dataesr/scientific-paragraphs-categorization huggingface dataset:
The EMT status of the immunofluorescence images was evaluated via blind scoring from two independent researchers following clear scoring criteria and TGF-β-treated samples as positive controls. The criteria for EMT-positive were increase in cell size and cell elongation, combined with (1) relocalization of vimentin (BEAS-2B cells) or (2) loss of E-cadherin (HBEC-3KT). The results from the blind scoring were combined and reported. For the cell size measurements, we used the manual cell analysis tool from the ImageJ 1. 04 (National Institutes of Health, Bethesda, MD, USA, http://imagej. nih. gov/ij/, 1997-2016) software. Briefly, the cells were marked manually and included in the Region Of Interest (ROI) tool. The measure function from the ROI menu was used to create a table including the area information of each cell.
The blots were incubated with secondary HRP-conjugated antibodies (GE Healthcare) for 1 h at room temperature followed by treatment with Super-Signal ECL (Thermo Fisher Scientific, Rockford, IL, USA) and developed on an X-ray sensitive film (GE Healthcare, Buckinghamshire, UK). Protein expression was quantified using the semiquantitative gel analysis function of the ImageJ 1. 04 (National Institutes of Health, Bethesda, MD, USA, http://imagej. nih. gov/ij/, 1997-2016) software. The results of each individual protein were normalized to the GAPDH expression. 2. 6. 8-oxo-dG Analysis. For the 8-oxo-dG analysis, medium was collected simultaneously with cell harvesting and kept frozen at -20 °C. The 8-oxo-dG was measured using a modified ELISA assay as described previously  [31, 32]. The ELISA kit was provided by Health Biomarkers Sweden AB.
Acknowledgments This work has been supported by an  EPIRADBIO grant  ( FP7-FI-2010-269553 ) under the  Euratom-specific program  for research and training on nuclear energy, 7th Framework Program, the  Swedish Radiation Protection Authority (SSM)  and the  Swedish Cancer Society. The authors would like to thank  Prof. Jerry Shay ,  UT Southwestern Medical Center, Dallas, TX, USA , for kindly providing the HBEC-3KT cell line, and to the team at  CRPR ,  Stockholm University, Stockholm, Sweden , for the help with the protracted irradiations. The authors would like to express their deep grief from the early loss of V. L. She was the engine of this project and at the same time a respected colleague and supportive supervisor. Her joyful personality and positive attitude to the work-related challenges are greatly missed by all her colleagues.
The technique assumes and requires excellent agreement between data and simulated samples for the construction of the templates, which must describe all reconstruction, resolution and migration effects. In this case the fit reduces to a linear sum of twelve independent templates, preserving the inherent model independence of the angular method. Eleven I X parameters are measured (one is fixed by I X = 1), and the model dependence is confined to one overall signal yield. The expected resolutions and covariance matrix of the I X parameters are determined under realistic experimental conditions emulating both hadron collider and B-factory scenarios; assessment of the I X sensitivity to NP is beyond the scope of the work. For this demonstration, the angular coefficients are measured integrated over all values of q 2.
Monte Carlo simulation Monte Carlo (MC) signal samples of B 0 → D * -τ + ν τ decays are generated using the RapidSim package  [33]. RapidSim is a fast MC generator for simulating heavy-quark hadron decays. It uses TGenPhaseSpace  [34]  to generate b-quark hadron decays and FONLL  [35]  to give the b quark the correct production kinematics for the Large Hadron Collider (LHC). Exclusive decays of the D * -meson decay to D 0 π -are generated, with the D 0 meson decaying to K + π -. The three-prong τ + → π + π + π -ντ decay, rather than the more abundant muonic τ decay, is the focus of this study. This is because the presence of a τ decay vertex results in lower backgrounds, and with only two neutrinos in the final state, this mode has the best decay angle resolution. Signal B 0 mesons are decayed using EvtGen  [36].
Using this PDF, a binned maximum likelihood fit to the reconstructed decay angle distribution is demonstrated using the TensorFlowAnalysis package  [40] , which provides an interface between TensorFlow  [41]  and the MINUIT  [42]  minimisation package. For this demonstration, N sig = 100, 000 signal events are generated across n bins bins in each of the three angular variables, where n bins = 16 is chosen to ensure that there are approximately 25 signal events in each bin. An alternative binning scheme based on placing approximately 50 events into each bin has been tested and shows consistent behaviour. The I X values found by the binned fit are summarised in Tab. 3 (a). -0. 00 Table  3 : Results of the (a) binned template fit to the reconstructed angles, and (b) unbinned parametric fit to the truth-level angles.
