dataesr/scientific-paragraphs-categorization huggingface dataset:
Consensus substructure and molecular network chemical classes We implemented a new algorithmic approach to deal with the high number of annotations retrieved from the various in silico MS/MS spectral databases. To this end, we used annotations retrieved form Sirius (confidence score above 0. 65), 1M-DB searched with modified cosine (score above 0. 5 and 5 matching peaks), CFM-DB 1M searched with spec2vec (score above 0. 5), Jassbi-CFM (score above 0. 5 and 5 matching peaks) and Jassbi-molDiscovery. These annotations were retrieved at the molecular network or at the MS2LDA mass motif level in order to calculate consensus substructures for a given network (NCS) or mass motif (MCS). Main steps involved in consensus substructure calculations involved the following commands (Script S16, S19, see Code Availability and Description): (1) fragment structures, (2) get the most common fragments, (3) select the top 50 and only keep the ones with more than 12 atoms, (4) cluster by structural similarity,  (5)  sort by cluster size,  (6)  calculate the maximum common substructure within the cluster,  (7)  retrieve the top 4 results.
To harness the vast amount of structural information classified by molecular networking, we selected the top 252 networks sorted by only picking networks containing more than 10 nodes. The peak areas within these networks were summed with Script S24. Peak areas were normalized (Excel's STANDARDIZE function) by cluster id and the maximum on tissue level per species was kept. The propagated CANOPUS classes were grouped their peak areas summed (Script S28) and the resulting data was used to create per species treemaps in Excel. A summary of the Top252 molecular networks, their calculated consensus substructures and their propagated CANOPUS classes can be found in Data S3. Additionally, Data S4 and S7 allow to navigate this multi-level information at mass motif and network levels.
Ancestral state reconstruction for the relative occurrence of N-acylnornicotines We adapted the concept of ancestral state reconstruction (ASR) classically employed for the evolutionary analysis of quantitative phenotypic traits for the exploration of NANNs' relative occurrence. To this end, we first constructed a phylogenetic tree of the focal Nicotiana species based sequences of the matK gene obtained from a previous study  (71) , the sequence of N. maritma was used to account for N. wuttkei position within the species tree due to unavailable genome data for the latter species. Laskowska and Berbec (2003)  previously suggested the very close relationship between the latter two species as well as reported their successful hybridization in the wild. Nicotiana setchelli matK gene sequence was obtained from the assembly of transcriptomics data publicly available for NCBI SRA accession SRR2106530.
The species tree was constructed using NGPhylogeny. fr (73)  with default one click options and the PhyML Maximum Likelihood method. For ASR, feature intensities accounting for the species and tissuewide NANN diversity were retrieved using the above-described mass motif characterization approach. ASR was performed with the MBASR package (Heritage, 2021; Script S25) on peak areas of the root that have been transformed into a ordered trait of 5 categories (Fig. 8 , Fig. S1 ).
α-Diversity analysis and CANOPUS class distance computation The alpha-diversity was calculated for each species based on Shannon Entropy (Script S29) using the scikit-bio package and sample features as OTUs. The top 252 networks as mentioned previously were selected their raw peak areas summed based on propagated CANOPUS classes (Script S28) and then converted to integers, networks without class annotations were discarded. The vegan package was used to perform non-metric multidimensional scaling (NMDS) followed by the calculation of intrinsic variables (CANOPUS classes) with 999 permutations (Script S30). The resulting vectors were used to calculate the per species cosine distances (Script S31). experiments, analyzed the data, and wrote the paper with inputs from the other authors. D. P. S2 ).
A close-up view on two superclasses ("Organic acids and derivatives" / "Phenylpropanoids and polyketides") detected in N. glauca (Ngla) is presented. (C) Network Consensus Structure (NCS) computations from hits obtained from the interrogation of in silico generated MS/MS spectra (Fig. S10). Hits obtained for each MS/MS feature-level database search within a network were compiled input to compute a consensus (sub)structure for each network. (D) NCS computed for network #486 whose MS/MS features were classified in (A) as those of "Amino acids and derivatives". The library of feature/network/NP-CANOPUS/NCS associations is reported in Data S1, S3 and S4.
