series of beach/foredune ridges (yellow with high crest in red, Figure  1a ) as well as interactions from inlets, tidal creeks and open ocean (green and off-white colours, Figure  1a ). This barrier morphology can be used to either: target these areas modified by natural or human processes to understand their impact or avoid them to isolate the influence of storms, sea level, and sediment supply versus accommodation space. The display or rendering chosen to analyse and present LiDAR data can impact interpretations.
The LiDAR data guided research on the remnants of the four relict barrier islands displaying classic foredune ridge sequences preserved in the eastern section of the embayment (white circles in Figure  2d ). Each sequence likely formed across the entire embayment, similar to the present-day prograded barrier island (black circle in Figure  2d ). However, subsequently they became modified by tectonics and river dynamics that completely eroded the central to western portion of the barrier islands (see insets a and b in Figure  2d ). Given how rapidly and drastically coastal landscape changes, being able to select a Google Earth image collected around the same time as the LiDAR is instrumental to providing good correlation in the overlay. It is optimal to use and publish LiDAR data augmented with aerial imagery.
GPR Once the surface morphology is analysed, the next step to determine how a barrier formed is to study the history preserved in the shallow subsurface. The LiDAR data can be used to inform where best to acquire detailed stratigraphy using geophysics. Ground-Penetrating Radar (GPR) is a high-resolution geophysical technique can image dune, beach and nearshore facies with decimetre resolution over kilometres of coast (e. g. Buynevich et al. , 2009; Jol et al. , 1996). To achieve subsurface imaging, GPR emits short pulses of electromagnetic energy (microwave radiation) into the ground  (Jol et al. , 1996). These transmitted high-frequency radio waves are sensitive to the electrical conduction properties of the material being penetrated (dielectric permittivity) and differences in permittivites cause them to be reflected or refracted or scattered back to the surface.
Concluding remarks Utilizing GOaL on prograded barriers provides insights on coastal evolution over spatial and temporal scales spanning form the present-day beach to paleo-beachfaces formed over millennia. LiDAR produces 3D images of the barrier morphology informing where best to collect 2-3D GPR records of dune, beach and nearshore stratigraphy, which intern informs which specific stratigraphic layers should be targeted for OSL dating. In addition to following the simple order to this methodological approach, a few general recommendations can maximize building and interpreting these GOaL datasets: 1) diligence in rendering LiDAR data and overlay with aerial imagery, 2) use appropriate gain control on GPR data and ground-truth, and 3) identify exactly what facies within the stratigraphy is dated and choose the most appropriate age model for the sample.
Acknowledgements We would like to thank  Duncan FitzGerald  and  Ilya Buynevich  for sharing their knowledge and enthusiasm for GPR and coastal science. Many thanks to:  Peter Annan  of  Sensors and Software  for helping me customize GPR specifically for this research during a three day Pulse Ekko course in Canada,  Mads Toft of Mala GPR Australia  for insights gained while trying to get UOW's unit fixed,  GBG Australia  for offering replacement units and geophysical advice, as well as everyone at  Geophysical Survey Systems, Inc. (GSSI) in New Hampshire  for their collaboration and support over the past 20 years. Much appreciation to  John Begg ,  Navin Juyal , and  Vikrant Jain  (in New Zealand) as well as  Seoyoung Heo ,  Christina Neudorf ,  Luke Gliganic ,  Daniela Mueller ,  Thomas Doyle ,  Heidi Brown , and  Zenobia Jacobs  (in Australia) for sharing their expertise in LiDAR and OSL.
The data obtained during the two NETCARE ship campaigns, together with data previously available in the PMEL sea surface database (https://saga. pmel. noaa. gov/dms/, last access: 16 February 2019), were used to develop a new satellite-based model allowing the estimation of DMS at the global and regional scales  (Galí et al. , 2018). As can be seen in Fig. 5d , the satellite-based model provides a DMS map with an unprecedented spatial resolution (8 days, 28 km × 28 km pixels). The DMS climatology based on the 3-D process-based model simulation shows a range similar to the  Lana et al. (2011)  climatology, but higher spatial variability, in line with the satellite-based climatology (Fig. 5e ). Together with the remote sensing approach, the numerical model is being used to help interpret the new NET-CARE DMS data set, as well as to investigate longer-term and larger-scale variability, such as impacts of sea-ice reduction on DMS production  (Hayashida, 2018).
