dataesr/scientific-paragraphs-categorization huggingface dataset:
BiPOm rules and properties According to Fig. 2 , BiPOm core-ontology contains 28 properties. Among them, 7 properties were built to be explicitly asserted in the ontology: has_input, has_output, mediated_by, requires, starts_with, ends_with, and has_intermediary_process (see A:1-7 in Additional file 4: Table  1 ). Other properties can be automatically inferred using automatic reasoning thanks to 10 inverse properties definitions (see IP:1-10 in Additional file 4: Table  1 ) and to 27 SWRL (Semantic Web Rule Language) rules definition (see R:1-27 in Additional file 4: Table  1 ). Notice that a same property can be inferred using different SWRL rules (i. e. has_molecular_part). Moreover, some SWRL rules infer both properties as well as instance typing, i. e. to which class an instance is belonging to (see R:7-13 in Additional file 4: Table  1 ).
Then, the HermiT reasoner automatically infers additional information (called inferredclass or -property assertions) from the instances  [48]. The BiPOm core-ontology and its instantiation after logical rules application are available at https://github. com/SysBioInra/ SysOnto. The different steps of instantiation and reasoning are summarized in Additional Table  3  for the RPPC of A. thaliana and in Additional Table  6  for the metabolic network of E. coli.
Availability of data and materials All versions of BiPOm can be downloaded at https://github. com/SysBioInra/SysOnto: (i) BiPOm-core ontology, (ii) BiPOm after instantiation with RPPC and E. coli information and (iii) BiPOm after automatic reasoning. Moreover, BiPOm-core can also be downloaded from Bioportal at http://bioportal. bioontology. org/ontologies/BIPOM.
Context. Interferometric observables are strongly correlated, yet it is common practice to ignore these correlations in the data analysis process. Aims. We develop an empirical model for the correlations present in Very Large Telescope Interferometer GRAVITY data and show that properly accounting for them yields fainter detection limits and increases the reliability of potential detections. Methods. We extracted the correlations of the (squared) visibility amplitudes and the closure phases directly from intermediate products of the GRAVITY data reduction pipeline and fitted our empirical models to them. Then, we performed model fitting and companion injection and recovery tests with both simulated and real GRAVITY data, which are affected by correlated noise, and compared the results when ignoring the correlations and when properly accounting for them with our empirical models.
Results. When accounting for the correlations, the faint source detection limits improve by a factor of up to ∼ 2 at angular separations > 20 mas. For commonly used detection criteria based on χ 2 statistics, this mostly results in claimed detections being more reliable. Conclusions. Ignoring the correlations present in interferometric data is a dangerous assumption which might lead to a large number of false detections. The commonly used detection criteria (e. g. in the model fitting pipeline CANDID) are only reliable when properly accounting for the correlations; furthermore, instrument teams should work on providing full covariance matrices instead of statistically independent error bars as part of the official data reduction pipelines.
In the future, infrared interferometry will be a promising opportunity for studying giant planet formation (e. g. with Hi-5,  Defrère et al. 2018 ) and potentially even characterising terrestrial exoplanets from space (e. g. with a formation-flying nulling interferometer,  Léger et al. 1996; Mennesson & Mariotti 1997; Kammerer & Quanz 2018; Quanz et al. 2018 Quanz et al. , 2019)). However, significant improvements are required on the technical side (e. g. Based on observations made with ESO telescopes at Paranal Observatory under programme IDs 60. A-9801(U) and 0101. C-0907(B). kernel nulling,  Martinache & Ireland 2018) , in addition to on the data reduction side in order to achieve these ambitious goals. Because they use the dual-feed mode of GRAVITY, the aforementioned observations are not conducted anywhere close to the diffraction limit of the interferometer, but rather the diffraction limit of a single telescope.
Methods In Section 2. 1 we show how we extract the correlations from individual GRAVITY pipeline products and describe their nature. In Section 2. 2 we introduce our empirical model for these correlations and in Section 2. 4 we present the model fitting routines with the aid of which we show the improvements that come from using our empirical correlation model. The Python code that we developed in the scope of this paper is publicly available on GitHub (https://github. com/kammerje/InterCorr).
