dataesr/scientific-paragraphs-categorization huggingface dataset:
A symbolic verification procedure We use the constraints from Sec. 4 as a symbolic representation in the adaptation of the classical working-list based backward procedure described below. This procedure corresponds to an instantiation of the framework of Well-Structured-Transition-Systems  [1, 8]. The procedure takes as arguments a program prg and a ⊑-minimal set Φ of constraints denoting the targeted set of configurations. Such sets can be easily built from the partial configurations described in Fig. 3. The predecessor computation rules in Figures  7  and 8  that are used by the procedure need to first concretize the given constraint to explicitly contain the required task(s), phaser(s), phaser variable(s), and control sequence(s). For this purpose, they make use of the rules concTasksPhasers (a wrapper for concretizeTask and concretizePhaser), concretizeSeq, and concretizeVar.
In addition to noting that H-G functions are ad hoc, recent results further challenge their accuracy  (Milli et al. 2017; Hughes et al. 2018; Goebel et al. 2018). Empirically derived scattering functions of debris disks more closely resemble a combination of Henyey-Greenstein functions, similar to the phase function of zodiacal light  (Graham et al. 2007; Ahmic et al. 2009 ). Disk modeling with these functions, rather than H-G, may lead to a revision in disk properties. Additionally, spectroscopy covering a wider wavelength range may clarify whether HD 115600's disk composition shows evidence for a single constituent or has mixed dust composition. In this study, we present new, multi-wavelength images and spectra of the debris disk around HD 115600 taken with VLT/SPHERE. The processed data are presented and described in Section 2.
We used our own principal component analysis (PCA) based Karhunen Loéve image projection (KLIP;  Soummer et al. 2012 ) algorithms of ADI and SDI, which are described in  Hanson & Apai (2015)  and  Apai et al. (2016). We used annular PCA, with 15 annuli, 14 pixels in width each, and six angular segments. We retained 7 KL basis vectors for ADI and SDI, with a minimum field rotation of 0. 5 × FW HM for ADI, and a minimum speckle radial movement of 1. 5 × FW HM for SDI between images. Before the PSF subtraction, we applied a sharpening highpass filter with a width of 11 pixels to the data to reduce lowfrequency noise. The reduced images were stored in x-y-λ (Cartesian and wavelength) cubes, as well as band-averaged flux density images in the Y-and J-bands, which greatly improves signal to noise (S/N) compared to the detection in the individual IFS channels.
The first seven frames of the cube were not included in the band-averaged images to eliminate frames impacted by detector persistence from the flux calibration frames. All images for a respective wavelength were combined with a noise-weighted mean  (Bottom et al. 2017). Basic reduction steps for IRDIS data were similarly performed using custom IDL tools for bad pixel correction, dark current subtraction, flat fielding, and star centering. We again used classical and KLIP ADI and SDI techniques for PSF subtraction.
DISK MODELS Our aim with disk modeling is to identify the simplest physically viable model of the disk, accounting for projection and light-scattering effects. By understanding the fundamental geometry, we can later attempt to make predictions about disk sculpting by possible exoplanets. We perform the disk modeling by two independent methods, ellipse fitting and with the model ZODIPIC, in order to asses the accuracy of both. We make no attempt to model the disk at the wavelengths of IRDIS, given the benefits of the IFS data such as extended wavelength coverage compared to previous studies and the smaller inner working angle. We performed primary modeling of the data using the model ZODIPIC  (Kuchner 2012) , originally written to model exozodiacal dust, but which is generally applicable to optically thin debris disks.
ZODIPIC is based on the radiative model of zodiacal dust in  Kelsall et al. (1998) , derived from all-sky data collected by the Cosmic Background Experiment/Diffuse Infrared Background Experiment (COBE/DIRBE). This model was chosen not only for ease-of-use, but because the scattering phase function for zodiacal light  (Hong 1985)  contained in ZODIPIC resembles those empirically derived from debris disks  (Graham et al. 2007; Ahmic et al. 2009). This setup allows us to control the disk size, inclination, offset, and scattering phase function, among other parameters. More complex radiative transfer models could be used, but were not warranted by the data quality and the goals of our study. The general procedure we used to find a best-fit disk model with ZODIPIC was to produce an evenly spaced grid of disk models with varying geometric parameters, and to subtract them from the raw data to find the model that minimizes the residual disk signal in our processed images without considering noise.
