dataesr/scientific-paragraphs-categorization huggingface dataset:
Field measurements for providing RATP inputs On six of these silver lime trees and in their surroundings, several measurements are continuously registered  (Najjar et al. , 2015)  and some of them are detailed in Table  4. Based on these field measurements, three input files of RATP model can already be determined: the 3D reconstruction of tree foliage, the vegetation file and the meteorological file. The climatological data are continually recorded with 3 sensors on a 20 meters mast and 3 other sensors located on the roof (300 m) of a neighboring building. Concerning the leaf physiological data, a LI-COR 6400 was used to measure gas exchanges between plant and environmental variables which control stomatal conductance of leaves. For the allometric statistics related to the position, size and orientation of leaves on shoots, manual measurements were carried out on leafy shoot samples.
For the moment, the four reconstructed 3D tree models concern October. Concerning June, given that shoots are visible only after the leaf fall, the skeletonization based on point clouds acquired in June is not possible. That is why another method was used for reconstructing the shoots of June. Based on the four skeletons representing the tree in October and the leafy tree point cloud in June, four new models related to the tree in June can be computed with a Matlab script. From the tree point cloud of June, a convex hull of the tree crown is created. Then, as illustrated in Figure  9a , all the nodes of the 3D curve skeletons of the October model (in brown), which are outside the convex hull (in green), are removed from the skeleton. As a consequence, 3D curve skeletons used for the creation of the June foliage are about 20 % smaller than for the October foliage.
1 M KPi buffer (pH 7) and mounted on the LabTek II chamber with KPi agarose pads for imaging. Single focal plane (SFP) images were acquired in TMR and DAPI channels, TMR images were deconvolved. Particles were counted from 83-105 nuclei. To visualize dynamics of Ace1p-3xGFP at the CUP1 array (Figure  4A , B and C), 11 z-sections with 0. 4 m step size were acquired with 4 min time lapse for 48 min total. These images were deconvolved and projected onto a single plane using SoftWoRx software (GE Healthcare Bio-Sciences, Pittsburgh, PA, USA). Cells containing visible arrays were counted from these projections. For SMT, SFP time lapse movies were acquired on a custom-built Highly Inclined Laminated Optical (HILO) sheet, (18)) imaging system (see Supplementary Methods for details). All the lasers were set at 2 mW output power, and images were acquired as fast as possible with constant laser exposure.
SMT analysis For each experimental condition, we obtained ∼250 tracks which were sufficient to generate a reasonably smooth cumulative histogram. We tracked the single molecules and analyzed the data with TrackRecord software  (19)  based on MATLAB (freely available at https://sourceforge. net/ projects/single-molecule-tracking). TrackRecord identifies particle positions based on their peak intensities and fitting to a 2D Gaussian function. Tracks are generated using a nearest-neighbors approach that we prefer over other algorithms due to its use of parameters that are more readily interpretable by biologists and to its low computational demands. This software permits us to assay (i) the fraction of molecules that are freely diffusing and those that are immobilized due to binding (ii) to discern long-residence molecules within the fraction of immobile molecules (iii) to calculate residence times for long-residence and shortresidence binding events.
We introduced the following improvements to this software: (i) the ability to select multiple regions if interest (ROI) that allows tracking in only the nuclei of all cells located in the field of view; (ii) additional noise removal by Wiener and morphological top-hat filters to reduce speckle noise and uneven illumination, respectively; (iii) display and use of particle kymographs to simplify the manual correction of erroneous automatically generated tracks. We used kymographs to manually bridge the gaps generated by blinking of the ligands. To extract binding times, the cumulative distribution of residence times ('survival plot') is fit with least-squares to both a one-and two-component exponential decay. Because of the chromatin motion, it is required to set a threshold on the allowable motion for a bound particle, called r max.
Higher particle densities make it impossible to follow each particle as the tracks overlap and cross each other. In a mammalian cell nucleus, the limit for successful tracking is ∼30 molecules per cell  (4, 19). The area of the yeast nucleus is ∼100-fold smaller than that of a mammalian nucleus. To establish a limit of labeling for yeast, we performed tracking of Hht1p-HaloTag fusion stained with JF 646 , using a previously developed semi-automated tracking software  (19). Briefly, the software takes images such as shown in Figure  2A , and filters out noise (Figure  2B ) to identify single molecules. Tracking is performed within user-defined ROIs (Figure  2C  and D ) delineating the nucleus to confine analysis to molecules inside the nuclear compartment (see Materials and Methods for more details).