Correlations extracted from GRAVITY data In order to extract the correlations between the different spectral channels, baselines and triangles from GRAVITY data we use the P2VM-reduced files from the GRAVITY data reduction pipeline. These files are intermediate pipeline products which contain the individual measurements (detector read-outs) before they are averaged together. Having access to the individual measurements enables extracting the correlations from the (complex) coherent flux VIS mbλ which is stored in the P2VM-reduced file as a data cube of shape M × B × Λ, where m = 1. M is the number of individual measurements, b = 1. B is the number of baselines and λ = 1. Λ is the number of spectral channels. From the coherent flux, we compute the squared visibility amplitudes VIS2 mbλ = |VIS mbλ | 2 F1F2 mbλ , (1) where F1F2 mbλ is the product of the total fluxes, and the closure phases T3 mtλ = K • ∠VIS mbλ , (2) 2 https://ascl.
(5) The correlations between the VIS2 and the T3 then follow by dividing the covariances by the standard deviation σ i = √ Σ ii of the corresponding observables, that is (C X ) i j = (Σ X ) i j (σ X ) i (σ X ) j. (6) The diagonal of the covariance matrix Σ equals the square of the standard deviation and that the diagonal of the correlation matrix C equals one by definition. For developing an empirical correlation model we use data taken with GRAVITY (Gravity Collaboration et al. 2017) at the Very Large Telescope Interferometer (VLTI) during technical time (programme 60. A-9801(U)). GRAVITY operates in the Kband (2. 0-2. 4 µm) and combines the light from either the four Unit Telescopes (UTs) or the four Auxiliary Telescopes (ATs) of the VLTI in order to perform interferometric imaging and astrometry by phase referencing 3.
Empirical model for the correlations An empirical VIS2/T3 sample covariance with fewer frames than the product of the number of baselines/triangles and spectral channels is necessarily singular. It takes a number of frames much greater than this to estimate a sample covariance matrix with a condition number approaching that of the true sample covariance. For this reason, we choose to develop an analytic model for the covariance matrix Σ of the VIS2 and the T3. This model can then be fitted to the (potentially under-conditioned) covariance extracted from an arbitrary GRAVITY data set and can be used for model fitting based on log-likelihood maximisation. Most model fitting routines (e. g. LITpro,  Tallon-Bosc et al. 2008; CANDID, Gallenne et al. 2015)  are based on χ 2 minimisation, which is equivalent to log-likelihood maximisation, where χ 2 = R T • Σ -1 • R (9) and R = D -M is the residual between data and model (cf.
Section 2. 4). Our approach is to model the correlation matrices C VIS2 of the VIS2 and C T3 of the T3 which have the relatively simple structure observed in Figures  1  and 2. Moreover, the observed structure of the correlations is consistent for different data sets with different exposure times (1 s with the UTs for programme 60. A-9801(U) and 10 s with the ATs for programme 0101. C-0907(B), cf. also  Figures A. 1 and A. 2). Then, we compute Σ i j = C i j σ i σ j , (10) where σ denotes the standard deviation of the data which can be obtained from the VIS2ERR and the T3PHIERR columns of the OIFITS files for example. We note that these standard deviations are used to build diagonal covariance matrices in LITpro and CANDID which assume uncorrelated data only. Of course, assuming uncorrelated data is a simplification and we discuss the problems that arise from this in Section 3.
Results We evaluate the impact of our full covariance model by performing model fitting and companion injection and recovery tests with both simulated and real GRAVITY data. In Section 3. 1, we simulate data without any astronomical object, that is correlated noise only, and use model fitting to determine the fundamental detection limits when assuming uncorrelated data (i. e. a diagonal covariance) and correlated data (i. e. our full covariance model). In Sections 3. 2 and 3. 3 we inject companions with different relative fluxes and separations into simulated and real GRAVITY data and try to recover them, again assuming both uncorrelated and correlated data.
Model fitting to correlated noise In order to compare the fundamental detection limits when assuming uncorrelated and correlated data, we simulate 100 GRAVITY data sets of an unresolved host star without any companion (i. e. θ = f = 0 in Equation  17 ) affected by realistic correlated noise (Σ VIS2,fit and Σ T3,fit , cf. Section 2. 3). Then, we use Equation 31 in order to compute the best fit relative flux f fit on a ∆ RA -∆ DEC grid for each of the 100 simulated data sets, first assuming uncorrelated data (i. e. a diagonal covariance diag(Σ VIS2,fit ) and diag(Σ T3,fit )) and then assuming correlated data (i. e. our full covariance model) in Equation  31. Since no companion was injected into the data, the best fit flux ratios of these grids represent the fundamental contrast floor. Any companion with a higher contrast (i.
