dataesr/scientific-paragraphs-categorization huggingface dataset:
To determine if our sample population possessed this greater regulation described above, we first used these morphometric measures to calculate a "balance number" or B value  [14]  for each coleoptile from the tilted group. By comparing these B values to previous results from the AC model, we predicted which coleoptiles form the tilted condition would overshoot the vertical. Our data demonstrated that fewer coleoptiles than predicted overshot the vertical (Figure  7 ), strongly suggesting the presence of additional gravitropic movement regulation in our coleoptile sample. We then attempted to find this regulatory mechanism by comparing the kinematics of tilted and untilted coleoptiles. First, we examined average measures of elongation growth rate and found that straight coleoptiles had a slightly higher average REGR than those in the tilted group.
Since this time, the problem of understanding and quantifying curvature has remained, notably because of a rapidly increasing database of experimental and computationally derived structural information on deformed DNA. Such deformation may be caused in many ways: by the base sequence alone, by bound ligands, by bound proteins or protein complexes or by topological constraints, as in looped or circular DNA. In developing the DNA conformational analysis program Curves  (8, 9) , and its more recent incarnation Curves+  (10) , we aimed at defining not only helical, backbone and groove parameters, but also a curvilinear helical axis that would help to resolve some of the questions raised above. Although we believe that such a helical axis has been a very useful guide to interpreting DNA curvature in a visual sense, it did not provide quantitative information on curvature.
Here, the sequence-dependent twists were obtained from the ABC dataset (20) by summing up the twists of the central base pair steps of the corresponding tetranucleotide fragments. The results are shown in Table  1 , where we can see that RE 94 is actually slightly overwound (σ = 0. 055), UN 94 is underwound by roughly the same magnitude (σ = -0. 063) and OV 89 is significantly overwound (σ = 0. 113). These values result from an average reduction of twist of 1. 6 • compared to the currently accepted sequence-averaged value of 34. 3 • (i. e. 10. 5 bp/turn) for DNA in solution. This difference can be partially attributed to the force field and partially to the specific sequence used for these minicircles. The initial construction of each minicircle was made using the internal coordinate nucleic acid modeling program JUMNA, using its superhelical symmetry and ring closure options  (12, 21).
Molecular dynamics (MD) simulations Simulations were performed using the Amber 12 program suite  (22, 25)  with the BSC0 modifications  (24)  to the parm99 parameter set  (22, 23). Each minicircle was placed in a truncated octahedral cell and solvated with SPC/E water (26) creating a solvent layer at least 10 Å thick. The minicircle charge was neutralized with an appropriate number of potassium cations and then sufficient K + Cl -ion pairs were added to achieve a bulk ionic strength of 0. 15 M, using the Dang force field parameters  (27). All ions were initially placed randomly, but at least 5 Å from the solute and at least 3. 5 Å from one another. The resultant system size varied from 131,409 to 164,196 atoms. After heating and equilibrating the systems, using a protocol described earlier  (28, 29) , simulations were carried out for at least 300 ns using periodic boundary conditions.
Ions lying 'inside' the local direction of curvature are those for which |A-G D | < 90 • , while those for which |A-G D | ≥ 90 • are classed as 'outside'. Note that the register vector defining G D at any position within a given base pair step can be obtained from the corresponding G i using the screw transform discussed in the previous section. Canion has been modified to allow the user to optionally limit any analysis to either the inside or the outside face of a chosen nucleic acid fragment. This distinction is only relevant in zones where the curvature C is significant. We have therefore introduced a cutoff below which inside/outside distinctions are ignored. The default is set to C = 0. 4, but this can be modified by the user. In order to check the choice of volumes in which ion densities will be calculated, Canion now also includes an option to visualize the volumes (by generating a Gaussian cube density matrix that can be read into in a graphic program such as Chimera  (35, 36) ).
Third, in the case of under-or overwound minicircles, it is possible to analyze heterogeneous curvature and to determine how this is coupled to changes in other structural parameters. Last, it is possible to analyze the links between curvature and the environment of DNA, notably the significant impact curvature has on the distribution of cations around the double helix. In the cases studied here, curvature concentrates potassium cations toward the interior of the minicircle, this effect being strongest in the minor groove. This method is now a part of the freely available Curves+ software (and the accompanying utilities Canal and Canion, aimed at analyzing MD trajectories) that can be found at http://curvesplus. bsc. es/. Although we have concentrated here on applying our new technique to circularized DNA in this work, the method is equally applicable to linear DNA and it should be of interest for analyzing the more subtle curvatures induced in DNA either by its own base sequence, or by external factors.
