dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This study was supported by the  National Natural Science Foundation of China  (grant nos. 41561134016  and  51879068 ); the  CHINA-TREND-STREAM French national project (ANR grant  no. ANR-15-CE01-00L1-0L ); the  National Key Research and Development Program  ( 2018YFC0407900 ); the  Global Environmental Research Fund  ( S-14 ) of the  Ministry of the Environment, Japan ; and the  China Scholarship Council  (CSC, 201506710042). The work was supported by computing resource of the  IPSL ClimServ cluster at École Polytechnique, France. We also thank the editor and the anonymous reviewers for their contribution to the improvement of this paper. Edited by: Zhongbo Yu Reviewed by: three anonymous referees.
Code and data availability. The land surface model is free to download on the website https://forge. ipsl. jussieu. fr/orchidee/ browser/branches/publications/ORCHIDEE_gmd-2018-57  (Nguyen-Quang et al. , 2018). The WFD dataset can be obtained from http://www. waterandclimatechange. eu/about/ watch-forcing-data-20th-century  (Weedon et al. , 2010). The WFDEI dataset can be obtained from http://www. eu-watch. org/ gfx_content/documents/README-WFDEI(v2016). pdf (Weedon et al. , 2014b). The CRU datasets are available through http: //catalogue. ceda. ac. uk/uuid/ac3e6be017970639a9278e64d3fd5508  (Jones and Harris, 2013). The CMA dataset is available through website http://data. cma. cn/data/detail/dataCode/SURF_CLI_ CHN_PRE_MON_GRID_0. 5/ (Zhao et al. , 2018).
We integrate these phylogenies with large species occurrence datasets, spatial information on Neotropical regions and biomes, and their evolution and connectivity through time. We address the following questions: was the amount of biotic interchange similar among Neotropical regions, or have particular regions contributed substantially more or less than others? How often were dispersal events associated with shifts between major biome types? To what extent was the direction of interchange determined by taxonomic group? Did biotic interchange among regions occur evenly through time, or were there periods of more frequent dispersal events? Which general descriptors of a region and its biota predict the amount of biotic interchange with other regions?.
Results Data Compilation and Analyses. Our vetted dataset encompassed 1,331,323 georeferenced records representing 68,329 species, derived from the integration of densely sampled molecular phylogenetic trees with a large dataset of species distributions (SI Appendix, Figs. S1 and S2, Table  S1 , and Supporting Methods). Our analytical pipeline identified 214 predominantly Neotropical clades including 4,450 species. These comprised 104 clades of flowering plants or angiosperms (2,114 spp; representing 46. 7% of the total number of species in the utilized phylogenies), four clades of ferns (53 spp; 1. 16%), 54 of birds (1,237 spp; 27. 5%), 11 of frogs (155 spp; 4. 13%), 13 of mammals (359 spp; 8. 23%), and 28 of squamates, which comprised lizards, snakes, and worm lizards or amphisbaenians (532 spp; 12.
The position of the circles in the layout reflects the biotic connection among regions, as calculated by a force-directed placement algorithm implemented in the R package qgraph  (56). Dispersal events out of Amazonia are highlighted in red. The color of the circles corresponds to those in Fig. 1. AGL, Andean Grasslands; AMA, Amazonia; ATF, Atlantic Forests; CAA, Caatinga; CEC, Cerrado and Chaco; DNO, Dry Northern South America; DWE, Dry Western South America; Mes, Mesoamerica; PAS, Patagonian Steppe; WIN, West Indies. (B) Number of nonambiguous dispersal events associated with shifts in major biome types compared with shifts to other regions within the same biome type. Shifts involving Mesoamerica and the West Indies are considered ambiguous (n = 2,068; SI Appendix, Table  S6 ). The ranked values correspond to dispersal events inferred from timecalibrated molecular phylogenies estimated under an unconstrained and a time-stratified biogeographic model of region connectivity (SI Appendix, Tables  S5  and S6 ).