32 ± 0. 008 s, and 14% with a residence time averaging 2. 2 ± 0. 06 s (Figure  5E ). Although this residence time appears to be longer than the long residence time observed in histones (1. 7 ± 0. 03 s, Figure  5C ) further studies are required to determine if upon heat shock, the binding of Hsf1p is indeed stronger than Hht1p. In absence of HS we obtained data on 72 700 particles in 226 Hsf1p-HaloTag cells (0. 5/cell/timepoint). The residence time analysis of these particles revealed that 68% were freely diffusing, 25% showed short binding events with an average residence time of 0. 21 ± 0. 008 s, and 6% showed longer binding events with an average residence time of 1. 1 ± 0. 07 s (Figure  5F ). Thus in absence of HS, the distribution of three fractions of molecules of Hsf1p (not-bound and bound with short and long residence time) is similar to Ace1p (No Cu 2+ ) and dramatically different from Hht1p.
Thus, a great majority of Ace1p binding that we may observe in SMT experiments must be non-specific. Previously, we demonstrated by FRAP that Ace1p is capable of nonspecific binding to nucleoplasmic targets, as opposed to GFP  (22). Therefore, by quantifying the behavior of Ace1p we may obtain the baseline for the nonspecific binding. By SMT we observe 20 ± 0. 9% of bound molecules of Ace1p; 86 ± 0. 7% of those have a residence time of 0. 13 ± 0. 003 s. A small fraction (14 ± 0. 7%) of Ace1p is bound with an average residence time of 1. 3 ± 0. 07 s. To compare the non-specific residence time of Ace1p to other proteins we estimated residence times of the similar inducible TF -heat-shock factor, encoded in yeast by a single HSF1 gene. We tracked Hsf1p-HaloTag with and without HS. Our data show that upon HS the estimated fraction of freely diffusing molecules decreases by a factor of 1.
Addressing blinking of the dyes in SMT analysis. The ligands we tested exhibit different rates of photobleaching and blinking. By using computational simulations, we established that the TrackRecord software performs a very good automatic correction for photobleaching  (19). However, blinking is not corrected adequately if the setting for the maximal number of frames for the gap in the track generated by blinking is arbitrarily set to one frame. Although the residence time is not that much affected, in simulations we observed that the long binding events were completely ignored, evidenced by the fact that the data analyzed with the 1-frame gap setting was adequately fit with a singlecomponent exponential function. Thus, the correct parameters for blinking gap correction have to be determined from real samples.
An example is the European Southern Observatory's VISTA InfraRed CAMera (VIRCAM). Methods. We developed a method to determine more accurate redshift and line flux estimates from the ratio of apparent fluxes measured from observations in different narrowband filters and several matching broadband filters. A parameterized model of the line and continuum flux is used to predict the flux ratios as a function of redshift based on the known filter curves. These model predictions are then used to determine the most likely redshift and line flux. Results. We tested the obtainable quality of parameter estimation for the example of Hα in the VIRCAM NB118 filters both on simulated and actual observations, where the latter were based on the UltraVISTA DR2 data set. We combined the narrowband data with deep broadband data in Y, J, and H.
We find that by using this method, the errors in the measured lines fluxes can be reduced up to almost an order of magnitude. Conclusions. We conclude that existing narrowband data can be used to derive accurate line fluxes if the observations include images taken with sufficiently different filter curves. For the UltraVISTA survey, the best suited narrowband filter combinations allow to achieve an accuracy in wavelength of better than 1 nm, and in flux of better than 15% at any redshift within the bandpass of the filters. By contrast, analyzing the data without exploiting the difference in filter curves leads to an uncertainty in wavelength of 10 nm, and up to an order of magnitude errors in line flux estimates.
As strong emission lines cause a flux excess even in wide filters (e. g. Guiderdoni & Rocca-Volmerange 1987; Zackrisson et al. 2001; Schaerer & de Barros 2009; Shivaei et al. 2015) , broadband photometry contains very useful additional information. In the following, we refer to our method as the throughput-variations method (TPV). We investigate the method carefully for the VISTA NB118 filters with a special focus on Hα, both with simulations and application to data. In VIRCAM, there is one individual copy of the NB118 filters above each of its 16 non-contiguous detectors. Although produced to be as similar as possible, the transmittance curves of the individual filters are unavoidably slightly different from each other, and hence useful for the proposed TPV. The extremely deep and homogeneous BB data also available from the UltraVISTA survey are well suited to constrain the continuum and to measure the broadband excess.
