dataesr/scientific-paragraphs-categorization huggingface dataset:
These could be degradation products, or silencing RNAs. Some differentially expressed regions are located inside gene promoters, which often also contain some small RNAs  [43]. A significant proportion also comes from introns, which are also known to contain regulatory elements, as well miRNAs  [44] , and snoRNAs  [45]. Fig  6  shows the size distribution of these regions. The regions could be attributed to a specific class, depending on their sizes: the 15-20 peak could be putative tRFs, 20-24-nucleotide long regions could be miRNAs, 26-31, piRNAs. Longer regions, often colocalizing with exons or introns, could be degradation products. We used several additional databases (see Methods) to check whether these unknown differentially expressed regions could be further annotated. We could label two new regions as tRFs, 31 as snoRNAs, and 30 as piRNAs.
Synthetic reads The "truth" set contained 44 differentially expressed features. The results of each tool were plotted as receiver operating characteristic (ROC) curves, given in Fig 7. The ROC curve provides the sensitivity (or recall) against (1 -specificity). The specificity is defined by TN TNþFP , where TN is the number of true negatives, and FP, the number of false positives. Informally, the area under the curve (AUC) gives the probability that a tool ranks a randomly chosen true   We also compared, in this controlled dataset, the effect of the differential analysis package. Time and memory usage  Fig 9  shows that derfinder is the fastest tool, and ShortStack the slowest. However, srnadiff still provides results within 15 minutes. The reason of the difference between srnadiff and derfinder is that the former implements two methods, and thus processes the data twice.
Second, derfinder uses bigWig files, whereas srnadiff readily uses BAM files (and internally converts them into a similar format, which is the bottleneck of the method). ShortStack is a Perl file, which requires significantly more time to process the data. Concerning the memory usage, derfinder is also the most efficient, and srnadiff usually the least efficient. However, all these computations fit in a standard computer. All the computation has been performed on a personal computer running Linux Ubuntu 19. 04, with Intel Xeon Processor E5-1650 v4 running 6 cores at 3. 6 GHz and 32GB RAM. Benchmarking the preprocessing steps (i. e. conversion from BAM to bigWig, and merging the BAM files for input of ShortStack) is not straightforward, because several pipe-lines are possible. We provide the usage we observed with deepTools  [46]  to convert BAM files to big-Wig and samtools  [47]  to merge the BAM files in S1 Appendix.
The tool presented in the paper is freely available through BioConductor. Published datasets are available from these GEO ids: H. sapiens: GSM927308 GSM927309 GSM927310 GSM927311 GSM927312 GSM927313 GSM927314 GSM927315 https://doi. org/10. 15454/ PA1CW6 The preprocessed data has been made available at the Data INRAE repository. The permanent link is: https://doi. org/10. 15454/ 0DCIGO.
Introduction Three-dimensional (3D) volumetric images of vitrified cell sections (slices) can be obtained using cryogenic electron tomography (cryo-ET). The 3D nature of cryo-ET data permits studying macromolecular complexes despite the crowded cell environment. The most common cryo-ET data acquisition scheme is the acquisition of multiple 2D projection images of a specimen rotated around a single axis (perpendicular to the electron beam) inside the electron mi-croscope. The obtained tilt-series 2D projections are used to reconstruct a 3D volume (referred to as tomogram) based on the Fourier slice theorem and backprojection. The tomogram of a cell section typically contains hundreds of copies of different macromolecules at random orientations. The copies of the macromolecule under study (target macromolecule) can be identified and extracted into individual volumes called subtomograms, either manually or via template matching.
HEMNMA-3D is based on elastic and rigid-body 3D registration between simulated shapes and cryo-ET data. The simulated motions are obtained by normal mode analysis of a high-or low-resolution 3D reference model of the complex under study. The rigidbody alignment is achieved via fast rotational matching with missing wedge compensation. HEMNMA-3D provides a visual insight into molecular dynamics by grouping and averaging subtomograms of similar shapes and by animating movies of registered motions. This article reviews the method and compares it with existing literature on a simulated dataset for nucleosome shape variability.