Materials and Methods A full description of the methodology used and known limitations of our data and methodology, such as those caused by incomplete sampling of species occurrences and molecular sequences in public databases  (49, 50) , are described in SI Appendix, Supporting Methods. Data Compilation. We focused our analyses on the Neotropical realm as defined by Morrone  (1). Within the Neotropics, we defined and delimited 10 regions, broadly corresponding to environmentally and biologically different terrestrial biomes proposed by Olson et al. (47)  (Fig. 1  and SI Appendix, Fig. S22 ). We reconstructed the biogeographic history of two groups of plants (angiosperms and ferns) and four groups of vertebrates (birds, frogs, mammals, and squamates). We downloaded occurrence records for all species from the Global Biodiversity Information Facility (SI Appendix, Supporting Methods).
We implemented the dispersalextinction-cladogenesis model  (53)  in the R package BioGeoBEARS (54) to infer geographical range evolution of lineages. We developed two sets of analyses: one in which biotic connectivity among all regions was allowed at any point in time, and one in which we modeled connectivity based on paleogeographic and geological evidence (SI Appendix, Table  S3 ). We identified shifts between regions in 5-My time bins. We computed the absolute number of dispersal events through time by extracting the areas and ages of all nodes from each phylogeny. The number of branches and total amount of branch lengths in any phylogeny of extant species increase with time, potentially increasing the number of dispersal events toward the present even under a constant dispersal rate  (55).
ACKNOWLEDGMENTS. We are very grateful for the constructive comments by three anonymous reviewers and the handling editor, which helped to substantially improve this manuscript. We also thank  Søren Faurby ,  Allison Perrigo ,  Isabel Sanmartín ,  Carlos Jaramillo ,  Lucia Lohmann ,  James Albert ,  Mark Torchin , and  Michael Donoghue  for discussions and feedback at various stages of this project. Funding for this work was provided by the  Swedish Research Council  ( B0569601 ), the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013,  ERC Grant Agreement 331024 ), the  Swedish Foundation for Strategic Research , the  Faculty of Sciences at the University of Gothenburg , the  David Rockefeller Center for Latin American Studies at Harvard University , the  Wenner-Gren Foundations , and a  Wallenberg Academy Fellowship  (to A.
A. ); the  Swedish Research Council  ( 2015-04748  to D. S. ); the  Carl Tryggers Stiftelse  grant ( CTS 12:24 ) and an " Investissements d'Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01  to F. L. C. ).
Numerical Results and Discussions Here, we developed a program in FICO XPRESS to validate and explore the proposed mathematical model. We had five sets of  [5, 10]  customers. We focused on the variation of the time windows from [0. 1, 0. 5] h in order to evaluate three different objectives: the fleet size, the traveled distance and the energy consumption through different objectives shown in Equation (1). Our analysis highlights three parts of this sizing objective function independently:.
For example, there is often a bias against fast-moving particles because the laser only lights up a thin layer of the cell. The proteins travelling slowly stay in focus long enough to be detected across many images; the fast ones quickly move out of the layer and are therefore counted less often. Hansen, Woringer et al. designed a free and user-friendly algorithm package called Spot-On to correct for this issue. Spot-On was thoroughly benchmarked against other solutions, demonstrating both its accuracy and robustness. Single-particle tracking can lead to misleading results if used incorrectly. It is essential to publically share solutions that help make this technique more rigorous, especially since a growing number of scientists have already started to use the method. Spot-On is available as a web-interface (https:// SpotOn.
Overview of Spot-On Spot-On is a user-friendly web-interface that pedagogically guides the user through a series of quality-checks of uploaded datasets consisting of pooled single-molecule trajectories. It then performs kinetic model-based analysis that leverages the histogram of molecular displacements over time to infer the fraction and diffusion constant of each subpopulation (Figure  2 ). Spot-On does not directly analyze raw microscopy images, since a large number of localization and tracking algorithms exist that convert microscopy images into single-molecule trajectories (for a comparison of particle tracking methods, see  (Chenouard et al. , 2014) ; moreover, Spot-On can be one-click interfaced with TrackMate  (Tinevez et al. , 2017) , which allows inspection of trajectories before uploading to Spot-On).
