dataesr/scientific-paragraphs-categorization huggingface dataset:
For 2D experiments, histon-GFP expressing cells were used and imaged in fluorescence to make cell detection easier. For TFM experiments, the focus was done on the substrate's surface and fluorescent images of the beads were acquired at each frame. At the end of the experiment, the cells were detached using Sodium dodecylsulfate (SDS, Sigma, Saint Louis, MO) at 10% and an image of the beads was taken as a force-free reference. The imaging of PBD-YFP was done using an inverted microscope (Leica, Wetzlar, Germany) with a CSU-W1 confocal spinning-disk module (Nikon, Tokyo, Japan) and a 40X oil-immersion objective. The acquisition was done using Metamorph (Molecular Devices, San Jose, CA), at a 6-10 minutes acquisition rate. The focus was done on the basal plane of the cells and the microscope's hardware autofocus was used to ensure the absence of defocusing.
Finally, the sample was either placed in PBS (for 6-well plates) or mounted on a glass slide with Moewiol. Image analysis-cell migration. The image treatment was performed using homemade ImageJ (FIJI distribution) and Matlab (The Mathworks, Natick, MA) software. In brief, patterns were first detected using semi-automated methods, images were rotated so as to align all patterns on the same direction and positioning noise was removed if necessary using Template Matching ImageJ plugin. Cell contours were detected using the Edge detection function of ImageJ and thresholding with filters on the detected features' size and circularity, and the positions of both cell ends were recorded. For 2D experiments, the nucleus centreof-mass was detected using an in-house program based on the Find Maxima function of ImageJ 17.
The cells were then tracked in time using the track. m (http://site. physics. georgetown. edu/matlab/) program in Matlab to reconstruct full cell trajectories. The trajectory data were organised as in  17  and adapted software was developed for all further analysis (footprint, velocity, auto-correlations, oscillation detection, MSDs. ). TFM analysis. The TFM data were analysed as previously described  29. First, the displacement field of the beads was measured using particle-image velocimetry (PIV), using the matpiv (https://www. mn. uio. no/math/english/people/aca/jks/ matpiv/) function in Matlab. Then the force field was computed using the FTTC 67 program in ImageJ (https://sites. google. com/site/qingzongtseng/tfm). For each time frame, the cell mask was extended so as to engulf all the corresponding force patch, and the force profile along the cell length was computed by averaging the forces within this mask along the line direction.
Statistics and plotting. To test the statistical significance of the difference between data distributions, we used the two-sample Kolmogorov-Smirnov test implemented in Matlab. Throughout the manuscript, to describe the data distributions accurately we used violin plots generated with the violinplot. m function in Matlab  68. Briefly, this data representation includes all the data points as a scatter, superimposed with a smoothed (vertical) histogram of the data and a box-andwhisker plot displaying the median, the lower and upper quartiles and the minimum and maximum of the distribution. The figures were organized and prepared in Inkscape.
Acknowledgements We thank the group members from 'Cell adhesion and migration' team for helpful discussions. This work was supported by the  LABEX Who Am I?  ( ANR-11-LABX-0071 ), the  Ligue Contre le Cancer  (Equipe labellisée 2019), and the  Agence Nationale de la Recherche  (' POLCAM ' ( ANR-17-CE13-0013  and ' MechanoAdipo '  ANR-17-CE13-0012 ). We acknowledge the  ImagoSeine core facility  of the  IJM, member of IBiSA and France-BioImaging  ( ANR-10-INBS-04 ) infrastructures.
Experiments with Human Subjects We validate how multi-session diversity improves user satisfaction in two Web applications: music recommendation, where we generate music channels, and task recommendation in crowdsourcing, where we generate task sessions. 3 https://github. com/Multi-Session-Diversity/WWW2021 4. 1. 1 Music Recommendation. We generate music playlists for users and consider different contexts namely music for long drive, theme party, Sunday morning, and learning a new music style, to observe how diversity affects user satisfaction in different contexts. Dataset. The dataset consists of 727 songs from 54 albums, 47 artists, and 10 genres. The songs are from albums that won the Grammy Best Album of the Year Award between 1961 and 2020. The list of albums and their corresponding genres are from Wikipedia while the other information such as artist, period, popularity, tempo, and duration are from Spotify.