Acknowledgments The authors would like to acknowledge the  High Performance Computing Center of the University of Strasbourg  for supporting this work by providing scientific support and access to computing resources. Some of these computing resources were funded by the  Equipex Equip@Meso project  ( Programme Investissements d'Avenir ) and the  CPER Alsacalcul/Big Data. The authors thank  N. Navrot  and  I. Grubor  for comments on the manuscript,  L. Malherbe  for help with plant sample collection,  J. Zumsteg  for support with preparative HPLC purifications, the Plant Imaging Mass Spectrometry platform of the IBMP for instrument access,  P. Dorrestein ,  M. Wang  and  M. Panitchpakdi  for help with the upload of the in-silico 1M-DB on the GNPS platform. Funding: D. E. , D. P. , C. V. , L.
M. and E. G. were funded by the  CNRS. D. E. and E. G. were supported by a  IdEx (Investissement d'Avenir)  Grant and PhD fellowship to D. E from the  University of Strasbourg. Initiation of this study by E. G. was further supported within the framework of the  Deutsche Forschungsgemeinschaft Excellence Initiative  to the  University of Heidelberg. Author contributions: D. E. and E. G. conceived the study, performed the.
Sumner et al. , 2007. Level 1 in our priority assignment of annotations corresponded to hits retrieved from experimental spectral databases and or NMR structural confirmation. Highest priority within level 1 of annotated spectra (level 1a) was given to hits confirmed by NMR in this work. Level 1b annotations correspond to hits from spectral alignments (and correspondence of precursor m/z values) using a local MatchMS (score above 0. 65 and more than 6 matching peaks) implementation (Script S8, see Code availability) with the modified cosine score, from an in house high-resolution experimental MS/MS spectra database of Nicotiana attenuata specialized metabolites and/or manual inspection of spectra. Level 2 corresponded in our annotation approach to hits retrieved, with the cosine score from high-resolution MS/MS spectra of the GNPS database.
Level 3 annotations were considered for hits from alignments with in silico MS/MS spectra or in the case of network propagation of hits from the experimental databases, both after manual inspection. Jobs for the recently developed molDiscovery approach (Version 1. 0. 0, Cao et al. , 2021) were submitted through GNPS with both the molDiscovery built-in library and the Jassbi compound database created as part of this study. The Jassbi compound database (429 structures) was compiled from structures extracted from a recent Nicotiana phytochemistry review (32). In silico MS/MS spectra for the Jassbi compound database were also produced with the fragmentation tool CFM-predict 4. 0 (23) (Script S5, see Code availability) database searching was performed with MatchMS (70) (Script S9).
contributed bioinformatics support. C. V. performed mass spectrometry measurements. B. M. and L. M. performed NMR analyses. Competing interests: The authors declare that they have no competing interests. Data and materials availability: Metabolomics raw data and. mzml files were deposited on MassIVE https://doi. org/doi:10. 25345/C5QB9V93Q. MSI data was deposited at metaspace https://metaspace2020. eu/project/nicotiana_msi-2022 Additional data is available on Zenodo https://doi. org/10. 5281/zenodo. 6536010. All scripts used in this study are available at the Github repository: https://github. com/volvox292/Nicotiana_metabolomics. All data needed to evaluate the conclusions in the paper are further present in the paper and/or the Supplementary Materials.
For the port-based D&C systems (other than the bulky infrared (IR) polarimetry/interferometry), two independent instances of each system are proposed to be installed in different toroidal locations. The overall goal is to achieve a reliable knowledge of the plasma state over a wide range of parameters, and at the same time be able to distinguish between correct and faulty measurements by means of integrated data analysis. The key elements and the general approach for the control strategy for the DEMO plasma are summarized in the following, including the current understanding on the diagnostics, actuators and control margins needed for reliable control. This selection and allocation of diagnostics and actuators has been mainly based on considering the stationary burn phase, however, according to the current understanding no additional systems are expected to be necessary for the transient phases, but some parameter ranges of individual subsystems might need to be extended.