The paper is organized as follows: In sec. 2, we motivate the throughput-variations method (TPV). After describing estimation algorithm and estimation model in sec. 3, we test usefulness and caveats of the technique based on simulations, which emulate UltraVISTA DR2 observations of Hα emitters (sec. 4). An application of the method to actual UltraVISTA DR2 data is presented in sec. 5. Finally, we discuss a possible modification to the UltraVISTA NB118 observing pattern for the purpose of the TPV (sec. 5. 9). Where needed, a (flat) standard ΛCDM cosmology with H 0 = 70 km s - 1  Mpc -1 , Ω m,0 = 0. 3 and Ω Λ,0 = 0. 7 was assumed. Furthermore, we used throughout this paper AB-magnitudes  (Oke 1974). All numbers referring to specific VISTA NB118 filters are in line with the standard VISTA detector (filter) numbering scheme (cf.
Ivanov & Szeifert 2009; Milvang-Jensen et al. 2013 ). All stated wavelengths are in vacuum, except when we use common identifiers like [O iii]λ5007, which are based on wavelengths in air. All assumed VISTA/VIRCAM filter curves include quantum-efficiency and mirror reflectivities, and the broadband filters curves include in addition an atmosphere with PWV = 1 mm at airmass 1. For the broadband filters, Y, J, and H, we used the same filter curve for all 16 detectors, as available from the ESO webpage. 1 Details about the individual NB118 filter curves are given in Appendix A. Parts of the field covered by NB118 observations include data from a single filter/detector, while other parts include data from two filters (cf. sec. 5. 1). Throughout this paper we will refer to 'combined effective filters', which are the effective filter responses, if data from two similar filters is combined into a single stack.
Curves for five different w 6583 between 0. 0 and 1. 5 are shown for the example filter combination 14 & 15 in the lower panel of Fig. 3. We can conclude, while the impact is not negligible, the resulting systematic wavelength errors are even in the worst case not larger than ∼ 3 nm, compared to the assumption of w 6583 = 0. 3. In addition to w 6583 , also the shape of the Hα line could differ. 3 Whereas line widths beyond 1000 km s -1 are not expected for solely star-forming galaxies, line widths of several 1000 km s -1 are possible when originating from a type I AGN. AGNs can be recognized by the use of the extensive multiwavelength data available in fields like COSMOS. We tested the impact of the line width on the λ 0 estimation by determining ∆mag-λ 0 curves for Gaussians with FWHM of 100, 500, 1000, 5000 km s -1 , as shown in the upper part of Fig.
For the UltraVISTA DR2 data the S/N for an infinite EW line is in J a factor 4. 1 lower than e. g. at the peak of NB118 filter 15 (For more details see Appendix D). Therefore, for the faintest lines detectable in the NB filter, the J-based estimate will not be very useful. Secondly, it needs to be possible to determine the continuum contribution to J with very high precision. For example, an EW obs = 10 nm line has a J excess of only 0. 06 mag, while the same object causes an excess of 0. 64 mag at the peak of NB118 filter 15. Hence, deviations between estimation power law and actual continuum SED are for fluxes measured through the broadband excess more critical. Notes. ( a) [nm] (b) [erg s -1 cm -2 ].
Implementation of estimation code Finding the right, global, maximum in a complicated probability landscape is not always easy. We implemented two versions of a python code to determine the parameter-set p est maximizing the posterior probability for a given set of measured magnitudes, M. One version of our code makes use of the implementation of the Nelder-Mead algorithm  (Nelder & Mead 1965)  within python/scipy. minimize. Using the downhill simplex method, it allows to efficiently determine the maximum. 4 In order to improve the success rate of finding the global maximum, we combined it with a pre-calculation on a coarse grained grid. The parameterrange of this pre-grid is given in Table  1. Then, we run the Nelder-Mead algorithm three times, starting from the grid points with the three highest probabilities.