We set the inner and outer disk radius to the approximate visual size, 39 to 53 au, and did not vary these parameters, first to reduce the grid dimensions, and also because we found their variance produced negligible effects in the subtraction residuals. The grid was centered on the best-fit parameters determined by  Currie et al. (2015b)  using ellipse fitting, which we also performed as a secondary analysis. Table 2 contains our range and step size for the varied parameters. To subtract a geometric model from our data, we first convolved the model with the measured SPHERE PSF. The width of this PSF was measured from the flux calibration data cube in each wavelength channel. The convolved model was then multiplied by a brightness scale factor, which was initially the same for every wavelength, to bring the brightness of the model to the same approximate brightness as the disk.
All of these results were based on measurements shortward of 70 µm. In order to better characterize the far-IR SED of our target we examined unpublished 100 and 160 µm observations obtained with the PACS instrument onboard the Herschel Space Observatory. We used the Herschel Interactive Processing Environment (HIPE v13, Ott 2010) to process the data following the basic steps described in  Moór et al. (2015). HD 115600 is detected at both wavelengths, however at 160µm it is contaminated by surrounding extended emission therefore no photometry was derived. At 100µm our aperture photometry yielded a flux density of 138±12 mJy. The aperture radius was set to 6 , the sky background was measured in an annulus between 40 and 50. The quoted uncertainty is a quadratic sum of the measurement and calibration (7%,  Balog et al.
2014 ) errors. To compile the final SED, previously obtained infrared photometric and spectroscopic data taken from the literature and public databases  (Wright et al. 2010; Ishihara et al. 2010; Lebouteiller et al. 2011; Chen 2014)  were supplemented by our new 100µm and a recent 1. 24 mm (?) photometry of the source. To estimate the photospheric contribution at relevant wavelengths an ATLAS9 model  (Castelli & Kurucz 2003)  was fitted to the optical and near-IR photometric data of HD 115600 (taken from  Perryman 1997; Høg et al. 2000; Cutri et al. 2003). Based on our analysis of the source's Strömgren photometry  (Paunzen 2015)  the influence of reddening was neglected, while for the metallicity a value of [Fe/H] = -0. 16 was derived using the method proposed by  Casagrande et al. (2011).
Dust-rich Debris Disks around F-G-type Sco-Cen members HD 115600 is a member of the Sco-Cen association. To put into context our results on its disk parameters in the following we make a comparison with other dust-rich debris disks in the same group. Using Spitzer 24µm photometric data  Chen et al. (2011)  inferred an excess fraction of ∼33% for F-G-type members of the Sco-Cen association. Eleven out of the revealed disks were found to exhibit fractional luminosity higher than 10 3 , and ten from this bright disk subsample, including HD 115600, harbored dust colder than 150 K. Observations of these ten dust-rich systems with groundbased high-contrast imaging systems such as SPHERE and GPI in scattered light and with the ALMA interferometer at millimeter wavelengths resulted in spatially resolved disk images in all but one case (HD 117214).
PLANET DETECTION LIMITS While no wide-orbit giant planet is obviously detected in our data, the lack of detection places useful constraints on the presence of planets external to the disk (projected radii > 50 au). To analyze our detection threshold we injected false-positive point sources at 4×10 -5 contrast into the raw data prior to processing. We then measured the brightness of the recovered sources compared to the originally injected PSF, and utilized the average recovered contrast to estimate the throughput correction appropriate for the data processing. We find that the throughput correction is essentially uniform with radius exterior to the disk, and thus we adopt a constant correction throughout the image. To compute a detection threshold for each radius, we integrated the flux in non-overlapping apertures of radius equivalent to the beam FWHM while accounting for small sample statistics  (Mawet et al.