To use Spot-On, a user uploads their SPT trajectory data in one of several formats (Figure  2 ). Spot-On then generates useful meta-data for assessing the quality of the experiment (e. g. localization density, number of trajectories etc. ). Spot-On also allows a user to upload multiple datasets (e. g. different replicates) and merge them. Spot-On then calculates and displays histograms of displacements over multiple time delays. The next step is model fitting. Spot-On models the distribution of displacements for each subpopulation using Brownian motion under steady-state conditions without state transitions (full model description in Materials and Methods). Spot-On also accounts for localization errors (either user-defined or inferred from the SPT Figure  1. Bias in single-particle tracking (SPT) experiments and analysis methods.
We labeled Halo-tagged proteins in live cells with the HaloTag ligands PA-JF 549 or PA-JF 646  (Grimm et al. , 2016a ) and performed spaSPT using HiLo illumination (Video 2). To generate a large dataset to comprehensively test Spot-On, we performed 1064 spaSPT experiments across 60 different conditions. Validation of Spot-On using spaSPT data at different frame rates First, we studied whether Spot-On could consistently infer subpopulations over a wide range of frame rates. We experimentally determined the axial detection range to be ~700 nm (Figure  4 -figure supplement 1) and performed spaSPT at 200 Hz, 167 Hz, 134 Hz, 100 Hz, 74 Hz and 50 Hz using the four cell lines. Spot-On consistently inferred the diffusion constant (Figure  4G ) and total bound fraction across the wide range of frame rates (Figure  4H ).
This is notable since all four proteins exhibit apparent anomalous diffusion (Figure  4 -figure supplement 2) and this demonstrates that Spot-On is also robust to anomalous diffusion despite modeling Brownian motion. While the ground-truth is unknown when considering experiments, Spot-On gave biologically reasonable results: histone H2B was overwhelmingly bound and free Halo-3xNLS was overwhelmingly unbound (comparison with vbSPT: Figure  4 -figure supplement 3). These results provide additional validation for the bias corrections implemented in Spot-On. We also note that although Spot-On was validated on spaSPT data, SPT data with non-photoactivatable dyes is also suitable for Spot-On analysis provided that the density is sufficiently low to minimize tracking errors (see also Appendix 3: "Which datasets are appropriate for Spot-On?").
For time delays from 1Dt to 15Dt, the fraction of molecules that were lost was calculated in the range of D=[1;12] mm 2 /s. a Dz; Dt; g ð Þ and b Dz; Dt; g ð Þ were then estimated through leastsquares fitting of P remaining Dt; Dz corr ; D ð Þ to the simulated fraction remaining. The process was repeated over a grid of plausible values of (Dz; Dt; g) to derive a grid of 134,865 (a,b) parameter pairs. This pre-calculated library of (a,b) parameters enables Spot-On to perform model fitting on nearly any SPT dataset with minimal overhead. Thus, the 2-state model Spot-On uses for kinetic modeling of SPT data is given by: P 2 r; Dt ð Þ ¼ F BOUND r 2 DBOUNDDtþs 2 ð Þ e Àr 2 4 D BOUND Dtþs 2 ð Þ þZ CORR Dt; Dz corr ; D FREE ð Þ 1 À F BOUND ð Þ r 2 DFREEDtþs 2 ð Þ e Àr 2 4 D FREE Dtþs 2 ð Þ where: Z CORR Dt; Dz corr ; D FREE ð Þ ¼ 1 Dzcorr R Dzcorr 2 ÀDzcorr 2 1 À P ¥ n¼0 À1 ð Þ n erfc 2nþ1 ð ÞDzcorr 2 Àz ffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 4DFREEDt p þ erfc 2nþ1 ð ÞDzcorr 2 þz ffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 4DFREEDt p ! & ' dz Having derived the 2-state model, generalization to a 3-state model with 1 bound and 2 diffusive states is straightforward.
