dataesr/scientific-paragraphs-categorization huggingface dataset:
E. Tracking In order to track a large amount of particles, we have developed a Python script based on a neuronal network detection algorithm  [52]. This neuronal network is taught to recognize a particle from a video frame and to discard anything else. Once trained, we use this neuronal network to detect particles on every frame of the video. The experiments are recorded using a contrast microscope (Leica) with a 10x objective at 2 frames per second. A particle fits in a box of 8-9 px 2 on the resulting video. Our software relates the detected particles between frames to find the trajectory of a given particle. The following paragraphs detail the procedure to train the neuronal network and to construct trajectories from the particle we detect on each frame. To train the neuronal network, we construct a dataset of images that show one or many particles.
We perform this step manually by cropping a square image from a video frame. This cropped image is centered close to a particle's center, keeping the selected particle within, and is three times the size of a particle. We also draw a square region of the size of the particle on this cropped image, like the squared, coloured, boxes in Fig. 1e , that indicate the location of the particle on the cropped image. We construct our dataset by applying this procedure on many particles. We extend this dataset by also adding cropped images that do not contain any particle. This way, our dataset is composed of 8700 images with particles and 8700 images that do not show any particle. We input this dataset to a neuronal network based on tiny YOLOv3  [52]. We simplify the network by transforming the images to 8-bit grayscale and considering there are only two sizes for the boxes surrounding the particles.
Particles may leave the sample; if this happens their position is fixed to that frame and is removed after a period of inactivity. Also particles can adhere electrostatically to the PDMS, or to each other, or be inactive. Therefore we end the trajectory if the particle is inactive for a long period of time. We use GPU acceleration for the neuronal network detection, based on the latest NVIDIA™ drivers. The filtered trajectories are given in x,y pixel coordinates, which we map to the cell coordinate of our model. This transformation is obtained after aligning the axes of the recorded videos and decomposing the image over the lattice structure in Fig. 1 e , shown with dashed lines. We then assign each of the now aligned (x,y) coordinate to a cell index, using the Python library shapely.
F. Post-selection of trajectories We organize the recorded trajectories in a database for each experimental configuration. We only keep track of the position in the unit-cell index to eliminate any intrinsic motion of a particle within a cell and thus to reduce noise. In the manuscript we consider two post-selected configurations, (1) where particles start at the top left and bottom right corners (see Fig. 1g ) and,  (2)  where particles start from a uniform distribution (see Fig. 1h ) over the lattice. The two post-selection procedures are as follows: 1. Post-selected corner. We select particles starting from the corners in two steps. We first select trajectories that go through one of the two corners. Then, among the selected trajectories, we remove all frames before the one where the particles first enters one of the two corners.
Acknowledgements. A. G. G and S. T. are grateful to  M. Brzezinska ,  M. Denner ,  T. Neupert ,  Q. Marsal ,  S. Sayyad , and  T. Sépulcre  for discussions. A. G. G and S. T acknowledge financial support from the  European Union   Horizon 2020 research and innovation program  under grant agreement No  829044  (SCHINES). A. G. G is also supported by the  ANR  under the grant  ANR-18-CE30-0001-01  (TOPODRIVE). L. P. is grateful to  J. Katuri  for discussions about ratchet design and to  J. Fuentes  for the PDMS wells fabrication. L. P acknowledges financial support from  MINECO  for the  FPI   BES-2016-077705  fellowship. M. G. thanks  MINECO  for the  Juan de la Cierva fellowship  ( ĲCI2016-30451 ), the  Beatriu de Pinós Programme  ( 2018-BP-00305 ) and the  Ministry of Business and Knowledge of the Government of Catalonia.
Versatile Video Coding (VVC) is the most recent international video coding standard jointly developed by ITU-T and ISO/IEC, which has been finalized in July 2020. VVC allows for significant bit-rate reductions around 50% for the same subjective video quality compared to its predecessor, High Efficiency Video Coding (HEVC). One year after finalization, VVC support in devices and chipsets is still under development, which is aligned with the typical development cycles of new video coding standards. This paper presents open-source software packages that allow building a complete VVC end-to-end toolchain already one year after its finalization. This includes the Fraunhofer HHI VVenC library for fast and efficient VVC encoding as well as HHI's VVdeC library for live decoding. An experimental integration of VVC in the GPAC software tools and FFmpeg media framework allows packaging VVC bitstreams, e.
