dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods and results We developed a regression equation-based cost-effectiveness model, using individual patient data from three randomized control trials (FIX-HF-5 Phases 1 and 2, and FIX-HF-5C) to populate the majority of parameters. A series of regression equations predicted NYHA class over time, mortality, all-cause hospitalization rates, and health-related quality of life. We conducted the analysis in line with the National Institute for Health and Care Excellence reference case, modelling costs from an English National Health Service perspective, and considering outcomes in quality-adjusted life years (QALYs) over a patient lifetime perspective. Our base case analysis produced an incremental cost per additional QALY of GBP22 988 (€25 750) when comparing Optimizer + SoC to SoC alone. This result was not sensitive to parameter uncertainty but was sensitive to the time horizon over which costs and QALYs were captured and the duration over which a survival benefit with Optimizer + SoC can be assumed to apply.
The purpose of the current study was to assess whether CCM plus standard of care (SoC) is a cost-effective treatment for systolic HF compared with SoC alone. We developed a cost-effectiveness model that focused on NYHA III patients with LVEF of 25-45% and used data from the three FIX-HF-5 RCTs to model longer term health outcomes and cost implications associated with adopting the Optimizer System from the perspective of the English National Health Service (NHS).
Study design and model structure We developed a regression equation-based costeffectiveness model, using individual patient data from three RCTs (FIX-HF-5 Phases 1 and 2, and FIX-HF-5C) to populate the majority of parameters. Figure  1  shows the model structure. Age, sex, presence of ischaemia (yes/no), diabetic status (yes/no), LVEF, and MLWHFQ score determined the patients' baseline characteristics on entering the model. We determined treatment-specific clinical outcomes (e. g. adverse events and hospitalizations), costs, utilities, and mortality at a 'cohort-level' on the basis of patients' NYHA classification. A series of regression equations predicted NYHA class as a function of time (which determined the proportion of patients in each NYHA class in each cycle), mortality, all-cause hospitalization rates, and health-related quality of life (HRQoL).
The analysis grouped NYHA Classes I and II due to a lack of available trial data in these groups. At the beginning of the first model cycle, all patients in the Optimizer + SoC arm underwent device implantation, which could either be successful or result in procedure/devicerelated adverse events. During each subsequent 30 day cycle, patients could remain stable, improve (move to NYHA I or II), deteriorate (move to NYHA IV), or die. We used a lifetime time horizon in the analysis. We conducted the analysis in line with the National Institute for Health and Care Excellence reference case, modelling costs from an English NHS perspective, considering outcomes in quality-adjusted life years (QALYs), and discounting all costs and outcomes at 3. 5% per year. 15 ach arm of the model captured treatment efficacy via the distribution of patients across NYHA classes [e.
Patients Of all patients included in the three source trials, we only included data from those who were NYHA III at baseline, had LVEF of 25-45%, and had narrow QRS (<130 ms), excluding patients who did not meet all of these criteria before performing any analyses. This is aligned with the population expected to use the device in routine clinical practice. In the resulting cross-trial cohort (N = 415), 194 received the Optimizer System + SoC and 221 received SoC alone. We used the individual patient data from the CCM registry 9 to inform a scenario analysis, rather than in the model base case, because these data were non-comparative. Standard of care differs between patients depending on their baseline LVEF. Patients with a baseline LVEF of <35% can potentially receive an implantable cardioverterdefibrillator (ICD) as part of SoC, whereas those with a LVEF of >35% are not eligible.
Study limitations The study results indicate that the Optimizer System is costeffective when a lifetime horizon for costs and benefits is considered. Follow-up in the clinical trials of the Optimizer System was relatively short, restricted to 12 months in the RCT data, and our statistical analyses extrapolated the trial data over a lifetime. However, the consistency between model predictions and observed mortality through 10 years of follow-up does provide reassurance of model validity. Nevertheless, as we increase the time horizon, more uncertainty is introduced into the analysis. Over shorter time horizons, where this uncertainty is reduced, the ICER increases. However, these shorter time horizons are not aligned with National Institute for Health and Care Excellence recommendations for lifetime cost-effectiveness estimation as the basis of assessment of value.