Numerical implementation of models in Spot-On Spot-On calculates the empirical histogram of displacements based on a user-defined bin width. Spot-On allows the user to choose between PDF-and CDF-fitting of the kinetic model to the empirical displacement distributions; CDF-fitting is generally most accurate for smaller datasets and the two are similar for large datasets (Figure  3 -figure supplement 9). The integral in Z CORR Dt; Dz corr ð Þ was numerically evaluated using the midpoint method over 200 points and the terms of the series computed until the term falls below a threshold of 10 À10. Model fitting and parameter optimization was performed using a non-linear least squares algorithm (Levenberg-Marquardt). Random initial parameter guesses are drawn uniformly from the user-specified parameter range.
Cell culture Halo-Sox2  (Teves et al. , 2016)  knock-in JM8. N4 mouse embryonic stem cells (  (Pettitt et al. , 2009)  Research Resource Identifier: RRID:CVCL_J962; obtained from the KOMP Repository at UC Davis) were grown on plates pre-coated with a 0. 1% autoclaved gelatin solution (Sigma-Aldrich, St. Louis, MO, G9391) under feeder free conditions in knock-out DMEM with 15% FBS and LIF (full recipe: 500 mL knockout DMEM (ThermoFisher, Waltham, MA, #10829018), 6 mL MEM NEAA (ThermoFisher #11140050), 6 mL GlutaMax (ThermoFisher #35050061), 5 mL Penicillin-streptomycin (ThermoFisher #15140122), 4. 6 mL 2-mercapoethanol (Sigma-Aldrich M3148), 90 mL fetal bovine serum (HyClone Logan, UT, FBS SH30910. 03 lot #AXJ47554)) and LIF. mES cells were fed by replacing half the medium with fresh medium daily and passaged every two days by trypsinization.
Halo-3xNLS, H2B-Halo-SNAP and knock-in C32 Halo-CTCF  (Hansen et al. , 2017)  Human U2OS osteosarcoma cells (Research Resource Identifier: RRID:CVCL_0042) were grown in low glucose DMEM with 10% FBS (full recipe: 500 mL DMEM (ThermoFisher #10567014), 50 mL fetal bovine serum (HyClone FBS SH30910. 03 lot #AXJ47554) and 5 mL Penicillin-streptomycin (ThermoFisher #15140122)) and were passaged every 2-4 days before reaching confluency. For live-cell imaging, the medium was identical except DMEM without phenol red was used (ThermoFisher #31053028). Both mouse ES and human U2OS cells were grown in a Sanyo copper alloy IncuSafe humidified incubator (MCO-18AIC(UV)) at 37 ˚C/5. 5% CO 2. Cell lines were pathogen tested and authenticated through STR profiling (U2OS) as described previously  (Hansen et al.
The excitation settings were as follows: 1 ms, 561 nm 100% AOTF, 633 nm 100% AOTF; 2 ms, 561 nm 43% AOTF, 633 nm 40% AOTF; 4 ms, 561 nm 28% AOTF, 633 nm 27% AOTF; 7 ms, 561 nm 20% AOTF, 633 nm 19% AOTF; constant illumination, 561 nm 17% AOTF, 633 nm 16% AOTF. spaSPT data was analyzed (localization and tracking) and converted into trajectories using a custom-written Matlab implementation of the MTT-algorithm  (Sergeet al. , 2008)  and the following settings: Localization error: 10 -6. 25 ; deflation loops: 0; Blinking (frames): 1; max competitors: 3; max D (mm 2 /s): 20. The spaSPT trajectory data was then analyzed using the Matlab version of Spot-On (v1. 0; GitLab tag 1f9f782b) and the following parameters: dZ = 0. 7 mm; GapsAllowed = 1; Time-Points: 4 (50 Hz), 6 (74 Hz), 7 (100 Hz), 8 (134 Hz), 9 (167 and 200 Hz); JumpsToConsider = 4; Model-Fit = 2; NumberOfStates = 2; FitLocError = 0; LocError = 0.
