dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, clinical evidences supporting that observation begin to be compiled. For example, a cohort study of patients with RA previously treated with TNFi or ABA or tofacitinib who newly start TCZ or TNFi showed no increased CV event occurrence among patients with RA under TCZ versus TNFi with a combined HR of 0. 84 (95% CI 0. 56 to 1. 26). 30 Another cohort study confirmed the equivalent impact on CV events incidence of TCZ and TNFi (etanercept). 31 However, these results need to be confirmed by randomised studies with clinical CVD outcomes, such as the ENTRACTE study (NCT01331837); the preliminary results of that study do not highlight a difference in major adverse CV events between TCZ and etanercept-treated patients. 32 o statistical analyses were performed regarding RTX and ABA, since few patients were treated with these drugs.
1665): a painting in the collection of the Mauritshuis, the Hague. These three techniques are: multi-scale optical coherence tomography, 3D scanning based on fringe-encoded stereo imaging (at two resolutions), and 3D digital microscopy. Additionally, scans were made of a reference target and compared to 3D data obtained with white-light confocal profilometry. The 3D data sets were aligned using a scale-invariant template matching algorithm, and compared on their ability to visualise topographical details of interest. Also the merits and limitations for the individual imaging techniques are discussed in-depth. We find that the 3D digital microscopy and the multi-scale optical coherence tomography offer the highest measurement accuracy and precision. However, the small field-of-view of these techniques, makes them relatively slow and thereby less viable solutions for capturing larger (areas of ) paintings.
Calibration and data processing For the MS-OCT setup the desired scanned area was correctly aligned, by means of a built-in video camera, The focal plane of the OCT sample arm optics was set to 0. 4 mm below the zero delay position; this to reduce auto-correlation artefacts in the measured spectra. Then, sample regions in the scanning area were captured, to ensure that there was no height variation larger than 1. 89 mm (depth-of-field) within the dimensions of a single tile. Next, a self-developed program in LabVIEW  [36]  was activated, which fully automatically scans the artwork and keeps it in focus. MS-OCT tile stitching was performed off-line, based on segmented surface data in volume scans. This image stitching algorithm is not dependent on tile overlap (due to high precision translation stages), but image segmentation artefacts can in rare cases result in stitching errors.
Calibration and data processing For both 3D scanning systems, the lens distortion of cameras was calibrated following a multi-view geometry calibration procedure  [37] , using a checkerboard pattern. The white balance and illumination non-uniformity were calibrated using a 300 mm × 300 mm Spectralon panel. The colour and 3D topography of the surface were captured using a hybrid solution of fringe projection and stereo imaging. To measure the topography, a 6-phase shifting sinusoidal grey-scale pattern (fringe) was projected horizontally and vertically on the painting's surface, capturing in total 24 images for a single scan area. One additional image was captured with uniform illumination from the projector, which is used as the colour image. This imaging module was then moved to the next   4 ) the horizontal stage, β the imaging angle, α the tilt angle of the lenses and l the working distance to the painting position and the procedure was repeated.
Offline, fringe unwrapping is applied and a sparse stereo matching was carried out to match the fringes of both camera images. A look-up table was generated for both cameras, encoding both images. Taking into account the camera calibration, a dense stereo matching was performed, using the principle of ray-tracing. The RGB values of the uniformly illuminated image were then mapped onto the XYZ datapoints. Next, a plane was fitted through the datapoints and the data was sampled in a regular XY-grid at respectively 25 µm or 7 µm intervals, which approximates the reconstructed resolution. If a single pixel contained more than one value (due to our non-parallel configuration this can occur), the average of the values was taken. Missing values were interpolated based on surrounding pixels. A detailed description of the image processing can be found in  [19, 20].
Calibration and data processing Prior to starting the scanning, the alignment of the axis and the planarity of movement relative to the platform (and painting) was checked using Hirox-supplied reference targets. A Hirox calibration grey card was used to set the white-balance of the microscope. XY-scale was calibrated using the Hirox-supplied, certified glass scale. The Hirox software was used for programming the automatic acquisition of the individual tiles. The microscope created a series of images in the Z-direction capturing each focus layer individually, between the predefined top and bottom set point. These are then combining into one single all-in-focus image (multi-focus or extended depth-of-field). The microscope then moves to the next position and the multi-focus capturing procedure is automatically started again.
