dataesr/scientific-paragraphs-categorization huggingface dataset:
B Ripr PF3D7_0323400 3261 Full-length NA Cyrpa PF3D7_0423800 1188 Full-length PDB Code: 6MPV. A Msp1 PF3D7_0930300 5163 Msp1-19 Model based on 1OB1 template Msp3 PF3D7_1035400 1065 Full-length NA Msp4 PF3D7_0207000 963 Full-length NA Msp6 PF3D7_1035500 1116 Full-length NA Ralp1 PF3D7_0722200 2250 Full-length NA Resa PF3D7_0102200 3464 Full-length NA Sera5 PF3D7_0207600 3435 C-terminal PDB Code: 2WBF Sera8 PF3D7_0207300 2536 Full-length NA Gametocyte Tramp PF3D7_1218000 1059 Full-length NA Pfs48/45 PF3D7_1346700 1347 Full-length, 6C PDB Code: 6E62. A Celtos PF3D7_1216600 549 Full-length Model Based on 5TSZ template https://doi. org/10. 1371/journal. pcbi. 1009801. t001 Fig 1. Distribution of haplotype diversity, nucleotide diversity, and Tajima's D values amongst countries for each antigen.
The breadth and variety of educators at the seminar was further evident when we discussed our teaching practices, materials used, assessment methods -collectively we covered the full range of coursework, graded exercises, and end of course examination -and activities. By discussing and summarising our backgrounds, we set different tasks and got learners to implement solutions using different tools; including Processing, JavaScript, R/ggplot, or Tableau. Through this discussion, we realised that by choosing one of these tools, we (as the teacher) were developing different skills in the students. For instance, Processing encourages people to break down the solution into marks on the page, whereas Tableau encourages people to think at a higher level of abstraction. Together, we discussed visualization activities such as the Five Design-sheet method for visualization design  [63] , workshops for data comics and storytelling  [6] , methods for design immersion  [33] , design spaces and patterns to practice visualization design  [46] , tangible visualization with physical tokens  [38] , sketching exercises for visualizing two quantities  [60] , gamification  [1, 43] , methods for eliciting self-reflection on personal data  [78] , critical thinking sheets  [66] , engaging people with physicalization  [40, 61] , stealth learning  [20] , and activities to improve data literacy  [10, 34].
In another example, context not only provides relevant data, but engenders an emotive response. In 2015, during a refugee crisis that was happening in the European Union (EU), Huron et al. [41]  ran a physicalization creation workshop at the Design Research Society's conference. Two participants presented a paper chain physicalization (Fig. 3 ), where each human figure on the paper chain represented an asylum seeker. When presenting their work, they announced emotively "they are your responsibility now". The migration crisis provided the context, which impacted the entire experience: the organizers' choice of dataset to share, the participants' creative process and delivery performance, and finally the public's reception.
Considerations: • When designing an activity, consider both the contexts of the creative process (e. g. , where and when it is taking place, what conditions surround it, social pressures, cultural conditions) and the contexts of the activity itself (e. g. , datasets, medium, audiences, expectations). • Devise activities that are open to accommodate examination of diverse contexts by those involved in the creative process (e. g. , social, environmental, political, cultural). • Participant diversity can benefit the creative process as tasks are examined and explored from different contexts and perspectives.
PROVIDING FREEDOM TO ENCOURAGE CREATIVITY One way to encourage creativity in learners is to give them freedom in what and how to design and develop their visualization representation ideas. It is important to allow and foster creativity in learners when teaching how to design and build data visualizations. For example, as first-time data visualization instructors, we experimented with a free-form data visualization teaching approach. We used the "Design Study 'Lite' Methodology"  [75]  to outline the course. While this course was offered for the first time in an engineering department in this format, students from different disciplines or backgrounds could join our data visualization class, pick their own dataset and choice of the target audience, and choose a developing platform to build an innovative data visualization.
We collected informal feedback from students throughout the semester about students' experiences as learners. From the collected feedback and personal observations, we share insights and the lessons learned. Although the course was offered in an engineering department, students from other programs, including Human-Computer Interaction, Data Science, and Medicine, registered for the course. Having students from different disciplines laid the grounds for each individual to offer their unique perspectives during in-class discussions. Flexibility in data selection. Students were given the option to bring their own data for their course project. Subsequently, they chose a variety of datasets such as results of research projects, performance data at the workplace, and entertainment and leisure-related data.