To demonstrate this, the signal-only template fit is repeated with per-event weights applied to the generated signal sample in order to align it with the CLN form factor scheme  [43]. The reweighting is performed using the HAMMER package  [44, 45]. Both the parametric unbinned fit and the binned template fit are rerun; the h I X templates are unaltered as they do not contain any form factor dependence. The I X coefficients resulting from the fits are displayed in Fig. 5 , and are seen to agree well with the SM values calculated using the CLN scheme in Ref. [30]. Furthermore, the longitudinal D * -polarisation fraction calculated from this signal-only template fit, F D * L = 3I 1c -I 2c 3I 1c -I 2c + 6I 1s -2I 2s = 0. 446 ± 0. 004 , (3. 3) aligns satisfactorily with the SM expectation noted in Sec.
Final signal plus background sample To create a realistic total dataset that includes both signal and background events, the generated signal and background samples are summed using the relative fractions listed in Tab. 4 ; the values used are based upon those measured in LHCb data  [8]. The signal comprises f sig = 11. 8% of the total sample, while the total feed-down contribution is 11% of the signal fraction (1. 3% in total). The majority of the sample is composed of B → D * -D + s (X) background decays (62. 1%) with smaller fractions assigned to the B → D * -D + (X) and B → D * -D 0 (X) backgrounds. Mode Fraction Value B 0 → D * -(τ + → π + π + π -ντ )ντ f 3π × f sig 0. 78 (fixed) × 0. 118 B 0 → D * -(τ + → π + π + π -π 0 ντ )ντ (1 -f 3π ) × f sig 0. 22 (fixed) × 0. 118 B → D * -D + s (X) f D + s 0.
Four-dimensional fit To reliably measure the signal fraction f sig and I X coefficients, a four-dimensional binned maximum-likelihood fit to the total dataset is performed using the PDF P ((cos θ D , cos θ L , χ) Reco , BDT) = f sig h sig + f D + s h D + s + f D + h D + + f D 0 h D 0 + f D * * h D * * + (1 -f sig -f D + s -f D + -f D 0 -f D * * )h prompt , (4. 1) where h sig represents the signal PDF, defined in (3. 1). In the fit, f sig , f D + s , and f D + freely vary, as do the eleven I X coefficients within h sig. The fractions f D 0 and f D * * are fixed, matching the procedure in Ref. [8]. The prompt fraction is constrained such that the fractions sum to unity. The twelve signal template histograms h I X are created following a procedure that is essentially identical to that described in Sec.
Hadron collider scenario The four-dimensional fit is applied to datasets corresponding to three scenarios: N sig = 8000, N sig = 40, 000 and N sig = 100, 000. These are calculated by extrapolating the yield measured in Ref. [8]  to 9 fb -1 (Runs 1 + 2), 23 fb -1 (Runs 1-3), and 50 fb -1 (Runs 1-4), as anticipated by LHCb  [47]. The 23 and 50 fb -1 scenario yield expectations account for additional improvements in the upgrade LHCb detector performance relative to LHCb in Run 1 and 2. The signal and background fractions listed in Tab. 4 are used to create the data sample for each of these cases, maintaining the same signal purity throughout. 9 fb -1 (N sig = 8000): The 4D template fit functions stably in this lowest-statistics case, and the resulting fit projections are shown in Fig. 9. The signal fraction is measured to be f sig = 0.
Both fits operate with binning schemes that require approximately 25 signal events per bin, and the increased number of bins in the 23 fb -1 fit provides greater differentiation between the I X signal components and the backgrounds. 50 fb -1 (N sig = 100, 000): With the largest dataset, the I X values are measured with absolute statistical uncertainties in the range 0. 01-0. 06, as shown in Fig. 10. The statistical correlation matrix for the fit is provided in App. C; the other fit scenarios show a consistent pattern of parameter correlations. The signal fraction is measured to be f sig = 0. 116 ± 0. 002 (1. 5% relative uncertainty). Given the considerable reduction in I X uncertainty between Density Data B 0 → D * τ ν B → D * * τ ν B → D * Ds(X) B → D * D + (X) B → D * 3π(X) B → D * D 0 (X) Figure  9 : One-dimensional projections of the N sig = 8, 000 binned fit, where the solid points represent the data and the filled histograms represent each fit component.
