dataesr/scientific-paragraphs-categorization huggingface dataset:
The distribution of the gene length of overlapping genes is plotted in Supplemental Figure  S6B  as a control. Enrichment of NSL3 at the boundaries of conserved TADs was shown using NSL3 ChIP-seq data from D. melanogaster S2 cells  (Lam et al. 2012). FASTQ files were mapped using Bowtie 2 (Langmead and Salzberg 2012) with default parameters. Log2fold ratio over the input sample was calculated using bamCompare (deep-Tools v3. 0. 2) with settings "-bs 5 -ignore chrX. " Overlap of conserved TADs with chromatin states was calculated using chromatin states reported in D. melanogaster Kc cells with the accession number GSE22069  (Filion et al. 2010 ). The bed file was lifted over from Dm3 to Dm6 using CrossMap  (Zhao et al. 2014). The significance of the difference to unconserved TADs and random regions was tested using Wilcoxon rank-sum tests (Supplemental Fig.
S6C ). Enrichment of H3K4me3, H3K36me3, H3K27me3, and Hp1α in conserved TADs was shown using ChIP-seq data of 14-to 16h-old D. melanogaster embryos from the modENCODE Project (modEncode accession 5096, 4950, 3955, and 3956)  (Celniker et al. 2009). Data processing included sequencing quality and adaptor trimming of single-end reads using trim_galore_v0. 4. 5 (bioinformatics. babraham. ac. uk) with setting "-q 5," mapping using Bowtie 2 v2. 3. 4. 1  (Langmead and Salzberg 2012). The coverage of mapped reads was calculated using deepTools v3. 0. 2  (Ramírez et al. 2016)  with settings "-bs 25. " Log2fold ratio over the input sample was calculated using bamCompare (deepTools v3. 0. 2) with settings "-bs 25. " Plots representing the mean and the 95% confidence interval (CI) of the log2ratio over input have been performed using deepTools "computeMatrix" followed by deepStats "dsCompareCurves" (Richard 2019).
H4K16ac ChIP-seq data processing We analyzed our generated H4K16ac ChIP-seq data from D. virilis and D. busckii (see paragraph before) as well as our already published H4K16ac ChIP-seq data from D. melanogaster (GSE109901). Data processing included sequencing quality and adaptor trimming of paired-end reads using trim_galore v0. 4 (bioinformatics. babraham. ac. uk), mapping individual replicates using bwa v0. 7. 12 (arXiv:1303. 3997v2) followed by sorting and indexing of bam files using SAMtools-1. 2  (Li et al. 2009 ). The coverage of mapped reads was calculated using deepTools v2. 0. 1  (Ramírez et al. 2016 ) with settings "-bs 10 -minMappingQuality 2 -normalizeTo1x {EFFECTIVE_GENOME_SIZE}. " We defined the effective genome size as the total genome size minus the number of Ns, which is 117 Mb, 188 Mb, and 120 Mb for D.
Polytene chromosome spreads Polytene chromosomes from separated male and female third instar larvae in all three Drosophila species were prepared as previously described  (Zink and Paro 1995). Briefly, fixed and blocked polytene chromosome spreads were incubated with a homemade primary anti-MOF antibody  (Mendjan et al. 2006 ) (1:400 in goat serum). The secondary antibody (Alexa 488 goat anti-rabbit, A-11034 from Thermo Fisher Scientific) was used in a 1:500 dilution together with Hoechst in a 1:500 dilution. Images were obtained with a Zeiss Elyra system (Carl Zeiss Microscopy) and processed using Fiji  (Schindelin et al. 2012). roX2 ChIRP-seq data processing ChIRP-seq data from D. melanogaster, D. virilis, and D. busckii was downloaded from GEO (GSE69208)  (Quinn et al. 2016). Data processing included sequencing quality and adaptor trimming of single-end reads using trim_galore v0.
HAS to HAS Hi-C contact visualization Figure  5B  shows total HAS to HAS obs/exp Hi-C contacts (red arcs) displayed using pyGenomeTracks v2. 0 (https://github. com/deeptools/pyGenomeTracks). The total HAS to HAS contacts were retrieved by converting Hi-C matrices at restriction enzyme sites resolution with three adjacent bins merged using "hicMergeMatrixBins" into obs/exp matrices with "hicTransform" that were then exported in GInteractions format using "hicExport" from HiCExplorer v2. 1. 4. Then anchors comprised in GInteractions files were overlapped with the HAS defined in each species using the "InteractionSet" R package  (Lun et al. 2016).