SUMMARY We present new extreme adaptive optics (VLT/SPHERE) imaging of the debris disk surrounding the young star HD 115600. Our dataset improves characterization of the surrounding debris disk and upper limits on possible exoplanets embedded in the disk. We model the debris disk to investigate structure that could indicate unseen exoplanet(s), as suggested by  Currie et al. (2015b)  and  Thilliez & Maddison (2017). We determine disk geometry by two methods, using the code ZODIPIC  (Kuchner 2012)  to inject and subtract a grid of disk models into the raw data, and by a 'maximum merit' ellipse fitting method on the ADI images, as described in  Thalmann et al. (2011). The key findings of our study are as follows: 1) We confidently detect the disk (SNR∼ 20) at separations between 0. 35 and 0.
Metabolite annotation, in silico databases and molecular networking For early NP identication, recently developed approaches have relied on the combined use of high-resolution mass spectrometry (HRMS) for molecular formula assignment, on the comparison of fragmentation MS/MS spectra against databases (DBs) and on the visualisation of groupings of these spectra into a molecular network (MN). 29 n LC-MS metabolite proling, two forms of MS/MS data acquisition are available: data independent or data dependent acquisition (DIA versus DDA). 20 DDA is the standard method for running LC-MS/MS experiments and involved executing a short HRMS survey scan of currently eluting molecules followed by a series of MS/MS scans. DIA can generate a more comprehensive survey for the metabolite proling of CNEs but is not yet widely used because informatics retreatment and DIA spectra libraries are still unavailable.
When hundreds to thousands of MS/MS spectra are collected by DDA, automatic classication is required. For this purpose, the Global Natural Product Social Molecular Networking (GNPS) web platform allows the generation of an MN. 29  In an MN, two molecules with several structural similarities normally exhibit considerable similarities in their fragmentation patterns. Such spectral similarities are calculated from a cosine score used in the high-throughput identication of metabolites. 30, 31 A newly developed soware program (MetGem) allows for the efficient and reliable calculation of cosine scores and plots using a GNPS-like format or t-distributed Stochastic Neighbour Embedding (t-SNE) algorithm. 32 The procedure offers a complementary view of data clusters while preserving interactions between related groups of MS/MS spectra.
When using online experimental DBs, an MN can be marked with standards to annotate each molecular cluster. For this purpose, the use of spectral databases among other approaches constitutes a key element from which annotation becomes effective. 33 While collections of pure NPs are available in many academic laboratories, large collections of spectra are still missing and efforts to share spectroscopic data should be made in the future. As an example, the efficiency of MNs combined with MS/MS searches through a restricted spectral library of monoterpene indole alkaloids for novel NP prioritisation has been demonstrated in the investigation of Apocynaceae species Geissospermum laeve. 34 he quality and completeness of public or commercial MS/ MS DBs are however still lacking with regard to the total number of NPs described to date.
Relatively few studies, however, integrate biological assays in their workows. The combination of biological screening with metabolite proling data through chemoinformatics, however challenging, has the potential to become the most effective approach NP discovery to date. A proof of concept study on 10 cyanobacteria species belonging to the Symploca genus was performed using integrated an LC-MS/MS-MN cancer cell cytotoxicity assay. 12 This approach allowed for the targeted isolation of samoamide A, a new cytotoxic NP, from a cluster representative of a novel cyanobacterial chemotype. While such a strategy involves a preliminary fractionation step, no iterative bioassay steps were required to isolate the desired compound. For prioritisation, the type of bioassays used is also crucial and for this purpose high content screening methods are of special interest.