Learners found the ability to choose a dataset of their interest valuable as they could get creative and offer an innovative visualization beyond the already available representations. Flexibility in audience selection. By having the option to choose the audience of data visualization, students said they could target their visualization design for audiences beyond their classmates. For example, one student chose to design a data visualization representing all air flight accidents over the past 100 years for people who have a flying phobia. Their goal was to show the infrequency of flight accidents. Other students mentioned visualizing their research data empowered them to better articulate their idea and communicate important insights about their data to their supervisors and peers. Flexibility in selecting tools for creating the visualization systems ensured that students could learn and apply visualization techniques regardless of their programming knowledge.
Choosing a tool. The question about which visualization authoring methods and tools as strategies to support learning is a often a tricky one for instructors when designing activities and assessment for a given audience. Choosing the right programming language, is one of the major decisions that defines how visualization design and creativity is taught. Computational solutions such as D3, R/ggplot or Python are extremely powerful in that they support both reproducibility and expressiveness, but they also require to integrate programming instruction to teach these languages, or setting stringent pre-requisites, as the learning curve is steep. Light-weight computational solutions include more approachable wrapping libraries such as Observable  [17] , Vega  [70] , or Processing  [30] , allowing to lower the threshold for novice learners, while supporting stealth development of programming skills  [20].
ACKNOWLEDGMENTS This work was funded by  BMK  under the  ICT of the Future program  via the  SEVA  project (no. 874018 ), by the  Austrian Science Fund  as part of the  Vis4Schools  project ( I 5622-N ), by the  GFF N Ö  as part of the dissertation project  VisOn  ( SC18-008 ) and  VisToon  ( SC20 -014 ), by  French government  funding managed by the  National Research Agency  under the  Investments for the Future program (PIA)  grant  ANR-21-ESRE-0030  ( CONTINUUM ), and by  Canadian NSERC  funds ( RGPIN-2018-05072 ) and ( RGPIN-2021-04222 ). We thank  Prof. Tatiana von Landesberger  for her contributions to the discussions, and the reviewers for their comments and suggestions.
However, analyzing these data can potentially also allow quantification of membrane dynamics  (Holcman et al. , 2015)  and may give insight into organelle organization. Software developed to analyze SPTs generally fall into two categories: (1) those where parameters are extracted along individual trajectories, and (2) those based on spatially combining these trajectories to recover intrinsic properties. The first category includes SpotOn  (Hansen et al. , 2018) , used to fit multi-states diffusion models from the distribution of displacements, the 4P-parameter algorithm, used to reconstruct chromatin dynamic  (Amitai et al. , 2017; Shukron et al. , 2019) , and, more recently, the 4P-Gaussian-Mixture  (Basu et al. , 2021) , used to subsegment trajectories into confined and unconfined regions.
For the second category, common methods include diffusion/drift maps and potential well extractions pioneered in  Hoze et al. (2012)  (https:// bionewmetrics. org/super-resolution-single-particle-trajectoriesusing-stochastic-analysis/) or the SR-Tesseler algorithm  (Levet et al. , 2015) , used to reconstruct and extract biophysical parameters from the local density of points. Another package is TRamWAy  (Beheiry et al. , 2015) , used to reconstruct diffusion and energy maps, but this approach does not reconstruct the potential well boundaries. Here, we have developed a method based on hybrid algorithms and automated an analysis pipeline for SPTs (Figure  1C ) based on the Langevin  (Schuss, 2009)  equation of motion to provide statistical analysis of these data. This method estimates biophysical characteristics and is capable of reconstructing nanodomain sizes and boundaries using the classical physical model of a potential well  (Kramers, 1940; Schuss, 2009) , well known since Kramer's work in 1940  (Kramers, 1940).