Reference structure The 3D registration is performed using a reference structure in the Protein Data Bank (PDB) format. If there is an existing atomic model of the macromolecule under study (e. g. , a model available in the PDB database and obtained by X-ray crystallography, nuclear magnetic resonance, or derived from 3D reconstructed density maps from cryo-EM images), this model can be used as the reference structure for the method. However, if no atomic model is available but a lower-resolution density map can be obtained by SPA reconstruction or subtomogram averaging, this density map can be converted into a pseudoatomic model  [19, 20]  and, then, this model can be used as the reference structure for the method. The PDB file of the reference structure contains 3D Cartesian coordinates of atoms or pseudoatoms.
Macromolecular shape variability visualization The number of elastic registration parameters (normalmode amplitudes) is determined by the number of normal modes used for the elastic registration. If more than three normal modes were used, the ensemble of normal-mode amplitudes (for all subtomograms) can be projected onto a lower-dimensional space using a dimensionality reduction technique, e. g. Principal Component Analysis (PCA). The obtained high-or low-dimensional space of normal mode amplitudes (conformational space) allows a global data display for interpreting macromolecular shape variability. Each point in this space represents a subtomogram, and close points correspond to similar registered shapes. In this conformational space, the shape variability can be analyzed in the following two ways: 1) by averaging subtomograms of similar registered shapes; and 2) by animating the registered motion of the reference structure along different directions.
Implementation The software of HEMNMA-3D is open-source and available on GitHub  [10, 9]. It is built as part of the Continuous-Flex plugin of the open-source Scipion software package  [27] , commonly used for cryo-EM data processing. Our software provides a graphical user interface (GUI) and is empowered with a C++ backend with a message passing interface (MPI) parallelization scheme. The number of subtomograms that can be processed simultaneously depends on the number of available CPUs. The current implementation takes around 10 minutes to analyze a subtomogram of size 64 3 voxels using three normal modes (tested on 2. 2 GHz Intel Xeon Silver 4214 CPU processor and 64 GB RAM). The more normal modes are used, the slower the processing.
Simulating nucleosome shape variability To review and compare HEMNMA-3D performance with existing literature, we synthesized a dataset comprising 1000 subtomograms with an imagined continuous shape variability of the nucleosome. We generated a linear combination of two reported motions for the nucleosome, breathing and gaping  [28] , with a linear dependence between the amplitudes of normal modes corresponding to the two motions. First, we performed NMA of the nucleosome atomic structure available in the PDB database under the code 3w98 and we visualized the motions carried by the different computed normal modes. Among these modes, we identified the modes describing breathing and gaping motions as normal modes 9 and 13, respectively (note here that the mode number corresponds to the frequency of the mode and that higher numbers correspond to higher frequencies).
We generated a dataset using a linear relationship between the amplitudes of normal modes 9 and 13 so that the nucleosome is simultaneously breathing and gaping. Precisely, at one end of the generated ground-truth conformational distribution, the nucleosome's two DNA ends (arms) are moving away from each other, and at the same time, the gap between the two DNA gyres increases. At the other end of the generated conformational distribution, the DNA arms approach each other and the gap between the two DNA gyres decreases. We simulated a gradual transition between the two ends, representing a continuum of nucleosome shapes, combining breathing and gaping. Equal random amplitudes Euler angles and random x, y, z shifts (the random shift range is ±5 pixels from the center). 4. Tilt and project the randomly rotated and shifted volume, using the tilt angle from -60°to +60°with 1°step, to obtain a collection of 2D projection images (i.
e. tilt series). 5. Simulate microscope conditions by adding heavy noise (SNR = 0. 01) and modulating the images with the contrast transfer function (CTF) of the microscope (using the defocus of -0. 5 µm). 6. Invert the CTF phase (a common CTF correction). 7. Reconstruct a volume (our synthetic subtomogram) from the obtained tilt series using a Fourier reconstruction method  [29]. Figure  3  shows an example subtomogram from the synthesized dataset and the corresponding ideal volume, for comparison in real space and in Fourier space.