The result for each tile is a TDR file (Hirox 3D file format) which includes a all-in-focus colour image (JPG) as well as altitude in the Z-axis for each pixel. The raw data-the individual focus images-was not saved due to size limitations. Hirox e-Tiling software  [38]  was used to stitch offline the individual 3D tiles (providing identical functionality to the online stitching of the microscope), resulting in large 3D-stitched files.
Pre-processing and alignment of data sets The height data was measured by the researchers and stored in an array that is sampled in µm (rather than pix- els). This enables interchangeable dataset comparison. 3 ) the motorised X-axis which moves the microscope, (  4 ) the motorised Y-axis used to move the painting platform, and l the working distance to the painting Consequently the data was processed by the alignment algorithm, making use of the (open-source) implementations of SciPy  [39]  (scientific computational libraries) and OpenCV  [40]  for Python. The data comparison flowchart is shown in Fig. 5. First, missing data is replaced by a linear interpolation result of the respective height map (Fig. 5a ). Secondly, the data is de-trended in order to minimise mismatching due to angled scanning, orientation of the painting during measurements and bending of the painting (which is a non-rigid object).
Alignment and stitching (of very large datasets) The alignment between data sets was performed using only the 3D data, aligned at the pixel level, applying only rigid transformations. A consequence of such an alignment approach is that, if a data has lateral distortions (e. g. caused by calibration errors or imaging system imperfections), the features of the height maps will not align perfectly. Whether such systematic errors occur remains to be investigated. It then also remains to be investigated if a sub-pixel, non-rigid alignment can be achieved, to improve comparison results (e. g. following the 2D alignment approach applied to other types of multi-modal painting scan data in the Bosch Research and Conservation Project  [42] ). Additionally, as the painting was measured at different resolutions-and therefore needs re-sampling-sampling errors can also influence the comparison between height data.
Furthermore, as all systems offer more than one imaging modality-such as colour information-these might also be included in the alignment algorithm, potentially leading to more robust alignment results. However, care should be taken with this, as it will almost certainly lead to a different result, which then leads to the issue of determining which result is better: all are merely based on optimisation. For instance, in the case of using colour data, robustness of the algorithm to illumination differences-leading to differences in shadowing and shading-needs to be assured. On the scale of the ROI, selected from our case study painting, only MS-OCT and 3D digital microscopy are made up of merged tiles. For the stitched data sets, which was either based on XY-axis displacement (MS-OCT, no blending), or done by the Hirox e-tiling software (3D digital microscopy, method unknown) no obvious tiling artefacts were observed.
It remains to be determined, which strategy is the most suitable for tile stitching and/ or blending, especially in the case when data in the overlapping regions are not in agreement with each other. With the stitching of (very) large datasets, we also run into the issue of error propagation, in the lateral (XY) as well as axial (Z) direction. If tiles a stitched in a sequential manner, small errors in misalignment (XYZ) can lead to much larger errors across the complete surface. Currently no topographical scans were made at the scale of the complete painting, or intermediate resolutions, which might be used for a global-to-local optimisation step in alignment, (potentially) minimising the influence of local errors and the potential for error propagation. We envision that an approach like the one used in the Bosch Research and Conservation Project  [42] , might be extended for topographical data, also dealing with the height (Z) data.
Furthermore, we encountered issues with stitching the High-Res 3D data set, as our current approach to stitching (as is used in for instance  [20] ), is limited by its memory requirements for such large data sets. Also the Hirox e-tiling software did not allow stitching the larger ROI at full resolution. Given the currently unsolved challenges with the nonrigid nature of canvas paintings (also encountered in the case study data sets), and the various challenges related to alignment and stitching (i. e. sub-pixel, non-rigid alignment, (multi) modal alignment considerations, dealing with stitching and blending of overlapping regions, error propagation, memory issues), we deem large-scale painting data height comparison to be beyond the scope of this paper.