Injection and recovery tests (simulated data) As a next step, we perform companion injection and recovery tests with simulated data, in order to compare the empirical detection limits when assuming uncorrelated and correlated data. Therefore, we simulate GRAVITY data sets (affected by correlated noise) of a 1 mas uniform disc (the host star) and inject companions with a range of relative fluxes and at different positions around the host star, that is f ∈ [10 -4 , 10 -3. 75 , 10 -3. 5 ,. , 10 -1. 5 ], (34) ∆ RA ∈ [-30, -25, -20,. , 30] mas, (35) ∆ DEC ∈  [-30, -25, -20,. , 30]  mas, (36) using Equation  17. Then, we perform model fitting with priors on a ∆ RA -∆ DEC grid in order to find the global minimum of the χ 2 (cf. Equation  23 ). We note that this method is similar to how CANDID searches for companions for example.
Injection and recovery tests (real data) In the previous Section it is obvious that our full covariance model would outperform the conventional diagonal covariance model, since we simulated data affected by correlated noise. Therefore, the crucial next step is to validate our methods with real GRAVITY data sets. For this purpose, we extract the correlations of the VISAMP and the T3 from the files belonging to programme 0101. C-0907(B) listed in Table  1 , fit our empirical models to them and use the VISAMP and the T3 data from the corresponding final GRAVITY pipeline products as noise model. Since the real data is affected by bright speckles arising from an imperfect calibration, for which our correlation model does not account, we subtract the theoretical VISAMP and T3 of the best fit companion from the data before performing the injection and recovery tests.
The fraction of correctly recovered companions as a function of the relative flux of the companion for both scenarios (uncorrelated noise: blue points and correlated noise: orange points) is shown in the right panel of Figure  7 , again overplotted with logistic growth functions fitted to the data points (cf. Section 3. 2). The plot looks similar to the one from the injection and recovery tests with simulated data and confirms the applicability of our full covariance model to real GRAVITY data. Of course, the empirical detection limits are about one to two orders of magnitude worse and the improvement that comes from our full covariance model (i. e. the lateral shift of the orange curve with respect to the blue curve) is only a factor of ∼ 2 (consistent with the right panel of Figure  6  which also shows an improvement by a factor of ∼ 2) due to weaker correlations being present in the data used for the injection and recovery tests with real data.
Conclusions Correlated noise is placing fundamental detection limits on interferometric data. From on-sky VLTI/GRAVITY data, we extract and illustrate the correlations present in the data and develop an empirical model in order to describe them. This empirical model is sufficiently simple for it to be fitted to the correlations extracted from a single GRAVITY data product and could therefore be directly integrated into the GRAVITY data reduction pipeline and made available to the community as part of the OIFITS 2 file (which has a well-defined standard for providing covariance matrices  Duvert et al. 2017). Then, we evaluate the impact of our full covariance model by performing model fitting and companion injection and recovery tests with both simulated and real GRAVITY data. Our methods are based on χ 2 = R T • Σ -1 • R minimisation, where we compare the scenarios assuming uncorrelated data (i.
The empirical correlation model presented in this paper is a simple one-parameter model derived from GRAVITY data, but is arguably also applicable (with small modifications) to other instruments such as VLTI/PIONIER for example. It only treats the correlations between the different observables, but not yet between different frames or targets (such as the science and the calibrator target). We choose this approach in order to enable a simple implementation into existing data reduction and model fitting pipelines. Especially with the increasing availability of computing power, the use of full covariance matrices for describing the correlated noise in interferometric data should become a standard. Collaborations around future instruments should provide estimated data covariances as part of the official data reduction pipelines.
Unique reads were counted and gathered under a single identifier in fasta format. Reads obtained from the sequencing of the non-converted RNA sample and the converted RNA samples were aligned on the pre-miRNA hairpin sequences downloaded from miRBase 21. Alignments were performed using an ad hoc python script designed for the mapping of RNA sequences on RNA references. When mapping converted RNA sequences, it has the particularity of authorizing mismatches between a nucleotide 'T' from a read, with a nucleotide 'C' from the bank. Otherwise, this script is a classical seed-andextend heuristic. The 'T'-'C' mismatches are authorized during both the seed indexation phase and the extension phase. This tool is available under the GNU affero general public license from here: https://github. com/ pierrepeterlongo/MethMap.