Molecular density analysis The algorithm work flow is shown in Fig. 1. All acquired image sequences are first drift corrected with sub-pixel precision. Using ThunderSTORM  23  , we measured lateral drift using fluorescent beads (fiducial markers) present in the images. These drift corrected image sequences were then processed by our bSOFI algorithm using 2nd, 3rd and 4th order cumulant analysis (see Methods). We extracted molecular density maps by combining the cumulant images in a system of linear equations, (see Methods). Fig. 2  shows a data processing example (molecular density analysis) for a single cell. As shown previously  21  , the accuracy of the density calculation is mainly determined by the size of the input image sequence. We acquired image sequences of 5000 frames for each dataset, choosing the number of frames by analysing the signal to noise ratio (SNR)  21.
We further evaluated the density maps generated by bSOFI by systematically increasing the density threshold (see Methods). Starting with a low threshold, large regions with a low average density are segmented. Increasing the threshold step by step allows precise density quantification (see Supplementary Fig. 1 ). We analysed each region of interest (ROI) by calculating, for each density threshold, the average number and area of high density regions (HDRs), as well as the relative area occupied by the HDRs. The averaged data across all cells for each protein variant over the range of density thresholds is shown in (Fig. 3a-c ). This analysis provides an overview of HDR parameters in relation to the density threshold, unravelling the overall clustering behaviour of the cell samples under study.
Protein nanoscale organization We expressed four different mutant variants of mEOS2labelled CD4 and analysed individual protein distributions on the plasma membrane of resting T cells immobilised on poly-L-lysine coated glass coverslips. Using TIRF microscopy, we imaged 20 cells for each CD4 variant (i. e. 80 in total) acquiring 5000 frames per cell. Tested mutants were native CD4 protein (WT), palmitoylation mutant (CS1) and truncated variants lacking the extracellular (dD1D4) and cytoplasmic (dCT) domains (Supplementary Fig. 4 ). Segmentation of SMLM data acquired for CD4-mEos2(WT) indicated the accumulation of native CD4 in HDRs with irregular shape, frequently forming networks (Fig. 2  and Supplementary Fig. 5 ). SMLM-based cluster analysis of these localisation data would be a challenge  13.
Molecular density analysis The SOFI based molecular density analysis was programmed in MATLAB taking into account a linearization procedure as described in  21. Combining SOFI images of different orders allows one to extract density maps (see Supplementary Note). Molecular density (i. e. number of emitters per pixel area) at pixel position r is given as 2 2 2 2 2 2 1 1 1 2 2 1 1 1 2 2 1 2 2 2 1 2 1 2 ( ) ( ) , 3 3 2 2 3 3 2 2 3 2 (1 ) 2 ( ) ( 3 2 ) 2(3 2 ) g N K K K K K K K K K K K K K K K K = - - - - - - - - - - r r where 2 3 1 3 2 ( ) ( ) g K g µ µ = r r, 2 4 2 4 2 ( ) ( ) g K g µ µ = r r, 2 3 4 , , g g g represents cumulant images of 2 nd , 3 rd and 4 th order, respectively. { ( )} n n V U µ = r  , where { ( )} n V U r  is the expectation value of the PSF ( ( ) n U r ) of the n th order cumulant image.
Each test scenario was repeated 10 times. In total, 360 datasets were generated and evaluated (120 datasets for each HDR/background case). The number of photons per emitter per frame was set to 100, which corresponds well to the experimental conditions. The number of frames of each image sequence was 5000. Dashed blue line in (a),(b),(c) marks the ground truth. Overall, the simulation validates the algorithm and estimation under a broad range of conditions. (d) HDR detection efficiency score is a probability that the estimated number of HDRs is in the range (7-13). The accuracy of the estimation increases with increasing HDR/background ratio and increasing HDR density. The simulations were calculated for the grid points. The dashed line marks the conditions where the signal to noise ratio (SNR) and signal to background ratio (SBR) of simulated data correspond to SNR and SBR of the real cell datasets in our experiments.
