dataesr/scientific-paragraphs-categorization huggingface dataset:
Measuring the effects of different models on estimates of branch lengths We compared the estimates for the deuterostome, protostome, and bilaterian stem branch lengths across four models accommodating or not rate and state compositional heterogeneity across sites. We performed Downloaded from https://www. science. org at Universite Pierre Et Marie Curie -Paris 6 (Upmc) on  November 15, 2021  this analysis only for the Laumer dataset that showed the strongest support for DM. To lessen the computational burden, we reduced the dataset to 36 taxa that cover all major branches of the phylogeny, selecting taxa with fewest missing data and randomly selected 50,000 of the original 106,186 alignment sites ("reduced-Laumer"). Using the reduced-Laumer dataset and assuming the DM topology, we performed a phylobayes-mpi [version 1.
Cross validation to measure fit of different models To compare how the branch length estimates relate to the degree of fit of each of the four models, we performed cross-validation analyses as described in the phylobayes-mpi manual. Using the reduced-Laumer dataset, we created 10 training datasets each consisting of 10,000 sites and 10 test datasets of 2000 sites each. We ran phylobayes-mpi for 3000 MCMC cycles for each of the training datasets and for each of the four models. Subsequently, using the "readpbmpi" program (distributed with the phylobayes-mpi) under the "-cv" option, we calculated the posterior mean cross-validation score with a burnin of 2000 and sampling a frequency of 0. 1. As expected, less complex models have significantly worse fit to the data than the more complex models (average cross validation scores: LG, -74,877.
Simulations: Systematic error We simulated sequence alignments using parameters that match those measured from empirical sequences under the three topological hypotheses relating the deuterostome clades. As before, we based these analyses on the reduced-Laumer dataset. The parameter estimates and simulations were carried out with phylobayes-mpi. Identical steps were taken when repeating these analyses with a dataset from which Xenoturbella had been excluded. The two steps were as follows: 1) We estimated the posteriors of branch lengths and model parameters using the CAT + LG + G model on three alternative fixed topologies relating the deuterostome clades. For each fixed topology, we performed 20,000 MCMC cycles with a sampling frequency of 1. 2) Using the final 5000 posterior samples, we subsampled with a frequency of 1 in 50, which gave us a subset of 100 posterior samples.
Using these combinations of branch lengths and model parameters, we simulated data with the "readpb_mpi" tool under the "ppred" option. For phylobayes runs using all three fixed topologies, the effective sampling size (ESS), for some of the model parameters, was lower than 100, but in all cases >50, possibly suggesting inadequate MCMC mixing and lack of convergence. This is a known problem of phylobayes when using large datasets. To ascertain whether the results from our simulations were affected by inadequate mixing (or by a sampling effect), we repeated the procedure for a smaller fraction (10,000 randomly selected sites) of the Laumer alignment. In this case, we performed 10,000 MCMC samples, and the ESS values were above 100 for all parameters with a 25% burnin. The results of the simulations based on this dataset were in broad agreement with the results based on the larger dataset (table  S4 ).
For each of the simulated datasets (simulated under the bestfitting state-frequency heterogeneous CAT model), we performed two phylogenetic inference analyses using IQ-TREE: (i) under the state-frequency homogeneous model LG + F + G and (ii) the state frequency heterogeneous model C60 + LG + F + G under the PMSF (posterior mean site frequency) approximation  (40). The C60 model implements an approximation of the CAT model of Phylobayes but with 60 precomputed site frequency categories  (5)  and is considerably faster to run in a maximum likelihood framework  (40) , enabling the analysis of multiple simulated datasets. We wanted to test the possible influence of an LBA attracting long-branched protostomes to the long branch leading to the outgroup resulting in artifactual support for monophyletic deuterostomes.
