dataesr/scientific-paragraphs-categorization huggingface dataset:
As suggested by  Xavier et al. (2015) , the similarity in depth ranges between these records supports the idea that P. amadou occurs in bathymetrically constrained bands. However, depth is a proxy for other environmental variables including temperature, salinity, oxygen, food availability, pressure, near-bottom current speed and sediment type  (Harris and Whiteway, 2009). In this study, available data from the CTD casts included temperature, salinity and oxygen. Although these variables were not suitable to be included in the model because of their high level of correlation with depth and higher level of uncertainty (only 36 points for the whole seamount), they can be used to help explain the observed depth range of P. amadou on the Tropic Seamount. For instance, the predicted distribution of the sponge grounds of the hexactinellid Vazella pourtalesi on the Scotian Shelf, off Nova Scotia, Canada, was driven by minimum bottom temperature.
The homogeneity in the backscatter intensity is probably because these data cannot distinguish between clean hard substrate and hard substrate covered by a thin layer of sediment  (<50 cm, Lo Iacono et al. , 2008) , which reduces its usefulness in DMs approaches in such environments. BPI (i. e. , the relative elevation of the seabed in comparison to neighboring areas) could partially resolve this limitation, as areas with high BPI generally correspond to regions of hard substrate; however, the importance of this variable in the final model is uncertain. The aspect of the seabed, in terms of northness and eastness, predicted the highest probability of presence of P. amadou on the northern, eastern and western slopes, which coincided with the direction of the modeled currents on the seamount.
Predicted habitat for P. amadou was found to be favorable on the deep flanks of the seamount within a very narrow oceanographic regime. Thus, any deep-sea mining operation on these flanks should consider sediment deposition throughout the water column and potential for sediment plumes to spread from adjacent non-flank areas including those on the shallower summit. Other VME taxa observed on Tropic Seamount, such as coral gardens and patches of S. variabilis are also likely to provide important ecosystem functions on the seamount, indeed one type of coral garden hosted a nursery ground for deep-sea squid: here too, predictive species and habitat models could greatly aid in building the evidence base for VME occurrences. Combined with the multiple ground-truthed records of VME taxa, new classes of predictive habitat models may provide powerful new lines of evidence to help sustainably manage seamounts in the High Seas from impacts of resource exploitation as these new data can help verify VME occurrences and/or identify marine protected areas (MPAs), for which there are some management and avoidance measures for industries to take.
FUNDING Thanks are due to the  NERC  funded  MarineE-Tech  project (ref:  NE/MO1151/1 ) and the crew of the  RRS James Cook cruise JC142  for the data and samples. BR-S would like to thank  T. Morato  at  IMAR-UAzores  for facilitating a course on SDMs that allowed this study to be developed. This study is a contribution to the  ATLAS  project with funding to BR-S, JG-I, L-AH, MC-S, and JR provided from the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement no. 678760. JX research is funded by the  H2020 EU Framework Program for Research and Innovation Project SponGES  under grant agreement no. 679849. This output reflects only the authors' views and the  European Union  cannot be held responsible for any use that may be made of the information contained therein.
This first requires a computer code which simulates the CTS spectra for the given geometry of the setup and a set of plasma parameters, based on a physics model of the microscopic wave-plasma interaction. Second, this forward model must be combined with an integrated data analysis system which provides all required additional information, such as the density profile, measured by independent diagnostic systems, as well as the information on the (modulated) probing beam. The W7-X integrated data analysis environment Minerva is a conglomeration of (Bayesian) probability theory and graph theory. 21 It provides a standardized data analysis infrastructure, taking into account the measurements of every implemented diagnostic. In addition, it facilitates the exchange of any physics models underlying a particular diagnostic.
This feature will allow the comparison of CTS physics models in a completely new way. To implement the CTS model into Minerva, it was necessary to build a new CTS code as pre-existing CTS codes are not open source. It should be remarked here that there is no common view in the literature on what the most appropriate physics model of CTS is. The basis is formed by plasma kinetic theory and Maxwell's equations. A physics model is then constructed either by considering the full electromagnetic response of the plasma to the fluctuations or by making the electrostatic approximation. The adequacy of the two descriptions (electromagnetic vs. electrostatic) in a particular experimental situation has been debated in the past by several authors,  [22] [23] [24] [25]  who independently developed CTS codes.
We present formulas to evaluate these sums effectively, which are otherwise not found in the literature. In this paper, we will • describe the newly developed CTS code, in particular the numerical methods employed to deal with infinite sums (Sec. II) • demonstrate the modeling of CTS spectra on the new fusion experiment Wendelstein 7-X (Sec. III) • outline the implementation of our CTS code into the Bayesian data analysis framework Minerva (Sec. IV) • compare the results of the code to the output generated with a code based on the full electromagnetic treatment  [25] [26] [27]  (Sec. V) • present the first CTS spectrum measured on Wendelstein 7-X and analyzed by our model (Sec. VI) • discuss the implications of the results (Sec. VII) • conclude and give the outlook in Sec. VIII.