Fit stability validation To demonstrate the stability and accuracy of the three fit scenarios, many pseudoexperiments ("toys") based on the fits are run. Using the template PDFs and the yields from the 9, 23, and 50 fb -1 fits, toy datasets are generated where the number of events is independently determined in each bin according to Poisson variations of the bin content. The template fit is applied to each toy dataset, and pull distributions are created for all freely varying fit parameters. All pull distributions have mean values close to zero and widths close to unity, as expected for an unbiased fit returning the appropriate uncertainties.
B-factory scenario Complementary to LHCb, the Belle II experiment  [48, 49]  can use an anticipated 50 ab -1 dataset to measure the angular coefficients in B → D * τ ν τ decays. The b b production cross section is much lower in e + e -collisions compared to pp, but the well-defined initial state and the absence of other tracks in BB events give considerable advantages when reconstructing final states with missing particles. By reconstructing the second B meson, the momentum of a signal B 0 meson can be well defined in spite of the missing neutrino. To estimate the performance of the template fit at an e + e -experiment, the signal and background samples generated to emulate the LHCb resolution and acceptance are reprocessed using the true B meson four-vector in the decay angle calculations rather than relying on the estimation in Eq.
6%. The statistical uncertainty of a model-dependent fit to the same dataset may be under 3%, based on an extrapolation of the statistical uncertainty in Ref. [8]. The inferior precision is due to the fact that (1) the model-independent fit has twelve parameters to describe the signal rather than one overall yield, and (2) the angular variables are less discriminating between signal and background than the model-dependent variables currently used such as q 2. To confirm these assertions, a test is performed using the generated 9 fb -1 LHCb dataset, where all of the I X coefficients are fixed and the signal-background separation is artificially improved by 20%. In this test, the fit uncertainty on f sig indeed reduces from 8. 6% to 3. 3%.
PoS(Asterics2019)031 The era of multi-messenger astrophysics with Gravitational Waves (GW) requires the exploration and development of suitable methods and tools for real-time analysis as well as post-processing activities. The irregular and complex shapes of the GW sky localizations represent a new challenge for observational astronomers, who need to work with fast tiling, catalog queries, transient localizations, visibility and sky map comparisons. Here we show how gravitational-wave sky maps can be easily and efficiently visualized and processed using Multi-Order Coverage (MOC) maps. These maps are based on HEALPix sky tessellation which uses both Python language and the recent implementation in Aladin Desktop/Lite. In addition to this, we describe a specific interactive script, named GWsky, that we developed to effectively tile the sky localization of a gravitational-wave event providing accurate telescope pointings.
The NUNIQ encoding scheme defines an algorithm for packing an (ORDER, IPIX) pair into a single integer for compactness  [1]. More details about the HEALPix Unique Identifier and NUNIQ schemes are in the HEALPix Primer  4. MOCs are serialized as FITS or JSON files. The MOC resolution is determined by the map resolution parameter N side , which is used for defining the resolution of the grid. For a typical N side = 512, the MOC order resolution is order = 9; (N side = 2 order ). The MOC maps make database queries for retrieving objects and logical operations (such as union, intersection, subtraction, difference) extremely simple and fast (generally a few seconds) even for very complex sky regions. If databases are adapted to support MOC based queries, such as VizieR  5  , they offer a useful method allowing any support of sky region query.
PoS(Asterics2019)031 Working with Gravitational-Wave sky localizations Giuseppe Greco In this context, we develop a tool named GWsky 9 which is a Python script devoted to run a sequence of telescope pointings across the sky localization of gravitational-wave signals. A simple GUI supports the users to interactively tile the gravitational-wave sky map taking into account information for each assigned telescope pointing, such as airmass plot, integrated probability and the conditional distance distribution for the 3D sky map, catalog query table. A semi-transparent background is applied to the movable windows. This graph attribute combined with the reduced window size makes easy to work in the overlay mode between the different Aladin planes. GWsky interacts with the Aladin Desktop via SAMP (Simple Application Messaging Protocol).