Task Recommendation. In these experiments, we recommend short and long task sessions to workers in crowdsourcing. The short sessions consist of 3 sets each with 3 tasks. The long sessions consist of 5 sets and each set consists of 10 tasks. Dataset. Experiments flow. For each session type (short and long), we collect 100 user profiles, where workers indicate (from 1 to 5) their interest in completing tasks, which are described by a given set of keywords. For each user profile, we generate task sessions using the algorithms in Table  3  and a combination of the following dimensions: skill, reward, duration, and creation date. Additionally, we generate a no-diversity baseline session. In this session, we randomly pick a task type and tasks belonging to that type. Next, workers complete the recommended sessions.
We measure task throughput, quality of the completed tasks with respect to a ground truth, and worker satisfaction. Throughput refers to the average number of tasks completed per minute. Quality refers to the percentage of correct answers from the tasks completed by a worker. To measure quality, we use the answers obtained from the dataset as the ground truth. We use a naïve script that relies on basic equality to evaluate answer correctness. Satisfaction refers to how satisfied workers are with the task sessions (a rating from 1 to 5 provided by each worker). We recruit 200 workers, pay each $0. 03 for profile collection and at least $0. 35 for task completion. Summary of Results. We present the average throughput, quality, and worker satisfaction for short and long sessions in Table  7. Similar to the music experiments, our sample size (n=200) allows our results to achieve 99% confidence level with 10% margin of error.
Large Data Experiments The goal here is to evaluate our algorithms with appropriate baselines (including exact solutions) and compare them qualitatively (approximation factors, objective function value) and scalabilitywise (running time). All algorithms are implemented in Python 3. 6 on a 64-bit Windows server machine, with Intel Xeon Processor, and 16 GB of RAM. All numbers are presented as the average of five runs. For brevity we present a subset of results that are representative. 4. 2. 1 Data Sets. a. 1-Million Song: We use the Million Songs Dataset  [2, 9]  that has 1 million songs (please note the Spotify dataset used in Section 4. 1 is small in scale). We have normalized the data to be between [0, 1]. This dataset also includes artist popularity and hotness, genre, release date and etc.
The presented results are representative and consider tempo and loudness as dimensions. b. Synthetic dataset: The presented results on this are the ones that vary distributions of the underlying dimensions. We sample from three distributions: Normal, Uniform, and Zipfian. For Normal distribution, data is sampled with mean and standard deviation,  = 250,  = 10. For Uniform, dataset is sampled from Uniform distribution between [0,500], and for Zipfian distribution default exponent is set to  = 1. 01. We produce a pool of 2 30 items for each of our three distributions.
4. 2. 4 Quality Evaluation. We vary  (the number of sessions),  (the number of items), and the data distribution. The default values are  =2 13  Comparison against Optimal. Table  8  shows the approximation factors for our algorithms for two default settings: ( = 2 13 ,  = 2 4 ) and ( = 2 10 ,  = 2 7 ) using 1-Million dataset. We can see that our algorithms produce an approximation factor equal to 1 when Intra diversity is minimized and a factor very close to 1 when Intra diversity is maximized. When Inter diversity is minimized, the resulting approximation factors are close to 1. However, when Inter diversity is maximized, the approximation factors are slightly low as our algorithm solves the Intra part of the problem before ordering the sessions to maximize Inter diversity. It is hence bound by the constraints of the solution to Intra.
Nevertheless, the solution formulated by our algorithm for Min-Intra,Max-Inter and Min-Intra,Min-Inter is able to produce a point on the Pareto Front in the optimal solution region which meets both the Intra and Inter objectives. The synthetic dataset mimics this trend as well. Based on the approximation factor results and the above analysis, we conclude that our algorithms produce good and in some cases the best possible solution for the 4 problems we attempt to optimize. Varying. Figure  1  shows how Inter scores change as we vary  from 2 10 to 2 16 for Baseline-MMR, Random and our algorithms. We have omitted the plots for Synthetic data experiments since those results closely follow the result for 1-Million Songs dataset. Figures  1(a)(c ) confirms that our algorithm performs best when Inter diversity is minimized.
Interestingly, Random produces an approximation factor close to 1 for  > 2 13 when maximizing Intra. This is because Intra is maximized when the variance of the sessions are maximized which is one of the side effects of Random. However, Baseline-MMR and Random produce very poor approximation factors when minimizing Intra. Contrarily, our solutions stay close to 1 approximation factor for both minimization and maximization of Intra diversity. As  increases, the Intra scores do not see any drastic change in approximation factors, and always stays close to 1. Varying. Figure  2  presents how Inter scores evolve as we vary  between 2 4 and 2 11 for different baselines compared to our algorithm. We keep  constant at 2 13. The synthetic dataset also mimics this trend. We observe figures 2(a)(c) that our algorithm performs significantly better than other baselines in minimizing Inter diversity.