IV. INTEGRATION OF THE CTS FORWARD MODEL INTO THE MINERVA FRAMEWORK The implementation of the CTS forward model into the Minerva framework will allow the inference of the model parameters, given the measured data, in a Bayesian fashion. Several other plasma diagnostic models operational on W7-X are already implemented in the framework. It has also been used in a number of other experiments. 21, 35 The physics model of each diagnostic is represented in the Minerva framework as a graphical model. In a forward model of a diagnostic, measured data are derived from the model parameters by using the code implementation of physical processes. According to Bayesian probability theory, the Bayes formula can be used to solve the inference of the parameters of the model, P(M|D) = P(D|M)P(M) P(D) , ( 22 ) where P(M|D) represents the posterior probability distribution for the model parameters M with respect to the measured data D, P(D|M) is the likelihood of the data given model parameters, P(M) is the prior probability distribution assigned to the model parameters before data acquisition, and P(D) is a normalizing factor.
25 The data have then been fed into the node Spectrum (see Fig. 9 ), and the parameter values have been inferred from this node. Depending on the data set, Minerva fits either the scattering function, Σ(k, ω), or the scattered FIG. 10. Gaussian noise (5% of the maximum value) was added to a spectrum obtained by the full electromagnetic code  25  for the scattering channel X → X in a deuterium plasma (black and blue dots). The spectrum has been fitted by the MAP inversion (the red line). Final temperatures obtained by fitting are indicated in the legend. power P s. In order to fit the data, Minerva performs the maximization of the posterior probability distribution (MAP) on the free parameters and updates the input to the integrated CTS forward model at each iteration. The process is repeated until convergence.
The coordinates of A1 were fixed for the entire duration of the experiment, while the coordinates of F1 were changed in order to sweep along the expected region of overlap with A1. In Fig. 15 , we present the result of the overlap sweep. In each discharge of the experiment, the overlap was measured before and after gas puff. An increase in the CTS signal was observed after each gas puff. The maximum overlap was obtained in the discharge 20 171 011. 053 at an azimuthal angle of -19. 3 •. The data were recorded by a sensitive heterodyne receiver equipped with a National Instruments fast data acquisition card with a sampling rate up to 12. 5 GS/s. The probing beam was modulated during every discharge in order to obtain a measurement of the background ECE radiation. The gyrotron on and off periods were 55 ms and 5 ms, respectively.
VIII. CONCLUSION AND OUTLOOK The developed code, eCTS, is a forward model of collective Thomson scattering in the electrostatic approximation. It has been integrated into the Bayesian data analysis framework Minerva and successfully used for the analysis of synthetic and measured spectra. Taking into account that the electrostatic approximation is not valid for arbitrary plasma conditions, or for arbitrary scattering geometries, we have shown that the model can be used for the measurements of the bulk ion temperature and plasma composition in W7-X plasmas. The model presented here will be expanded into a full electromagnetic model which will also be implemented into Minerva. This will allow for a new and unique comparison of the two treatments. Furthermore, the subsequent versions of the code will include relativistic effects both in the geometrical form factor and the spectral density function.
More details about a very similar setup can be found in  [30]. In this setup, the impedance measurements were performed using a two-electrode arrangement. In this configuration the two metallic plates of the cuvettes were used as the measuring electrodes both for current injection and voltage measurement. A fast time-domain measuring technique based on multisine excitations was used to acquire impedance spectral information in the gap between consecutive pulses at a temporal resolution of 1 spectrum/ms. The multisine excitation burst contained 26 frequencies distributed from 1 kHz to 1 MHz. A custom software was developed in LabVIEW (National Instruments) to control the whole system. The measurements sequence consisted in a continuous acquisition of 100 spectra (100 ms) after a fixed delay of 500 ms after each pulse application until completing the 20 pulses protocol.
Exponential model In an attempt to mathematically model the ensemble of the data, and to describe in a more exhaustive way the relation between pulse number, the amplitude of the applied electric field and the observed outcome, we fitted the data to different mathematical functions and we found that the exponential function was the simplest and the most convenient approach. The analysis was performed in Matlab (The MathWorks, Inc. ) using a nonlinear Least Squares approach and the Trust-Region algorithm. The data corresponding to a constant bleomycin concentration of 30 nM and different electric fields  reached by the model, corresponds to the plateau level in cell survival discussed before. The parameter a corresponds, thus, to the maximum effect attainable in the present experimental setup.