The plasma current (inversely related to the edge safety factor q 95 ) will be controlled based on signals from magnetic sensors (invessel and ex-vessel inductive sensors and Hall sensors) measuring the related poloidal fields, and for the case of a pulsed DEMO tokamak the actuators are the central solenoid current as well as the (limited) auxiliary plasma current drive. The edge safety factor has to be kept precisely in the desired range (see physics requirement 3. 1a above), with a relatively small control margin, since it belongs to the characterizing quantities for the plasma scenario, and it is relevant for certain MHD instabilities  [37]. Additional ex-vessel magnetic measurements are foreseen for the diamagnetic energy and the loop voltage, respectively. Diamagnetic loops are needed to evaluate in real time the poloidal beta and hence the plasma energy  [38] , which are important input data for the burn control.
The maximum target value for the plasma beta (requirement 3. 2e) given above) has been defined to account for the current understanding of the required control margins. For the purpose of this plasma pressure limitation, a measurement of the diamagnetic energy will be performed via diamagnetic loops. This magnetic measurement will be amended by deriving a coarse electron temperature profile from ECE measurements (limited to the radial region where the electron density is high enough), and by deriving an ion temperature profile from gamma and neutron spectra, also taking into account high resolution X-ray spectra from the plasma centre. The plasma density profile follows from reflectometry and IR interferometry/polarimetry. The knowledge of plasma density and temperature profiles will not only improve the knowledge of the plasma energy (via integrated data analysis), but also allow performing a consistency-check of the fusion power and radiation power profiles in the plasma and thus enhance the robustness of the control concept.
The Python Library Pyrotor The above optimization methodology is aimed at being used in a wide range of applications, from path planning for industrial robots  (Chettibi et al. , 2004)  to fuel-efficient aircraft trajectories  (Rommel et al. , 2019; Dewez et al. , 2020). We therefore contribute a generic Python library PyRotor (standing for Python Route trajectory optimizer) which is intended to the largest audience. PyRotor is the backbone to numerical results given in this paper. When using the PyRotor library, the practitioner has to define the endpoints conditions as a dictionary, the additional constraints in a list of functions, the name of the basis and the dimension for each variable. The current version of the library covers only the case of Section 2. 5, that is to say the cost is given by a quadratic instantaneous function.
This permits to make use of Proposition 5 in which a quadratic objective function is given. We mention that future releases of PyRotor are intended to cover more general cost functions. The value of the parameter ν max in (19) can also be manually set depending on the application. The Legendre basis and B-splines are currently the bases implemented in the first version of PyRotor (via the legendre module from NumPy package  Harris et al. , 2020  and the interpolate module from Scipy package  Virtanen et al. , 2020)  but future developments including other general bases are planned. Further, the user indicates a path to a directory containing the data, each reference trajectory being contained in a csv file. The covariance matrix Σ is here estimated by using the sklearn. covariance package from the Python library scikit-learn  (Pedregosa et al.
, 2011). Two optimization solvers are proposed: the generic solver minimize(method='trustconstr';  Conn et al. , 2000)  from SciPy and the quadratic programming solver from CVXOPT software  (Andersen et al. , 2020). The latter is intended to speed up the execution in case of convex quadratic objective function. Once the arguments are given by the user, a class is created and the optimization is performed by executing a method from this class. At the end, the optimized trajectory is provided in a dataframe: at each time, the position of the trajectory is given together with the value of f. The total cost is also computed and a quantitative comparison in terms of savings with the reference trajectories can be also displayed. The open source PyRotor library is developed on GitHub and welcomes contributions from its users: we favor a community-based development to foster the diffusion of our work toward practitioners.