In Figures  3(b)(d) , Baseline-MMR outperforms our algorithm due to the same reasons mentioned in the varying  and  section of this paper. We also observe that across all 4 scenarios, Zipf produces scores much larger in magnitude than Normal or Uniform distribution. This is due to the range of values in Zipf, which results in larger Intra and Inter scores. Overall, our algorithms stand out to be the best choice, with its best performance being on Uniform distribution. 4. 2. 5 Scalability Evaluation. We compare the running time of the three algorithms for 1-Million dataset. In Figures 5, we vary  and set  to 2 13. Naturally, as  increases, the running time of our algorithms increases. We observe that our algorithms scale very well but are sometimes slightly slower than Random. This is unsurprising, as Random does not even have to do much work to generate sessions (recall that however it performs poorly qualitatively).
Existing works on diversification could be classified as set-based only  [3, 17, 26, 29, 30, 34]  or sequence-based only  [7, 12, 22, 40]. As an example, in  [40] , the authors study sequence-based diversity that is defined as the diversity of any permutation of the items. Another example is  [7] , in which taxonomies are used to sample search results to reduce homogeneity. In  [3] , the authors propose an algorithm with a provable approximation factor to find relevant and diverse news articles. In the database context, Chen and Li  [12]  propose to post-process structured query results, organizing them in a decision tree for easier navigation. In  [8, 24] , the notion of diversity is used in the results of queries to produce closest results such that each answers is different from the rest.
Acknowledgments The work of  Mohammadreza Esfandiari ,  Sepideh Nikookar ,  Paras Sakharkar , and  Senjuti Basu Roy  are supported by the  National Science Foundation ,  CAREER  Award # 1942913 ,IIS # 2007935 , IIS # 1814595 , and by the  Office of Naval Research  Grant No: N000141812838.
From Gonesse to the Val d'Oise, and up to the national-global matrix EUROPACITY project demonstrates multiscalar territorial stakes combining the international ambition of the Greater Paris metropolis, and the issue of local development of a socioeconomically fragile territory. The planned location for the project was in the commune of Gonesse, which is included in larger administrative and political areas, the department and the urban community, which share the management and the development of a group of communes. This set of scales was overwhelmingly favorable to the Europacity project, mostly for economic development reasons, but also for social and cultural ones. Indeed, this part of the Ile-de-France region is unattractive because of its limited public transport services, but also because of its lower socio-economic data and characteristics compared to the Ile-de-France region as a whole.
Acknowledgements We are grateful to  Pauline Vilain  for the exchange of ideas and reflections on Europacity as part of the pedagogical co-animation of a Master's seminar in Paris 8, and for the co-presentation of a paper at the congress of the Association of Tunisian Geographers in 2018. Thanks also to  Louis Dupont  for his insightful proofreading, and his talent as a cross-cultural translator between French and English.
Real-Time Cell Analysis (xCELLigence) MiRNA-and CDDP-mediated cytotoxicity were initially monitored using high-throughput screening with the Real-Time Cell Analyzer (RTCA) multi-plate instrument (xCELLigence System; ACEA, Ozyme, Saint-Quentin-en-Yvelines, France) under normoxia. This system monitors cellular events in real-time by measuring electrical impedance across interdigitated micro-electrodes integrated into the bottom surfaces of 96-well E-plates VIEW (Ozyme, Saint-Quentin-en-Yvelines, Trapp, France). RTCA 2. 1. 0 software calculates the cell index (CI) based on impedance. CI correlates with the area of cells attached to the bottom of the plate as described elsewhere  [58]. Briefly, 3. 5 × 10 3 SW1353 cells/well were plated in 96-well E-plates VIEW. They were left to grow for 24 h before transfection with 20 nM miRNAs using Interferin TM (Polyplus-Transfection, Strasbourg, France).
In each plate, miR-Ctrl was used as a negative control of cytotoxicity. The cells were treated with or without CDDP 24 h post-transfection. Impedance was continuously measured until the end of the treatment (i. e. , 96 h post-transfection and 120 h after seeding). MiRNA effect was established according to three criteria: the shape of the curve, the area under the curve (AUC) and the CI at the end of the experiment. Standard deviations of triplicates were analyzed with the RTCA software. Endpoint morphological analysis of cells was also done in a high-throughput cell imaging system (Cellavista; Roche, Basel, Switzerland) at the end of the experiment.