15 Ongoing data collection in the Optimizer CCM registry 9 will increase confidence in the durability of the treatment effect. Scenario analyses indicate that the survival benefit associated with the Optimizer System is a key driver of the costeffectiveness results, and therefore, the Optimizer System is likely to be most cost-effective in individuals at higher risk of mortality but for whom a survival benefit can be obtained. The RCT data on short-term survival suggest a benefit, but this fails to reach statistical significance at 12 months. In order for the Optimizer System to be cost-effective at a GBP30 000 threshold, the hazard ratio for all-cause mortality required with the Optimizer System + SoC is 0. 81 compared with SoC alone. Whilst the mortality data used in the model are uncertain, the impact of treatment on symptoms (as measured by NYHA class) is highly statistically significant and likely to be associated with better longer term survival, particularly as CCM therapy is also associated with reverse remodelling of the left ventricle 32 similar to that reported for CRT, which is known to be associated with longer term survival benefit.
Background: The segmentation of a 3D image is a task that can hardly be automatized in certain situations, notably when the contrast is low and/or the distance between elements is small. The existing supervised methods require a high amount of user input, e. g. delineating the domain in all planar sections. Results: We present FitEllipsoid, a supervised segmentation code that allows fitting ellipsoids to 3D images with a minimal amount of interactions: the user clicks on a few points on the boundary of the object on 3 orthogonal views. The quantitative geometric results of the segmentation of ellipsoids can be exported as a csv file or as a binary image. The core of the code is based on an original computational approach to fit ellipsoids to point clouds in an affine invariant manner. The plugin is validated by segmenting a large number of 3D nuclei in tumor spheroids, allowing to analyze the distribution of their shapes.
User experiments show that large collections of nuclei can be segmented with a high accuracy much faster than with more traditional 2D slice by slice delineation approaches. Conclusions: We designed a user-friendly software FitEllipsoid allowing to segment hundreds of ellipsoidal shapes in a supervised manner. It may be used directly to analyze biological samples, or to generate segmentation databases necessary to train learning algorithms. The algorithm is distributed as an open-source plugin to be used within the image analysis software Icy. We also provide a Matlab toolbox available with GitHub.
Starting observation Segmenting ellipsoidal structures in 2D or 3D imagescan be used to characterize the shape of organs, tissues, cells, nuclei or other cell organels  [1] [2] [3] , or serve as an initialization for more advanced algorithms such as active contours  [4] [5] [6] [7]. While fully automatic detection algorithms  [8] [9] [10] [11]  are probably the ideal tool to limit subjectiveness and time of analysis, existing strategies are not sufficient to provide convincing segmentation results when images suffer from strong degradations (e. g. blur, noise, low resolution) or contain densely packed objects. In addition, automatic methods usually require tuning a few parameters, which may be more time consuming than using a simple supervised segmentation algorithm. Finally, the generation of learning databases or gold standards to test and compare existing segmentation algorithms still requires efficient supervised algorithms.
An important feature of the proposed algorithm is affine invariance: the point cloud is registered prior to computation, ensuring a robust behavior whatever the shape of the point cloud. The proposed algorithms are shared not only within the Icy plugin, but also through a set of Matlab codes delivered on a Github repository  [27]. To the best of our knowledge, FitEllipsoid is the first open-source toolbox that allows fitting ellipsoids and not more general quadrics (e. g. hyperbolas). In order to showcase the usefulness of the plugin, we propose to examine the morphology and the distribution of shapes of nuclei in a 3D tumor spheroid. Are they rather elongated, spherical or none of these? Using FitEllipsoid, we obtained the shape of hundreds of nuclei from 3D SPIM images of optically-cleared spheroids.
