dataesr/scientific-paragraphs-categorization huggingface dataset:
This is quite remarkable since there is almost a decade between the period measurements. 3. 1. Pulse period long-term evolution 2RXP J130159. 6-635806 regularly fell into the FOV of various X-ray telescopes thanks to extensive observational campaigns dedicated to PSR B1259-63 which is located only 9. 55 ′ away. This allows us to investigate the long-term evolution of the pulse period. We analyzed the XMM-Newton  (Jansen et al. 2001 ) archival data from 2007-2011 using the procedures described by  Chernyakova et al. (2005)  and Science Analysis Software (SAS) version 14. 0. 0. The list of selected XMM-Newton observations with corresponding period measurements are shown in Table  2. Fig. 2  presents the long-term evolution of the period, showing that the 2RXP J130159. 6-635806 neutron star has undergone very strong and steady spin-up during the last 20 years.
However, the evolution of the PDS shape aw a function of mass accretion rate in such systems is not well studied. In Fig. 5 , we show the PDS of 2RXP J130159. 6-635806 obtained with the NuSTAR data in the 4th observation after subtracting the pulse profile folded with the measured period from the light curve. The solid line represents the fitting model in the form of a broken power law. The measured break frequency is 0. 0066 Hz (shown by dotted line), and it is clear that it is shifted towards higher frequencies relative to the spin frequency in this observation (0. 0015 Hz; shown by dashed vertical line). The power-law slope above the break frequency is fixed at -2  (Revnivtsev et al. 2009 ). The best-fit value of the slope below the break is -0. 33. Given the steady persistent luminosity, we conclude that the spin-up observed during last ∼20 years is caused by a long-term mass accretion rate that is high enough to squeeze the magnetosphere inside the corotational radius.
Spectral analysis We used nuproducts, a part of the NuSTAR-DAS package, to extract source and background spectra and to generate NuSTAR response matrix (RMF) and effective area (ARF) files for a point source. In our analysis we utilized the most recent calibration database (CALDB), version 20150316. The extracted spectra were then grouped to have more than 20 counts per bin using the grppha tool from the HEAsoft 6. 15. 1 package. We fit the NuS-TAR spectra using xspec version 12. 8. 1  (Arnaud 1996).
As the working energy range of the NuSTAR observatory begins at 3 keV, it is not very sensitive to measuring low absorption columns. Therefore, in the following analysis, the interstellar absorption was fixed to the value N H = 2. 48 × 10 22 cm -2 measured by  Chernyakova et al. (2005). Note that we simultaneously fitted spectra obtained by both NuSTAR modules. To take into account the uncertainty in their relative cali-   , c, d ) show corresponding residuals from the models 'cutoff', 'highecut', and 'highecut' with the 6. 4 keV line, respectively (see text and Table  3 ). brations, which may be even more of a concern for the observations where the source is highly offset from the optical axis, we added a cross-calibration constant between the modules. The fitting parameters for different data sets are shown in Table  3.
72 for 920 dof ). The high-energy cutoff value E cut is found to be 6. 48 +0. 22 -0. 15 keV, which is significantly lower than the value of ∼ 25 keV reported by  Chernyakova et al. (2005)  using simultaneous XMM-Newton and IN-TEGRAL data. The apparent discrepancy is probably due to the lower statistical quality of the INTEGRAL data and the gap between the energy bands covered by the XMM-Newton and INTEGRAL observatories. An additional possible problem is that the cutoff energy E cut = 6. 48 keV is very close to the energy of the iron fluorescent line at 6. 4 keV. The simplistic 'highecut' model might hide the presence of the emission line in the source spectrum. Observed deviations of the measured spectrum from the model near this energy argue in favor of this possibility. To investigate this issue, we added a Gaussian emission line to the 'highecut' model, fixing its energy to 6.