These could involve methods that are still practiced (like locally flattening lifting paint with heat), and those (like wax-resin lining) that are seldom used by conservators nowadays  [43]. As the used 3D imaging systems were built with standard equipment (lamps, cameras, projectors, microscope) that are well documented by the supplier and software based on standardised image processing and calibration procedures (using either Matlab, LabView of Hirox supplied software) the scanners can be rebuilt in the future with the same quality. However, currently no (dedicated) software exists for data comparison (over time), which can adequately deal with the global shape variations nor the data size. It also remains to be investigated if the accuracy of these systems is sufficient for monitoring paintings over time, as currently no data exists on the magnitudes of topographical changes occurring in paintings, including those kept under museum conditions.
Acknowledgements The project The Girl in the Spotlight is a Mauritshuis initiative, and involves a team of internationally recognised specialists working within the collaborative framework of the  Netherlands Institute for Conservation+Art+Science+(NI CAS ), with some scientists from other institutions. The authors would like to thank  Hirox Japan  ( Mr. Takeuchi  and  Mr. Nakajima ) for modifying the RH-2000 software specifically for this project, allowing extended XYZ-movement control and offline stitching, and  Mr. V. Sabatier  ( Hirox Europe ) for his help in the construction and alignment of the Hirox bridge frame as well as his support during the 3D digital microscope scan. The authors would also like to thank  Ms. T. T. W. Essers M. Sc. and  Mr. M. van Hengstum M. Sc. for their support with carrying out the 3D scanning.
The present experimental data are analyzed in terms of apparent temperature, resulting from thermal processes occurring in the sample, taking into account the photo-thermal heating and thermal exchanges between the sample and the thermal bath whose temperature is monitored. Indeed, as a consequence, the actual compound temperatures may sizably differ from the data displayed by the temperature controller, which is denoted as "apparent temperature". All the OM, photo-magnetic, and photo-thermal effects results are presented, discussed, and modeled using a microscopic description based on a non-equilibrium extension of the Ising-like model, combined with an equation of the heat balance between the SCO system and its environment (i. e. , light and thermal bath) which gives the temporal dependence of the SCO compound's (or crystal's) temperature.
Acknowledgments We are grateful to  Manuel Houzet  and  Julia Meyer  for inspiring comments on the poisoning processes, and  Teun Klapwijk  for unvaluable discussions. We thank  Jacques Marcus  for his support on the crystal growth. Samples were prepared at the  Nanofab platform and at the "Plateforme Technologique Amont" of Grenoble. This work was supported by the  LANEF framework  ( ANR-10-LABX-51-01 ) and the  H2020 ERC  grant  QUEST No. 637815.
By employing Fast Fourier Transform (FFT) using a Matlab ® script, the waveform of electric potential (*) for a given y-position can be decomposed into multiple harmonics. Figure  9  represents the ten first Fourier transform coefficients for , = 0. 1 over the surface in the case of 2 and 4-phase configurations. In the case of 2-phase SWC, the first harmonic value is very high compared to the others. The coefficient value of the first harmonic is 0. 925, the other harmonics values are less than 5 % compared to the first one. So the effect of harmonics having an order higher than ; = 1 can be neglected. In the case of 4-phase TWC, the strongest harmonics are the fundamental (0. 749) and the third harmonic (0. 201); the other harmonic's values are very weak so their effect can be neglected. Those harmonics are due to the deformation of the electric potential caused by the rectangular shape of the electrodes.
, 2018, for a summary). This reflects that the data and computational methods needed to construct such a model through geological time are very different from present-day plate modeling approaches. The relevant geological and geophysical data are cumbersome to compile and to synthesize, and until recently, there was no community software available to build a deep-time deforming plate model. This changed recently with the release of version 2. 1 of the GPlates software  (Müller et al. , 2018) , which includes the capability to build global tectonic reconstructions with continuously evolving deforming and rigid plates ©2019. The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
In the case of orogenesis, the crustal thickness evolution implied by our approach is generally an overestimation as it does not account for the inevitable erosion that would accompany uplift. However, future links to surface process models will address the lack of erosion and deposition that would occur as a result of compression or extension of lithosphere. We assimilate observations constraining plate deformation into a model of rigid plate motions and compute time-dependent uniform stretching, compression, and strain rates within deforming regions based on the kinematic motions of the rigid surrounding blocks. The technical details of implementing plate deformation can be found in  Gurnis et al. (2018)  and in GPlates software tutorials (https://sites. google. com/site/gplatestutorials/).