Description The need for a free software dedicated to biomedical imaging oriented us to the recently developed imaging tool Icy  [12]. It is based on VTK (Visualization ToolKit)  [28] , allowing for nice 3D visualization. An ellipsoid can be represented in different ways: • A center (3 parameters), three angles of rotations and the length of each axis (3 parameters). • A center (3 parameters), the three axes (9 parameters linked through orthogonality relationships) and the length of each axis (3 parameters). • A center (3 parameters) and a positive symmetric definite matrix (6 coefficients). Unfortunately, none of these representations can be easily used by a human. For instance, finding the center of the ellipsoid precisely by just looking at the image would result in inaccurate results. The strategy that is adopted in FitEllipsoid is to ask the user to select a few points in 3D on the object's boundary and the plugin then creates an ellipsoid that passes through them approximately.
Segmentation experiments on synthetic data In order to assess the plugin's efficiency in terms of: accuracy, reproducibility and time of user's interaction, we designed a synthetic 3D image composed of 145 oblate 1 ellipsoids mimicking a tumor spheroid, see Fig. 4. This image can be reproduced using the codes available on GitHub. The image was blurred with a Gaussian kernel   1. The column labelled 'time' displays the average time spent by the user (in seconds) to segment one ellipsoid, the column 'center' provides the average error (in pixel) between the true location of the center and the estimated one, the column 'angle' corresponds to the average error (in degrees) of the orientation of the minor axis, and the column 'length' corresponds to the average error (in pixels) on the 3 axes lengths.
Conclusions FitEllipsoid is a powerful tool for supervised ellipsoids segmentation, with a user-friendly interface. The computational part of the software is based on a novel algorithm that is invariant under affine transforms. It allowed to segment hundreds of cell nuclei in order to analyze statistically their shape. Endnote 1 We used this geometry since we thought that it would correspond to what is observed in real tumor spheroids. We will see later that this is actually not the case.
Other methods may be preferable for large datasets unless extensive computing resources are available. The better performance of profile methods may be due to their fitting the simulation conditions better. However, it is unclear how we could have simulated convergent evolution realistically without using mutation-selection models that use profiles of amino acid frequencies. In the end, this indicates that profile methods may perform better on empirical data as well; apart from diffsel which always comes out first, the variability of the AUC ranks among trees however indicates that using several methods on a data set is recommended. We then studied the performance of the methods for a wider range of genome-wide selection efficacies, focusing on the Cyperaceae tree (see Figure  S7  for the three other trees).
Pipeline and implementation of the methods The results in this paper were obtained using an all-in-one pipeline that encompasses simulations, detection and post-simulation analysis, including the generation of the plots used for Figure  3  and Figure  4. The pipeline itself was implemented in OCaml using bistro ( https://github. com/pveber/bistro ), a library to build statically-typed reproducible workflows. Special attention was paid to reproducibility, in particular by following the guidelines given in  (33). Instructions to reproduce our results are given in the supplementary material. The implementations of the methods used in the pipeline are as follows: • The Multinomial method has been implemented de novo in Python as well as the Identical and Topological methods which additionally use executables from the bppsuite  (31).
They are available via the pipeline. • The TDG09 implementation we used is a slightly modified version of the one available on github (see Table  1  ) where multithreading has been removed to avoid multithreading-related problems. Results should be identical to github version. In addition, a script available in the pipeline repository was written to adapt input alignments and trees to TDG09 expected formats. • For diffsel, we used an optimized version of the original implementation that is faster but implements the same model. The implementation we used is available on github (see Table  1  ). In addition, we use a different approach to establish MCMC convergence. The original method compares two MCMC chains using the tracecomp program from the PhyloBayes suite  (34). Instead, we run only one chain, use the Raftery and Lewis's Diagnostic implemented in the R package coda (v0.
CONCLUSION We have reviewed existing definitions of convergent amino acid evolution. We have built upon them to distinguish between convergent amino acid profile changes and convergent scalings in the efficacy of selection. When existing methods are tested on simulated data in a range of conditions, probabilistic methods that rely on models of sequence evolution detect convergent profile changes better than other methods. However, none of them performs well at detecting convergent scalings of selection efficacy, and they perform poorly when selection efficacy is low over the entire tree. Improved models would allow distinguishing between different types of convergent evolution, and should use mathematical and algorithmic tricks to improve computational efficiency. Data, code and materials Our pipeline's code is available at https://gitlab.