Several approaches connecting high-content datasets to biological annotations although highly challenging are now effective for the early prioritisation of extracts and compounds with unique biological properties. The main untargeted proling methodologies applied indifferently for mammalian, yeast or bacterial strain cell screening or in vivo screening in zebrash have been recently reviewed. 45 Employed strategies, which can be numerous, rely on various methodologies such as image-or biomap-based screening or gene proling methods, most of which focus on comprehensive biological characterisation or allow for phenotypic annotations of natural extracts to investigate those with unique or atypical biological proles. As an example, the BioMAP high-throughput platform providing detailed biological characterisations of antibacterial properties of CNEs by applying activity prole matching to a set of 15 clinically relevant strains has been designed to discover novel antibiotics.
g. , statistical total correlation spectroscopy (STOCSY)). 52 In this respect, heterocovariance (HetCA)-based metabolomics have been found to serve as a powerful tool. 53 n a proof of concept study, the variance of extract constituents was rst assessed in plants of different families and correlations with tyrosinase or 5-lipoxygenase enzymes inhibition were calculated. 53 Correlations between spectral features and activity levels were, for example, examined by tracking the efficiency of a single species to identify the structural characteristics of bioactive components. In this case, the covariance between NMR and activity data was plotted for the most active plant species. Spectral lines showing strong correlations with activity were found to be indicative of specic molecular scaffolds, demonstrating that the HetCA approach can provide structural information prior to purication and showing that in the case of Dorycnium hirsutum, activity is correlated with the presence of kaempferol 3-O-(4-O-acetylrhamnosyl)-7-O-rhamnoside.
This also shows that spectral data reecting the concentration differences of components of an extract can correlate statistically with measurable dose-dependent properties of bioassays. To limit extract complexity, which can hinder analysis, HetCA has also been applied to relatively coarse fractions obtained from an extract that may reveal the concentration variance of the same compound in subsequent fractions. This has led to the identication of a series of 1 H NMR signals across fractions that correlate with activity. In addition, similar correlations has been recorded in MS and have been correlated with NMR by statistical heterospectroscopy (SHY). 54 The integration of HetCA STOCSY and SHY allows for the identication of stilbene derivatives as active tyrosinase inhibitors in Morus alba without the need for full purication.
Thus, it is important to carry out the purification of a representative number of molecules within a considered cluster. A similar approach is possible using different colour tags for taxonomy mapping, leading to genus or sample-specific clusters. The combination of biological and taxonomical mapping leads to more stringent targeting. This journal is © The Royal Society of Chemistry 2019 Nat. Prod. Rep. a dedicated tool integrated with the LC-HRMS database allowing for the novelty, complexity and diversity of samples to be ranked according to various calculated metric scores. It has been applied to a set of eight marine sponges and to six tunicate samples previously fractionated by solid-phase extraction (SPE). 16 Although the study presents some limitations, the proposed strategy's efficiency was demonstrated through the isolation and characterisation of two new eudistomins and two new nucleosides.
In this context, biochemometrics approaches, which rely on the use of statistical modelling tools to correlate metabolite proles with biological datasets, are very useful for assigning a biological activity to a particular compound detected from complex mixtures. The advantages and disadvantages of three of these used in metabolomics (partial least-squares (PLS), Splot and selectivity ratios) were critically assessed and the latter was found best identify MS features correlated to bioactivity. 58 More recently, selectivity ratio models have been applied to inactive botanical mixtures spiked with known antimicrobial compounds to assess how biochemometrics may highlight the active constituents of CNEs. This shows that data-processing approaches (data transformation and model simplication tools using a variance cut-off) have signicant impacts on model capacities to detect active constituents in CNEs.
Samples prepared at the same concentration are first injected in LC-MS/MS (A). Data are acquired using the data-dependent acquisition (DDA) mode, then converted through a MZmine 2 software processing. The final. mgf file is then used for the generation of the MN by GNPS web-platform or MetGem software. In parallel, bioassays (B) are conducted to record activity and taxonomic information are tabulated (C). Finally, the MNs are annotated by the interrogation of public or private databases, and bioactivity together with taxonomy can be mapped on the MNs for sample prioritisation and targeted isolation. taxonomic considerations rather than biological ones (CHIKV, DENV and ZIKV assays) to nd new or uncommon structures. Taxonomic mapping involved overlaying the 20 Euphorbiaceae genera found in the extract collection and detecting genus-or sample-specic clusters.