Each of those random starting rotations is then minimized within its local neighborhood by iteratively perturbing the pole latitude, longitude, and angle of the starting rotation, evaluating a cost equal to the sum of individual constraints (involving global net rotation, trench migration, and hot spots) and converging to the locally perturbed rotation with the minimum cost. See  Tetley et al. (2019)  for additional details. Since the local minimizations are independent of each other, they are distributed over multiple cores in a high-performance computing cluster using mpi4py (Python bindings for Message Passing Interface). The net rotation constraint cost is based on computing the global net rotation ω net calculated as implemented in GPlates  (Müller et al. , 2018) , following  Torsvik et al.
Acknowledgments We thank two anonymous reviewers for their detailed, constructive comments, which improved the manuscript substantially. S. Z. and R. D. M. were supported by Alfred P. Sloan grants  G-2017-9997  and  G-2018-11296  through the  Deep Carbon Observatory , and J. C. and R. D. M. were supported by the  AuScope National Collaborative Research Infrastructure System (NCRIS) program. M. G. was supported by the  National Science Foundation  through award  EAR-1645775 , and E. L. B. was supported by the  German Research Foundation  (DFG;  BR 4900/2-1 grant ). The  Sydney Informatics Hub  provided support for the Artemis highperformance computer used for our plate model optimization. We thank  Wenchao Cao ,  Joanna Tobin ,  Maxim Adams , and  Joe Ibrahim  for assistance with plate model editing and figure production.
To this effect, lower optical prop-156 erties of the CLLB2 with respect to the CLLB1 may also con-157 tribute. 158 Performing a linear fit on the data distributions of fig. 3  we 159 could estimate that the deviation from linearity is less than 1% 160 for all the tested crystals, in the investigated energy range. The FWHM-energy resolution was measured in the Gamma 163 Spectroscopy Laboratory in Milan, for all the tested crystals. 164 For this study we irradiated the scintillators in the energy range 165 between 276 keV and 1. 33 MeV and we acquired the spectra 166 with a standard spectroscopic chain, selecting a shaping time 167 of 2 µs for the CLLBs and for the CLLBC and 0. 5 µs for the LaBr 3 :Ce and the LaBr 3 :Ce,Sr in order to optimize the energy 169 resolution measurements.
In this paper we propose a role for the CDC6 protein in the entry of cells into mitosis. This has not been considered in the literature so far. Recent experiments suggest that CDC6, upon entry into mitosis, inhibits the appearance of active CDK1 and cyclin B complexes. This paper proposes a mathematical model which incorporates the dynamics of kinase CDK1, its regulatory protein cyclin B, the regulatory phosphatase CDC25 and the inhibitor CDC6 known to be involved in the regulation of active CDK1 and cyclin B complexes. The experimental data lead us to formulate a new hypothesis that CDC6 slows down the activation of inactive complexes of CDK1 and cyclin B upon mitotic entry. Our mathematical model, based on mass action kinetics, provides a possible explanation for the experimental data. We claim that the dynamics of active complexes CDK1 and cyclin B have a similar nature to diauxic dynamics introduced by Monod in 1949.
The structure of the paper is as follows. In Section 2 we present the material and methods used to obtain experimental results that inspired our subsequent research. In Section 3, at first, we introduce the biochemical model describing the basic events occurring when a cell enters mitosis. Then, we present the experimental results on the CDC6 protein that motivate our work. We show both the data reprinted from El Dika et al. [1]  in Figure  1  and original results in Figure  2. We formulate a new hypothesis that captures the role of CDC6 in the process and the mathematical model corresponding to the biochemical one. Next, we present the numerical simulations of the proposed model and finally, Section 4 provides conclusions and directions for further research. In Appendix A we present the mathematical analysis of the presented model.