For comparisons of Spot-On, MSD i and vbSPT using a 2-state scenario, we parameterized simSPT to consider two subpopulations of particles diffusing in a sphere (the nucleus) of 8 mm diameter illuminated using HiLo illumination (assuming a HiLo beam width of 4 mm), with an axial detection range of ~700 nm, centered at the middle of the HiLo beam with Gaussian edges. Molecules are assumed to have a mean lifetime of 4 frames (when inside the HiLo beam) and of 40 frames when outside the HiLo beam. The localization error was set to 25 nm and the simulation was run until 100,000 in-focus trajectories were recorded. More specifically, the effect of the exposure time (1 ms, 4 ms, 7 ms, 13 ms, 20 ms), the free diffusion constant (from 0. 5 mm 2 /s to 14. 5 mm 2 /s in 0. 5 mm 2 /s increments) and the fraction bound (from 0% to 95% in 5% increments) were investigated, yielding a dataset consisting of 3480 simulations.
More details on the simulations, including scripts to reproduce the dataset, are available on GitLab as detailed in the 'Computer code' section. Full details on how the simulations were analyzed by Spot-On, vbSPT and MSD i are given in Appendix 1. We also considered a 3-state scenario featuring a bound subpopulation ('bound'), a relatively slow diffusing free subpopulation ('slow') and a relatively faster diffusing free subpopulation ('free'). In this case, we only compared Spot-On and vbSPT (Figure  3 -figure supplement 6), since the MSD i methods did not perform well. As in the 2-state simulations, we parameterized simSPT to consider that three subpopulations of particles diffusing in a sphere (the nucleus) of 8 mm diameter illuminated using HiLo illumination (assuming a HiLo beam width of 4 mm), with an axial detection range of ~700 nm, centered at the middle of the HiLo beam with Gaussian edges.
Molecules are assumed to have a mean lifetime of 4 frames (when inside the HiLo beam) and of 40 frames when outside the HiLo beam. The localization error was set to 40 nm and the simulation was run until 100,000 in-focus trajectories were recorded. We considered three different subpopulation conditions: (1) F BOUND = 25%; F SLOW = 25%; F FAST = 50%; (2) F BOUND = 25%; F SLOW = 50%; F FAST = 25%; (3) F BOUND = 50%; F SLOW = 25%; F FAST = 25%. Specifically, for each of these condition, the effect of of the exposure time (1 ms, 4 ms, 7 ms, 10 ms, 13 ms, 20 ms), the slower free diffusion constant (from 0. 5 mm 2 /s to 2. 5 mm 2 /s in 0. 5 mm 2 /s increments) and the faster free diffusion constant (from 4 mm 2 /s to 11 mm 2 /s in 1 mm 2 /s increments) were investigated, yielding a dataset of 720 simulations.
Fitting of simulations using Spot-On, vbSPT and MSD i To systematically evaluate the performance of Spot-On as well as other common analysis tools such as MSD i and vbSPT  (Persson et al. , 2013) , we developed simSPT, a simulation tool to generate a comprehensive set of realistic SPT simulations spanning the range of plausible dynamics (almost a billion trajectories were simulated in total). simSPT is freely available at GitLab: https://gitlab. com/tjian-darzacq-lab/simSPT. simSPT simulates 3D SPT trajectories arising from an arbitrary number of subpopulations confined inside a sphere under HiLo illumination and takes into account a limited axial detection range, realistic photobleaching rates and optionally state interconversion. The simulation methods are described in detail at GitLab. Briefly, we parameterized simSPT to consider that particles diffuse inside a sphere (the nucleus) of 8 mm diameter illuminated using HiLo illumination (assuming a HiLo beam width of 4 mm), with an axial detection range of ~700 nm with Gaussian edges, centered at the middle of the HiLo beam.