For standard development and verification, the VVC test model (VTM) reference software has been used. VTM is well suited to evaluate the compression potential of VVC but not optimized for runtime and lacks important realworld features on the encoder side such as rate control, subjective optimizations, parallelization as well different efficiency / runtime trade-offs. Furthermore, VVC specifies the bitstream of coded video as well as the decoding processes. To use a video coding standard in real world application scenarios, its integration into transport systems layer such as MPEG-2 transport stream (MPEG2-TS)  [5] , ISO base media file format (ISOBMFF a. k. a. MP4 file format)  [6]  tracks is key. In addition, VVC encapsulated in MP4 file format can be used in dynamic adaptive streaming over HTTP (DASH)  [7].
Real-world VVC implementations in chipsets and devices together with mp4 file format support for file-based playback and streaming typically emerge 2-3 years after finalization of a standard. However, already today open-source software packages allow to use VVC with Fraunhofer HHI's encoder VVenC and decoder VVdeC. VVC bitstreams can be encapsulated in mp4 file format and packetized into DASH segments using GPAC while extraction and playback is feasible with VVdeC integrated into FFmpeg and GPAC. This paper describes these software packages in Section 2. Section 3 demonstrates how to use them for file Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.
VVenC The Fraunhofer Versatile Video Encoder, VVenC, is an optimized software encoder implementation of the VVC standard. Its source code is freely available on GitHub under a 3-clause BSD copyright license for both commercial and non-commercial use  [8]. After the initial release in September 2020, which is only two months after finalization of VVC, version 1. 0. 0 has been released in May 2021, for which the results are presented. VVenC has initially been derived from the VTM reference software. Its main purpose is to maintain the high coding efficiency of VTM while running significantly faster and providing real-world application features. While VVenC itself is implemented in C++, the software package includes a C library interface as well as two sample applications that can be used as standalone command line encodersa simple application designed for ease-of-use as well as an expert app resembling the VTM interface.
Currently, only one slice and tile per picture is supported, without sub-pictures. VVenC already anticipates versatile application scenarios by incorporating all Main 10 profile screen content coding tools and providing a constrained encoding mode optimized for use in open-GOP adaptive streaming with resolution change  [11]. 2. 1. 2. Results. The multi-threaded runtime and PSNR-based Bjøntegard Delta (BD) rate compression performance (negative numbers mean bit-rate reduction) for VVenC 1. 0. 0 relative to the HEVC reference software (HM-16. 22) is shown in Figure  1. The results are based on encoding of the HD and UHD sequences from the JVET common test conditions  [12]  test-set under JVET random access conditions. Additionally, the figure contains results for VTM 12. 0 as well as for two alternative freely available encoders, for HEVC (x265 3.
VVdeC The Fraunhofer Versatile Video Decoder, VVdeC, is an optimized VVC software decoder implementation which is freely available on GitHub under a 3-clause BSD copyright license  [13]. Analogue to VVenC, the license covers both commercial and noncommercial use. The latest release, v1. 1. 1, was published in May The decoder software has been derived from VTM as well, with subsequent optimizations and parallelization. Being based on VTM, it has performance limitations beyond the complexity of the VVC standard itself. The decoder allows playback of HD video at 60 frames per second (fps) on most modern computers using only 2 or 3 threads, and 60 fps UHD playback on more powerful modern workstations with sufficient processing cores to fully exploit the multi-threading potential  [15]. The VVdeC package contains a simple and easy-to-use C library interface and a standalone decoder application capable of decoding elementary VVC bitstreams into raw YUV video data.