Gene order Gene order was determined from the National Center for Biotechnology Information (NCBI) genome gff files, or gff files distributed with sequence data from the Marine Genomics Unit of the Okinawa Institute of Science and Technology (https://marinegenomics. oist. jp/), or other online resources related to original publications. Orthologs of selected genes were determined from phylogenies of corresponding Pfam domains (e. g. , Homeobox, Forkhead, etc. ). Briefly, Pfam hidden Markov models were searched against a metazoan protein database (hmmsearch), and sequences were aligned (hmmalign) and then trimmed (esl-alimask and esl-alimanip) on the basis of posterior probabilities of correct alignments and length of remaining sequence. Phylogenies were constructed using IQ-TREE, allowing the optimum model to be selected from the LG set (i.
e. , "-mset LG" in IQ-TREE). Orthologs were identified by inspection of the phylogenetic tree around the relevant gene (e. g. , msxlx, pax1/9, etc. ). Testing the phylogenetic distribution of supposed deuterostome-specific genes Deuterostome novelties of Simakov et al. (16)  were searched against selected protostome and nonbilaterian metazoan datasets. Most gene sets (Lingula, Priapulus, etc. ) were obtained from the NCBI genomes resource (accessible at www. ncbi. nlm. nih. gov/genome/browse#!/ overview/), except Phoronis australis (OIST; see above). Sponge transcriptomes were assembled from reads retrieved from the European Nucleotide Archive: Sequences were processed into "left" and "right" read files adding "/1" and "/2," respectively, to identifiers (e. g. , for a "_1. fastq" file: perl -pe 's/^@(SRR\S+)/\@$1\/1/') and then assembled using Trinity-v2.
8. 4  (42)  with default parameters: trinityrnaseq-Trinity-v2. 8. 4/ Trinity --seqType fq --max_memory 128G --left fastq/left. fastq --right fastq/right. fastq --CPU 32. Oscarella carmela proteins were downloaded from compagen (www. compagen. org/datasets. html; file OCAR_T-PEP_130911) and P. australis from OIST (see above). Candidate orthologs were identified via reciprocal best hits and phylogenetic analysis of relevant Pfam domains, as described above (section "Gene order"). Sequences were also searched against the NR database of the NCBI to test likely monophyly of metazoan (in this analysis, generally, sponge and bilaterian) proteins based on clear separation of bit scores, using blastp (v2. 10. 0+) (37).
In all organisms, diffusion is the primary mechanism by which DNA-binding proteins locate their targets  (Erbas and Marko, 2019; Erbas ¸et al. , 2019; Schavemaker et al. , 2018). The diffusion coefficient of a particle in a dilute solution is determined by its size and the viscosity and temperature of the medium. In the crowded and heterogeneous intracellular environment, however, a myriad of specific and non-specific interactions and steric effects influence the mobility of macromolecules. Because of this complexity, efforts to understand molecular mobility have relied on phenomenological models  (Kalwarczyk et al. , 2012; Mika and Poolman, 2011)  or coarsegrained simulations of the cytoplasm  (Chow and Skolnick, 2017; Feig et al. , 2015; Hasnain et al. , 2014). In this context, anal-ysis of in vivo experimental data is crucial not only to determine parameter values but also the structure of such models by informing which cellular components and interactions should be included in a model.
0 mm 2 /s and D* = 1. 2 mm 2 /s, respectively) (Figure  5B ), consistent with our previous observations  (Badrinarayanan et al. , 2012; Stracy et al. , 2015; Uphoff et al. , 2013). Despite the differences in the diffusion profiles, a unifying feature volume  (English et al. , 2011; Uphoff, 2016). To determine unbiased D values from experimentally measured D*, we applied stochastic Brownian motion simulations to generate artificial single-molecule tracks using an identical number of molecules in the same segmented 3D cell volumes as in the experimental data (Figure  6A ). Localization error and stochastic disappearance of tracks because of photobleaching were also modeled, resulting in the same sampling and biases as in the experiments. We determined an unbiased estimate of the diffusion coefficient D from the best match (according to a least-squares metric) be-tween measured D* distributions and those obtained from simulations with a range of input diffusion coefficients.
Wide-field epifluorescence microscopy imaging Wide-field epifluorescence microscopy imaging of DAPI-stained cells was carried out on an Eclipse Ti-E microscope (Nikon), equipped with 100 s/1. 45 oil Plan Apo Lambda phase objective, Flash4 V2 CMOS camera (Hamamatsu), and using NIS Elements software for image acquisition. Acquisition was performed in phase contrast and epifluorescence mode using 50% power of a Fluo LED Spectra X light source at 405 nm and 560 nm excitation wavelengths for DAPI and ParB-mCherry, respectively. Wide-field imaging of MinC-Ypet was carried out on a Nikon Eclipse TE2000-U microscope equipped with a 100X objective, CCD camera (Cool-SNAP by Photometrics) and Metamorph 6. 2 acquisition software. Time-lapse movies were acquired in phase contrast and epifluorescence at 2 s intervals with 50 ms exposure for MinC-Ypet at 30 C.