Gene Ontology enrichment of biological processes Gene Ontology Biological Processes (GOBP) enrichment was computed using DAVID  (Huang et al. 2009a,b)  with default parameters by assigning HAS ± 30 kb overlapping genes as target, and Random ± 30 kb overlapping genes as background. We furthermore compared the same gene lists (HAS ± 30 kb and Random ± 30 kb overlapping genes) using the goProfiles R package (https://doi. org/doi:10. 18129/B9. bioc. goProfiles) at the second level of the GOBP hierarchy using the "basicProfile," "mergeProfiles," and "plotProfiles" functions. A Holm corrected Fisher exact test has been performed using the "fisherGOProfiles" function, as suggested by the goProfiles vignette, to compare the terms enrichment between HAS ± 30 kb and Random ± 30 kb genes.
Data access The raw PacBio and Hi-C data have been deposited to the NCBI Sequence Read Archive (SRA) under accession numbers SRR7029387-SRR7029398. ChIP-seq and processed Hi-C data including both genome assemblies have been deposited to the NCBI Gene Expression Omnibus (GEO; http://www. ncbi. nlm. nih. gov/geo/) under accession number GSE120752. HiCAssembler is freely available at https://github. com/ maxplanck-ie/HiCAssembler.
Acknowledgments We thank  Aline Gaub  for help with polytene chromosome squashes,  Vivek Bhardwaj  for helpful discussions, and  Plamen Georgiev  and  Maria Shvedunova  for critically reading the manuscript. We also thank the  Deep Sequencing Facility (Ulrike Boenisch  and team) and the  Bioinformatics Facility  ( Thomas Manke  and team) of  MPI-IE  for their help. G. Ri. was funded by  French National Institute for Agricultural Research Young Scientist Contract (INRA CJS). C. I. K. V. was supported by a  Human Frontier Science Program (HFSP)  long-term fellowship (000233/ 2014-L). L. A. was funded by the  German Epigenome Programme "DEEP " ( BMBF  project number  01KU1216G ). This work was supported by  DFG  funded  CRC992 ,  CRC1381  awarded to A. A. and by the  German Research Foundation (DFG)  under  Germany's Excellence Strategy  ( CIBSS EXC-2189 -Project ID 390939984 ).
Synthetic spectra in the infrared region We used the 3D pure-LTE radiative transfer code Optim3D  (Chiavassa et al. 2009)  to compute synthetic spectra from the snapshots of the RHD simulations reported in Table  1  and taken from the Stagger-grid  (Magic et al. 2013). The code takes into account the Doppler shifts due to convective motions. The radiative transfer equation is solved monochromatically using pretabulated extinction coefficients as a function of temperature, density, and wavelength. The lookup tables were computed for the same chemical compositions as the RHD simulations using the same extensive atomic and molecular continuum and line opacity data as the latest generation of MARCS models  (Gustafsson et al. 2008). We assumed microturbulence equal to zero since the velocity fields inherent in RHD simulations are expected to self-consistently and adequately account for nonthermal Doppler broadening of spectral lines  (Asplund 2000).
One peculiar aspect of these observations is that the temporal information is preserved, i. e. each couple of A-B or B-A frames is combined and the corresponding one-dimensional spectrum extracted, which differs from the common practice of co-adding all the spectra obtained during an observing night. This strategy allows us to have a time resolution between 1 and 2 minutes per combined exposure, enough to prevent the change in orbital radial velocity of exoplanets from broadening the planet spectral lines. Extraction of the one-dimensional spectra is obtained via the standard ESO pipeline. Each dataset described below has been published at different stages of the lifetime of CRIRES, and therefore different pipeline versions have been used. The data of 51 Pegasi  (Brogi et al. 2013 ) and the thermal emission data of HD 189733 (de Kok et al.