Real-time impedance measurements Because all the data presented in the previous sections were somehow based in post-pulse evaluations of the effects of the entire trains of pulses (no data was collected on the effects of each additional pulse inside a given train of pulses), we considered that real-time data could give complementary information about the effects on cells with increasing pulse number. To this end we performed electrical impedance measurements of cell suspensions during the application of electroporation pulses. The measuring system allowed us to obtain measurements before, during (in the time gap between consecutive pulses) and just after a complete 20 pulses procedure. It is important to stress the fact that these measurements were performed in very different experimental conditions with respect to the previous experiments.
Acknowledgements The authors thank,  CNRS ,  Gustave Roussy ,  Univ. Paris-Sud  and  Université Paris-Saclay  for financial support of this work. This work was funded by  La Ligue contre le Cancer postdoctoral fellowship program. We also acknowledge the financial support of the  ITMO Cancer  to the projects PLASCANCER ( N°17CP086-00 ) and  Dynamo  ( N° PC201517 ;  Modélisation dynamique de l'électroporation)  granted within the Plan Cancer. This study was conducted in the scope of LEA EBAM ( European Laboratory of Pulsed Electric Fields Applications in Biology and Medicine ).
Animal Experiments Animal handling was performed according to the Dutch Law on Animal Experimentation and the European Directive for the Protection of Vertebrate Animals Used for Experimental and Other Scientific Purpose. The protocol was approved by the Animal Experimental Committee of Maastricht University. The animal experiments have been conducted in 2010-2011 and the methodology used has partially been described in a previous publication  (Strik et al. , 2013b) , reporting on the LV pressure data. In the current study we analyzed both RV and LV pressure data and related them to the electrical conduction measurements. In short, 6 adult mongrel dogs were anesthetized using midazolam (0,25 mg/kg/h) and sufentanil (3 µg/kg/h) and a complete AV-block was induced by radiofrequency ablation.
Data Analysis Local electrical activation times were determined as the duration between the atrial pace and the timepoint of steepest negative deflection of the electrogram using custom software (Figure  1B , green line in local EGM's). For quantification of intraventricular electrical dyssynchrony we calculated total activation time as the difference between earliest and latest activation time of the RVFW (RV TAT) and of the whole LV (LV TAT). The latter was calculated from the combination of epicardial LVFW and endocardial septal electrodes. To quantify electrical interventricular dyssynchrony we used the ventricular electrical uncoupling (VEU) index. VEU was defined as the difference between mean LVFW and mean RVFW activation times  (Ploux et al. , 2013). For both the LV and the RV, response to pacing was defined as the relative change in dP/dt max compared to the baseline setting.
We applied quadratic LOESS fitting to account for measurement variability within each dog  (Cleveland, 1979). Furthermore, in order to quantify a generic canine response to changes in pacing delay, we created a single representative canine dataset by taking the mean values of the dogs for each setting. We also applied linear 2D interpolation between the measurements in the heat map visualizations of all pacing delay settings. All these calculations were performed in Matlab 2016A (The Mathworks Inc. , Natick, MA, United States).
Computer Simulations The CircAdapt model of the entire heart and circulation  (Arts et al. , 2005; Lumens et al. , 2009; Walmsley et al. , 2015a) , which can be downloaded from www. circadapt. org, was used to simulate cardiovascular mechanics and hemodynamics during pacing delay variations as applied in the animal experiments (Figure  1C ). Previous experimental and clinical studies have shown that the CircAdapt model realistically relates local ventricular myofiber mechanics to global cardiovascular hemodynamics in dyssynchronous and paced hearts  (Leenders et al. , 2012; Huntjens et al. , 2014; Lumens et al. , 2015; Walmsley et al. , 2015a,b). The source code of the CircAdapt model used for all simulations as well as the Matlab R (The MathWorks, Natick, MA, United States) scripts to perform all simulations and analysis thereof are provided as an online supplement (Supplementary Data Sheet S1).
In summary, the CircAdapt model is a reduced order model of the human four-chamber heart connected to a closed-loop cardiovascular system, with lumped pulmonary and systemic circulations. It uses a simplified ventricular geometry, where cardiac walls are represented by thick spherical shells consisting of contractile myocardium. The MultiPatch module enables cardiac walls to be subdivided into an arbitrary number of wall segments (patches). Tissue properties and activation time can differ between patches, but all patches within a wall share a common wall tension and curvature. Since wall tension is the same in all patches within a wall, spatial location within a wall is not required to calculate deformation in a patch. In CircAdapt wall tension and curvature determine cavity pressure through Laplace's law  (Lumens et al.
Metabolomics generates massive and complex data. Redundant different analytical species and the high degree of correlation in datasets is a constraint for the use of data mining/statistical methods and interpretation. In this context, we developed a new tool to detect analytical correlation into datasets without confounding them with biological correlations. Based on several parameters, such as a similarity measure, retention time, and mass information from known isotopes, adducts, or fragments, the algorithm principle is used to group features coming from the same analyte, and to propose one single representative per group. To illustrate the functionalities and added-value of this tool, it was applied to published datasets and compared to one of the most commonly used free packages proposing a grouping method for metabolomics data: 'CAMERA'.