Molecules are assumed to have a mean lifetime of 4 frames (when inside the HiLo beam) and of 40 frames when outside the HiLo beam. For the 2-state comparisons, the localization error was set to 25 nm and the simulation was run until 100,000 in-focus trajectories were recorded. More specifically, the effect of the time between frames (1 ms, 4 ms, 7 ms, 13 ms, 20 ms), the free diffusion constant (from 0. 5 mm 2 /s to 14. 5 mm 2 /s in 0. 5 mm 2 /s increments) and the fraction bound (from 0% to 95% in 5% increments) were investigated, yielding a dataset consisting of 3480 simulations. All 3480 simulated datasets are also available (see Data Availability section). The advantage of simulations is that the ground truth is known. For the 3-state comparisons (Figure  3 -figure supplement 6), the localization error was set to 40 nm and the simulation was run until 100,000 in-focus trajectories were recorded.
7 mm; 1 gap: a = 0. 0387 s 12 ; b = 0. 3189 mm; Dt = 4 ms; Dz = 0. 7 mm; 1 gap: a = 0. 1472 s 1/2 ; b = 0. 2111 mm; Dt = 7 ms; Dz = 0. 7 mm; 1 gap: a = 0. 1999 s 1/2 ; b = 0. 2058 mm; Dt = 10 ms; Dz = 0. 7 mm; 1 gap: a = 0. 2379 s 1/2 ; b = 0. 2017 mm; Dt = 13 ms; Dz = 0. 7 mm; 1 gap: a = 0. 2656 s 1/2 ; b = 0. 2118 mm; Dt = 20 ms; Dz = 0. 7 mm; 1 gap: a = 0. 3133 s 1/2 ; b = 0. 2391 mm; CDF-fitting was then performed in MATLAB R2014b using the Matlab version of Spot-On (v1. 0; GitLab tag 1f9f782b) and the estimated free diffusion constant, D FREE , and bound fraction, F BOUND , recorded for each of the 3480 simulations. The estimated D FREE and F BOUND were then compared to the ground truth known from the simulations. Three parameters were estimated in the fit.
, 2016) ). Each trajectory with at least five localizations are then fit, often using the Matlab library MSDAnalyzer  (Tarantino et al. , 2014) , and thus assigned an apparent diffusion constant. An additional threshold is then applied: only if the fit to the MSD curve is judged sufficiently good, is the diffusion constant then used. Otherwise the trajectory is ignored. This fitting threshold is frequently set based on the coefficient of determination as R 2 >0. 8 in some recent papers  (Knight et al. , 2015; Li et al. , 2016; Schmidt et al. , 2016). Next, after analyzing all trajectories in this way, a distribution of diffusion constants is then obtained. The analysis is then performed on the logarithm of these diffusion constants ('LogD histogram')  (Knight et al. , 2015; Li et al. , 2016; Schmidt et al.
Please note that fitting a sum of normal distributions to the LogD histogram is equivalent to fitting a sum of log-normal distributions to the D histogram. We also note here, that in a theoretical study Michalet previously showed that the distribution of diffusion constants is approximately Gaussian, but only under a set of stringent criteria  (Michalet, 2010). Since CDF-fitting is generally less susceptible to noise from binning and since in this comparison Spot-On also uses CDF-fitting, we fit the LogD histogram with a sum of 2 Gaussians using CDF-fitting. We refer to this whole procedure as MSD i (R 2 >0. 8). Examples of fits are shown in Figure  3  and Figure  3 -figure supplement 3 and the Matlab code to perform the fitting is available together with the data (see "Data availability').
8; at least five frames). Why is MSD i (R 2 >0. 8) fitting so unreliable? It is instructive to consider an example. In the example dataset provided with the MSD i code (simulation with D FREE =2; F BOUND ¼0. 75; 1 ms frame rate), the estimated D FREE =2. 06 is very good, but the estimated F BOUND ¼0. 16 is extremely poor. Even though the simulation dataset contains 100,000 simulated trajectories, only 3726 of them actually pass the threshold (R 2 >0. 8; at least five frames). Thus, MSD i (R 2 >0. 8) only uses around 4% of the data. Since the tiny fraction of the dataset that is used for analysis is chosen based on how well it fits an MSD-curve and since displacements of bound molecules are dominated by localization errors and therefore generally poorly fit by MSD-analysis, the procedure enriches for the free population, which is why the estimated bound fraction (16%) is so much lower than the true bound fraction (75%).