dataesr/scientific-paragraphs-categorization huggingface dataset:
The performance of the two methods have been compared using five terms of comparison, three of which are specific to metabolomics, such as (1) compounds' detectability (2) Pezzatti score  (Pezzatti et al. 2018 ), (3) intra-day precision (repeatability), (4) ease of automatic analysis of the data (through a common deconvolution alignment and extrapolation software, MS-DIAL, and (5) time & cost analysis. From this study, HILIC-MS proved to be a better tool for polar metabolome analysis, while CE-MS helped identify some interesting variables that gave it interest in completing metabolome coverage in metabolomics studies. Finally, in this framework, MS-DIAL demonstrates for the first time its ability to process CE data for metabolomics, although it is not designed for it.
Mass spectrometry and data acquisition The MS detector used in this work was an Agilent 6550 iFunnel QToF LC/MS model, a quadrupole-time of flight-mass spectrometer equipped with a JetStream electrospray ion source (ESI-Q-TOF-MS). The instrument was operated in positive and negative ionization modes in both HILIC and CE analysis. The instrument was set to acquire in both MS and data dependent acquisition (DDA MS/MS) mode. High-energy (40 V) spectra were used to obtain fragment ions. Instrumental parameters were optimized for every combination of the separation technique and ionization polarity (Table  1 ). The fragmentor and octopole RF voltages were set at 380 and 750 V, respectively. The instrument was calibrated and tuned according to the procedures recommended by the manufacturer. MS and MS/MS data were stored using the centroid mode.
Accurate mass spectra were acquired in MS and MS/MS mode over a m/z range from 50 to 1000 and with an acquisition frequency of 3. 33 Hz. For CE-MS analysis, the ESI source included a coaxial sheath liquid interface with a sprayer and a platinum needle. The sheath liquid consisted of 5 mM acetic acid in a 50/50 (v/v) isopropanol/water including mass reference (0. 1 µM purine and 0. 015 µM HP-0921 from Agilent Technologies) for online reference mass calibration. Sheath liquid was delivered at a flow rate of 6 μL/min by an Agilent 1260-Infinity II isocratic pump. The instrument was operated using the MassHunter WorkStation Data Acquisition software (version 10, Agilent).
Data processing and analysis Manual detection and identification of the peaks resulting from the injection of the STD mix and the QC samples spiked with STD mix were performed using Agilent MassHunter Qualitative Analysis 10. 0 software, while quantification of the peaks was performed using Agilent Mass-Hunter Quantitative Analysis software. Each chromatographic/electrophoretic peak was manually curated. The tolerance limit was set at 10 ppm for accurate mass and 0. 4 min for retention time (RT) in HILIC, and at 10 ppm for accurate mass and 3 min for migration time (MT) in CE. For automatic deconvolution and analysis of the data, the chromatograms (or electropherograms) were converted to the open-source. mzML format using the MSConvert software from ProteoWizard  [22] , using the following parameters: peak picking (vendor algorithm) MS levels 1-2, subset polarity according to the polarity of the chromatograms (or electropherograms).
The. mzML files were deconvoluted and analyzed using MS-DIAL software  [23]. This software has been chosen because it has been recently ranked first in a direct comparison with further popular peak picking software  [24]. Comparative evaluation of the two separation methods was performed using five terms of comparison. The first was compound detectability, quantified as the number of compounds that could show a clear peak in the chromatograms/electropherograms. The second, developed by Pezzatti et al. , was a scoring approach expressly developed for the comparison of metabolomics methods  [21]. This approach evaluates the retention/migration time, signal-to-noise ratio (S/N), relative intensity, and peak shape of each peak. The sum of all the scores from each peak gives a cumulative score for each method using different analytical platform (i.
e. , the higher the score, the better the overall performance of the platform). The third term of comparison was the intra-day precision. Analytical signals from 10 consecutive injections of the spiked QC were integrated for all the spiked compounds, and the unit-variance was scaled to attain all the compounds at the same scale. Thus, the coefficient of variation (CV) was calculated for all compounds. The fourth term of comparison was the performance of automatic data deconvolution and analysis. The capability of automatic data analysis of the datasets obtained from the two platforms was evaluated in terms of accuracy, reproducibility, and prognostic capabilities. The last term of comparison was the analysis time and cost.