This method allowed us to characterize calcium channel organization in the membranes of hippocampal neurons. We also present an algorithm that reconstructs a network from SPTs based on the clustering of low-velocity trajectory fragments and use it to reconstruct ER network topology as well as estimate the timescale of lysosome trafficking and ER network interactions. Finally, these methods allowed us to extract the motion of trajectories relative to the reconstructed network, thereby revealing the redistribution of trajectories inside the ER of normal and atlastin-null mutant cells  (Niu et al. , 2019). The diversity of datasets used here demonstrate the broad applicability of these methods to cell biological processes. The algorithms we developed here are available in an ImageJ plugin with a graphical interface for processing individual experiments and a programming interface for batch processing.
Automatic determination of bins with the highest density of points (Figure  S3Ba ). 2. For each such bin, we iterate over square regions of increasing width w k around the bin center. For each iteration k, we apply a principal-component analysis to estimate the semi-axis a k ; b k , the center c k , and a score L (Equation  24 ) based on the points falling in the square of size w k (Figures  S3Bb  and S3Bc ). We iterate until we reach the maximum size of the well set by the user (Figure  S3Bc ). 3. In the termination step, we select the optimal value of the iteration providing the optimal parameter (see Method details). The reconstruction is illustrated in Figure  S3C  for three wells. To evaluate the performances of this hybrid algorithm, we constructed ground-truth datasets consisting of trajectories following Equation (2), with the energy field given by 12 for different types of potential wells (see Methods S1 for the detail of the procedures).
Moreover, for sufficiently large regions, the algorithm can distinguish between a parabolic field and a Brownian motion confined by impenetrable walls (see Methods S1 and Data S1). At this stage, we have thus validated the hybrid algorithm by using ground-truth datasets to identify and automatically reconstruct nanodomains. This hybrid algorithm outperformed the others when reconstructing the exact shape of the nanodomain and its energy. Analysis of SPTs for the endogeneous voltage-gated calcium channels reveals their organization and weaker stability in nanodomains We applied the new hybrid algorithm to reanalyze trajectories of calcium voltage channels (CaV2. 1) on the surface of neuronal cells for two overexpressed splice variants, CaV2. 1 D47 and CaV2. 1 + 47, previously shown to shape synaptic short-term plasticity  (Heck et al.
The starting point is to color trajectory displacements depending on their instantaneous velocity, which reveals a dynamical segregation of the ER into nodes and tubules (Figures  S7A-S7C ). Based on this segregation, we developed the GRA to recover the ER structure from SPTs (see Methods S1). This procedure allows us to reconstruct a two-dimensional graph for the organelle network that can be used to study further statistical properties. Finally, we tested the GRA on a groundtruth dataset exacted from a live-cell image (Figure  S8 ; Methods S1). We simulated trajectories on the extracted graph and applied our algorithm on these trajectories to reconstruct the topology of the underlying ER network. This procedure gave a satisfactory reconstruction result (8. 1% error), thus validating the present algorithm (Table  S7 ).
The values of R and N depend on the local number of recorded trajectories and can vary inside the image. For each dataset, these values can be determined such that the computed clusters overlap with the structure of the organelle formed by the trajectories. We now present the steps of the algorithm: 1. We form the ensemble of points belonging to low-velocity trajectory fragments S = & X i ðt j Þ; kDXi ðtj Þk Dt % v th '.
ACKNOWLEDGMENTS D. H. 's research has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Programme  (grant agreement no. 882673 ),  Plan Cancer-INSERM   Projet19CS145-00 , and  ANR NEUC-0001. E. A. and P. P. are supported by the  UK Dementia Research Institute , which receives its funding from  UK DRI, Ltd. , funded by the  UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK. J. H. is currently funded by the  EMBL   Interdisciplinary Postdoc Program (EIPOD)  supported by the  European Union 's  Horizon 2020 Research and Innovation Programme  under  Marie Sk1odowska-Curie Actions  (grant agreement no. 847543 ). 2. We apply the dbscan procedure with parameters ðR U ; N 1 Þ to obtain a first ensemble of K clusters c 1 ;.