4 keV and width to 0. 1 keV, allowing its normalization to be a free parameter. This resulted in an additional improvement of the fit to χ 2 = 916. 97 for 919 dof for a normalization of (2. 31±0. 70)×10 -  ph cm -2 s -1. The corresponding equivalent width of the line is EW = 44 +48 -32 eV (3σ error). We determined the significance of the line using the XSPEC script simftest with 4 × 10 4 trials, and found that presumption against the null hypothesis, or no line required by the data is 3 × 10 -3 , which corresponds to ∼ 3σ line detection, assuming a normal distribution. The residuals of the 'highecut' model with the 6. 4 keV iron line are shown in Fig. 6 (d). The model itself, together with spectral data points, is shown in Fig. 6  (a). Considering the moderate spectral resolution of the NuSTAR observatory and the low significance of the detected iron line, we investigated the possibility of the presence of an iron fluorescence line in the XMM-Newton spectrum of 2RXP J130159.
Pulse phase-resolved spectroscopy In order to study the evolution of the source spectrum over the pulse period, we performed pulse phase-resolved spectroscopy using the data from the 4th observation. The period was di- vided into 10 phase bins with zero phase coinciding with the main minimum in the pulse profile. According to the pulse phase-averaged spectral analysis, the fitting model was chosen in the form of (wabs*(powerlaw*highecut+gau)). However, due to much lower statistics, the photon index was fixed at the value from the averaged spectrum (Γ = 1. 37). We selected this parameter due to its virtual constancy over the pulse in our preliminary analysis of the same data. The N H value is not very well constrained by the NuSTAR data; however we checked that it is consistent with being constant in phase, and we fixed it as well.
• The phase-resolved spectroscopy shows some differences in source spectrum with phase. The cut-off energy is very stable over the pulse except at zero phase where its value increases by a factor of two. The apparent correlation of the folding energy with pulse intensity is attributed to the change in hardness of the source spectrum with orbital phase. This research has made use of data obtained with NuSTAR, a project led by Caltech, funded by NASA and managed by NASA/JPL, and has utilized the NUSTARDAS software package, jointly developed by the ASDC (Italy) and Caltech (USA). This research has also made use of data obtained with XMM-Newton, an ESA science mission with instruments and contributions directly funded by ESA Member States. AL and ST acknowledge support from Russian Science Foundation (grant 14-12-01287).
Second, taking advantage of both the availability of increased computer power and novel computing techniques we were able to design a model tissue with cellular resolution in 3D. This integrates a new data structure to handle multicellular tissues  [38]  used by other groups for tissue simulation (e. g. [18] ) and the use of the modeling software platform SOFA for mechanical simulation in biology  [39]. The platform makes it possible to express the mechanical, geometrical and simulation components in a completely modular way and optimizes the computational overhead during simulations by making use of efficient implicit integration solvers. As a result, simulation in 3D of flower development can be achieved in near interactive time. The model is able to handle several hundreds of cells, and can take into account both biochemical and physical properties of cells.
Correlations between organ outgrowth and the expression of genes involved in wall modifications can also be made. Modifications in wall anisotropy at the abaxial side of the primordia -as suggested by the model -can even be monitored in vivo by direct observation of microtubule dynamics  [3]. Metrics will also have to be developed to compare quantitatively results of mechanical simulations and the observed, actual geometry of developing organs. Based on the recent progresses in imaging protocols, cell segmentation and tracking softwares, e. g. [19, 40] , it will now become possible to routinely compare the simulated development of particular cellular regions (e. g. central zone, primordium, abaxial/adaxial, frontier zone,…) with the observed ones based on a quantitative comparison of their principal direction of growth, rate of growth, and of various shape factors such as local curvature, degree of symmetry, compacity, etc.
Turgor pressure is converted into nodal external forces by spatial integration: f ext i ~1 3 X j[T(i) DP j S j N j ð30Þ where DP j is the pressure (supposed uniform) on triangle j. N j is the outward unit normal (current configuration). Time evolution. Solving the weak form of Eq. 14, turns out to minimize the total elastic. We solve this by gradient descent: dx i ~{(f i zf ext i )h ð31Þ where h is the step size. For faster convergence and stability, we use an implicit scheme. At iteration k, we have: x(kzh){x(k)~(I{hK) {1 f(k)h ð32Þ where K~L f Lx is the stiffness matrix. x and f are the position and force vectors of all nodes (concatenation of the x i and (f i zf ext i )). This linear system is solved using the conjugate gradient algorithm. Computational simulation framework. We have implemented our mechanical model in the open source software SOFA  [39].