[2]. (a) Original dataset composed of 1,747,681 localizations, scale bar 2 µm. (b) Magnification of the central area of the cell. Segmented HDRs are displayed in red, scale bar 1 µm. (c) Zoom on a HDR composed of 65 localizations, scale bar 50 nm. As shown by its time trace, the localizations in this region are originating from a single fluorophore, making its blinking correction simple. (d-e) Zoom on a denser HDR (d, 384 localizations, scale bar 50 nm) and interconnected HDRs (e, 12,886 localizations, scale bar 200 nm) with their time trace. Even for a small HDR, the presence of multiple emitters complicates the blinking correction. The problem becomes even more apparent with interconnected HDRs because of their high-density of molecules. (f-g) Three frames of the original image acquisition, pixel size 105 nm.
Acknowledgements We would like to thank  Guy Hagen  for his helpful remarks and comments. We acknowledge  Peter Kapusta  for technical assistance and professional advice. This work was funded by  Czech Science Foundation  (M. C. :  15-06989S ). T. La. acknowledge the partly funding of the  Horizon 2020  project  AD-gut  ( SEFRI 16. 0047 ) and the  Swiss National Science Foundation  (SNSF, http://www. snf. ch/)under grants  200020-159945  and  205321-138305. T. Lu. acknowledges a  SCIEX scholarship  ( 13. 183 ) and a  CTU student grant  ( SGS16/167/OHK3/2T/13 ).
Supplementary Figure 6 : 6 [2]. (a) Original dataset composed of 1,747,681 localizations, scale bar 2 µm. (b) Magnification of the central area of the cell. Segmented HDRs are displayed in red, scale bar 1 µm. (c) Zoom on a HDR composed of 65 localizations, scale bar 50 nm. As shown by its time trace, the localizations in this region are originating from a single fluorophore, making its blinking correction simple. (d-e) Zoom on a denser HDR (d, 384 localizations, scale bar 50 nm) and interconnected HDRs (e, 12,886 localizations, scale bar 200 nm) with their time trace. Even for a small HDR, the presence of multiple emitters complicates the blinking correction. The problem becomes even more apparent with interconnected HDRs because of their high-density of molecules. (f-g) Three frames of the original image acquisition, pixel size 105 nm.
Multi Body (MB) models of child occupants were presented by van Rooij et al. [19]. A 3 year-old child facet model was developed and extended to 6 and 10 year-old child HBMs in the MAthematical DYnamic MOdel (MADYMO) code (TASS International, Helmond, Netherlands)  [20]. The anthropometry was based on the CANDAT database  [21]  and mechanical properties were scaled down from the 50 th percentile male model  [20]. Validation of the 6 and 10 year-old models was presented in the MADYMO Human Models Manual  [20]. Their response was compared with scaled corridors obtained from the dynamic hub impactor tests of Neathery  [22]  (impactor speed: 4. 3 m. s -1 and 6. 7 m. s -1 ). The 6 year-old model was also validated against: frontal thoracic pendulum tests (impactor mass: 3. 5 kg, impactor speed: 6 m.
Materials and Methods The MADYMO HBMs representing 6 year-old (Child6YO) and 10 year-old (Child10YO) children  [20]  were used to simulate experiments previously performed with child volunteers in braking  [26]  and steering events  [27]. The MADYMO Release 7. 4. 1 code was used for all simulations and post processing was done using MATLAB version R2010b (MathWorks, Natick, Massachusetts, U. S. A. ).
Experimental Data Previously published studies have presented child volunteer data for braking  [26]  and steering  [27]  events performed on a test track with a Volvo XC70. The study protocol the authors are referring to was reviewed and approved by The Ethics Board of Gothenburg, Sweden  [26, 27]. The driving study was conducted on a closed-circuit test track by a professional driving instructor. Child volunteers were safely restrained and accompanied by a parent. Every child and parent were aware of the test protocol. An informed consent form was signed by the parents, and a small gift was given to the participating children. The test could be aborted at any time by the child or parent. Children were not exposed to any injury risks or major discomfort. The aims of this study were to quantify the kinematics of child occupants.
The braking event was divided into three phases (Fig  1 ). The first phase corresponded to a decrease of the longitudinal acceleration until reaching a plateau. The acceleration level was maintained during the second phase, and the third phase was the end of the event with the increase of the acceleration. Phase I finished at 0. 3 s, and Phase II at 2 s. The steering event was performed at 50 km/h with a mean maximum lateral acceleration of 0. 8 g. The vehicle was turned to the right resulting in an inboard motion of the volunteers. The steering event pulse (Fig  1 ) presented the same shape as the braking event, but at a lower plateau value and with a longer duration. Data was presented for three points in time. A reference point T1, before the event started corresponded to the initial position of the volunteer.