We adopt (x 1 , x 2 , x 3 ) = (268, 512, 768) to evenly sample the 1024 pixels of a CRIRES detector. 2. To assess the goodness of the wavelength solution, we compare the position of spectral lines in the observed spectra to those in model spectra via cross correlation. As results are almost exclusively dependent on line position, we do not need to exactly match the amplitude of spectral lines at this stage. Therefore, we choose a telluric model as close as possible to the average airmass of each night of observations, and with PWV=1. 5 mm, the median value at Cerro Paranal. 3. We launch a Markov-Chain Monte Carlo (MCMC) using the Python package emcee (Foreman-Mackey et al. 2013) where we vary the λ i (3 parameters) at each step of the Monte-Carlo. We interpolate the telluric and stellar models (the latter Doppler-shifted accounting for the systemic, barycentric, and orbital radial velocity) to the tested solution, cross correlate the combined model with the data, and translate the cross correlation value into log-likelihood as in Zucker (  2003 ) to drive the MCMC.
g. ,  Ramírez et al. 2008; Gray 2009). In this section, we aim to check weather our use of numerical stellar models is correct. Initially we cross correlate the stellar spectrum 51 Pegasi, resolved temporally, with both the 3D RHD simulation from Table  1  and the 1D hydrostatic model from MARCS package  (Gustafsson et al. 2008)  with the same stellar parameters as of 3D simulation. We run the cross correlation at R = 10 6 , i. e. 10× the resolution of CRIRES, resulting into cross correlation functions sampled with a step size of 0. 3 km s -1. Subsequently, we compute the bisectors for the resulting cross correlated functions as reported in Fig. 12  (light blue for 1D and black for 3D). A straight and zero-centered bisector would indicate that the model we used to correct the stellar signal is perfectly matching the stellar properties of the star.
State of the Art of MEC Modeling The magnetic equivalent circuits (MECs) modeling approach has been introduced in the late nineteen-sixties  [76, 77]  and early nineteen-seventies  [78]. More lately, the MEC modeling started to regain popularity among machine designers but MEC methods lack the genericity when compared to FEA. From the commercial software side, the MEC software is far less widespread than the FEA software. Two types of MEC approaches are mainly employed: Expert reluctance network (also called in literature lumped parameter MEC models) on one side and mesh-based reluctance network (MbRN) on the other side. Lumped parameter models are specifically developed for a dedicated topology and are based on the expertise of the designer. These models often need a prior knowledge of flux paths in the studied topology as shown in the works of Liu et al.
in  [88]  to model a linear actuator. The FEA-MEC combination has been applied to model a permanent magnet machine by Philips in  [89]. Regardless, a little number of computer-aided design (CAD) software exploiting RN modeling has appeared. In this section also, existing tools based on RN modeling are overviewed. On the academic side and in literature, we can mention Turbo-TCM  [90]  dedicated to small-power turbo-alternator modeling. On the commercial software side, RMxprt ® in the ANSYS ® Electromagnetic package  [91]  includes pre-defined designs of stator and rotor topologies that can be combined into one whole machine model for performance assessment but very few information on its working principle are available. SPEED  [92]  developed by Speed Laboratories (University of Glasgow), uses various analytical formulations as complementary to FEA but again with pre-defined geometries.
In another approach, Reluctool ® developed by G2ELab (Grenoble, France) is based on lumped parameter MEC for the modeling of electromagnetic devices and includes an optimization module for pre-design purposes  [93, 94]. Reluctool ® models are intimately linked to a given topology, and the reluctance network needs to be built based on the expertise of the designer. All the previously mentioned software come with a graphical interface that allows interactions with the user/designer but none of them allows the automated processing of an arbitrary geometry. On this aspect, for a given structure, a dedicated MEC model needs to be developed. This makes model development duration longer for MEC methods as compared to FEA. Furthermore, if geometry parameters vary in a large scale, the model will no longer be valid and will have to be readjusted.
µ r is the relative permeability of ferromagnetic parts. µ rz = µ r k f + µ r • (1 -k f ) (3) As for the finite element computations, two air volumes at axial ends of the machine are considered in the MEC model. The lamination effect is also taken into account in the same way as the finite element model. The nodal method is used to formulate the MEC equations system. The unknowns for the generated circuit equations system are the magnetic scalar potentials at each node. The equations system referred to in Equation (  4 ) is solved using the MATLAB software. For that purpose, it is expressed using the matrix formulation where, [P] is the permeance matrix, [U] is the magnetic scalar potential vector, [Φ] is the flux source vector and n is the total number of nodes. For each moving armature/stator relative position a new equations system is established.