To overcome this, two methods were used to identify miRNA sequences: bisulfite conversion followed by highthroughput sequencing analysis and adequate small RNAs databases (miRNA-BSeq) and 5mC immunoprecipitation followed by miRNA array analysis. Figure  S3  shows the miRNA-BSeq workflow. A total of 2565 unique mature miRNA sequences were downloaded from miR-Base-21. Among them 892 (35%) had at least one CpG dinucleotide. Results obtained from three biological replicates (bisulfite-converted samples BS1, BS2, BS3 and corresponding non-converted control samples noBS1, noBS2, noBS3) are presented in table_ sequencing_results. xls (Raw FASTQ data have been submitted to ArrayExpress). As previously described in the non-converted samples, the oncogenic miRNA-21-5p was over-expressed, and represented around 25% of the total mapped reads (data not shown).
xls). These results were validated by miRNA Array technology on 5mCimmunoprecipitated miRNAs. Of the 12/17 methylated miRNAs (percentage of methylation ≥5), five (miRNA-16-5p, miRNA-181a-5p, miRNA-181b-5p, miRNA-181d-5p and miRNA-210-3p) were also found methylated in miRNA Array analysis (FC enrichment ≥3) (Fig. 1e ). In addition, the sequence of these 5 miR was not recognized as piRNA or tRF (tRNA-derived fragments) following the use of two adequate databases (piRBase / http://www. regulatoryrna. org/database/piRNA and tRF/ http://genome. bioch. virginia. edu/trfdb/ [17, 18] ). To determine whether the 5-cytosine-methylation of miRNA-16-5p, miRNA-181a-5p, miRNA-181b-5p, miRNA-181d-5p and miRNA-210-3p was restricted to the U87 cells, we analyzed the 5-cytosine methylation level of miRNA in 3 other glioblastoma cell lines: T98G, U118 and LN229 using the miRNA Array technology on 5mC-immunoprecipitated miRNAs.
"S" sample of miRNA extracted to glioblastoma cells. In an arbitrary manner, we decided to use the synthetic methylated miRNA-4665-3p as control as this miRNA was composed of 6 CG (MIMAT0019740 # CUCGGCCGCGGCGCGUAGCCCC CGCC, according to the miRBase website. ) Image acquisition was performed on ChemiDoc MP (Bio-Rad, France). d Graph illustrates the quantification of the miRNA sample using the ELISA method (Methylamp Global DNA Methylation Quantification kit, Epigentek-Euromedex, France). Data (averagestandard deviation) are representative of three independent experiments. e Schematic representation of miRNA bisulfite sequencing and miRNA Array analysis (Qiagen, France) of anti-5methylcytosine-mediated miRNA immunoprecipitation. F. Heatmap representation for adenosine methylated enrichment of miRNA in three gliobklastoma cell lines: T98G, U118 and LN229 IgG had no effect (Fig.
Luciferase activity was determined 48 h after transfection methylated or with both mutated forms of miRNA-181a-5p (Fig. 3d ). Overall, our data demonstrate that the presence of 5mC on miRNA-181a-5p abolished its repressive function towards BIM. In addition, the mutation of cytosine-10 and -16 showed the same effect as the presence of 5mC on the function of miRNA-181a-5p towards BIM, suggesting that these two cytosines play a crucial role in the repressive function of miRNA-181a-5p. Cytosine-methylation of miRNA-181a-5p abolishes the formation of the miRNA-181a-5p-3'UTR/BIM duplex We then studied the formation of miRNA-mRNA duplex by performing biotin-tagged miRNA experiments  [22, 23]. In these experiments, RT-qPCR quantified the amount of endogenous 3'UTR/BIM recruited on synthetic unmethylated or methylated biotin-tagged miRNA-181a-5p.
S13 ). By performing miR-BSeq and miRIP-5mC/Array analyses, our work permits to identify miRNA-16-5p, miRNA-181a-5p, miRNA-181b-5p, miRNA-181d-5p and miRNA-210-3p as cytosine-methylated miRNAs in U87 cells. miRIP-5mC/Array analyses perfomred on other glioblastoma cell lines supported that the cytosine methylation of miRNA-181a-5p was commonly observed in GBM. This identification is based on three points: i) the primers specificity used in the miR Array (Qiagen, France), ii) the sequence matching against the mature miRNA sequences were downloaded from miRBase-21, and iii) the absence of sequence matching with piRNA and tRF (via the use of two databases: http://www. regulatoryrna. org/database/piRNA/ and http://genome. bioch. virginia. edu/trfdb/). As to the putative overlapping between miRNA and tRF, we noted that the five cytosinemethylated miRNA in our study is not included in the miRs overlapping with tRF  [31].