Such efforts must also be made in good agreement with newly enforced ABS legislation. 3 o achieve such goals, developments are still needed in various elds. Key to the whole process is the design of CNE libraries and this may vary according to nal objectives. All data associated with such libraries should be of high quality and acquired in formats that would ease their mining with advanced bioinformatics tools. Efficient means of standardisation and dissemination and the open sharing of relevant datasets are prerequisites to advancing in this eld. 67 In this vein, initiatives for sharing metabolite proling data with data-driven socialnetworking platforms such those of GNPS provide a means to collectively translate big data into shared knowledge across the entire NP research community. 29 This is central to rendering natural extract metabolome data of a similar level as genome data, which are also decisive for prioritisation.
In this context, in NP research, massive metabolite annotation with high levels of condence remains a major challenge. Here, the methodical approaches vary and cannot be standardised to the level of proteomics and genomics. Having at hand massive and partly redundant MS datasets serves as one way to gain condence for annotation especially when contextualising such data (e. g. , by taxonomy or genetics) is done carefully. NMR remains a key complement to MS for unambiguous identication. In this respect, new initiatives for the community sharing of raw NMR data in public repositories are emerging. 68 All these efforts of mutual data sharing  29, 68  should nally yield results of higher quality and integrity and are also well in line with the requirements of data management plans of funding agencies.
45 n addition to this method, the multi-informational MN approach embedding various information layers appears to be a powerful and efficient approach to the prioritisation and targeted isolation of novel bioactive compounds. 10 The combination of phenotypic-based proling methodologies with such a multi-informative MN approach would likely prove to be one of the smarter and most effective means to identify new drug leads. Efficient biochemometric tools for the co-correlation of bioassay and composition data are needed and must be carefully evaluated. 58 Adequate algorithms should enable one to efficiently highlight spectroscopic features of large datasets to nally guide the targeted isolation of NPs of interest. To be effectively implemented by the NP community, all necessary statistical and bioinformatics tools of prioritisation workows should ideally be user-friendly and intuitive.
Acknowledgements JLW is grateful to the Swiss National Science Foundation (SNF) for supporting its natural product metabolomics projects (grants no. 310030E-164289, 31003A_163424 and 316030_164095). This work has beneted from an "Investissement d'Avenir" grant managed by Agence Nationale de la Recherche (CEBA, ref ANR-10-LABX-25-01). We acknowledge Tomas Knoop and CombineDesign from the Noun Project (https://thenounproject. com/) and for the icons used in the Fig. 3. The authors acknowledge Mr Adriano Rutz and Dr Laurence Marcourt for their help in the realisation of Fig. 3 , and Florent Olivon for Fig. 1  and Dr Pierre-Marie Allard for fruitful discussions in the preparation of the manuscript.
In each school, children were tested for the first time (T1) in mid-September (6-to-7-year-olds) at the beginning of 1st Grade and for the second time (T2) in mid-September 1 year later (7to-8-year-olds) at the beginning of 2nd Grade. Children's age in months was similar across the five different socio-economic groups (with the largest age-difference in terms of months between two groups belonging to socio-economic index 12 and 13, p = 0. 08; all other contrasts, p > 0. 3). Data were collected by only one person. Children who took part in the study had no history of developmental disorders and were considered as typically developing children by the Belgian psycho-medicosocial services.
IHDTA is then extended to 9 ternary Ag-Cd-In compositions in order to achieve a more complete description of the liquidus surface (Section 2). In a first step, a self-consistent set of thermodynamic descriptions of the binaries is required. For this purpose, we have reviewed the available information for the Ag-Cd (Section 4), Ag-In (Section 5) and Cd-In (Section 6) binary systems. The objective is to reassess the 3 binaries in order to represent the extension of the hcp_A3 phase in the ternary system and to be self-consistent with the current lattice stabilities of the SGTE unary database  [10]. The second step is to perform the modeling of the ternary system (Section 7) starting from the combination of the three binary sub-systems. The ternary interaction parameters are optimized based on experimental phase diagram information and calorimetric data for ternary alloys.