MS-DIAL automatic data analysis evaluation MS-DIAL software was deployed to automatically analyze and extract the data from the 10 consecutive injections of each QC-mix on both analytical platforms. To our knowledge, this is the first time that MS-DIAL has been used to analyze metabolomics data produced with CE-MS. The idea was to evaluate and compare the feasibility of automatic data analysis for both methods using the different platforms. A first term of comparison was the ability of MS-DIAL to correctly detect the compounds spiked in each different QC. As shown in Table  4 , in both cases, MS-DIAL was able to detect most but not all of the peaks (>80%) in any of the evaluated methods. In particular, MS-DIAL showed the highest "sympathy" with CE-ESI(+)-MS, while the lowest affinity with CE-(ESI-)-MS.
Both HILIC-MS modes showed a similar affinity with MS-DIAL peak detection. In both HILIC-MS and CE-MS, peak detection was affected mainly by low peak intensity and the presence of double peaks. A second term of comparison was the ability of MS-DIAL to correctly integrate the detected peaks. This was measured by a correlation analysis between the peak area detected by MassHunter quantitative software and MS-DIAL in 10 consecutive injections (Table  4 ). The results show that MS-DIAL was able to correctly integrate most of the peaks in HILIC-ESI(-)-MS (>90%), while its performance dropped in HILIC-ESI (+)-MS (<70%), likely due to lower peak intensity and worse peak shapes. In CE-MS data, in both ESI+ and ESI-ionization modes, this correlation was less than 70%, similarly to HILIC-(ESI+)-MS. Given that in the metabolomics workflow the statistical analysis of the data is performed on the dataset resulting from this data processing step, it is important that this last step is as efficient as possible, which depends mainly on the ability of the software to correctly integrate chromatographic/electrophoretic peaks.
Migration time correction Migration time fluctuation in CE-MS strongly affects peak alignment and identification, making migration time correction an essential step in CE-based metabolomics. Recently, the effective electrophoretic mobility (μ eff ) has been introduced as an effective parameter able to increase the reproducibility and identification capability of CE-MS compared to the relative migration time (RMT). An specific software (i. e. , ROMANCE) has been developed by González-Ruíz et al. to correctly return μ eff in open format files (. mzML)  [19, 37]. The results revealed that conversion of migration times into μ eff reduced variability from 10. 9% in RMT to 3. 1% on the μ eff scale for 20 compounds using the same BGE composition  [28]. A similar μ eff variability was achieved in this study (see supplementary material, Table  S5 ).
Unfortunately, the resulting. mzML files from ROMANCE retain only the MS data, losing all information about the MS/MS acquisitions. This is a very limiting factor in automatic data analysis, because MS/MS data is necessary to automatically interrogate MS/MS libraries and in-silico fragmentation tools; therefore, increasing the odds of compound identification. Despite being very promising, ROMANCE-based μ eff has been set aside in this study. In this framework, migration time correction has been performed using the retention time correction wizard of the MS-DIAL software (ver. 4. 92)  [23]. MT correction was executed applying the following settings: linear interpolation method, extrapolation method to MT beginning at 0 min and ending at linear extrapolation, and calculation of MT difference by "sample average" method.
Time & cost analysis To increase comparability of the methods applied, several steps of the analysis have been kept equal, including the same type of samples obtained from the same sample-set, the same extraction method (with the exception of a necessary filtering step in CE-MS at these conditions), the instrumental brand (Agilent), the MS system, the calibration mixes, the standards, and internal standards used, the open-source software (MS-DIAL). Nevertheless, there were small adjustments that remained different between the two methods and that increase the time-cost   tradeoff of each of them (Table  5 ). From an instrumental point of view, there is no doubt about the fact that the use of a CE system is advisable, due to the lower cost of both the instrument and maintenance. Nevertheless, a comparison of the costs in terms of consumables is necessary to understand the differences between the two techniques.