CDK1 Activity Measurements Samples of cell-free extracts were diluted in MPF buffer supplemented with: 0. 5 mM sodium orthovanadate, 5 µg/µL of leupeptin, aprotinin, pepstatin and chymostatin, 0. 4 mg/mL H1 histone (type III-S), 1µCi [γ32P] ATP (specific activity: 3000 Ci/mmol; Amersham Biosciences, UK) and 0. 8 mM ATP. After incubation at 30 • C for 30 min, phosphorylation reactions were stopped by adding Laemmli sample buffer and heated at 85 • C for 5 min. Histone H1 was separated by SDS-PAGE and incorporated radioactivity was measured by autoradiography of the gel using a STORM phosphorimager (Amersham Biosciences, Buckinghamshire, UK) followed by data analysis with ImageQuant 5. 2 software.
The formation of CDK1/CYC B/CDC6 prolongs a very slow increase in the appearance of CDK1/CYC B A complexes. A slowdown in CDK1/CYC B A increase is visible as the flattening of the curve in Figure  1a. The experimental data leads to a new hypothesis on the mutual interaction between CDC6 and CDK1/CYC B N , which determines the dynamics of CDK1/CYC B A upon mitotic entry. Our mathematical model, based on the law of mass action, bolsters this hypothesis. We suggest that the dynamics of CDK1/CYC B A are similar to diauxic dynamics introduced by Monod  [2]. In mathematical terms, we state it as the existence of more than one inflection point on the curve defining the dynamics of the complexes, cf. Figure  3. Indeed, in the present model, we observe three or four inflexion points. The second part of our hypothesis is that the reaction speed of CDK1/CYC B N and CDC6 binding depends on active CDK1/CYC B A in a switch like mode.
This means that when the concentration of CDK1/CYC B A is less than the concentration value, then the reaction speed of CDK1/CYC B/CDC6 formation is low. When the CDK1/CYC B A is higher than the threshold value the reaction speed becomes much faster resulting in a two-step CDK1 activation visible in biological experiments as an inflection of the activation curve of CDK1. Experimental data show clearly that this activation depends on the presence of CDC6  [1]. Summarising, we consider the biochemical model that takes into account eight species, the descriptions of which are provided in Table  1 , whereas the scheme of their mutual interactions is provided in Figure  4. Table  1. Species of the biochemical model involved in Equations (  1 )-(  5 ). We consider the following five reactions (i.
The function f is bounded as it plays the role of a rate coefficient. The typical way of modelling such a nonlinear dependence is based on the Hill function, see, e. g. ,  [40]. Taking into consideration the biological constraints, we propose the following initial data x(0) = K CDK1 -ε x a -ε x n -ε w > 0 , x a (0) = ε x a K CDK1 , x n (0) = ε x n K CDK1 , y a (0) = ε y a K CDC25 , y n (0) = K CDC25 -ε y a , z(0) = K CDC6 -ε w , w(0) = ε w min{K CDK1 , K CDC6 } , c(0) = 0. (7) Equation (  6 ) have the following conservation laws x a + x n + x + w = K CDK1 , y a + y n = K CDC25 , (8) z + w = K CDC6 , where K CDK1 , K CDC25 , K CDC6 denote constants given at the initial time. In Appendix A we provide the mathematical analysis of the model. We provide the standard non-dimensionalisation of Equation (  6 ).
Numerical Simulations To carry out the numerical simulations we use the Runge-Kutta 4th order method provided by Matlab. Parameters values used to carry out the numerical simulations are given in Table  2. Figure  5  shows the concentrations of CDK1, CDK1/CYC B A , CDK1/CYC B N , CDK1/CYC B/CDC6. The most interesting curve is CDK1/CYC B A , where we observe three inflection points. The concentrations of species containing CDK1 are shown in Figure  6  with the concentration of CDC6 set to zero. Figure  7  shows the difference in activation: The timing and dynamics of the activation of CDK1/CYC B A in the presence and absence of CDC6. When CDC6 is present the activation has more than one inflection point, and mitosis starts later, whereas when CDC6 is absent, the activation is fast. In Figures  5  and 7  we observe diauxic-type behaviour for the curve of CDK1/CYC B A.