Neglecting justifiably uncertainties from read-out noise, dark-current, linearity corrections, flat fielding, background subtraction, and converting electrons through the gain, g, to digital-units, DN, the S /N can be calculated as: S /N = Ṅ * DN √ g √ t Ṅ * DN + n pix ḃ * DN (6) Here Ṅ * DN is the total number of DN per second created within the aperture due to the source, while ḃ * DN is the number of DN produced per pixel and second by the sky-background. n pix is the number of pixels constituting the aperture and t is the exposure time. We assumed an observation time of 11. 4 hr in each of two NB118 filters through which an object is simulated to be observed, which sums to the typical per-pixel integration time available in the UltraVISTA DR2. The expected per-pixel integration time in the finished survey will be 112 hr, meaning that the DR2 NB118 data includes only ∼ 20% in time or 45% in depth of the final UltraVISTA survey goal.
Application to UltraVISTA data The six contributing pawprints are three steps in one direction of the sky (y), which are performed for two steps in the perpendicular direction (x). For the NB118 part of UltraVISTA, only one of the two x-positions is observed, resulting in four stripes (cf. Fig. 9 ). Each pointing in the y-direction is separated by 47. 5% of a detector (or 5. 5 ). 11 Consequently, observations of at least two pawprints contribute to the covered field with the exception of the outermost parts. The filter numbers in the different patches of the pawprint can bee seen in Fig. 9. Most important for our method, in 20. 5% percent of the stripes two pawprints contribute with two different filters. In addition for 6 tiny patches, which total 4. 8% percent of the stripes, two pawprints contribute with one filter, while one pawprint contributes with a second filter.
Data The controlled environment of our simulations demonstrated that the TPV is expected to work. Here, we apply the method to the actual UltraVISTA DR2 data. A stack of NB118 data is available as part of this data release. In regions of overlap, this stack includes data from both contributing NB118 copies. For the purpose of the TPV, we need these data separately. Therefore, we produced 16 custom NB118 stacks, each of which including only the data from one filter/detector. Reduction, stacking, and flux calibration were basically done in the same way as for the publicly available joined NB118 DR2 stack (cf. Milvang-Jensen  et al. 2013; McCracken et al. 2012 9 ) and the same observations were included. Employing SExtractor's  (Bertin & Arnouts 1996)  doubleimage mode, we obtained in each of the individual NB118 filters photometry in the same 2 circular apertures as in the detection image, where the latter was the joined NB118 DR2 stack including data from all 16 filters.
In the final UltraVISTA data we expect to select about twice as many Hα emitters within the same sub-field. In addition to the NBES, we picked 100 comparison samples, each of which having in each of the filters the same number of objects as the NBES with the same stack NB118 distribution. More precisely, we split the range between 19 < NB118 < 24 into 40 bins. The objects in the comparison samples (CS) were randomly drawn from a selection, where we did by contrast to the NBES not impose a color-significance or color-cut. In order to completely avoid NB excess objects in the comparison sample, we also created stricter versions, where we imposed J corr -NB118 < 0 (SCS).
The angle of incidence of the annulus's center is related to the object's position on the sky by 1. 208 • × d per 100  (Findlay 2012). The position d per is stated here in percentages of detectors. With this input, the effective filter curve for the complete beam can be calculated by (e. g. Lissberger 1970): T (λ) = Ω (φ, θ)T θ (λ)dΩ Ω (φ, θ)dΩ (A. 2) Consequently, we can finally estimate the shape of the filter curves in VIRCAM's convergent beam by combining equations A. 1 and A. 2. T (λ) = annulus T 0          1 cos √ (θx +θ c x ) 2 +(θy +θ c y ) 2 ne f f λ          dθ x dθ y annulus dθ x dθ y (A. 3) We tested our script doing the actual convergent beam conversion based on a 1% top-hat filter and comparing the results to those presented in  Bland-Hawthorn et al. (2001).
For human sequencing data, BCALM 2 reduces the computational burden of compacting the de Bruijn graph to roughly an hour and 3 GB of memory. We also applied BCALM 2 to the 22 Gbp loblolly pine and 20 Gbp white spruce sequencing datasets. Compacted graphs were constructed from raw reads in less than 2 days and 40 GB of memory on a single machine. Hence, BCALM 2 is at least an order of magnitude more efficient than other available methods.