Cell Cycle Analysis Adherent cells were harvested by trypsinization. The supernatant, containing the detached cells, and the trypsinized cells were pooled, centrifuged (1400 rpm, 10 min), rinsed with PBS, fixed in 70% ethanol, and stored at -20 • C until analysis. Fixed cells were then centrifuged (3000 rpm, 10 min) and rinsed with PBS to remove all traces of ethanol. After centrifugation (2000 rpm, 5 min), the cells were resuspended and incubated for 30 min in a PBS solution containing 20 µg/mL propidium iodide (Sigma-Aldrich, Saint-Louis, MO, USA) and 100 µg/mL RNase (Fisher Scientifics SAS, Illkirch, France). Cell cycle phase distribution was analyzed by flow cytometry using the Cytoflex S Flow Cytometer (Beckman Coulter France SAS, Paris, France). Data were computed with the Cytexpert ® acquisition software (Beckman Coulter France SAS, Paris, France).
We also used McL-1 (S-19), GAPDH (FL-335) and β-Tubulin (D-10) antibodies from Santa Cruz Biotechnology (Dallas, TX, USA), and the Bcl-2 (M0887) antibody from DAKO. After incubation, membranes were washed in TBST, followed by an incubation with HRP-conjugated goat anti-rabbit or mouse IgG antibody (Jackson Immunoresearch, West Grove, PA, USA). Signals were visualized with a chemiluminescence imaging system (ChemiDoc TM Touch imaging system; Biorad, Hercules, CA, USA). Each immunoblot is representative of at least three distinct experiments. Protein expression was estimated by quantifying the density of immunoblots bands adjusted to β-Tubulin or GAPDH (ImageLab ® software; Biorad, Hercules, CA, USA).
Real-Time Detection of Caspase-3/7 Mediated Apoptosis Caspase-3/7 activity was assessed using the Incucyte ® caspase-3/7 green apoptosis assay reagent (Essen BioScience, Ltd. , Royston, UK). This reagent couples the activated caspase-3/7 recognition motif (DEVD) to NucView™488, a DNA intercalating dye. Thus, caspase-3/7 activity correlates with an increase in the green fluorescence in the nuclei. Briefly, 3. 5 × 10 3 SW1353 cells/well were grown in 96-well plates and monitored in the Incucyte ® S3 acquiring images (objective × 10) every 3 h in two separate regions per well after transfection of 20 nM miRNA. Each experiment was done in triplicate and analyzed using Incucyte ® software.
RNA Isolation and PCR Assay Total RNA (including miRNA) was isolated from transfected cells using the NucleoSpin ® miRNA kit (Macherey-Nagel SAS, Herdt, France) according to the manufacturer's instructions. RNA quantity and quality were assessed using the NanoDrop TM 2000 spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA). Reverse transcription was performed to generate cDNA from 1 µg of total RNA with the miScript II RT kit (Qiagen SAS, Courtaboeuf, France). PCR was performed on a CFX96 Touch real Time PCR (Biorad, Hercules, CA, USA). Data were analyzed using CFX manager software. For the detection of miRNAs, we used QuantiTect™ SYBR Green PCR master mix (Qiagen SAS, Courtaboeuf, France) with an universal miScript primer and specific miScript primers provided by Qiagen: Hs_miR-491-5p_1 (miR-491-5p), Hs_miR-342-5p_1 (miR-342-5p), Hs_miR-541*_1 (miR-541-5p), Hs_miR-625_3 (miR-625-5p), Hs_miR-149_1 (miR-149-5p), Hs_RNU6-2_11 (U6 small nuclear 6 or RNU-6B) and Hs_miR-15a_1 (miR-15a).
The cycling conditions were 15 min for polymerase activation at 95 • C, followed by 40 cycles at 94 • C for 15 s, 55 • C for 30 s and 70 • C for 30 s. In addition, melting curves were performed to ensure specificity of PCR products. Expression values of miRNAs were normalized to RNU-6B and to miR-15a with CFX manager software. They were expressed as mean of triplicate samples ± SD. For RNA transcript quantification, we used GoTaq ® qPCR Master Mix (Promega, Charbonnières-les-Bains, France). The following primers were purchased from Eurogentec SA (Angers, France): EGFR F-5 -TGGCATCTTTAAGGGCTCCA-3 , EGFR R-5 -TGGCTAGTCGGTGTAAACGT-3 , PPIA1 F-5 -CGGATTTGATCATTTGGTG-3 , PPIA1 R-5 -CAGGGAATACGTAACCAG-3 , GAPDH F-5 -CCTGCACCACCAACTGCTTA-3 , GAPDH R-5 -GGCCAT CCACAGTCTTCTGGG-3. GAPDH and PPIA1 were both used as endogenous reference genes to normalize EGFR expression, expressed as the mean of triplicate samples ± SD.