Acknowledgements We would like to thank  G. Didier  for providing a modified version of msd that can work on individual sites, and  T. Latrille  for providing the profiles and fruitful discussions. This work was performed using the computing facilities of the  CC LBBE/PRABI. We would like to thank the  French Institute of Bioinformatics -IFB CNRS UMS 3601  -(funded as part of  Investissement d'avenir program  managed by  Agence Nationale pour la Recherche , contract  ANR-11-INBS-0013 ) for providing life science data and tools, storage and computing resources on the IFB national service infrastructure in bioinformatics.
Motivation: We present Link-HD, an approach to integrate multiple datasets. Link-HD is a generalization of 'Structuration des Tableaux A Trois Indices de la Statistique-Analyse Conjointe de Tableaux', a family of methods designed to integrate information from heterogeneous data. Here, we extend the classical approach to deal with broader datasets (e. g. compositional data), methods for variable selection and taxon-set enrichment analysis. Results: The methodology is demonstrated by integrating rumen microbial communities from cows for which methane yield (CH 4 y ) was individually measured. Our approach reproduces the significant link between rumen microbiota structure and CH 4 emission. When analyzing the TARA's ocean data, Link-HD replicates published results, highlighting the relevance of temperature with members of phyla Proteobacteria on the structure and functionality of this ecosystem.
Introduction The reduction of 'omics' technology costs now enables collection of data from multiple sources. This allows researchers to simultaneously study several datasets and investigate their relationship with complex traits. The integration of these heterogeneous datasets is not trivial and several statistical methods have been developed to address this challenge  (Argelaguet et al. , 2018; Mariette and Villa-Vialaneix, 2018; Meng et al. , 2014). In particular, the amalgamation of multiple microbial ecosystems poses unique challenges as these are compositional and sparse data. MixKernel (Mariette and Villa-Vialaneix, 2018) is a well-known tool designed to integrate heterogeneous datasets including microbial communities, but no method to perform a taxonomic enrichment analysis is available.
Another popular integrative approach is MOFA  (Argelaguet et al. , 2018) , however, it is unable to deal with compositional data. Here, we present Link-HD, a tool to integrate and explore multiple microbial communities based on STATIS  (Des Plantes, 1976) , a family of multivariate methods to integrate multiple datasets. Link-HD generalizes STATIS with Regression Biplot  (Ter Braak, 1997) , clustering, differential abundance, enrichment taxonomic analysis and visualization tools. Link-HD analyzes distance tables computed from numerical, categorical, or compositional data as a generalization of multidimensional scaling  (Abdi et al. , 2007). Furthermore, Link-HD performs variable selection and can link the obtained common sub-space with phenotype information.
Materials and methods Like STATIS, Link-HD aims to compare and analyze the relationships between datasets with a shared set of observations or variables. However, our package was specifically designed to integrate microbial communities and incorporate distances and transformations to deal with compositional data  (Aitchison, 1982). The method is implemented in three main phases (Fig. 1 ). i. Inter-structure step: The algorithm first assesses the similarity between transformed distance tables using the vector correlation coefficient (Rv)  (Escoufier, 1973) , which can be interpreted as a general 'vector covariance' between matrices, i. e. this step evaluates similarity between the disparate datasets. ii. Compromise step: Next, the 'compromise' matrix is calculated, which is a weighted sum of each cross-product matrix.
Case studies We illustrate our approach with rumen microbial (Ramayo-Caldas et al. , 2019), TARA's Ocean expedition  (Sunagawa et al. , 2015)  and transcriptome NCI-60 cell line datasets  (Reinhold et al. , 2012). In the rumen study, we integrated Bacteria, Archaea and Protozoa from 65 Holstein cows. Link-HD was able to reproduce previous results  (Danielsson et al. , 2017; Kittelmann et al. , 2014; Ramayo-Caldas et al. , 2019) , showing a link between the structure of the rumen microbiota and CH 4 emission. We also identify microbial markers associated to CH 4. In the TARA's example, Link-HD replicates the relevant role of temperature and Proteobacteria phyla on the structure of this ecosystem, as described in  Mariette and Villa-Vialaneix (2018). Finally, we show the potential of Link-HD to integrate other omics layers by using transcriptome NCI-60 cell lines.