Data Analysis Braking event. For the braking event, the displacement of the forehead and upper sternum in the XZ-plane of the global coordinate system was output. In order to compare the volunteer data with simulation data, the vertical displacements were plotted according to the seated height of the models and volunteers, and the longitudinal displacements were considered to start in the same point. The forehead point was defined as a node on the forehead above the eyes. The upper sternum point was a node on the skin, at the level of T1. A point defining the auditory canal was used to calculate the change of head rotation in the sagittal plane. This point was defined as the node at the right-most point of the head in the mid-sagittal plane of the head. The shoulder belt force was measured.
Discussion The aim of this study was to evaluate the kinematic responses of 6 and 10 year-old child numerical models in emergency events. This was done by comparing the responses of the Child6YO and Child10YO models available in MADYMO with child volunteer data in braking and steering events. To the best of the authors' knowledge, this is the first study to present the responses of child HBMs in low acceleration loadings. The HBM forward and lateral displacements were within the range of volunteer data in the beginning of the braking and steering events, up to approximately 0. 3 s. Later in the events, the HBMs had greater displacements than the volunteers in all cases where the seat belt had slipped of the shoulder. The only case without belt slip off was the Child10YO on the seat cushion in the braking event, where the model response at 2 s was only 3 cm greater than the volunteers mean displacement, still within the ranges.
Nevertheless, in future work, these two parameters should be considered and measured to simulate child volunteer data. Regarding the other parameters, the belt routing had only a small effect on kinematic data and the contact friction coefficient between the booster cushion and seat had almost no effect on the results. This last result was in accordance with the work by Andersson et al. [29]  who also evaluated this parameter but for a 3-year-old multibody model in crash events. The discussion this far has highlighted that the HBMs behaved differently than the volunteers during the entire braking and steering events. To increase the biofidelity of these models, changes in their mechanical properties can be made. It is possible to increase the spine stiffness in both frontal and lateral bending.
The Andean mountains of South America are the most species-rich biodiversity hotspot worldwide with about 15% of the world's plant species, in only 1% of the world's land surface. Orchids are a key element of the Andean flora, and one of the most prominent components of the Neotropical epiphyte diversity, yet very little is known about their origin and diversification. • We address this knowledge gap by inferring the biogeographical history and evolutionary dynamics of the two largest Neotropical orchid groups (Cymbidieae and Pleurothallidinae), using two unparalleled, densely-sampled orchid phylogenies (including 400+ newly generated DNA sequences), comparative phylogenetic methods, geological and biological datasets. • We find that the majority of Andean orchid lineages only originated in the last 15 million years.
, 2006; Antonelli et al. , 2009b; Luebert et al. , 2011; Drummond et al. , 2012; Madriñán et al. , 2013; Lagomarsino et al. , 2016). Taken together, these studies suggest that rapid Andean uplift yielded new niches that fostered both adaptive and non-adaptive radiations  (Nevado et al. , 2016). Other Andean groups, such as hummingbirds, diversified mostly prior to Andean uplift  (McGuire et al. , 2014) , or after it had attained most of its currently height  (Smith et al. , 2014). We address the impact of the Andean uplift on the diversity and distribution of orchids by inferring the dynamics of speciation, extinction, and migration while simultaneously incorporating surface uplift of the two largest Andean Neotropical orchid clades Cymbidieae and Pleurothallidinae. We rely on model-based inference methods in historical biogeography, ancestral area and character estimation approaches, and a series of diversification analyses to investigate the following questions: (i) Where do Andean orchids come from? (ii) Is there evidence for the Andes acting as a dispersal barrier for epiphytic lowland taxa? (iii) Did the Andean uplift enhance orchid diversification, and if so was this effect evident on species at all or just the highest elevations? (iv) Is Andean diversity derived from pre-adapted lineages or rather descendant of lowland migrants? In addition, we use the limited available data to evaluate whether shifts in pollination syndromes are associated to changes in diversification rates.