S. cerevisiae Sen1 is a fail-safe transcription termination factor for RNAPIII One of the important findings of this study is the demonstration that Sen1 can directly promote the termination of RNAPIII transcription. Multiple lines of evidence support the notion that Sen1 functions to remove polymerases that have escaped primary termination at the very 3′ end of RNAPIII-dependent genes. These include the high-resolution detection of RNAPIII occupancy by CRAC and the analysis of the different RNA species produced from model tRNA genes (Figs. 2  and 4  and figs. S2 and S4). A mechanistic explanation for our observation that Sen1 cannot operate at primary terminators is provided by our finding that in vitro Sen1 function in termination is hindered by RNA secondary structures, which are typically present in RNAPIII transcripts close to the first terminator.
10 4. Monocharged peptides and unassigned charge states were excluded from the MS/MS acquisition. The maximum ion accumulation times were set to 100 ms for MS and 35 ms for MS/MS acquisitions, respectively. Label-free quantification was done on Progenesis QI for Proteomics (Waters, Milford, MA, USA) in Hi-3 mode for protein abundance calculation. MGF peak files from Progenesis were processed by Proteome Discoverer 2. 4 with the Mascot search engine. The Swissprot protein database was typically used for interrogation. A maximum of two missed cleavages was authorized. Precursor and fragment mass tolerances were set to 7 parts per million and 0. 5 Da, respectively. The following posttranslational modifications were included as a variable: oxidation (M) and phosphorylation (STY). Spectra were filtered using a 1% false discovery rate using the percolator node.
Dataset processing CRAC reads were demultiplexed using the pyBarcodeFilter script from the pyCRACutility suite  (57). Next, the 5′ adapter was clipped with Cutadapt, and the resulting insert was quality-trimmed from the 3′ end using Trimmomatic rolling mean clipping  (58). We used the pyCRAC script pyFastqDuplicateRemover to collapse PCR duplicates using a 6-nt random tag included in the 3′ adapter. The resulting sequences were reverse-complemented with the Fastx reverse complement that is part of the fastx toolkit (http://hannonlab. cshl. edu/fastx_toolkit/) and mapped to the R64 genome with bow-tie2 using "-N 1" option. Reads mapping to multiple sites (e. g. , most reads mapping to the body of tRNA genes) were attributed randomly to one of the matching sites. Therefore, the RNAPIII signal at those repeated sequences represents the average of all identical regions.
Bioinformatic analyses All sequence files and annotations were obtained from Saccharomyces Genome Database (S. cerevisiae genome version R64-2-1). T-tracts were annotated by first searching for sequences containing at least four consecutive thymines (for the plus strand) or adenines (for the minus strand) using the unix command line tool grep and then generating coordinate files by the awk command. The resulting files were then combined into a single BED file (table  S7 ) using BEDOPS suite  (60)  with the everything option. For each tRNA gene, the primary terminator was defined as the first T-tract after the 3′ end of the mature tRNA. These primary terminators were identified by comparing the mentioned T-tract annotations and the tRNA annotations with the closest tool from BEDTools  (61).
" Last, samples were subjected to 10% denaturing polyacrylamide gel electrophoresis (PAGE), running for 1 hour at 40 W in 1× TBE buffer. Gels were exposed on a PhosphorImager screen overnight at -80°C, and screens were scanned using a Typhoon scanner (GE Healthcare). Images were analyzed using the ImageQuant software (GE Healthcare). funding (F. R. M. ; programme Equipes 2019 to D. L. ), China Scholarship Council PhD fellowship (to J. X. ), Fondation pour la Recherche Medical PhD fellowship FDT202012010433 (to J. X. ), LabEx "Who Am I?" (ANR-11-LABX-0071 and the Université de Paris IdEx ANR-18-IDEX-0001) postdoctoral fellowship (to J. X. ), French Ministry of Education and Research PhD fellowship (to U. A. ), Fondation ARC pour la recherche sur le cancer PhD fellowship (to U. A. ), Boehringer Ingelheim Fonds PhD fellowship (to M.
G. ), EMBL International PhD program (to M. G. ), EMBL funding (to C. W. M. ), and Deutsche Forschungsgemeinschaft grant PE 2079/2-2 (to J. S. and V. P. ). Author contributions: J. X. conducted all experiments except for those in Table  1 , performed bioinformatic analyses together with Y. C. , and contributed to manuscript writing. U. A. and N. H. performed experiments in Table  1. M. G. and C. W. M. purified RNAPIII and RNAPI and provided experimental advice. J. S. and V. P. purified Sen1 protein. O. P. conceived the project. O. P. and D. L. designed experiments, supervised research, and wrote the manuscript with inputs from all coauthors. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The RNAPIII CRAC data have been deposited in NCBI's Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE174738.