Widefield epifluorescence image analysis Cells were automatically detected using the MicrobeJ plugin for Fiji  (Ducret et al. , 2016; Schneider et al. , 2012). Intracellular DAPI or ParB-mCherry mean fluorescence intensity (a. u. ) was automatically extracted and plotted using the MicrobeJ results interface. For analysis of MinC oscillation, cells were outlined using the MATLAB-based tool MicrobeTracker  (Sliusarenko et al. , 2011). The fluorescence signal was integrated across the cross-section of each cell to generate a one-dimensional fluorescence profile in each frame. The fluorescence signal was normalized to the total fluorescence in each frame to remove photobleaching effects and facilitate MinC-Ypet localization analysis. The fluorescence signals obtained from each cell were further analyzed by generating kymographs using custom MATLAB code.
Localization and tracking Single-molecule-tracking analysis was performed using custom-written MATLAB software (MathWorks) as previously described  (Uphoff et al. , 2014) : fluorophore images were identified for localization by band-pass filtering and applying an intensity threshold to each frame of the movie. Candidate positions were used as initial guesses in a two-dimensional elliptical Gaussian fit for high-precision localization. Free fit parameters were x-position, y-position, x-width, y-width, elliptical rotation angle, intensity, background. Localizations were segmented based on cell outlines obtained from MicrobeTracker applied to the brightfield snapshots. Single-particle tracking analysis was performed by adapting the MATLAB implementation of the algorithm described in  Crocker and Grier (1996).
e. , n = 4). The D Ã i distribution was then fitted with the equation for n = 4: p D Ã i À Á = 1 6 Ã 4 D 4 Ã D Ã i À Á 3 Ã exp À4D Ã i D Fits were performed using maximum likelihood estimation in MATLAB. For unperturbed cells the protein diffusion distributions were fit with a model containing two molecular species with diffusion coefficients D Ã 1 and D Ã 2 : representing immobile molecules bound to DNA for the entire trajectory, and mobile molecules diffusing and binding only transiently to DNA: p D Ã i À Á = A 1 6 Ã 4 D Ã 1 4 Ã D Ã i À Á 3 Ã exp À4D Ã i D Ã 1 " # + 1 À A ð Þ 6 Ã 4 D Ã 2 4 Ã D Ã i À Á 3 Ã exp À4D Ã i D Ã 2 " # where Aand 1 À A are the fraction of molecules found in each state. The localization uncertainty, s loc , manifests itself as a positive offset of s loc 2 /Dt in the D* value  (Michalet and Berglund, 2012).
ACKNOWLEDGMENTS The authors thank  Sophie Nolivos  for the pSN1 plasmid and the  Micron Advanced Bioimaging Unit (Wellcome Strategic  Awards  091911/B/10/Z  and  107457/Z/15/Z ) for support. M. S. was funded by a  Sir Henry Wellcome  Fellowship ( 204684/Z/16/Z ) and a junior research fellowship at  Trinity College Oxford. S. U. was funded by a  Sir Henry Wellcome  ( 101636/Z/13/Z ) and a  Sir Henry Dale Fellowship  ( 206159/Z/17/Z ), a  Wellcome-Beit Prize  ( 206159/Z/17/B ), and a  Hugh Price Fellowship  at  Jesus College, Oxford. C. L. acknowledges  ATIP-Avenir  and  FINOVI  ( AO-2014 ) and the  Schlumberger Foundation for Education and Research  ( FSER2019 ). D. J. S. was funded by a  Wellcome   Investigator Award  ( 200782/Z/16/Z ). A. N. K. was supported by  Wellcome Trust  grant  110164/Z/15/Z ,  European Council  grant  261227 , and  UK Biotechnology and Biological Sciences Research Council  grants  BB/N018656/1  and  BB/ S008896/1.
(a) An in silico model of the coevolution of transposable elements and host genomes To understand how the interaction between TEs and cells shapes genome architecture, we present an individual-based model of co-evolving TEs and host genomes packaged within cells. We first focus on simple bacteria-like cells, which engage in HGT via environmental pools of DNA, but later extend the model to encompass sexual reproduction. A brief overview of the model is given below. For more insights into the workings of the model, please see §4, the published code (https://github. com/bramvandijk88/selfishDNA) and the explorable model (https://bramvan dijk88. github. io/cacatoo/TEs_streamlining/). Individuals are simple cells that carry a genome with three distinct genetic elements: (i) ten host-essential genes (type A-J), which are necessary for survival/reproduction of the host, (ii) TEs, which are slightly costly to the host and (iii) non-coding DNA, which provides no function, but also carries no cost (figure  1a ).