Assuming it is tidally locked, the planet rotated by the same angle during the observation. We analysed the data using four independent pipelines: NAMELESS, nirHiss, supreme-SPOON and transitspectroscopy (see Methods). Beginning from the raw uncalibrated data or stage 1 products, we performed custom reductions and extracted 1D spectra from each integration using either a fixed-width aperture (NAMELESS, supreme-SPOON and transitspectroscopy) or an optimal extraction (nirHiss) technique. We put particular emphasis on the removal of 1/f noise (f is frequency), a signal with power spectrum inversely proportional to the frequency that is introduced through variations of the reference voltage as the detector is being read. Its removal requires careful treatment, as the spectral trace covers most of the SUBSTRIP96 subarray (see Methods).
nirHiss reduction We use the nirHiss Python open-source data-reduction pipeline as described in ref. 59. To summarize, this pipeline uses Eureka!  62  to go from the stage 0 JWST outputs to stage 2 calibration, which applies detector-level corrections, produces count-rate images and calibrates individual exposures. From the stage 2 'calints' FITS files, we use nirHiss to correct for background sources, 1/f noise, cosmic-ray removal, trace identification and spectral extraction. We follow two steps for trace identification. First, we use the (x, y) position of the trace from the 'jwst_niriss_ spectrace_0022. fits' reference file, with y values offset by about 25 pixels, to identify order 2. We mask this region, such that it does not contaminate the trace identification or background routine later on.
Next, we use the nirHiss. tracing. mask_method_edges function. This technique identifies the edges of order 1 using a canny edge-detection method from scikit-image, an open-source image-processing package  63. This method uses the derivative of a Gaussian function to identify regions with the maximum gradient. From this step, the potential edges are narrowed down to 1-pixel curves along the maxima. This results in an image in which the outline of order 1 is presented. We identify the median location along the column from the top and bottom edges of order 1 and smooth the trace by fitting a fourth-order polynomial. We find that a fourth-order polynomial best fits the overall shape of both orders, whereas a sixth-order polynomial overfits and a second-order polynomial underfits the order profile.
The visible fraction is computed using the normalized secondary-eclipse light curve modelled with the batman Python package  68. The second-order harmonic function provides sufficient precision for the noise floor of the JWST  69. We fit for the orbital parameters that dictate the shape and duration of the eclipse: the time of superior conjunction T sec (U[2459802. 78, 2459802. 98]), the impact parameter b (N[0. 360, 0. 026 2 ]), as well as the semi-major axis to stellar radius ratio a/R * (N[3. 496, 0. 029 2 ]). The normal priors considered for the semi-major axis and impact parameter use the median and 1σ uncertainties of the constraints obtained from TESS (see Extended Data Table  1 ) as the centre and standard deviation of the priors. Those priors are used because of the precise constraints obtained from several TESS transits and they are free from correlations with a potentially non-uniform dayside, as opposed to our secondary-eclipse light curve.
Free-chemistry and free temperature-pressure profile retrieval We use two independent atmospheric-retrieval codes to perform free-chemistry retrievals on the dayside thermal emission spectrum of WASP-18b: hydra  11, 26, 123, 124  , including the effects of thermal dissociation, and poseidon  125, 126  , which here assumes constant-with-depth abundances for all chemical species. hydra consists of a parametric forward atmospheric model coupled to a Python implementation of the MultiNest 127 nested-sampling Bayesian parameter-estimation algorithm 128 , PyMultiNest 103. The inputs to the parametric model are the deep-atmosphere mixing ratios for each of the chemical species included, the temperature-pressure profile parameters (six free parameters) and a dilution parameter (area fraction) to account for 3D effects on the dayside  51.
5 ). This is because of a difference in the thermal-dissociation prescriptions; in the hydra retrieval, thermal dissociation results in a markedly depleted VO abundance at the photosphere, whereas in the chemical-equilibrium retrieval, thermal dissociation begins at lower pressures. Furthermore, the posterior distribution for the VO abundance peaks at highly supersolar values (about 10 4 times solar). Such a high abundance is physically unlikely and may indicate the presence of further sources of optical opacity not included in the retrieval. We also conduct free-chemistry retrievals, without the inclusion of thermal dissociation, using poseidon. poseidon is an atmosphericretrieval code originally designed for the interpretation of exoplanet transmission spectra 125. We have recently extended poseidon to include secondary-eclipse emission spectra modelling and retrieval capabilities.