Upstream to the ionization and detection steps, metabolites from complex biological samples are separated most frequently using liquid chromatography (LC) or gas chromatography (GC)  [3, 4]. During the analysis, metabolites are ionised in the mass spectrometer source to produce several ions/analytical features (parent ion, isotopes, adducts, and fragments) that are part of the original molecule. Raw data obtained from metabolic profiles are processed to yield a data matrix containing retention times (time between sample injection and appearance of the maximum ion signal), masses, and peak intensities. This step results in thousands of features present in the final dataset with a high degree of correlation  [5]. In addition to this analytical redundancy, biological correlation issued from modulated metabolites coming from the same pathways does also exist.
This high degree of correlation in datasets is a constraint for the use of various data mining and statistical methods. For example, analytical redundancy highly affects multiple testing correction. Indeed, having non-independent variables (coming from the same metabolite) leads to an over-correction of data that can hide potentially relevant information. Moreover, for biological interpretation, experts are mainly focusing on metabolites rather than on the different analytical features. For all these reasons, considering metabolite as a unique entity instead of the individual ions, as a variable, is more relevant. In order to handle this data complexity and, in particular, identify subgroups of related features, tools have been developed, mostly for annotation purposes  [6]. In fact, the use of correlations between ions contributes to determining chemical identities and isolating species part of the same metabolite from those of other coeluting compounds.
Generally, the available packages consist of a two-step process: first, a grouping of all features derived from the same analyte, and then, an annotation of the ion species. The first category of tools is based on a grouping approach consisting of using chromatographic peak-shape similarity of coeluting features in raw data. Among these tools, CAMERA is a Bioconductor R package that is widely used in the field of mass spectrometry metabolomics  [7] , designed to post-process XCMS  [8]  feature lists and to collect all features related to a compound. In an iterative process, it first selects the most intense feature not yet assigned to a compound spectrum and determines an associated retention time (RT) window. All features within this range are then included in a new compound spectrum. Next, the algorithm excludes unfitting features using the chromatographic peak shape similarity of the extracted ion chromatogram (EIC) of each feature, as well as a Pearson correlation between intensities inside the chromatographic peak boundaries for all pairs of features within the compound spectrum.
xMSannotator  [11]  also incorporates multi-criterion scoring (both analytical and biological correlations, matches in databases, etc. ) for improving the annotation of high-resolution metabolomics data. Even though a grouping step does exist in these packages, most of the time all the generated information (correlation coefficient, retention time, mass difference between features) are not used together to form the groups. Moreover, users do not have the possibility of accessing the grouping information nor obtaining representative features among groups, which is essential for data reduction. Finally, most of those tools are not giving the possibility to build a workflow with other processing tools, as they require either a specific input format or specific approach for measuring similarities.
In the context of contemporary e-Science, it has been recognised that data have to be Findable, Accessible, Interoperable, and Reusable in the long-term  [12]. Workflow4Metabolomics (W4M, http://workflow4metabolomics. org; [13, 14] ) has been developed within this objective, for comprehensive metabolomics data pre-processing, statistical analysis, and interpretation. It is a fully open-source virtual research environment (VRE;  [15] ) built upon the Galaxy environment  [16]  for bioinformatics developers and metabolomics users and allows user-friendly functionalities for workflow management. In this context, we proposed a new stand-alone tool dedicated to data reduction based on the removal of analytical redundancies of MS-based metabolomics datasets. As a key element within the metabolomics data analysis workflow, as well as to ensure reproducible computational analyses, we made it available via Galaxy and provided it for W4M, with generic input files and different output files for visualisation and further data analysis steps within workflows.
Materials and Methods Our aim was to detect analytical correlations into MS-based metabolomics datasets (tabular files) without confounding them with biological ones that may exist within samples. To achieve our goal, we developed a Perl tool supported by metabolomics experts to translate and understand the chemical complexity of datasets as well as possible. The algorithm principle is to group features coming from the same metabolite and to suggest one single representative per group. In optimal settings, the grouping criteria include a similarity measure, retention time, and mass information from a reference list containing isotopes, adducts, and fragments. Thresholds for all these criteria can be fixed, and the representative feature can be determined following four methods according to the user's needs and the analytical technology used, either LC-or GC-MS.
As the output, the module returns the input file with new columns in relation to resulting groups (representative feature choice, grouping information, and annotation of features), as well as a. sif file allowing correlation network visualisation of the dataset of interest. The present tool "Analytic correlation filtration" (ACorF) is available via the web interface Galaxy as a single module and can be chained with other W4M modules. As CAMERA is also available in W4M; the present tool was compared to this package by using a published dataset, demonstrating its utility and various possibilities of use.