Results We propose a method for determining the optimal similarity threshold by minimizing the proportions of false-positive and false-negative similarity matches. We compared the distributions of the similarity values of pairs of similar genes and pairs of non-similar genes. These comparisons were performed separately for all three branches of the Gene Ontology. In all situations, we found overlap between the similar and the non-similar distributions, indicating that some similar genes had a similarity value lower than the similarity value of some non-similar genes. We then extend this method to the semantic particularity measure and to a similarity measure applied to the ChEBI ontology. Thresholds were evaluated over the whole HomoloGene database. For each group of homologous genes, we computed all the similarity and particularity values between pairs of genes.
Need for thresholds Comparing several gene sets to identify and quantify the features they share and the features that differentiate them is central to the functional analysis of gene sets  [1] [2] [3]. These operations hinge on comparing sets of Gene Ontology (GO) terms  [4]. The links between genes and GO terms are provided by the Gene Ontology Annotation (GOA) database for multiple species  [5]. Numerous semantic similarity measures have been developed  [6] [7] [8]. We recently proposed to combine semantic similarity measures and a new semantic particularity measure to improve the results of gene set analysis  [9]. The analysis of results on similarity and particularity is based on an interpretation that contrasts the genes with particular functions among similar genes. The main focus of studies to date has been on defining the measures, but there is no extensive study on the interpretation of the values obtained with these measures.
Classification of semantic similarity measures Pesquita et al. classified semantic similarity measures into two categories: node and edge-based measures, with some hybrid measures  [6]. Node-based measures assign an Information Content (IC) value to each ontology term, with the least-frequent terms given the highest IC value. This IC concept, borrowed from Shannon's information theory  [13] , was used to measure similarities using ontologies  [14] [15] [16]  such as WordNet  [17]. Node-based measures consider that the similarity between two terms relies on their most informative common ancestor. These measures developed in linguistics have been applied to GO  [18, 19] , where the IC of a GO term is inversely proportional to the frequency with which it annotates a gene using the Gene Ontology Annotations (GOA) database  [5].
Semantic similarity Lin developed a widely-used node-based similarity measure that employs the IC concept  [15]. Several of the tools available have implemented this measure. The IC of a term t depends on its log probability P(t). Working with GO terms, this IC is inversely proportional to the frequency with which the terms annotate a gene using the Gene Ontology Annotations (GOA) database. When comparing two GO terms t1 and t2 having a most informative common ancestor t0, Lin defines their similarity as follows: Simðt1; t2Þ ¼ 2 Â logPðt0Þ logPðt1Þ þ logPðt2Þ Wang's hybrid measure depends solely on GO graph and does not need an annotation corpus, thus allowing cross-species comparisons  [30]. For each term, the first step of the measure is to compute the semantic contributions of its ancestors, following: S A ðAÞ ¼ 1 S A ðtÞ ¼ maxfw e Ã S A ðt 0 Þ j t 0 2 children of ðtÞg if t 6 ¼ A ( where S A (t) is the semantic contribution of term t to term A and w e is the semantic contribution factor for edge e linking term t to its child term t'.
Following Wang, we used a semantic contribution factor of 0. 8 for the "is a" relations and 0. 6 for the "part of" relations, and we added a 0. 7 factor for the "[positively] [negatively] regulates" relations. Then, for each target term to compare, the semantic value (SV) is the sum of the semantic contributions of all its ancestors: SVðAÞ ¼ X t2T A S A ðtÞ The comparison of two terms A and B is computed as follows: S GO ðA; BÞ ¼ X t2T A \T B ðS A ðtÞ þ S B ðtÞÞ SVðAÞ þ SVðBÞ The similarity between a GO term "go" and a set of GO terms "Sg" is: Simðgo; SgÞ ¼ max 1⩽i⩽k ðS GO ðgo; go i ÞÞ Finally, the similarity between two genes G1 and G2 is: SimðG1; G2Þ ¼ X 1 i m ðSimðgo 1i ; Sg2ÞÞ þ X 1 j n ðSimðgo 2j ; Sg1ÞÞ m þ n Gentleman developed a graph-based measure for the R package GOstats called simUI  [36].