After these initial steps, the size of the data is reduced gradually, e. g. for a human dataset with 45Â coverage, To overcome the scalability challenges of fragment assembly of large sequencing datasets, there has been a focus on improving the resource utilization of de Bruijn graph construction. In particular, k-mer counting has seen orders of magnitude improvements in memory usage and speed. As a result, graph compaction is becoming the new bottleneck; but, it has received little attention  (Kundeti et al. , 2010). Recently, we developed a compaction tool that uses low memory, but without an improvement in time  (Chikhi et al. , 2014). Other parallel approaches for compaction have been proposed, as part of genome assemblers. However, most are only implemented within the context of a specific assembler, and cannot be used as modules for the construction of other fragment assemblers or for other applications of de Bruijn graphs (e.
g. metagenomics). In this paper, we present a fast and low memory algorithm for graph compaction. Our algorithm consists of three stages: careful distribution of input k-mers into buckets, parallel compaction of the buckets, and a parallel reunification step to glue together the compacted strings into unitigs. The algorithm builds upon the use of minimizers to partition the graph  (Chikhi et al. , 2014) ; however, the partitioning strategy is completely novel since the strategy of  Chikhi et al. (2014)  does not lend itself to parallelization. Due to the algorithm's complexity, we formally prove its correctness. We then evaluate it on whole-genome human, pine and spruce sequencing data. The de Bruijn graph for a whole human genome dataset is compacted in roughly an hour and 3 GB of memory using 16 cores.
There are also alternate approaches that do not use the de Bruijn graph at all (e. g. greedy or string graph). Numerous parallel assemblers are available for use, including ABySS  (Simpson et al. , 2009) , SOAPdenovo  (Luo et al. , 2012) , Ray  (Boisvert et al. , 2010) , PASQUAL  (Liu et al. , 2013) , PASHA  (Liu et al. , 2011) , SAND  (Moretti et al. , 2012) , SWAP-Assembler  (Meng et al. , 2014). Other methods for parallel assembly have been published but without publicly available software  (Duan et al. , 2014; Garg et al. , 2013; Georganas et al. , 2015; Jackson et al. , 2010). There has also been work done in reducing the overall memory footprint de Bruijn graph assembly. This challenge is most pronounced for k-mer counters. However, when scaling to mammaliansized genomes, memory usage continues to be an issue in downstream steps such as compaction.
Chikhi et al. (2014)  used minimizers to compact the de Bruijn graph of a human whole-genome dataset in under 50 MB of memory, but the algorithm did not improve the running time. Wu et al. (2012)  propose an approach based on dividing the assembly problem into mutually independent instances. Ye et al. (2012)  exploit the notion of graph sparseness for reducing memory use. Kleftogiannis et al. (2013)  perform a comparative analysis and propose several memory-reducing strategies. Chikhi and Rizk (2012)  use Bloom filters to reduce memory usage. Movahedi et al. (2012)  propose a divide-and-conquer approach for compacting a de Bruijn graph.
4: return w To perform these operations efficiently in time and memory, Reunite first partitions the strings of R so that any two strings that need to be reunited are guaranteed to be in the partition. Then, each partition can be processed independently. To achieve the partition, we use a union-find (UF) data structure of all k-mers extremities. Recall that a UF data structure is created by first assigning a set to each distinct element (here, an element is the k-mer extremity of a string). Then, the union operation replaces the sets of two elements by a single set corresponding to their union. Here, union is applied to both k-mer extremities of a string. After the UF is constructed, the set of strings to be reunited is partitioned such that k-mer extremities of sequences in a partition all belong to the same UF set.
PROOF. By Lemma 5, x appears as a lonely suffix of some string u 1 2 B and as a lonely prefix of another string u 2 2 B. As a consequence of the UF data structure, u 1 and u 2 belong to the same partition P at line 6 of Algorithm 3. We will show that u 1 and u 2 are consecutively selected at line 10 of the Reunite algorithm. Observe that in Reunite, strings selected at line 10 have a lonely prefix (as a consequence of Lemma 5), and strings selected at line 7 do not. If u 1 not does have a lonely prefix, u 1 must be selected at line 7 of Reunite. Then, u 2 is selected at the next execution of line 10. Now, assume that u 1 has a lonely prefix. Then by Lemma 7, there exists strings v 1 ;. ; v a such that suf k ðv i Þ ¼ pre k ðv iÀ1 Þ for 0 < i a and v a has a non-lonely prefix. Then, since v a does not have a lonely prefix, v a is selected at line 7 of Reunite, and it follows that v aÀ1 ;.