FFmpeg VVC integration with VVdeC FFmpeg is a well-known multimedia framework which provides a set of libraries to record, convert and stream any type of audio and video data. In order to support VVC in FFmpeg, a parser and a Code Bitstreams Type (CBT) were implemented in the development branch (currently under review). This first step enables support for VVC elementary streams but does not provide a system level support with standard delivery mechanisms such as MPEG2-TS or DASH. To further extend the VVC capabilities in FFmpeg, the support for VVdeC and MPEG2-TS as well as ISOBMFF/DASH binds have been implemented by ATEME in a public FFmpeg fork  [16]. The support for VVC video stream type has been added to MPEG2-TS demuxer in the libavformat. The ISOBMFF demuxer has been upgraded to support VVC tag 'vvc1' and 'vvcC' MOV table entry.
VVC support in GPAC The GPAC multimedia framework provides a set of tools to package, stream and playback multimedia content. It is wellknown for its MP4Box tool, an mp4 file packager and HTTP streaming (HLS) segmenter, but GPAC also enables end users to build custom multimedia processing pipelines through its filterbased architecture  [17]. The systems aspect of VVC have been implemented in the master branch of GPAC and are part of the nightly builds of the project. This covers the most common application use cases:  MPEG-2 broadcasting: multiplexing and demultiplexing an MPEG2-TS program with VVC content  MP4 file packaging: importing and dumping VVC bitstreams  MPEG-DASH and HLS content packaging GPAC supports all codecs integrated in FFmpeg's libavcodec library, including VVdeC as included in  [16].
VVenC can be used to prepare and encode the video sequence (Figure  2 ), while GPAC can package and multicast the services using DASH/ROUTE, in an ATSC-3. 0 fashion. At the receiver side, the GPAC client integrated with libavcodec/VVdeC can be used to deliver TV services to the viewer (Figure  2 ). The proposed  solution is currently part of a response to the SBTVD TV3. 0  [19]  call for technology, utilizing presented components for playback. In the following subsections two encoding and distribution scenarios using VVC are demonstrated step-by-step. In the presented examples the VVC elementary stream (ES) file extension does not matter, because GPAC including MP4Box always probe the input data.
0, Hamamatsu) with an effective pixel size of 65 nm, while phase-contrast and quantitative phase images were obtained with another CMOS camera (DCC3260M, Thorlabs) with an effective pixel size of 87 nm. For SLIM measurements we took six consecutive images with a phase delay of nπ/2, where n=  [1, 2, 3, 4, 5, 6] , with 200 ms exposure each. Out of these, we obtained three phase images (from images 1-4, 2-5, and 3-6, respectively), and took the average to obtain the final phase image. Including delays due to software, the acquisition of one final phase image took <3 seconds. Micro-manager was used to control the microscope and acquire images within MATLAB.
Measurement of cytoplasmic contour, dimensions, surface area, and volume Cell dimensions were obtained from phase-contrast images acquired using the SLIM module, essentially as described previously  (Oldewurtel et al. , 2019). Specifically, we used the MATLABbased tool Morphometrics  (Ursell et al. , 2017)  to determine cell contours. The image-formation process through the microscope, but also the contour-finding routines of Morphometrics can bias and distort the contour. We correct and calibrate for this based on epi-fluorescence images of cells stained with the fluorescent membrane stain FM 4-64. Since the calibartion is generally cell-shape dependent, we collected FM 4-64 images for wild-type cells and ponA-expressing mutant cells (yhdG::cat-pHyperSpank-ponA,ponA::kan) with different levels of inducer grown in S7 50 +GlcCaa medium.
For FM 4-64 image acquisition, we focused on the middle of the cell based on phasecontrast microscopy through the epi-fluorescence port, which yields a sharper cell contour than the SLIM module. To correct these images for diffraction, we simulated membrane-stained cells as described  (Oldewurtel et al. , 2019)  using the MATLAB based tool BlurLab  (Ursell et al. , 2017)  and using the point-spread function (PSF) of the microscope (based on 100 nm fluorescent beads (TetraSpeck, Thermo Fisher)). We applied the correction found in silico onto membrane contours obtained by Morphometrics to obtain the true (physical) contour of the periphery of the cytoplasm. In addition to the epi-fluorescence images, we obtained phase-contrast images of the same cells using the SLIM module. We then overlaid the measured contour of the phase-contrast cell with the corrected membrane contour obtained from the membrane dye and measured their respective offset as a function of cell width and as a function of the distance from the cell pole (Figure  1  -figure supplement 1B).