Traditional subtomogram averaging and post alignment classification StA provides a global average without considering the shape variability, and it provides a basis for performing classification of subtomograms (classification of the subtomograms aligned through StA). We applied StA on the synthesized nucleosome dataset, using the protocol based on the rigid-body alignment approach of  [26]  (recall that this rigid-body alignment approach is also used in the elastic and rigid-body alignment of HEMNMA-3D). This StA protocol uses an exhaustive angular search (with FRM method) and a shifts search within a region of interest, and compensates for the missing wedge by using the CCC (evaluation of the correlation between the subtomogram average of each iteration and the given subtomogram density maps, but excluding the evaluation in the missing-wedge region of the given subtomogram).
22. 1 and default parameters were used)  [30]. We note that applying the clustering algorithm directly on the CCC ij matrix gives identical results, and we used the convention proposed in the literature  [18, 17]. The clustering tree (dendrogram) and class averages are shown in Figure  5. The k-means clustering was performed following PCA on the CCC ij matrix. The clustering was done into 10 classes (k=10) based on the first two principal axes, using the k-means module of Scikit-Learn. In general, the choice of the number of principal axes to perform classification is arbitrary, as explained in  [17]. Since the dataset was synthesized with two degrees of freedom (nucleosome breathing and gaping), we set the number of principle axes to 2, to obtain the best results. Figure  6  shows the classification of the PCA space and the resultant class averages.
HEMNMA-3D Applying HEMNMA-3D to the synthesized nucleosome dataset aims at solving the inverse problem of finding the nucleosome shape variant in each subtomogram, i. e. estimating the amplitudes of normal modes 9 and 13 of the PDB structure 3w98 as close as possible to the generated ground-truth amplitudes. We set the method parameters as follows: • NMA settings: To make the elastic and rigid-body 3D registration task more realistic and challenging, we used three normal modes (modes 9, 10 and 13) instead of only two modes (modes 9 and 13 used to generate the dataset). • FRM settings The shift range for the rigid-body registration (FRM method) is set to 10 pixels. The amplitudes estimated for modes 9 and 13 using HEMNMA-3D are shown in Figure  7  (a). It is graphically intelligible that the linear relationship is retrieved between the estimated amplitudes of the two modes.
Discussion and conclusion This article presented a review on HEMNMA-3D, a method that addresses continuous macromolecular shape variability captured in cryo-ET subtomograms. HEMNMA-3D applies elastic and rigid body 3D registration via normal mode analysis, fast rotational matching and trust-region based numerical optimization. We tested HEMNMA-3D by synthesizing a dataset of nucleosome shape variability under challenging conditions of the simulated microscope and testing the method's capability to recover the ground-truth shapes. The test results indicate that the method recovers the ground-truth combination of two nucleosome motions (breathing and gaping) with subpixel accuracy. Also, it does not overfit the data with non-existing motions (normal mode 10 was not used to create the nucleosome dataset but used for 3D elastic registration, and it was retrieved with amplitudes near zero).
We applied two state-of-the-art methods for cryo-ET classification on the synthesized nucleosome dataset. Both methods gave similar outputs, showing different discrete class averages of the nucleosome at different breathing and gaping magnitudes. However, the choice of the number of classes is arbitrary in these methods and the shape transitions between the obtained class averages are ambiguous, probably because of the continuous nature of the shape variability. In contrast to these methods, HEMNMA-3D adopts a new scheme that permits revealing hidden macromolecular dynamics by i) grouping and averaging similar subtomograms at locations in the conformational space that reveal the shape transitions and ii) animating the reference structure by displacing it in different directions in the conformational space.
Acknowledgements We acknowledge the support of the  French National Research Agency -ANR  ( ANR-20-CE11-0020-03  and  ANR-19-CE11-0008-01  to S. J. ); the  Sorbonne University  ( 2019  " Interface pour le Vivant" PhD scholarship grant  to M. H. ); and the access to the HPC resources of CINES and IDRIS granted by  GENCI  ( A0100710998 ,  A0070710998 ,  AP010712190 ,  AD011012188  to S. J. ).