First, the mesh (spatial discretization) is not the same on both models. Another difference is that the 3D-FEA model is developed on a commercial FEA software and the implemented numerical methods (i. e. , derivatives calculations, non-linear behaviors considerations) are not the same as those of the 3D-MEC model developed in MATLAB (see Figure  20 ). Figure  30  compares the developed maximum hybrid torque evaluated by the FEA and MEC models with an armature current density of Jmax= 10 A/mm² at a rotational speed of 170 rpm for Iexc = -4 A, Iexc = 0 A and Iexc = +4 A, respectively. It can be seen from Figure  30  that magnetic saturation effects induce an increased error between models. The best agreement between both modeling techniques is obtained at Iexc = -4 A when the air-gap flux is weakened and magnetic saturation is  From Figure  29 , it can be seen that a wide range of air-gap flux control can be achieved.
The air-gap flux changes with a variation of +57% when air-gap flux is enhanced and -35% when it is weakened, with respect to the no-field excitation flux (Iexc = 0 A). Numerous reasons can explain the models discrepancies on global quantities such as flux and back-EMF. First, the mesh (spatial discretization) is not the same on both models. Another difference is that the 3D-FEA model is developed on a commercial FEA software and the implemented numerical methods (i. e. , derivatives calculations, non-linear behaviors considerations) are not the same as those of the 3D-MEC model developed in MATLAB (see Figure  20 ). Figure  30  compares the developed maximum hybrid torque evaluated by the FEA and MEC models with an armature current density of Jmax= 10 A/mm² at a rotational speed of 170 rpm for Iexc = -4 A, Iexc = 0 A and Iexc = +4 A, respectively.
e. , derivatives calculations, non-linear behaviors considerations) are not the same as those of the 3D-MEC model developed in MATLAB (see Figure  20 ). Figure  30  compares the developed maximum hybrid torque evaluated by the FEA and MEC models with an armature current density of J max = 10 A/mm 2 at a rotational speed of 170 rpm for I exc = -4 A, I exc = 0 A and I exc = +4 A, respectively. It can be seen from Figure  30  that magnetic saturation effects induce an increased error between models. The best agreement between both modeling techniques is obtained at I exc = -4 A when the air-gap flux is weakened and magnetic saturation is low. When air-gap flux is enhanced (I exc = +4 A) the difference between the MEC and FEA models is the greatest. low. When air-gap flux is enhanced (Iexc = +4 A) the difference between the MEC and FEA models is the greatest.
The DART code is based on the momentum exchange theory described in  [4, 5]  and has been verified using available experimental and numerical data  [6]. Although Gülich  [1]  demonstrated the high dependence of regenerative pump efficiency on the dimension of leakages in off-design conditions, the original version of the DART neglects the leakage flows' effect on the main-flow, thus providing inaccurate data when manufacturing uncertainty makes the dimension of the disk clearance relevant. The aim of this work is to develop a methodology to correctly evaluate the leakage flow through a stationary and a rotating disk in presence of tangential pressure non-uniformity and to implement it into the DART code. To comply with that aim, a theoretical study of the problem is performed and a new approach (partially based on the one originally proposed in  [7] ) is presented.
For the investigated cases, the radial clearance at the rotor tip has the same value considered for the disk clearance. The computational grid used for the three-dimensional RANS simulations of the pump is generated with the meshing tool of the commercial software Pumplinx. Attention is dedicated to the refinement of the critical region of the stripper leakage and of the cavity. The overall grid is composed by about 14. 8 M elements. Such grid resolution is appropriate to make the problem grid-independent according to the authors of  [11, 12] , who performed grid sensitivity studies on similar machines. A detail of the grid is reported in Figure  2a , where the surface meshes of the impeller (in blue), of the side channel (in red) and of the stationary wall (in green) are visible. The three-dimensional numerical campaign was carried out using the Pumplinx solver.
The DART Code The use of simplified models for the preliminary analysis and design of energy systems' components is widely accepted in the turbomachinery field (see, for example,  [14] [15] [16] [17] ). Concerning regenerative pumps, a one-dimensional tool called DART (Design and Analysis tool for Regenerative Turbomachinery) has been developed aiming at providing preliminary design parameters for the setup of detailed three-dimensional numerical simulations. A general scheme of the DART solver is reported in Figure  6. For the development and verification of the original version of the DART code, refer to the work in  [6]. A brief description of the solver follows. The original model for performance prediction is as described in  [4, 5]  and is based on the following hypothesis: • steady, adiabatic, incompressible flow; • pressure independent from axial and radial coordinates; and • leakage flow through disks does not have any impact on the main flow.