Numerical results We present now numerical results based on real flight data for the above aeronautic problem. Here, we have access to 2,162 recorded short and medium-haul flights performed by the same narrow-body airliner type, provided by a partner airline. In particular they cannot be publicly released for commercial reasons. The data is here recorded by the quick access recorder (QAR). Before considering the optimization setting, we estimate a fuel flow model specific to the climb phase and to the considered airliner type. To do so, we extract the signals of the four variables of interest (altitude, Mach number, engines rotational speed, and fuel flow) and keep the observations from the take-off to the beginning of the cruise without level-off phases. Smoothing splines are then applied to the raw signals to remove the noise.
We sample each 5 s to reduce the dataset size without impacting strongly the accuracy of the resulting models. At the end, we obtain 494,039 observations which are randomly split into training and test sets to fit a polynomial model of degree 2 using the scikit-learn library. The RMSE and MAPE values of this model on the test set are respectively equal to 3:64 Â 10 À2 kg/s and 1. 73%. Regarding the optimization, we are interested in climb phases from 3,000 to 38,000 ft. We mention that we remove lower altitudes because operational procedures constraint heavily the trajectory during the very beginning of the climb. Further the initial and final Mach numbers are required to be equal to 0. 3 and 0. 78. It is noteworthy that the optimization solvers used in PyRotor allow linear inequality conditions, permitting to slightly relax the endpoints conditions.
Since we consider narrow-body airliners, polynomials are expected to be relevant to describe the slow variations of such aircrafts. Here the dimensions associated with the altitude, the Mach number and the engines power are given respectively by 4, 10, and 6. The reference vectors c R i are then computed using the formula (2). At the end, we amount to solving a constrained optimization problem in a space of dimension 20. We are then in position to apply the optimization method developed in Section 2 using the PyRotor library. First of all a relevant value for ν max > 0 has to be fixed. In order to propose a realistic optimized climb, we choose a ν max relatively small so that the optimized climb remains close to the reference ones. In particular, the quadratic objective function in (19) turns out to be convex for all ν ∈ 0,ν max ½ permitting to use the quadratic programming solver from CVXOPT software imported in PyRotor.
Material and methods The sensitivity analysis applied in this study made intensive use of stochastic simulations. The compiled executable of the simulation tool and scripts to reproduce simulations and analyses can be retrieved from: https://data. mendeley. com/datasets/txx9zkb-d2g/2. This code needs to be run under Linux with the SLURM workload manager. In addition, it requires R statistical software  (R Core Team, 2021)  for data manipulations and statistical analyses as well as DMU version 5. 2  (Madsen and Jensen, 2013)  for the estimation of variance components. In this code, the seeds used for sampling random numbers were fixed at the beginning of each replicate to ensure the reproducibility of data. Datasets were generated for 12 different scenarios, considering different combinations of input parameters based on three different nutritional environments, two levels of heritability and three levels of CV for AAPs (Table  1 ).
(2021)  were used, i. e. heritability of 0. 35, a phenotypic CV of 10% and no genetic or phenotypic correlation between the input traits. These parameters were used to simulate milk production and FE data in all three nutritional environments and served as a baseline. Then, different sets of genetic parameters were considered (Table  1 ) to evaluate how sensitive the genetic and phenotypic correlations between simulated traits were to changes in the genetic parameters of AAPs. In all three environments, the effect of heritability was evaluated by decreasing it to 0. 15 for all four AAPs. Alternatively, the effect of CV was evaluated by increasing it to 20% for all four AAPs, whilst keeping heritability to 0. 35. These lower heritabilities and higher CVs are typical of fitness traits  (Hill et al.
Variance components were estimated using the Average Information Restricted Maximum Likelihood algorithm. At first, univariate analyses were carried out to estimate heritabilities, followed by bivariate analyses to estimate genetic and phenotypic covariances between traits. All calculations were carried out using the DMU software  (Madsen and Jensen, 2013)  that can be retrieved from https://dmu. ghpc. au. dk/dmu/DMU/Linux/Previous/.