Satellite observations of radiation in the microwave and sub-millimetre spectral regions (broadly from 1 to 1000 GHz) can have strong sensitivity to cloud and precipitation particles in the atmosphere. These particles (known as hydrometeors) scatter, absorb, and emit radiation according to their mass, composition, shape, internal structure, and orientation. Hence, microwave and sub-millimetre observations have applications including weather forecasting, geophysical retrievals and model validation. To simulate these observations requires a scattering-capable radiative transfer model and an estimate of the bulk optical properties of the hydrometeors. This article describes the module used to integrate single-particle optical properties over a particle size distribution (PSD) to provide bulk optical properties for the Radiative Transfer for TOVS microwave and sub-millimetre scattering code, RTTOV-SCATT, a widely used fast model.
Bulk optical properties can be derived from a range of particle models including Mie spheres (liquid and frozen) and non-spherical ice habits from the Liu and Atmospheric Radiative Transfer Simulator (ARTS) databases, which include pristine crystals, aggregates, and hail. The effects of different PSD and particle options on simulated brightness temperatures are explored, based on an analytical two-stream solution for a homogeneous cloud slab. The hydrometeor scattering "spectrum" below 1000 GHz is described, along with its sensitivities to particle composition (liquid or ice), size and shape. The optical behaviour of frozen particles changes in the frequencies above 200 GHz, moving towards an optically thick and emission-dominated regime more familiar from the infrared. This region is little explored but will soon be covered by the Ice Cloud Imager (ICI).
This produces the "bulk hydrometeor optical properties" that are the necessary input to a model for radiative transfer or radar propagation that is used to forward model the observed quantity, i. e. radiance or radar reflectivity. This work describes, from a scientific point of view, a widely used software tool for generating lookup tables of bulk hydrometeor optical properties for use in the forward modelling, and hence for use in numerous applications in atmospheric physics, retrievals, and weather forecasting. The tool to be described is known as the hydrometeor optical table (hydrotable) generator and is a self-standing component of the Radiative Transfer for TOVS (RTTOV,  Saunders et al. , 1999 Saunders et al. , , 2018) )  fast model. RTTOV provides tools to simulate observations from over 80 spaceborne sensors operating from the microwave to the visible parts of the spectrum, and it has over 1000 registered users including operational centres and scientists worldwide.
The original code for the hydrotable generator came out of the work of  Bauer (2001)  and was brought into RTTOV with the addition of RTTOV-SCATT  (Bauer et al. , 2006) ; this initial code used Mie spheres to represent the hydrometeor optical properties. The hydrotable generator was then extended to simulate ice cloud signatures at higher frequencies (e. g. 183 GHz) by  Doherty et al. (2007)  and then revised by  Geer and Baordo (2014)  to incorporate the database of non-spherical frozen particles from  Liu (2008). The move to representing snow as a non-spherical particle unlocked the use of the higher microwave frequencies in weather forecasting  (Geer and Baordo, 2014; Geer et al. , 2017). The code has been much updated for version 13. 0 of RTTOV, with improved models of water permittivity  (Lonitz and Geer, 2019) , a first treatment of hydrometeor orientation  (Barlakas et al.
, 2021) , and the addition of the Atmospheric Radiative Transfer Simulator (ARTS) scattering database with a wider range of frozen particles, such as aggregates and hail  (Eriksson et al. , 2018). There has also been a major expansion of the available PSDs (e. g. McFarquhar and Heymsfield, 1997; Petty and Huang, 2011; Heymsfield et al. , 2013). And although the tool is fully configurable, the default configuration is widely used, so these default microphysical choices have been carefully selected. Since the global physical properties of hydrometeors are not well known, the microphysical settings for v13. 0 were updated by parameter estimation, based on the fit between real observations and those simulated from a weather forecasting model  (Geer, 2021b). In that work, a particular effort was made to better represent ice hydrometeors in preparation for ICI: these are now represented by a large plate aggregate for snow, a column for graupel, and a large column aggregate for ice cloud.
, 2018) , the Community Radiative Transfer Model (CRTM, https://github. com/JCSDA/ crtm, last access: 30 November 2021), the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA,  Mech et al. , 2020) , and the radar and lidar forward simulator  Zm-Var (Di Michele et al. , 2012; Fielding and Janiskova, 2020) , which has also evolved from the original code of  Bauer (2001). However, the hydrotable generator in RTTOV is one of the most comprehensive available, and certainly it is widely used. This work aims to be both a scientific user guide to the hydrotable generator in RTTOV and also a helpful reference for users of similar tools. A more technically focused user guide is included with the source code, which is the ultimate reference. Here we concentrate on the broader science and on providing guidance on the physical choices available.