For an ultra-hot Jupiter such as WASP-18b, in which the dayside can be assumed clear, the emission forward model of poseidon calculates the emergent flux by means of a standard single-stream prescription without scattering (8) in which W are the Gaussian quadrature weights corresponding to each μ (taken here as second-order quadrature over the interval μ ∈ [0, 1]). I µλ I µ λ B T λ ( , ) = ( Finally, the planet-star flux ratio seen at Earth is given by in which R p,phot is the effective photosphere radius 137,138 at wavelength λ (evaluated at τ(λ) = 2/3). Because the calculation of R p,phot requires a reference radius boundary condition to solve the equation of hydrostatic equilibrium, we prescribe r(P = 10 mbar) as a free parameter.             F F λ R λ R F λ F λ * ( ) = ( ) * ( ) * ( ) (10) For the WASP-18b poseidon retrieval analysis, we calculated emission spectra through opacity sampling and explored the parameter space using MultiNest through its Python wrapper PyMultiNest 103,127.
Acknowledgements This work is based on observations made with the  NASA/ESA /CSA JWST. The data were obtained from the  Mikulski Archive for Space Telescopes (MAST)  at the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127. These observations are associated with program  JWST-ERS-01366. Support for this program was provided by  NASA  through a grant from the  Space Telescope Science Institute. The results reported herein benefited during the design phase from collaborations and/or information exchange within  NASA 's Nexus for Exoplanet System Science (NExSS) research coordination network sponsored by  NASA's Science Mission Directorate. L. -P. C. acknowledges funding by the  Technologies for Exo-Planetary Science (TEPS) Natural Sciences and Engineering Research Council of Canada (NSERC)   CREATE Trainee Program.
M. M. , R. J. M. and L. W. acknowledge support provided by  NASA  through the  NASA   Hubble Fellowship Program. A. D. F. acknowledges support from the  National Science Foundation  through the  Graduate Research Fellowship Program. I. W. acknowledges support provided by  NASA  through the  NASA   Postdoctoral Program. B. V. R. acknowledges support from the  51 Pegasi  b Fellowship funded by the  Heising-Simons Foundation.
Code availability The open-source pipelines that were used throughout this work are as follows: NIRISS/SOSS data reduction: nirHiss (https://github. com/ afeinstein20/nirhiss); supreme-SPOON (https://github. com/radicamc/ supreme-spoon); transitspectroscopy (https://github. com/nespinoza/ transitspectroscopy). Light-curve fitting: batman (https://github. com/ lkreidberg/batman); emcee (https://emcee. readthedocs. io/en/sta-.
Ion beam interaction simulations Implantation induced primary displacement damage profiles and implanted ion profiles were evaluated using GEANT4 10. 6  [11]  and SRIM-2013 (The Stopping and Range of Ions in Matter)  [12]  codes. Both GEANT4 and SRIM-2013 are Monte Carlo based simulation codes which use binary collision approximation (BCA) to obtain the displacement damage. In BCA, the interactions are treated as single atom-projectile pair events and the target lattice is simulated as being static (at 0 • K). Moreover, there is no displacement damage accumulation during the simulations and the material is treated as amorphous. Although SRIM-2013 code is used as an industry standard in such calculations, GEANT4 code can also be used for the evaluations as it is a versatile simulation toolkit capable of simulating displacement and implantation profiles with great accuracy.
ICRU73 (Stopping of Ions Heavier Than Helium) library does not contain stopping powers for graphite material and uses parametrization based on carbon atoms, while ICRU90 (Key Data For Ionizing-Radiation Dosimetry: Measurement Standards And Applications) library contains stopping powers of graphite. Moreover, G4IonParame-trisedLossModel was modified to allow the usage of SRIM stopping powers. The material used in the simulations was G4_Graphite with a density set to 2. 25 g/cm 3 for HOPG and 1. 7 g/cm 3 for RBMK graphite. For scattering events, the single scattering class G4CoulombScattering with G4IonCoulombScatteringModel model was used. The recoil threshold energy was set to 25 eV. The nuclear energy loss was simulated with the G4NuclearStopping class which uses ICRU49 (Stopping powers and ranges for protons and alpha particles)  [19]  stopping powers.