3c) For each possible threshold value, sum the FN and FP proportions obtained in steps 3a and 3b. The similarity threshold τ sim is the one that minimizes this sum. doi:10. 1371/journal. pone. 0133579. g001 The minimization at step 3c has to be done on FN and FP proportions as the N and S distributions have different sizes. We applied this method to compute Lin's and Wang's semantic similarity thresholds on GO, the corresponding IC-based and SV-based semantic particularity thresholds on GO, and the simUI and simGIC thresholds on ChEBI. For all the pairs of genes compared, we used the GO annotations from the August 2013 version of GOA. We computed Lin's similarity with the GOSemSim R package  [40]  (version 1. 18. 0) using its GO and IC tables and the best-match average approach to compare genes.
Similarity threshold determination using two groups of similar genes We first applied our method to determine the similarity threshold for the Biological Processes (BP) using two groups of similar genes. We determined thresholds using first Wang's and then Lin's similarity measures. Group determination. We composed two groups of similar genes from two families of the Protein ANalysis THrough Evolutionary Relationships database (PANTHER). The union of the pairs of genes within each family constituted the S distribution. The PANTHER database classifies proteins (and their genes) to facilitate high-throughput analysis  [42]. PANTHER families are composed of genes sharing evolutionary history, molecular functions and biological processes annotations, and involvment in the same biological pathways.
For MF, we used the same six genes families to compute our S and N distributions, as the PANTHER families are also homogeneous in term of molecular functions. For CC, we used the genes from five different pathways, each located in a different cellular compartment, to compute our S and N distributions. The lists of genes were borrowed from the Reactome database  [43]. Robustness of threshold determination. We validated our study using a leave-one-out approach that consisted in successively recomputing the thresholds using all the sets but one. This approach provides an evaluation of threshold stability.
Evaluation The evaluation study involved first quantifying the extent of the changes resulting from using the threshold computed by our method instead of the default 0. 5 and then determining whether these changes are biologically relevant. The first part of this study focused on the changes in the results of the whole HomoloGene database intra-group gene comparisons. HomoloGene is a system that automatically detects homologs, including paralogs and orthologs, among the genes of 21 fully-sequenced eukaryotic genomes  [44]. In the second part of this study, we computed the similarity and particularity measures on the well annotated peroxisome proliferator activated receptor (PPAR) multigene family. PPARα, PPARβ and PPARγ are involved in different processes  [45]  as transcription factors. Each member of this family uses the same molecular mechanisms in different metabolic pathways.
Robustness of threshold determination. In order to study the robustness of our optimization, we successively removed one gene set from our datasets and re-computed the similarity threshold. We performed this analysis on BP, MF and CC. Tables  3  and 4  present the results for Wang's and Lin's measures, respectively, giving the τ sim and the FP and FN proportions for each complete dataset and for all the groups of a dataset except one. The thresholds varied slightly over the different datasets. BP similarity threshold varied between 0. 4 and 0. 435. MF similarity threshold remained stable at 0. 41, except when not taking into account the family of genes related to gated channels (0. 49). CC similarity threshold was between 0. 475 and 0. 515. The MF case diverged from BP and CC on its similarity (FP + FN proportions) curve.
Indeed, the minimum value of 0. 41 was located at the extreme left of a part of the curve where (FP + FN proportions) varied slightly. Consequently, leaving out the "neurotransmitter gated ion channels" dataset that was causing this specific minimum position greatly affected the threshold. However, some perspective is needed: first, there was a relatively long interval in which the sum of FP and FN remained low, and second, the minimum of 0. 49 obtained without the "neurotransmitter gated ion channels" set was located at the opposite part of this range of stability. This table summarizes the similarity thresholds τ sim obtained considering each complete dataset or all the groups of a dataset except one, when using Wang's similarity measure. The numbers given for FP and FN are the proportions of false-positives and false-negatives that the threshold admits in the comparison results.