Nevertheless, the continuous measurements at Arctic ground stations remain our most valuable data set to assess long-term trends. There is a significant need to enhance the instrumental capabilities at these stations, for example with key continuous measurements of SO 2 , NH 3 , VOCs, and aerosol composition across all size ranges to further our understanding of many of the issues described above. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-19-2527-2019-supplement. Author contributions. JPDA coordinated and wrote substantial portions of the paper. All other co-authors contributed text and/or reviewed the paper. All co-authors either wrote a first-author paper as part of the NETCARE project or else contributed in a substantive manner to the research conducted in the project or presented in the paper.
Acknowledgements. NETCARE was funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada under its Climate Change and Atmospheric Research program, with additional financial and in-kind support from Environment and Climate Change Canada, Fisheries and Oceans Canada, the Alfred Wegener Institute, and the Major Research Project Management Fund at the University of Toronto. Colorado State University researchers were supported by the US Department of Energy's Atmospheric System Research, an Office of Science, Office of Biological and Environmental Research program, under grant no. DE-SC0011780, the US National Science Foundation, Atmospheric Chemistry program, under grant no. AGS-1559607, and by the US National Oceanic and Atmospheric Administration, an Office of Science, Office of Atmospheric Chemistry, Carbon Cycle, and Climate Program, under the cooperative agreement award no.
The MTase methylates self-DNA whereas exogenous DNA is cleaved by the cognate REase due to different methylation patterns. RM-MTases are classified into 4 different groups based on biochemical properties (e. g. their subunit organization, their recognition of palindromic vs asymmetric target DNA sequences…)  8. The REBASE database is designed to identify REases and MTases and currently lists thousands of putative RM-MTases  9. Many studies, particularly in Escherichia coli, have described the role of DNA methylation in discriminating between the parental and newly-synthesized strand using the DNA mismatch repair (MMR) system  10. During replication, errors may occur, and the MMR system excises the wrong base on the newly-synthesized unmethylated DNA strand  10. The MMR system requires Dam (DNA adenine MTase), an MTase that is not coupled with a restriction enzyme and so called "orphan" or "solitary" MTase  5.
Results Predicted MTases in P. luminescens TT01. The P. luminescens TT01 genome harbors 47 genes which are annotated as methyltransferase or methylase, most of them encoding putative RNA MTases or protein MTases. Only 8 genes (plu0087, plu0338, plu0600, plu2710, plu2942, plu3417, plu3449 and plu3462) are annotated as DNA methyltransferase or DNA methylase  45. Analysis of the P. luminescens TT01 genome using REBASE revealed 12 putative DNA MTase-encoding genes, i. e. the 8 genes cited above, plus plu0233, plu1935 and plu4197 which are annotated as encoding CHP (conserved hypothetical proteins) or unknown proteins, and plu4319 which is annotated as encoding a "Type I site-specific deoxyribonuclease HsdM". Prediction of the protein domains revealed an S-adenosyl-L-methionine-dependent methyltransferase domain (Interpro domain IPR029063) in all of the 12 MTases.
Methylome modifications by Dam-MTase overexpression. We then investigated whether the high proportion (99. 8%, Table  2 ) of the methylated GATC motifs identified in a reference condition could be increased by Dam MTase overexpression. The methylome of a strain overexpressing the Dam MTase was therefore analyzed and compared to its control strain (harboring an empty plasmid). The number of modification marks identified in the strain overexpressing the Dam MTase and in the control sample (harboring an empty plasmid) was 41175 and 41119, respectively (Table  2 ). The 68 modification marks differing between the two samples were distributed as follows: 57 in motif I (GATC) which is recognized by Dam MTase, 2 in motif II, 2 in motif IIIa, 4 in motif IIIb and 3 in motif IV (detailed data can be found in the genome browser).