We present the use of the Douglas-Gunn Alternating Direction Implicit finite difference method for computationally efficient simulation of the electric field propagation through a wide variety of optical fiber geometries. The method can accommodate refractive index profiles of arbitrary shape and is implemented in a tool called BPM-Matlab. We validate BPM-Matlab by comparing it to published experimental, numerical, and theoretical data and to commercially available state-of-the-art software. It is user-friendly, fast, and is available open-source. BPM-Matlab has a broad scope of applications in modeling a variety of optical fibers for diverse fields such as imaging, communication, material processing, and remote sensing.
Nevertheless, numerical tools must supplement these theoretical methods to accurately simulate beam propagation and to calculate the memory effect, especially for non-trivial fiber structures and for cases in which perturbation theory may no longer be valid. Multiple toolboxes such as BeamLab  [19]  and software packages such as Lumerical  [20] , based on the Beam Propagation Method (BPM) and/or the Finite-Difference Eigenmode solver and Eigenmode Expansion propagator, are widely used by the scientific community for optical fiber modeling. Although these simulation tools can model a multitude of applications in the regimes of free-space, waveguide, and non-linear optics, they are neither free as in beer (gratis) nor free as in speech (libre). Moreover, many of these tools make symmetry assumptions pertaining to the fiber/component geometry by simulating only a fraction (one half or one quarter) of the required geometry, thereby restricting complex arbitrary structure definitions.
To the best of our knowledge, they are also all restricted to CPU operation and do not support GPU acceleration despite the computation times in some cases being prohibitively slow. In this article, we present a numerical simulation tool called BPM-Matlab in which the Douglas-Gunn Alternating Direction Implicit (DG-ADI) method is used to efficiently model the electric field propagation in a wide variety of optical fiber geometries with arbitrary refractive index profiles. Our approach incorporates the important practical use cases of tapering, twisting, and bending deformations and may also offer solutions for modes for arbitrary refractive index profiles even in the presence of bending. The key aspect of this work lies in the use of the fast DG-ADI method to model field propagation through non-symmetric refractive index profiles of arbitrary shape combined with the software's availability for free (gratis) and as open-source (libre).
2. , explaining the employed DG-ADI method, the incorporation of fiber bending and LP mode analysis into the framework, and the corresponding implementations in MATLAB. In Sec. 3. , we discuss the applicability of BPM-Matlab and provide experimental validations for the numerical results. Finally, we conclude with comparisons to other state-of-the-art simulation software in Sec. 4. The applicability and versatility of BPM-Matlab is demonstrated with examples of single-mode, multi-mode, and multicore optical fibers, for conditions where the absence of any symmetry assumptions in geometry is necessary. The simulation tool is gratis, open-source, fast, and supports optional CUDA acceleration. It is available at https://gitlab. gbar. dtu. dk/biophotonics/BPM-Matlab.
Theory In this section, we will present the Finite Difference Beam Propagation Method (FD-BPM) for monochromatic light, motivate the use of the Douglas-Gunn Alternating Direction Implicit (DG-ADI) finite difference method for numerical calculations, and then present its implementation in MATLAB. The beam propagation method (BPM) solves the paraxial Helmholtz equation for the spatial distribution of the complex electric field E(x, y, z) at any transverse plane along z for the case when the electric field distribution at the input plane z = 0, E(x, y, z = 0), and the three dimensional refractive index distribution of the optical fiber, n(x, y, z) are all known. The paraxial approximation is well satisfied as long as the angles between the local k-vectors and the z-axis are small, which is the case for weakly guiding structures such as fibers with small difference in refractive index between the core and the cladding.
-a y -a y. -a x -a y -a x b ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ and C = ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ c a x a y a x c. a y a y. a x a y a x c ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ are sparse block-tridiagonal matrices of order N × N, where N = N x N y. Note that in these matrices, the displaced diagonals start after N x rows or columns, and correspond to partial differentiation in the y direction. To take the Dirichlet boundary conditions into account, every N th x entry in the diagonals containing a x or -a x elements must be set to 0. The right-hand side of Eq. (  4 ) is an explicit step that can be evaluated by a simple matrix multiplication whereafter E z+∆z can be solved for in a subsequent implicit step using various solvers such as the "matrix left division" operator in MATLAB.