Link-HD recapitulates the reported data structure  (Meng et al. , 2014)  and ontology analysis reveals several cancer-related pathways. In all, our results demonstrate that Link-HD is robust in combining several heterogeneous data types. A detailed description of these case studies and the theory behind Link-HD is available at https:// lauzingaretti. github. io/LinkHD/ and in Bioconductor (https://biocon ductor. org/packages/release/bioc/html/LinkHD. html).
Precipitation model for hydroxide magnesium in the culture medium used The concentration of magnesium hydroxide, calcium carbonate and dolomite were modeled as a function of the added NaOH concentration in the culture medium. To do so, we simulated the NaOH addition effects and developed a numerical model, using the software Phreeqc, based on the Pitzer equations to simulate the precipitate formation in our culture medium. Indeed, in order to assess the equilibrium state of hypersaline solutions in which D. salina is grown and understand the precipitation phenomena involved during NaOH addition, it is necessary to evaluate the activity of species in solution. This requires the evaluation of coefficients of activity, which take into account the various ionic interactions in these complex environments.
The whole set of parameters were validated and listed by  Harvie et al. (Harvie et al. , 1984) , the relevant database being "pitzer. dat" This database accounts for the significant elements for Mediterranean see water (Na-K-Mg-Ca-H-Cl-S-O-C-Fe-Mn-Ba-Sr-B-Li-Br) with a large spectrum of possible precipitates. Before its application for the simulations presented here, the model has been successfully  (Besson, 2013)  compared to the experimental results of sea water evaporation proposed by Baseggio  (Baseggio, 1974). The composition of the culture medium used for the simulations (Table  1 ) was entered using a temperature of 25°C and a total dissolved carbon concentration of 0. 00021 mol/kg_water.
We show that the stellar buoyancy radius can be used as a reliable evolution indicator for field stars on the main sequence. On the other hand, we computed rotating evolutionary models of intermediate-mass stars including internal transport of angular momentum in radiative zones, following the formalism developed by Zahn and Maeder, with the CESTAM code. This code calculates the rotational history of stars from the birth line to the tip of the RGB. The initial angular momentum content has to be set initially, which is done here by fitting rotation periods in young stellar clusters. Results. We show a clear disagreement between the near-core rotation rates measured in the sample and the rotation rates obtained from the evolutionary models including rotationally induced transport of angular momentum following Zahn (1992).
2005). Due to the structure of these stars, the detected oscillations are able to probe the deep internal region of these stars. With periods typically of the order of one day, these oscillations were extremely difficult to detect from ground. One had to wait for the four years of nearly continuous observations from Kepler to obtain data of sufficient quality in order to perform seismic studies of these stars. Moreover, unlike solar-type stars, the convective envelopes in γ Dor stars are too shallow to generate a magnetic field able to act as a magnetic torque and slow down their surfaces  (Schatzman 1962). For that reason these stars have projected rotation velocities of around 100 km. s -1 in average  (Abt & Morrell 1995). These high-rotation velocities have long hampered the interpretation of γ Dor stars seismology.
These studies allowed to find near-core rotation frequencies for these stars ranging from 5 to 25 µHz -periods of 0. 5 to 2. 5 days-for dozens of these stars. The present study is a first attempt to confront these findings with the models of angular momentum (AM) evolution in intermediate-mass stars. In particular, we make use of the stellar evolution code cestam  (Marques et al. 2013) , which models AM evolution from the stellar birth-line to the tip of the red-giant branch. These models are presented in Sect. 2, where the choices for angular momentum transport processes are explained. Such calculations require the definition of appropriate initial conditions, which set the initial AM content. These initial conditions are chosen using observations of young stellar forming regions as a constraint.