The second hypothesis makes the model one-dimensional, while the third hypothesis is the one that has to be overcome to increase DART's accuracy. The aim of the present work is to equip the DART code with a methodology for the prediction of leakage flows through stationary and rotating disks in the presence of a tangential pressure non-uniformity, which is a typical configuration during regenerative pump operation. DART is mainly based on the calculation of the equation for the circulatory velocity flow (Equation  (1) , where the notation is the same used by Yoo  [4] ). dV c dθ = A c Q + Q v (C 3 + C 2 V c (θ) -C 1 V c (θ)) (1) The resolution of Equation (  1 ) gives enough information to obtain the performance of the machine. It has to be noticed that both the derivative of the circulatory velocity and the coefficients C i depend on the flow rate Q.
Implementation of the Leakage Model The proposed model was initially implemented as a stand-alone tool in MATLAB to test its accuracy and to tune the method using the available data obtained from a specifically performed numerical campaign using Computational Fluid Dynamics (CFD). The final version of the model was implemented in DART and the new code was verified using both CFD and experimental data. To make the new model work in DART, the circumferential domain of the cavity is divided into n equal parts θ i , written in a vector ϑ. The matrix ∆p is also defined, where the ijth element represents the difference of pressure between ϑ i and ϑ j : ∆p = pΓ n T -pΓ n T T (17) In Equation (  17 ), Γ n is a n-dimensional vector that has a unitary value in every component. With the same method, the matrix containing the difference of the angular coordinate between ϑ i and ϑ j can be written: ∆ϑ = ϑΓ n T -ϑΓ n T T (18) To complete the implementation, it is necessary to define the matrices of the passage areas, of the length of the chords and of the square root of differences of pressure between ϑ i and ϑ j : S = h n -1 2r sin dθ 2 sin ∆ϑ 2 (19) L = 2r sin ∆ϑ 2 (20) K = ∆p - ∆p T ( 21 ) In the latter equation the operator refers to the real part of the elements of the matrix.
Comparison with CFD Data A comparison between the performance obtained with DART and with the CFD campaign introduced in Section 2. 1 is here reported. In Table  2 , the data obtained from DART with the leakage option switched on and off are compared with CFD data. Variables were reduced to non-dimensional values with reference to the non-dimensional ∆p value obtained using CFD for the case with a non-dimensional axial clearance equal to 0. 0177. As can be observed, both the ∆p and the η values were greatly overestimated with respect to CFD data when the leakage model was switched off. The accuracy of the DART code increased when the leakage flow model was used, especially in terms of ∆p for both the h/H = 0. 00886 and the h/H = 0. 0354 cases. In addition, the trend of variation of the performance was well captured, with some notable exception.
The relative dimension of the shaft with respect to the disk dimension could have an impact on the flow trajectory, but, in the present model, that aspect has been neglected. The theory underlying the methodology is explained and the implementation procedure is detailed. The outcome of the stand-alone methodology was initially compared with data obtained from the three-dimensional CFD campaign performed on the virtual model used as reference case. A tuning procedure is defined to correctly reproduce the shape of the tangential flow rate distribution neglecting the stripper flow rate contribution. The tuned model was coupled with the original algorithm in DART and the results were compared with CFD data, thus demonstrating that the new version of the code is able to capture the main-flow/leakage-flow interaction phenomenon.
Then, DART was used to analyze the performance of the reference case varying the clearance dimension and results were compared with CFD data. The obtained results show that, if the clearance dimension increases, the performance of the pump decreases. Furthermore, data demonstrate that the implementation of the calibrated leakage-flow model improves DART accuracy by a non-negligible factor. Finally, DART was used to analyze a heart pump geometry whose characteristics are available from the literature. Based on the data comparison, it can be concluded that the implemented method greatly increases the accuracy of the DART code by considering a real-machine effect that has a fundamental impact on regenerative pump performance.
This paper presents a machine learning based method to predict the forest structure parameters from L-band polarimetric and interferometric synthetic aperture radar (PolInSAR) data acquired by the airborne UAVSAR system over the Réserve Faunique des Laurentides in Québec, Canada. The main objective of this paper is to show that relevant parameters of the PolInSAR coherence region can be used to invert forest structure indicators computed from the airborne LIDAR sensor Laser Vegetation and Ice Sensor (LVIS). The method relies on the shape of the observed generalized PolInSAR coherence region that is related to the three-dimensional structure of the scene. In addition to parameters describing the coherence shape, we consider the impact of acquisition parameters such as the interferometric baseline, ground elevation and local surface slope.