Acknowledgements This project has received funding from  European Social Fund  (project No  09. 3. 3-LMT-K-712-14-0112 )under grant agreement with the  Research Council of Lithuania (LMTLT). Financial support and technical assistance were also received from  Campus France  and  Institut français de Lituanie  (Grant n  • 942172F ). Many thanks to  Marc Marteau  from  Pprime Institute  for carrying out the ion implantation procedures. Special thanks to  Julien Amalric  and  Delphine Pavon  from  SERMA Technologies/Science et Surface Laboratory  for helpful discussions about SIMS measurements. Many thanks to  Tomas Rakickas  and  Martynas Gavutis  from  Center for Physical Sciences and Technology, Vilnius , Lithuania for optical microscopy images. Also, special thanks to  Philippe Sainsot  from  INSA Lyon  for his help with interferometry measurements as well as to  Nelly Toulhoat  for the fruitful discussions.
Background: Recent advances in both single-cell sequencing technologies and gene expression simulation algorithms have led to the production of increasingly large datasets. Larger datasets (tens or hundreds of Gigabytes) can no longer fit on regular computers' RAM and thus pose important challenges for storage and manipulation. Existing solutions offer partial solutions but do not explicitly handle the temporal dimension of simulated data and still require large amounts of RAM to run. Results: VData is a Python extension to the widely used AnnData format that solves these issues by extending 2D dataframes to 3 dimensions (cells, genes and time). VData is built on top of Ch5mpy, a custom built Python library for easily working with hdf5 files and which allows to reduce the memory footprint to the minimum.
Background Since their introduction in  [1] , single-cell sequencing protocols have revealed with unprecendented details the highly dynamic and heterogeneous nature of gene expression. There now exists a wide variety of such protocols (10x Genomics  [2] , SMART-seq  [3] , MARS-seq  [4] , Split-seq  [5] ) which were leveraged by the systems biology community to gain insights into how genes are expressed and regulated inside cells. As a result, vast amounts of single-cell expression datasets have been produced, with an ever increasing number of cells and genes being captured in a single experiment. It has now become common to analyze datasets of tens or hundreds of thousands of single cells characterized by the expression levels of tens of thousands of genes  [6]. This is even truer in multiomics studies, in which multiple genetic parameters are measured in the same unique cell in parallel, thereby increasing again the size of the datasets (scMT-seq  [7] , SHARE-seq  [8] , FISH-Flow  [9] ).
Experiments are not the only source of gene expression datasets. Synthetic datasets are now beeing produced by a variety of simulation algorithms, used to test our understanding of gene expression and regulation mechanisms or to predict cellular cell fate (dyngen  [10] , Splatter  [11] , scDesign2  [12] , SNCS  [13] , WASABI  [14] , CAR-DAMOM  [15] ). Although similar to experimental datasets, synthetic data displays unique characteristics since 1) it is often non sparse (there are no technical dropouts and simulations are approximate), 2) there is potentially no limit to the number of cells or genes for which to record data and 3) simulations allow to observe the same cell at multiple timepoints (although some experimental protocols now also allow this, such as Live-seq  [16] ). Experimental datasets, and synthetic ones even more so, have become extremely large, to the point that they now frequently reach several Gigabytes.
As data analysis progresses, the total size of the datasets still increases as copies are made or new matrices are created. This poses important computational challenges that need to be addressed to ensure that current computers, with limited memory size, are still able to work on such large datasets. Algorithms dedicated to single-cell dataset storage and manipulation have been proposed (AnnData  [17]  in Python, SingleCellExperiment  [18]  in R) but do not specifically focus on working with large datasets (only the principal matrix in AnnData objects -the X layer -can be read from an on-disk file one small chunk at a time) or with synthetic data (there is no explicit handling of the time dimension, preventing the exact same cell to be found multiple times). Here we present VData, a solution for storing and manipulating single cell datasets that extends the widely used AnnData format and is designed with synthetic data in mind.
VData introduces temporally annotated dataframes To easily attribute data to a particular timepoint or to retrieve it, we needed to expand classical pandas DataFrames  [19]  to three dimensions. For that, we created Fig. 1  Structure of a VData object. As in AnnData objects, the bulk of the data is stored in multiple matrices called 'layers'. The data is described using 'obs' and 'var' dataframes for providing details about individual cells and genes respectively. Multi-dimensional or pair-wise data can be stored in 'obsm', 'obsp', 'varm' and 'varp' mappings (see  [17]  for details). Additionally, data independent from cells and genes can be stored in the 'uns' mapping.