The default settings for frozen hydrometeors were obtained from a multi-dimensional parameter search in order to produce the best fits between ECMWF modelled brightness temperatures and Special Sensor Microwave Imager Sounder (SSMIS) observations  (Geer, 2021b). The settings for rain and cloud water have been inherited from  Bauer (2001). Each hydrometeor type needs to be associated with one of the "placeholder" types listed in Table  2. This gives the hydrometeor a descriptive name and indicates whether it is frozen or liquid. It also associates a density and size range D min to D max , which are mainly relevant when the Mie sphere approximation is used to compute the optical properties. If optical properties are taken from a scattering database, D max Frozen [kg m -3 ] [m] [m] Rain 1000 1 ×10 -4 1 ×10 -2 F Snow 100 1 ×10 -4 2 ×10 -2 T Graupel 400 5 ×10 -4 3 ×10 -3 T Aggregate 900 5 ×10 -3 2 ×10 -2 T Cloud water 1000 5 ×10 -6 1 ×10 -4 F Cloud ice 900 5 ×10 -6 1 ×10 -4 T Totalice 500 5 ×10 -6 2 ×10 -2 T T: true; F: false.
then the density and integration size range are instead implied by the settings for that database. Any placeholder type can be associated with any hydrometeor from the databases. The number of hydrometeor types in the lookup tables is unlimited, so the placeholder types can be re-used multiple times. Their names should not be taken too literally, such as in the use of the "snow" placeholder type with large-scale snow, and the "graupel" placeholder for frozen particles in convective clouds. The "totalice" placeholder was designed for the Met Office global model, which represents all ice particles as a single species (e. g. Doherty et al. , 2007). To save time, the table generator first calculates optical properties for the full set of possible channels (currently 136) and then it composes these channels into sets corresponding to satellite instruments (where currently 34 are represented).
Single-particle optical properties Single-particle optical properties are derived using either Mie theory, which assumes spherical particles, or from scattering databases that summarise the properties of non-spherical particle habits, which have been computed using more sophisticated methods such as the discrete dipole approximation (DDA). Currently, the  Liu (2008)  and ARTS  (Eriksson et al. , 2018)  databases are available within the RTTOV-SCATT hydrotable generator.
This is primarily due to excessive forward scattering from the idealised spherical particle (Sect. 4). However, the Mie capability is still used, where appropriate, to fill gaps in the scattering databases. Specifically, this supports an optional extension of the size ranges below the smallest available particles from the databases. It is also used to fill the gap below 3 GHz where the  Liu (2008)  database does not provide data. Frozen Mie spheres are assumed to be composed of a mixture of air and ice at the relevant density from Table  2 , with pure ice assumed to have a density of 917 kg m -3 and air a density of 1. 225 kg m -3. Alternatively, formulations of density as a function of particle diameter can be used, from  Wilson and Ballard (1999) ,  Jones (1995) , or  Brown and Francis (1995).
These options were explored by  Doherty et al. (2007). Permittivity of ice uses the  Mätzler (2006)  formulation, consistent with the ARTS scattering database  (Eriksson et al. , 2018). Earlier versions of the table generator followed the  Mätzler and Wegmüller (1987)  formulation, which differs only slightly; the option is retained in case exact backward comparison is required. The permittivity of the ice-air mixture is computed using the Fabry and Szyrmer (1999) mixing rule.
Liu (2008) non-spherical frozen particles Use of the  Liu (2008)  scattering database for non-spherical ice particles revolutionised the quality of microwave scattering simulations made by RTTOV-SCATT  (Geer and Baordo, 2014). Table  3  lists the options, of which the sector snowflake was the previous default choice for snow. The other options are a dendrite snowflake and a variety of hex plates, columns, and rosettes. These habits are geometric models of ice particles which are rescaled and then discretised into a 3D grid of polarisable points for input to DDA scattering calculations. To create the database, single-particle scattering properties have been averaged over a large number of orientations in order to represent the average properties of an ensemble of totally randomly oriented particles.