Methods Strains and growth conditions. The P. luminescens TT01 bacterial strains were routinely grown in Luria broth (LB) medium with a 180 rpm agitation at 28 °C. As required, antibiotic concentrations used for bacterial selection were gentamycin at 15 μg mL -1 ; polymyxin B (polyB), 100 μg mL -1. The AMP-R subpopulation was isolated as previously described  44. Briefly, addition of polymyxin B to WT cells grown in LB at an OD 540 of 0. 3-0. 4 (EP) led to a decrease in OD due to the death of the AMP-sensitive population (about 0. 5% of the WT population was found to be AMP-R). The AMP-R cells were then collected when the OD 540 had returned to 0. 3 in the presence of polymyxin B. Genomic DNA was extracted on these cells for methylome analysis. Genomic analysis of MTases encoding genes. The REBASE database  9  was used to identify the putative DNA MTases in the P.
Distribution of the MTases in other Enterobacterial organisms was performed using a BlastP search on the NCBI non-redundant protein database with default parameters and a maximal target sequences set at 1000. Single-molecule real-time (SMRT) DNA sequencing. Genomic DNA was extracted from bacteria grown in LB and harvested at an OD 540 of 0. 3-0. 4 (EP), OD = 0. 9 (LE), OD = 1. 5 (SP), and after 24 h of growth (OD > 3, LS) as follows. Bacterial cells corresponding to 2 ml of culture were washed in PBS and pellets were stored at -80 °C. To perform lysis, cells were resuspended in 200 µl of TSE-lysozyme for 15 minutes at 37 °C, followed by addition of 640 µl EDTA pH8 0. 5 M and 160 µl SDS 10% and incubated 15 minutes at 60 °C. Lysates were incubated for 1 hour at 56 °C after addition of 20 µl proteinase K (20 mg•ml -1 ), cooled on ice and incubated 5 minutes at room temperature with 30 µl of RNAse A (20 mg•ml -1 ).
Precipitation of contaminants was performed by addition of chilled 350 µl potassium acetate 5 M and a centrifugation step (10,000 × g for 10 min at 4 °C). The genomic DNA was purified with magnetic beads (Sera-Mag Speed beads, Thermo-Scientific) as previously described  60. The DNA libraries were prepared according to PacBio guidelines: 20-kb Template Preparation Using BluePippin Size-Selection System (15-kb size cutoff); shearing at 40 kb was performed using Megaruptor system (Diagenode); sizing at 17 kb was performed using BluePippin system (Sage). Libraries were sequenced on one or two PacBio SMRT cells at 0. 25 nM with the Protocol OneCellPerWell (OCPW), P6C4 chemistry and 360 minutes movies on a Pacific Biosciences RSII instrument (GeT-PlaGe, Toulouse, France). The raw data were processed with the PacBio SMRT Analysis Suite (version v2.
3. 0 p4). For samples EP, SP and AMP-R, the reads were assembled de novo, with the high-quality Hierarchical Genome Assembly Process HGAP. 3 algorithm and no rearrangement was observed with progressiveMauve 2. 1. 0. a1  61. Conserved Synteny LinePlot revealed 100% conservation of synteny groups between the TT01 genomes studied, with a synton size ≥3 and the P. luminescens TT01 NC_005126 genome as the reference (data not shown). DNA methylation detection and motifs identification after SMRT sequencing. DNA methylation was determined using the RS_Modification_and_Motif_Analysis protocol within SMRT Portal 2. 3. 0p4 which uses an in silico kinetic reference and a Welch's t-test based kinetic score detection of modified base positions  62  with parameters set as follow: subread/polymerase read length > = 500, polymerase read quality > = 80 and modification QV > = 30.
A score of 30 for the "Modification QV" is the default threshold for calling a position as modified, and corresponds to a p-value of 0. 001. Homemade script was used to keep methylated bases for adenine or cytosine with score > = 30 and known motifs. Whole Genome Bisulfite DNA sequencing (WBGS) and DNA methylation detection. Genomic DNA from bacteria grown in EP and SP was extracted as described above and was sequenced using Illumina MiSeq technology as previously described  63. Fastq files were trimmed for adapters and low quality bases with Trim Galore! (v0. 4. 4)  64  then mapped to the public reference genome (NC005126) with Bismark (v0. 17. 0)  65. Picard tools were used to remove duplicated reads. Then methylation calling was performed with Bismark_methylation_extractor (v0. 17. 0) for every single cytosine  65.