Acknowledgements We also gratefully acknowledge funding from "En avant la vie". We thank the  Neurosurgery department of the Hôpital G  and  R Laennec ,  CHU Nantes , and the  Oncology department of the ICO-Centre René Gauducheau, Nantes-Atlantique  for the tumor samples. We thank microPICell ( Plateau technique d'imagerie cellulaire -Centre de Recherche en Cancérologie et Immunologie Nantes-Angers ,  INSERMU1232-IRF26 ) for its assistance for the confocal microscopy.
Funding This work was partially supported by grants from the  LIGUE NATIONALE CONTRE LE CANCER ,  Comité InterRégional Grand Ouest, département de Loire Atlantique, d'Ille et Vilaine, Vendée et Côte d'Armor  ( Subvention 2014-2016 ). This work was partially supported by grants from  CANCEROPOLE GRAND-OUEST  (Projet émergent#2015). MC was supported by a fellowship from  CANCEROPOLE GRAND-OUEST/REGION PAYS DE LA LOIRE. RP was supported by a fellowship from  Labex   IGO  project (n°ANR- 11-LABX-0016-01 ) funded by the  «Investissements d'Avenir» French Government program , managed by the  French National Research Agency (ANR). JB was supported by a fellowship from  EpiSAVMEN/REGION PAYS DE LA LOIRE  and by " En avant la vie " (http://enavantlavie. org), a French association fighting glioblastoma).
For the reformulation of (DC) we choose the concave objective function f (x, r). = rh(x). (2) The feasible set P is the epigraph of g. The concave minimization problem associated to (DC) min x,r f (x, r) s. t. (x, r) ∈ epi g (ConcMin) is equivalent to (DC) in the following sense: • If (x 0 , r 0 ) solves (ConcMin) then x 0 solves (DC). • If x 0 solves (DC) then (x 0 , g(x 0 )) solves (ConcMin). In the literature on concave minimization, many authors assume a compact feasible set in order to guaranty the existence of optimal solutions, see e. g. [1, 3, 12]. However, problem (ConcMin) always has a non-compact feasible set. In  [4] , algorithms for concave (even quasi-concave) minimization based on a modification of methods for vector linear programming (VLP) are studied. An implementation of these methods based on the VLP solver bensolve  [10, 11]  is provided by the Octave/Matlab package bensolve tools  [5, 6].
c. programs. Section 3 explains how a polyhedral d. c. program can be solved using a (quasi-)concave minimization solver like bensolve tools after the existence of optimal solutions has been verified. The last section presents two numerical examples for the case where both g and h are polyhedral. Both existence tests and solution methods are addressed. We use the following notation. The domain of a function f : R n → R ∪ {∞} is defined by dom f. = {x ∈ R n | f (x) < ∞} and the epigraph of f is the set epi f. = {(x, r) ∈ R n × R | r ≥ f (x)}. A convex function f is called closed if epi f is a closed set. We write R n + for the set of vectors with non-negative components. The recession cone 0 + C of a convex set C ⊆ R n is the set of all y with C + {y} ⊆ C. The lineality space of C is the set lineal(C).
Numerical results We implemented the results of this article in Matlab 9. 6 by using bensolve tools, version 2. 3, see  [5, 6]. The code and the test instances are available at http://tools. bensolve. org/dcsolve. By two (new) commands dcsolve and dcdsolve the user can run, respectively, the primal and dual method described in the previous section. The input arguments of both commands are two arbitrary polyhedral convex functions g and h in the usual format of Bensolve Tools. Both commands solve arbitrary polyhedral d. c. optimization problems (of small size) or certify that no optimal solution exists. The following two numerical examples were run on a computer with Intel R Core TM i5 CPU with 3. 3 GHz. Example 17. Let A ∈ R n×m be a matrix and denote by a i its columns. We define a polyhedral convex function f A : R n → R by f A (x).
3. 1 package. The experimental data obtained by using a small scale test apparatus at Japan Atomic Energy Agency (JAEA) was used to validate the RANS methodology. In the experiment, we measured the velocity field around the interaction region between vertical jet and stratification by using the Particle Image Velocimetry (PIV) system and time transient of gas concentration by using the Quadrupole Mass Spectrometer (QMS) system. Moreover, Large-Eddy Simulation (LES) was performed to phenomenologically discuss the interaction behavior. The comparison study indicated that the turbulence production ratio by shear stress and buoyancy force predicted by the RANS with the dynamic modeling for Sc t was a better agreement with the LES result, and the gradual decay of the turbulence fluctuation in the stratification was predicted accurately.