Douglas-Gunn alternating direction implicit FD-BPM The Crank-Nicolson model of finite difference procedure leads to Eq. (  4 ) where there are five non-zero diagonals in each of the matrices B and C. Despite the conceptual simplicity of the couplings that this system describes, being local in both the x and y directions, standard methods of solving such systems such as with the linear algebra package LAPACK are, in fact, orders of magnitude less efficient than for a simple tridiagonal system, such as representing coupling only in the x or the y directions  [35]. However, the alternating direction implicit (ADI) finite-difference method  [26]  splits Eq. (  3 ) into two tridiagonal linear equations of ∆z 2 step sizes each. Equation (  2 ) can be rearranged as ∂E p,q ∂z = E p-1,q -2E p,q + E p+1,q 2jk 0 n 0 ∆x 2 + E p,q-1 -2E p,q + E p,q+1 2jk 0 n 0 ∆y 2 (5) and k 0 2jn 0 [︁ n 2 p,q (z) -n 2 0 ]︁ E p,q can be considered as an additional phase term which we will include later.
Implementation in MATLAB We used MATLAB mex functions written in C for implementing the above DG-ADI method. Implementing the aforementioned computations as MATLAB mex functions enables combining the speed of subroutines written in C with the versatility and user-friendliness of MATLAB. In solving Eqs. (6) and 7, the tridiagonal matrix algorithm (Thomas algorithm)  [40]  was used to solve for the E column vectors on the left hand sides of the equations, corresponding to a MATLAB "matrix left division". Fiber bending is taken into account by modifying the refractive index as a function of its transverse coordinates  [41]  as n ′ (x, y) = n(x, y) [︂ 1 -n 2 (x, y) x -x 0 2R p e ]︂ exp (︂ x -x 0 R )︂ (8) where n(x, y) is the refractive index distribution in the absence of bending that transforms into n ′ (x, y) when the fiber is bent through a bending radius of curvature R measured from the center of the fiber located at x 0.
Both tapering and twisting functionalities of BPM-Matlab impose further restrictions on step size since a large ∆z could result in discretization artefacts. The size of the z-steps is user-specified independently for each segment of the fiber and it is up to the user to verify that the step size is small enough to obtain converged results. There are no symmetry conditions explicitly or implicitly assumed anywhere while modeling the fiber in BPM-Matlab. Hence, the code is highly versatile providing the capability to simulate refractive index profiles of arbitrary shape and the important practical use cases of bending, twisting, and tapering deformations.
Fiber modes and modal power distribution BPM-Matlab propagates the field in the space domain without decomposing the beam into modes. However, the mode picture can be very useful and is a common way to visualise the field distribution during propagation. The E-field envelope of linearly polarized (LP) modes for straight step-index fibers is described by a piece-wise combination of Bessel functions, the analytical expression of which can be found in many other works  [42, 43]. However, to be able to perform this visualization for more complicated fiber geometries and bending, one needs to be able to find the set of modes for refractive index profiles of arbitrary shape. BPM-Matlab includes a mode solver for that purpose, solving for the eigenvectors of the C matrix described above. The reason that it is not necessary to consider the whole of Eq.
4 is that the B matrix on the left-hand side describes the same propagation as C, just as an implicit step rather than an explicit step. When solving for eigenvectors, this difference is irrelevant and matrix C will have the same eigenvectors as the equation as a whole. The refractive index profile used here is n ′ (x, y) as described in Eq. 8, so that bending is also taken into account. The eigenvectors are found using the built-in Matlab eigs function, which in turn employs the Krylov-Schur method  [44] , finding a user-specified number of modes with eigenvalues closest to 1 in the complex plane. The LP modes' normalized fractional power η is defined as the squared norm of the inner product integral of the present field with the field distribution of the mode  [45] η = | ∬ E(x, y)E * l,m,p (x, y) dxdy| 2 ∬ |E(x, y)| 2 dxdy ∬ |E l,m,p (x, y)| 2 dxdy (9) where E(x, y) is the electric field distribution and E l,m,p (x, y) is the electric field of the mode with azimuthal mode index l, radial mode index m, and parity p (even or odd).