Taxon sampling, DNA sequencing and phylogenetic analysis To generate solid phylogenies of the tribe Cymbidieae and subtribe Pleurothallidianae, we newly generated a total of 420 sequences of the nuclear ribosomal internal transcribed spacer (ITS), and a ~1500 bp fragment of the gene ycf1 of under-represented lineages of key biogeographical importance. DNA amplification, PCR product purification and sequencing were conducted as previously described  (Pérez-Escobar et al. , 2016b). Voucher information and GenBank accession numbers are provided in Tables  S1  and S2. We merged our novel dataset with previously generated data from the studies of  (Blanco et al. , 2007) ,  (Whitten et al. , 2014) , and  (Ramirez et al. , 2011) , using the R-package MEGAPTERA v. 2  (Heibl, 2014). We retrieved 3541 sequences of nuclear (ITS) and plastid  (matK, trnL-F region, psbA, ycf1).
We selected outgroup taxa representing the old and new world subtribes Polystachyinae, Aeridinae and Laeliinae. Trees were rooted on Calypso bulbosa (for Cymbidieae) and Arpophyllum giganteum (for Pleurothallidinae) following  Whitten et al. (2014). Poorly aligned positions were excluded from the alignments using GBLOCKS v. 0. 9  (Talavera & Castresana, 2007). To statistically detect potential incongruences between plastid and nuclear DNA phylogenies, we used the tool Procrustes Approach to Cophylogeny (PACo)  (Balbuena et al. , 2013; Pérez-Escobar et al. , 2016a). Maximum-Likelihood (ML) tree inference was performed using RAxML-HPC v. 8. 0  (Stamatakis, 2014) , under the GTR+G substitution model with four gamma categories (best model for both datasets as inferred via AIC in jModelTest v. 2.
Ancestral range estimation Species ranges were coded from the literature  (Pridgeon et al. , 2009)  and from herbarium specimens through a survey of virtual collections and loans of several herbaria (AMES, COL, F, MO, SEL, US, M) as well as the GBIF repository. To query GBIF database, we relied on the function occ of the R-package SPOCC  (Scott et al. , 2015). A total of 19486 distribution records were compiled for the Cymbidieae, and 9042 records for the Pleurothallidinae. Protocols for distribution maps and species richness pattern analyses are detailed in Appendix S1. Distribution maps for Cymbidieae and Pleurothallidinae (summarized in Figs.
To infer the ancestral range of all examined lineages in Cymbidieae and Pleurothallidinae, we used the R package BioGeoBEARS v. 0. 2. 1  (Matzke, 2013 (Matzke, , 2014)). In addition, in order to estimate the number of migrations, dispersals, extinctions and sympatric speciation events from our phylogeny, we used Biogeographical Stochastic Mapping (BSM)  (Matzke, 2014)  under the best-fit model, as implemented in BioGeoBEARS (for detailed settings see Appendix S1).
Rates of species diversification To infer the diversification dynamics of the Cymbidieae and Pleurothallidinae, we first used a time-dependent model implemented in BAMM v. 2. 5. 0  (Rabosky, 2014)  to estimate rates of extinction and speciation across the phylogenies. Incomplete taxon sampling was accounted for by assigning a sampling fraction of 25% of the extant orchid diversity of Cymbidieae, and 13% of Pleurothallidinae (sampling fractions of every genus sampled was incorporated according to  [Chase et al. , 2015] ). We performed three runs with 1 million MCMC generations, sampling parameters every 10,000 generations. Diversification rates and rate shift configurations were plotted using the R package BAMMtools  (Rabosky et al. , 2014). We checked the convergence of the runs by plotting the log-likelihood across MCMC generations sampled in the "mcmc_out" file.
Geographic state-dependent analyses We used GeoSSE  (Goldberg et al. , 2011) , an extension of the BiSSE model that allows lineages to occur simultaneously in two areas and to test whether one area has overall higher speciation rates, as implemented in the R package diversitree v. 0. 9-7  (Fitzjohn, 2012). To test whether lineages restricted to the Northern Andes ("A") had higher diversification rates than lineages absent from the Northern Andes (collectively called "B" here), we used Bayesian MCMC GeoSSE analyses of 1 million generations on the maximum clade credibility tree from BEAST (in the particular case of Cymbidieae, only Neotropical representatives were included). Implemented models in GeoSSE and settings of tailored simulation to account for Type I error bioases in GeoSSE are provided in Appendix S1.