Study Sites, Population, and Procedures We included in our study pregnant women who participated in 3 clinical trials of intermittent preventive treatment during pregnancy (IPTp) during 2003-2005 in Mozambique (NCT00209781)  (20)  and during 2010-2012 in Mozambique, Benin, Gabon, Kenya, and Tanzania (NCT00811421)  (21, 22). Participants were recruited at their first antenatal visit, and all received a long-lasting insecticide-treated bed net. During 2003-2005, all received 2 doses of sulfadoxine/pyrimethamine  (20) ; during 2010-2012, they received 2 doses of mefloquine or sulfadoxine/ pyrimethamine if they were not HIV infected  (21)  and 3 doses of mefloquine or placebo plus daily cotrimoxazol prophylaxis if they were HIV infected  (22). At delivery, tissue samples from the maternal side of the placenta, as well as 50 μL peripheral and placental dried blood spots (DBS), were collected.
We assessed the modification of the associations by HIV infection or parity by including interaction terms into the regression models. To control the false discovery rate in the selection of antigens, we computed adjusted p values (q-values) by using the Simes procedure  (23). We used multilevel mixed-effect linear regression analysis to estimate half-life and time to double (T 2× ) IgG levels in the longitudinal cohort of pregnant women from Mozambique (Appendix 1). We identified spatial clusters of P. falciparum infection and seropositivity as well as the most likely hotspots by using the Ward hierarchical cluster analysis and Kulldorff spatial scan method (Appendix 1). We performed statistical analyses by using Stata/SE software version 12. 0 (StataCorp, https://www. stata. com), R statistics software version 3.
Acknowledgments We thank the women from  Benin, Gabon, Kenya, Mozambique, Tanzania , and Spain who participated in the study, as well as the staff of the hospitals, clinical officers, field supervisors, and data manager. We also thank  Laura Puyol ,  Diana Barrios ,  Gemma Moncunill ,  Pau Cisteró ,  Lazaro Mussacate ,  Nelito Ernesto Jose , and  Ana Rosa Manhiça  for their contribution to the collection and organization of sample shipment and  Joe Campo ,  Aida Valmaseda ,  Marta Vidal ,  Eduard Rovira-Vallbona ,  Pedro Aide ,  Beatriz Galatas ,  Patrick G. T. Walker , and  Peter Gething  for providing important inputs for standardization of the Luminex technology, the design of protein 3D models, the interpretation of results and PfPR 2-10 data.
Bragg coherent X-ray diffraction imaging (BCDI) has emerged as a powerful technique to image the local displacement field and strain in nanocrystals, in three dimensions with nanometric spatial resolution. However, BCDI relies on both dataset collection and phase retrieval algorithms that can induce artefacts in the reconstruction. Phase retrieval algorithms are based on the fast Fourier transform (FFT). We demonstrate how to calculate the displacement field inside a nanocrystal from its reconstructed phase depending on the mathematical convention used for the FFT. We use numerical simulations to quantify the influence of experimentally unavoidable detector deficiencies such as blind areas or limited dynamic range as well as post-processing filtering on the reconstruction. We also propose a criterion for the isosurface determination of the object, based on the histogram of the reconstructed modulus.
Often, a qualitative understanding of the strain and displacement components is possible, but since the technique is extending towards in situ and operando studies, it is of primary importance to get quantitative information and to probe subtle displacement and strain variations. For example, the concept of strain engineering appeared recently in catalysis, where the goal is to tune the surface strain of a catalyst to improve its activity and/ or selectivity in a particular chemical reaction  9, 10. Understanding the impact of artefacts induced by both dataset.
Results Mathematical formalism between retrieved phase and displacement field. Labat et al. mentioned   briefly that the retrieved phase and the retrieved displacement are of opposite sign as a result of the FFT convention used by their Python-based phasing algorithms  20. This is a critical point if one wants to perform a quantitative analysis of the displacement field and strain to distinguish compressive or tensile regions in a nanocrystal. We believe that it deserves a more in-depth description. Starting with a 2D model with a known real-space orientation, electron density and displacement u x (u y = 0, see Fig. 1(a, b ), we calculate the diffraction pattern using a Python-based FFT algorithm and the kinematical sum (sum over all nodes/atoms). The kinematical sum gives a unique diffraction pattern of intensity I(q x , q y ) of the complex-valued real-space object, whatever the (positive or negative) convention used for the phase term: ρ Ω = ∼ ±.