Statistical Analysis All experiments were repeated at least three times. Values are reported as means ± SD. Statistical significances of the mean of at least three independent experiments were assessed using one-way ANOVA corrected for multiple comparisons using Dunnett's test, or two-way ANOVA corrected for multiple comparisons using Sidak's test. Alternatively, two-tailed unpaired student's t-test with Welch's correction was used to analyze statistical differences within representative experiments performed in triplicate. Statistical analyses were carried out using GraphPad Prism 7 software (San Diego, CA, USA). p-values of less than 0. 05 were considered significant: *** p < 0. 001, ** p < 0. 01, and * p < 0. 05.
Such regions of synchrotron emission are expected to have a more organized magnetic field and higher linear polarization (e. g. ,  Ginzburg 1979) , which is supported by observations in radio  Lister & Homan (2005)  and optical  Kovalev et al. (2020). More comments on the above-cited sources are reported in Appendix B. Although a source-per-source examination of all radio-optical links is out of the scope of this paper, brief inspection of the MOJAVE data for these 12 sources suggests that, when the optical centroid can be identified with a radio feature in the jet (0346+800, 0749+540, 1652+398, 1742-078, 2223-052), the latter is a quasi-stationary component with a substantial fractional polarization. These results should encourage the MOJAVE team to pursue the regular follow-up of AGNs, including the measurement of radio polarization when possible.
Acknowledgements. The authors are grateful to the referee,  Matthew Lister , for his constructive review that helped in improving the manuscript. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the  Gaia Multilateral Agreement. This research has made use of data from the MOJAVE database that is maintained by the MOJAVE team. NL was funded by the  National Natural Science Foundation of China (NSFC)  under grant No. 11833004  and the  Fundamental Research Funds for the Central Universities of China  under grant No.
Synchrotron Radiation Circular Dichroism (SRCD) SRCD measurements were carried out on the DISCO beamline at SOLEIL Synchrotron as described previously  [45]  (proposal 20201013). Samples (~4 µL) were loaded into a CaF2 circular cell of 10 µm pathlength. Spectral acquisitions of 1 nm steps at 1. 2 s integration time were recorded in triplicates between 320 and 180 nm. (+)-camphor-10-sulfonic acid (CSA) was used to calibrate amplitudes and wavelength positions of the experiment. Data analyses (averaging, baseline subtraction, smoothing, scaling, and spectral summations) were carried out with CDtoolX  [69]. Spectra are presented in units of mdeg versus nm maintaining the same molar ratios for all presented samples. Due to the origin of absorption, spectra of mixed samples (polynucleotides + peptide) could not be standardized to ∆ε.
Synchrotron Radiation Circular Dichroism (SRCD) SRCD measurements were carried out on the DISCO beamline at SOLEIL Synchrotron as described previously  [45]  (proposal 20201013). Samples (~4 µL) were loaded into a CaF 2 circular cell of 10 µm pathlength. Spectral acquisitions of 1 nm steps at 1. 2 s integration time were recorded in triplicates between 320 and 180 nm. (+)-camphor-10-sulfonic acid (CSA) was used to calibrate amplitudes and wavelength positions of the experiment. Data analyses (averaging, baseline subtraction, smoothing, scaling, and spectral summations) were carried out with CDtoolX  [69]. Spectra are presented in units of mdeg versus nm maintaining the same molar ratios for all presented samples. Due to the origin of absorption, spectra of mixed samples (polynucleotides + peptide) could not be standardized to ∆ε.
Acknowledgements. The authors thank the anonymous referee for providing helpful comments that contribute to improving the paper. This work was supported by the  ANR  grant  LYRICS  ( ANR-16-CE31-0011 ). The grids of models have been run on the computing cluster Totoro funded by the  European Research Council , under the European Community's  Seventh framework Programme , through the  Advanced Grant MIST  ( FP7/2017-2022 , No  742719 ).