Author's point of views The main outcome of this study is a suite of scripts and a compiled version of the AQAL software to (1) simulate phenotypic trajectories of milk production and feed efficiency traits in a population of related cows and (2) estimate genetic parameters for traits derived from simulations. The sensitivity analysis performed in this study gives insight into the tuning of genetic parameters of AQAL input traits to simulate datasets of phenotypes with realistic means and genetic parameters. It confirmed that a heritability of 0. 35 for all four AAPs and a phenotypic CV of 10% permitted to simulate datasets of phenotypes with realistic variance and covariance components, as suggested empirically by  Puillet et al. (2021). Such genetic parameters are typical for production traits in domesticated species in farm or natural conditions  (Hill et al.
Those high culling rates are not realistic for heifers which suggested that, to be realistic, CV of acquisition and allocation input traits should be kept to values lower than 20%. This study confirmed that the nutritional environment has a much larger influence in the shaping of genetic and phenotypic covariances between traits than heritability and phenotypic CV of acquisition and allocation input parameters. Such information is critical for using the mechanistic AQAL software for breeding applications in the context of GÂE interactions. Indeed, the simulation of milk and feed efficiency performances should be relatively robust to deviations from true values of heritability and CV of AAP. The GÂE interactions emerging from the simulations are then mostly dependent on the nutritional environment specifications and resulting trade-offs in case of energy restriction.
We expect that the script and software attached to this study will help breeders (1) explore the interest in novel phenotypes derived from phenotypic trajectories and (2) define new breeding strategies for feed efficiency in dairy cows, with a focus on the influence of GÂE interactions on the trait expression. In conclusion, the code associated to this article enables the simulation of datasets of cow phenotypic trajectories for milk production and feed efficiency traits in different nutritional environments. The present sensitivity analysis confirmed the relevance of defining a heritability value of 0. 35 and a CV of 10% for acquisition and allocation input parameters in the AQAL simulation tool to simulate traits with realistic heritability and genetic correlation structure. Furthermore, the heritability and CV of AAPs had a more limited impact than the nutritional environment on genetic correlations among simulated traits.
Introduction Computing solutions such as 3D imaging and insilico simulation visualisation, Virtual/Augmented Real-ity (VR/AR) and visual analytics, among others, have seen an outstanding progress over the last years. Advances and improved access to big and high-resolution data, hardware infrastructure (e. g. High Performance Computing Clusters, Graphical Processing Units), affordable devices (e. g. , VR/AR glasses) and Open-Source codes thanks to the commitment to Open and Reproducible Science by researchers, have been made it possible. Biomedical applications are not an exception, with an unprecedented availability to medical datasets at different multi-scale and resolution levels. Therefore, some visual computing technologies are already disrupting traditional concepts of medical image exploration.
Complex medical interventions are usually planned by exploring preoperative medical images, where advanced visual computing solutions in combination with technologies such as 3D printing could contribute to reduce potential complications. Until recently, the visualisation and analysis of medical imaging data was always performed with visualisation tools, usually as 2D images and standard multi-planar reconstruction (MPR) visualisation viewed in 2D flat monitors. The most common situation is that these are the only tools that can be used during intervention since they are fully integrated with the acquisition systems in the operating room, with clinicians mentally integrating the 3D structure and functional information provided by multiple sources. Off-line image analysis can be performed with numerous tools including Open-Source software such as 3D Slicer  Kikinis et al.
Nevertheless, the complexity and inter-patient variability of the LAA 3D anatomy can be unnoticed with 2D imaging modalities. Three-dimensional echocardiography and more recently high-resolution Computerised Tomography (CT) images are increasingly becoming available, providing spatial information of the LAA to optimise the implantation strategy and device selection before the intervention  Saw et al. (2016). Commercial solutions are available with standard MPR visualisation of the 3D imaging modalities (e. g. , CT images) such as the 3mensio Structural Heart software (Pie Medical Imaging, Bilthoven, the Netherlands)  Hascoet et al. (2018) , which also includes advanced 3D photorealistic rendering of the heart for an advanced exploration of the LAA or Materialise Mimics (NV, Leuven, Belgium).