This correction was used to correct the contours of all cells measured with the SLIM module. Finally, given the calibrated contours of the cell, we used Morphometrics to apply a mesh-grid of 1 px (87 nm) step-size. This routine also gives the centerline of the cell, which is used to determine cell length. We then assume cylindrical symmetry around the centerline and infer cell surface and cell volume from the sum of the surfaces and volumes of truncated conical wedges with height and width given by the meshes. For the confirmation of continued MreB activity after cerulenin treatment through TIRF microscopy (Figure  4  -figure supplement 2) we also segmented phase-contrast images using the Morphometrics tool. However, those data were not calibrated against FM4-64 images. This is not relevant for the calculation of instantaneous surface-growth rate or density of moving MreB filaments.
Acknowledgements We thank  Richard Wheeler  and  Ivo Boneca  for assistance with cell wall extraction. This work was supported by the  European Research Council (ERC)  under the Europe Union's  Horizon 2020 research and innovation program  [Grant Agreement No. ( 679980 )] to SVT, the  French Government 's  Investissement d'Avenir program   Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ) to SVT, the  Mairie de Paris   "Emergence(s)" program  to SVT, a  National Institutes of Health  Grant [ DP2AI117923-01 ] to ECG, as well as support from the  Volkswagen Foundation  to SVT and ECG.
Statistical Analysis Data are presented as median values with interquartile ranges (IQR) since some variables were not normally distributed. Sd LDL-C and TG data were analyzed after log-transformation of values since these parameters are not normally distributed. Categorical variables are reported as frequencies and percentages. Analyses were performed by gender because of significant differences between men and women with regard to lipid and lipoprotein levels. Univariate analyses were performed by one-way ANOVA for continuous variables. When a significant group effect was observed, the Tukey post-hoc analysis for equally distributed variables and the Games-Howell post-hoc analysis for non-equally distributed variables were performed. Mantel-Haenszel test for trend was used for categorical variables.
The 75th percentile of serum sdLDL-C level in this cohort was 40. 1 mg/dL, which we used as the cut point. Multivariate linear regression models adjusted for baseline characteristics, such as age and other CVD risk factors were performed to examine the effect of smoking status on lipid profiles. Smoking status was analyzed after converting 3 categorical variables. Values of body mass index (BMI), systolic blood pressure, HbA1c, and drinking habits known as CVD risk factors were chosen as independent variables. No variables showed strong correlation with each other. Akaike's information criteria (AIC) were indicated as fit index. All statistical analyses were performed using SPSS software, version 23 (IBM, Armonk, NY). A P value of ＜0. 05 was considered to confer statistical significance in ANOVA, Mantel-Haenszel test for trend, and multiple regression analysis, while a P value of ＜0.
Subjects Our study population consisted of 40,871 subjects who received a medical examination at the health evaluation centers in Osaka, Japan, between April of 2016 and March of 2019. All subjects agreed to participate in this study and the study protocol had previously been approved by the Medical Research Ethics Committee of Tokyo Medical and Dental University, Tokyo, Japan (M2000-2209). In subjects who had more than one visit during the study period, data on only the first visit was used for this study. Subjects on lipid lowering medication as well as those with missing data were excluded from the analysis (Fig. 1 ). As CVD risk was reported to be reduced by about half after 4 to 5 years smoking cessation  4)  , subjects were classified into four groups, non-smokers (NON), exsmokers who had quit smoking for ≥ 5 years from the time of the examination (EX1), ex-smokers who had quit smoking for ＜5 years (EX2), and current smokers (CUS) according to self-reported results on a questionnaire.
Acknowledgements and Notice of Grant Support The contribution of all subjects in this study is gratefully acknowledged and gratefully appreciated. This study was supported by the university research fund (No. 32782 ) and the research grant (No. 2A232 ) from  Denka Co. , Ltd. , Tokyo, Japan to AM, and presented in part at the 51st Annual Scientific Meeting of the  Japan Atherosclerosis Society , July 11th, 2019 in Kyoto, Japan.