The variation of the "+ + +" and "---" profiles in our datasets was studied using the similarity threshold τ sim obtained in the previous section and sampling the value of τ par , the particularity threshold. Table  5  gives the particularity thresholds (τ par ) minimizing the sum of "+ + +" and "---" patterns for SV-based and IC-based approaches. S6 File presents the values that supported the thresholds determination. These thresholds differed between BP, MF and CC and between approaches. We performed the leave-one-out study in order to assess stability of the particularity threshold by removing one gene set from our datasets and re-computing the particularity threshold. This analysis was performed on BP, MF and CC. We obtained τ par and the proportions of non-informative "+ + +" and "---" cases for each complete dataset and for all the groups of a dataset except one.
The thresholds varied slightly among the different datasets. BP particularity threshold was between 0. 49 and 0. 515. MF particularity threshold was between 0. 35 and 0. 485. CC particularity threshold was between 0. 28 and 0. 335. S6 File provides the detailed results of the leave-one-out study using SV and IC as informativeness measures. With both SV-based and IC-based approaches, the minimum ordinate value of the sums "+ + +" + "---" was located in a relatively large range within which the ordinate varied only slightly. Consequently, we concluded that the particularity thresholds should be located in the range where the sum of the "+ + +" and "---" proportions varied the least. simUI and simGIC thresholds for ChEBI molecular entities. Fig 12 presents the distribution of the similarity values obtained for the intra and inter-groups comparisons using the two ChEBI groups composed of children of "monocarboxylic acid" (chebi:25384) and "glycoside" (chebi:24400).
Evaluation We evaluated the GO similarity and particularity thresholds in two different use-cases. First, we compared the interpretation of the results of semantic measures performed on homolog genes using a default threshold of 0. 5 vs our new thresholds. Second, we studied whether the thresholds determined via our new method led to biologically-relevant interpretations. Large-scale evaluation of the impact of threshold changes. We evaluated the impact of our new GO similarity and particularity thresholds over a large dataset characterization. We compared the distribution of semantic measures results among the different patterns proposed in Table  1  for the whole HomoloGene database considering an arbitrary 0. 5 threshold and our new method thresholds. Tables 6, 7 and 8 summarize the results for BP, MF and CC, respectively.
2%) and MF (-34. 8%) but increased for CC (+49%). This situation can be explained by the fact that the CC particularity threshold of 0. 335 was the lowest of all the computed thresholds, making the increase of "+ + +" cases more important than the decrease of the "---" cases. Furthermore, the average number of CC terms that annotate a gene in HomoloGene was only 1. 38 against 2. 45 for BP and 1. 63 for MF. Consequently, the similarity and particularity values measured on HomoloGene were less reliable for CC than for BP and MF. This situation could be attributed to a lack of CC annotations in our dataset. However, in the three branches of GO, the proportions of the least-informative cases were low at just 1. 62%, 0. 39% and 1. 30% for BP, MF and CC, respectively. Overall, the change of thresholds deeply impacted the distribution the HomoloGene intra-group comparison results between the different patterns.
Generic method and domain-dependent thresholds We computed thresholds for several semantic measures. We used them to interpret data from different mammal species. The gene groups used to compute the thresholds were related to six different families (BP and MF thresholds) and five pathways located in a different cell compartment (CC threshold). We believe that these thresholds are more relevant for the comparison of any mammal genes than the arbitrary threshold of 0. 5 used to date. We do not claim that these thresholds are universal. It is preferable to recompute the thresholds in order to compare genes for other species or simply to use thresholds that are up-to-date with the evolution of GO and GOA. Numbers of pairs of genes changing from one pattern to another when considering our optimal similarity and particularity thresholds instead of the default value of 0.
5. After the change of threshold, the most important transition consists in 25,089 results moving from the "+ --" category (size decreased by 9. 4%) to the "+ + -or + -+" category (size increased by 36. 7%). The new thresholds give more "+ + +" results but fewer "---" results. Globally, the sum of the numbers of the "+ + +" and "---" patterns has increased (+49%). doi:10. 1371/journal. pone. 0133579. t008 Overall, even if the thresholds are domain-dependent, our threshold computation method can be applied to any domain. It only requires some degree of domain expertise to build the most relevant "S" and "N" distributions. Once a threshold is determined with the help of an expert to compose the relevant datasets, the leave-one-out study indicates that the threshold is applicable to other similar datasets and is in this regard application-independent.