PluTORF2709 IPR029063, IPR025931, IPR025931 27200 II/R-M plu2710, PluTORF2710 CTCGm6AG (motif V) 3211074-3213464 RGP66 plu2942 M. PluTORF2942 IPR007757, IPR029063 24665 Solitary Gm6ATC 3452873-3453517 P71 plu3417 M. PluTORF3417 IPR002941, IPR029063, IPR001091 21450 Solitary ? 4034728-4035285 P80 plu3449 M. PluTORF3449 IPR012263, IPR029063, IPR023095 29802 Solitary ? 4059699-4060469 P80 plu3462 M. PluTORF3462 IPR002941, IPR001091, IPR029063, IPR002052 18578 Solitary ? 4068792-4069265 P80 plu4197 M. PluTORF4197 IPR009528, IPR029063 18824 Solitary CTGCm6AG 4911020-4911514 GI94 IPR003356, IPR022749, hsdS (plu4320), GGm6AN6RTGA(motif hsdM (plu4319) M. PluTORF4319 IPR004546, IPR029063, 57671 I/R-M S. PluTORF4319, hsdR IIIa)/TCm6AYN6TCC 5043966-5044616 GI95 IPR002052 (plu4322), PluTORF4319 (motif IIIb) a Gene name and protein label of cognate partners (if any) is indicated.
We were interested in individuals' actual question-asking in seminars, to quantify directly any disparity that might exist. With regard to the second aim, we were interested in perceptions of question-asking in seminars, to understand the motivations and beliefs that underlie any disparity. Thus, our data collection also took two approaches. First, we ran an online survey that collected data on over 600 academic respondents' self-reported attendance and question-asking in seminars, their perceptions of others' question-asking behaviour in seminars, and their beliefs about why they themselves and others do and do not ask questions in seminars. Second, we collected observational data at almost 250 seminars in 10 countries to quantify the attendance and questionasking behaviour of women and men in departmental seminars.
Using these two data sets, we asked three questions. First, we asked whether there was a gender disparity in the question-asking of audience members in academic seminars (Question 1). Using data collected in the survey, we asked academics whether they perceived a disparity in women's question-asking in seminars (Q1a). We also used our observational data to describe women's and men's actual question-asking behaviour at seminars (Q1b). Second, we aimed to understand why there is a disparity in women's question-asking in academic seminars (Q2). Using the survey data, we asked both women and men why they did not ask questions when they wanted to, and for those that thought there was a gender disparity in question-asking, we asked why they believed there to be a disparity (Q2a). Next, we used our observational data to identify factors associated with the disparity (Q2b).
Observation of seminar participation To determine the extent of the gender disparity in question-asking during academic seminars, we observed seminars and recruited colleagues through personal contact to do the same. Because these data were collected passively at public events, ethical approval was not needed (following https://memforms. apa. org/apa/cli/interest/ethics1. cfm). Observers were in the same fields as the authors (biology or psychology), chosen to represent as much geographic distribution as possible; they were based in 10 different countries and 35 different institutions. We solicited observers' help by explaining the motivation for the study and our preliminary findings (see Supplementary Material 2). In the end, more than 90% of people that were invited to act as observers reported observations.
Data were collected opportunistically during seminars that the observers normally attended in their institutions and these seminars are therefore likely to be a representative sample of the broader experiences of academics. We provided all observers with written guidelines prior to the start of their observations (see Supplementary Material 2). During the initial period of observations at the University of Cambridge, two of us (AJC and DL) attended six seminars together but independently scored them. This yielded identical observations regarding the gender of the first person to ask a question and the total number of questions asked by each gender, and the counts of the audience numbers were within 0-2 people, suggesting that the guidelines are sufficiently specific for comparison across observers.
Experimental manipulation of time given to questions We (AJC and DL) collected preliminary observations of question-asking from the University of Cambridge during the 2014-2016 academic years (N = 62, comprising 18, 18, and 26 seminars in each of three departments). These data indicated a correlation between the number of questions asked and the imbalance in questions, with the imbalance approaching 0 as more questions were asked (linear mixed effects model with department as a random effect: β ± S. E. = 0. 02 ± 0. 009, t = 2. 02). Based on this preliminary finding, we hypothesised that we could increase the number of questions asked by women by increasing the amount of time devoted to questions after seminars. We thus designed a manipulation at two institutions to test whether decreasing the length of talks (and thus, theoretically, increasing the time allotted to questions) would lead to more equal questionasking from male and female audience members.