These carbon equilibrium contents of the δ and γ phases in the δ + γ domain, as well as the diffusion coefficients as a function of temperature are obtained from the data tabulated in the ThermoCalc software with the TCFE5 and MOB2 thermodynamic and mobility databases. In the γ domain, the growth of the nucleus is only controlled by atomic diffusion, the final size rf of the new grain when Ae3 is reached is calculated according to the cooling speed using the following equation (adapted from  [19] ): 𝒓 𝒇 = 𝒓 𝜹+𝜸 + ∫ 𝟐 (𝑫(𝑻). 𝒅𝑻 𝒗 ) 𝟎. 𝟓 𝑨𝒆 𝟒 𝑨𝒆 𝟑 (3) To carry out these calculations, the Fe-9Cr-1Mo equilibrium phase diagram is used. The calculated two-phase δ + γ domain therefore extends from Ae5 = 1420 °C to Ae4 = 1240 °C, and the single-phase γ domain from Ae4 = 1240 °C to Ae3 = 850 °C (Figure  1 ).
However, it is important to keep in mind that powder particles are fully melted and mixed during the PBF-LB and DED-LB processes, meaning that their initial microstructure and eventual chemical segregations are erased during the building process. Prior austenite grains were determined using Kurdjumow-Sachs and Nishiyama-Wassermann relationships, with a method similar to the one used in ARPGE software  [34]. This allows to plot prior austenite grains boundaries (in white on Since the real thermal cycle undergone by the material during the building is not precisely measured in this case, it is not possible to compare the grain sizes obtained here with the ones computed in section 3. Only the order of magnitude could be compared. The increase in prior austenite grain size between the bottom and the top of the wall is explained by an increase of the temperature around the melt pool, as the height of the wall increases (cooling by conduction is reduced and the energy supplied by the laser accumulates)  [12].
ACKNOWLEDGEMENTS Authors would like to thank Patrice Peyre and Thierry Malot (PIMM Laboratory, Arts et Métiers ParisTech) for their expertise and their help in the production of the DED-LB samples used for this study, Fernando Lomello (CEA-Saclay) for his help in the production of the PBF-LB samples and Elodie Rouesne (CEA-Saclay) for her help in EBSD acquisition and analysis.
Acknowledgments We would like to thank all participants in the OMERACT  MRI special interest group  ( SIG ) for input in the project. Thanks to  Sjoerd van Beest  for participating in the first reliability exercise. We would also like to thank all participants in the  Nor-Hand study. PGC is supported in part by the  National Institute for Health Research (NIHR) Leeds Biomedical Research center, United Kingdom. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR  or the Department of Health.
All GIWAXS patterns were taken at 0. 15° incident angle with typically 5 second exposure. The GIWAXS patterns were analyzed using the GIXSGUI package and Single Crystal-Crystal Maker software. 58 -situ microscopic imaging. Few microliters of the precursor solutions were drop casted on a thin microscopic slide that was then placed in a Titan microscope. The thin film formation without annealing was imaged using an oil immersion 100X objective over time by taking continuous snapshot images every second. Dynamic light scattering measurements. The precursor solutions were loaded into cylindrical glass cuvettes and the DLS correlation curves were collected at different angle with respect to the incident laser beam. The data were fitted using a single-or bi-exponential decay model to extract the size of the particles in the solution (see details in the method section of the Supplementary information).
The fabricated wet film was immediately transferred to the x-ray diffraction instrument. The film growth was monitored over time for up to 120 minutes. The diffraction measurements were continuously recorded until the film is dried and the diffraction peaks were saturated. The peaks in the obtained diffraction patterns were assigned Miller indices based on the simulated diffraction pattern from the structural data available from our previous work. 25, 26 4 Grazing incidence wide angle x-ray scattering (GIWAXS) Experimental methods: All GIWAXS diffraction spectra used in this paper were high-resolution synchrotron patterns measured at two beamlines: 8-ID-E at the Advanced Photon Source (APS) and 11-BM at the National Synchrotron Light Source-II (NSLS II). At beamline 8-ID-E, samples were placed on a Linkam grazing incidence x-ray-scattering (GIXS) stage (temperature controlled at 25°C) inside a vacuum chamber (10 -4 torr) with the sample 228 mm away from a Pilatus 1M (Dectris) area detector.