Acknowledgments: We thank  G. Wentzinger  for technical assistance and other members of the  Libri laboratory  for discussions. We thank  F. Fiorini  and  H. Le Hir  for sharing the Upf1 protein. We thank the  Roscoff Bioinformatics platform ABiMS  (http://abims. sb-roscoff. fr)for providing computational resources and support. This work has benefited from the facilities and expertise of the high-throughput sequencing core facility of I2BC (www. i2bc. paris-saclay. fr/). We thank the proteomics facility of the  Institut Jacques Monod , supported by the  Region Ile-de-France (SESAME) ,  Université de Paris , and the  CNRS , for technical assistance. Funding: This work was supported by  Centre National de la Recherche Scientifique  (to D. L. and O. P. ),  Agence National pour la Recherche  grant  ANR-16-CE12-0001-01  (to O.
Acknowledgments We would like to thank  Yifei He ,  Alexandre Homrich ,  Slava Rychkov ,  Roberto Tateo ,  Alexander Zamolodchikov  and  Bernardo Zan  for useful and insightful discussions. Work of SN is supported by  NSF  grant  PHY-1915219. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( QUASIFT grant  agreement  677368 ).
, 2013; Schneider et al. , 2014)  is used here. Figure  11  shows on a Taylor diagram the quality of the simulated discharge when compared to observations at the 35 stations presented in appendix A. For discharge, the database of GRDC  (Fekete et al. , 2000)  (http://grdc. bafg. de) is used. It has been enhanced with national sources of information for a number of European countries. The stream temperatures were obtained through the GESM database  (Programme, 2017)  (https://gemstat. org/). The diagnostics are computed with the available observations over the period 1983-2002. For discharge these results are well in-line with previous validations of ORCHIDEE  (Ngo-Duc et al. , 2005)  and the experience gained during the data assimilation experiments performed  (Wang et al. , 2018). The discrepancies between the models in the Euro-Mediterranean region can be attributed to missing processes in the land surface model.
Others proposed a hybrid of deep architecture with hand-crafted techniques  [33, 34]. Some other works attempted to address fish detection and species classification within the same framework  [18, 19, 35]. All of these CNN-based works used classical multi-class CNN, which is trained at once on all classes in the dataset. With this structure, the CNN treats all classes the same. However, some classes are naturally more likely to be misclassified than others, especially for classes that have fewer samples or are difficult to classify. In a live fish recognition task, the species in the training set can be grouped into categories. We can group species according to their degree of difficulty. Difficult species require special treatment when learning the CNN model. On the other hand, incremental learning allows a model to integrate new examples without having to perform a complete re-training and without destroying the knowledge acquired from old data.
• Extensive experiments and comparisons of results with other methods are presented. The proposed approach outperforms state-of-the-art fish identification approaches on the LifeClef 2015 Fish ( www. imageclef. org/lifeclef/2015/fish accessed on 7 July 2022) benchmark dataset. The rest of this paper is organized as follows. Section 2 outlines related work. Section 3 presents the proposed approach for underwater live fish species classification. Section 4 describes the benchmark dataset used in this work, provides the experimental results and performs a comparative study. Finally, the conclusion and perspectives are discussed in Section 5.
Qin et al. [30]  proposed a CNN with three convolutional layers trained from scratch on the Fish Recognition Ground-Truth dataset. They also proposed in  [33]  a hybrid deep architecture with traditional methods to extract features from fish images. In this architecture, principal component analysis (PCA) is used in two convolutional layers, followed by binary hashing in the non-linear layer and block-wise histogram in the pooling layer. Then, spatial pyramid pooling (SPP)  [40]  is used. Finally, classification is performed with a linear SVM. Compared to their first work  [30] , the proposed hybrid deep architecture improved the accuracy by only 0. 07%. Sun et al. [34]  applied two deep architectures, PCANet  [41]  and NIN  [42] , to extract features from underwater images. A linear SVM classifier is used for classification.