+ +. x y x y e ( , ) ( , ) i q x u q y [ ( ) ] x x y. Figure  1(c, d ) correspond to diffraction patterns of intensity ρ = ∑ +   . + +.    ( ) q q x y e I , ( , ) x y x y i q x u q y , ( ) 2 x x y and ρ ∑ -  . + +.    x y e ( , ) x y i q x u q y , ( ) 2 (e) FFT of the complex object calculated using Python. (f) FFT of the complex object with displacement field of opposite sign, calculated using Python. A zoom on the center of the Bragg peak is shown at the bottom right corner. From this we conclude that the correct and unique diffraction pattern corresponding to our complex object is the one in (f) for FFT calculations. (g-j) show the displacement retrieved from diffraction patterns in (c-f) respectively using the Python-based phasing algorithm. respectively, which are identical.
I q q FFT x y e ( , ) ( ( , ) ) x y iq u ( ) 2 x x -see Fig. 1 (f) gives the same diffraction pattern as those calculated using the kinematical sum. Then, we use the Python-based phasing program to retrieve the complex-valued real-space object. Two solutions arise from phasing: the object and its complex conjugate. It appears that the phasing of the diffraction patterns obtained by the kinematical sum and Fourier transform (with a displacement field of +u x ), i. e. Figure  1(c, d, f ), gives a phase which has indeed an opposite sign compared to the displacement: ψ = -q x. u x. This is explained by the fact that Python uses a negative sign convention for the FFT. The reconstructions obtained by phase retrieval are shown in Fig. 1 (g-j) for each case and give the same result as already inferred from diffraction patterns.
For the sake of completeness, we show in Fig. S1  the FFT of the complex object calculated with Mathematica, which uses a positive sign convention. In that case, the FFT of the complex object gives directly the correct diffraction pattern. Most of the phasing algorithms available in the community are either Python or Matlab-based, which use the same negative convention for the FFT. Therefore, a negative sign should be applied to the phase before scaling it back to the displacement. Practically, however, there are only few experimental cases where the correct real-space orientation of the sample is known (e. g. , when there is an epitaxial relationship between the crystal and the substrate used for crystal growth). In many cases where there is no systematic orientation and asymmetry in crystal shape (e.
In order to assess the effect of the size of the FFT window, which corresponds to the size of the experimentally exploited detector area, we crop the array, and finally phase it using a Python-based phase-retrieval algorithm, the PyNX package  36. The phase obtained from the inversion of the simulated diffraction patterns at the 111 Bragg reflection encodes the displacement field along the [111] direction (u 111 ) of the crystal, which is parallel to the Y axis of Fig. 2  and pointing upwards. After removing the non-physical ramp and offset in the retrieved displacement and interpolating back into the laboratory frame, the strain can be derived from the displacement field:  3  displays slices in the XZ plane through the middle of the reconstructed array of the out-of-plane strain ε YY calculated after phasing.
The efficiency of this single step at removing the high frequency noise due to the limited size of the FFT window is striking, as shown in Fig. 8. The addition of a single post-phasing apodization step to the overall identical phase retrieval process of Fig. 3  significantly reduced strain noise at small FFT window sizes. It is well known that apodization affects the resolution: there is a tradeoff between cutting high-frequency noise and loss in resolution. The Blackman window, which falls quickly away from its center, has a large impact on the resolution. Tunable windows, like the Tukey window which spans from a rectangular window to a Hann window depending of its parameter α, can be optimized for a particular dataset. In Fig. S12 , we show the effect of apodization on the histogram of modulus distribution: the modulus is more homogeneous, and its mean value has increased.
Averaging the phase is a valid approach for this dataset where no high frequency variation is expected in the phase. Tick spacing corresponds to 50 nm. resolution function. The correct isosurface is the one for which those unphysical surface layers are removed. In Fig. 11 (g), we show the difference between the simulated and the retrieved strain ε YY for a too low isosurface level (32. 5%, which corresponds to the volume conservation of the support in Fig. 10(a) ), versus an isosurface determined using our criterion (~70%) in Fig. 11(h). While 2D slices in Fig. 11(g, h ) look similar (slight differences appearing only at the surface voxel layer), the effect of choosing a wrong isosurface is obvious in Fig. S17 , where we show the 3D isosurface representation of the simulated and retrieved crystals using the various post (pre)-processing filtering methods presented in this study.