dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimates of protein copy number per cell were calculated using the histone ruler method  [24]. 2. 6. Flow cytometry of macrophages Following treatments, BMDMs were harvested from cell culture plates using 5 mmol/l EDTA in PBS for 10 min at 37 C. Cells were washed in FACS buffer (PBS containing 1% (w/v) bovine serum albumin (BSA; Sigma-Aldrich)). Cells were incubated with 10 mg/ml anti-mouse CD16/ 32 (clone 93, BioLegend) to block Fc receptors, then stained with anti-F4/80-BV421 (1:400; clone BM8, BioLegend) and anti-CD11b-PE (1:400; clone M1/70, BioLegend) in FACS buffer for 20 min at 4 C. Stained cells were washed and resuspended in FACS buffer and acquired on a BD LSRFortessa (BD Biosciences) using FACSDiva software. Analysis was carried out using FlowJo software. For analysis, live cells were identified by gating based on forward and side scatter.
Statistical analyses For statistical analysis of proteomics data, four biological replicates were generated, and P values were calculated using a two-tailed t-test assuming unequal variance on log normalised "copy numbers per cell" values. For expression of individual proteins, data are presented as the mean AE s. d. Differences were considered statistically significant if p < 0. 05. Heat maps were generated using the Morpheus tool from the Broad Institute (http://software. broadinstitute. org/morpheus). For Gene Ontology (GO) Term enrichment analysis, GO terms enriched in proteins with statistically significant changes in expression were identified using the functional annotation tools within DAVID Bioinformatics Resources 6. 8, NIAID/NIH (https://david. ncifcrf. gov/). All other data are presented as mean values AE s.
2a ). Notably, AMPKa1 deletion reduced the expression of markers of anti-inflammatory M2 macrophages Ym1, Mannose receptor C-type 1 (MRC1), CD36, CD301 and Glutamine synthetase (GS) (Supplementary Fig. 2b  and c ), as well as CD169, a marker for M2-like tumour-associated macrophages  [25]. In contrast, deletion of AMPKa1 did not affect the expression of proteins associated with pro-inflammatory M1 macrophages. We also found that expression of ApoE, which promotes cholesterol efflux, was almost completely abolished in that genotype (Supplementary Fig. 2d ). ApoE is a reported target gene of LXRs, and whilst we did not detect expression of LXRs in our dataset, deletion of AMPKa1 resulted in decreases in the expression of a range of other known LXR target genes (Supplementary Fig. 2e ). Additionally, the expression of key nutrient transporters was enhanced in AMPKa1 KO macrophages, including those for system A þ N (SNAT1/2), system L (4F2, LAT1/2), cationic amino acids (CAT1/2), glucose (GLUT1), lactate (MCT4) and iron (NRAMP1) (Supplementary Fig.
ACKNOWLEDGEMENTS GR acknowledges research grants from  Academy of Medical Sciences   NIF\R5A\0017 ,  British Heart Foundation   PG/18/79/34106  and  Diabetes UK 19/ 0006045 ,  Medical Research Council   UK grants   MR/K012924/1and MR/X013057/1 , PhD scholarships supporting  Magdalena Sovakova  ( Medical Research Council UK ) and  Noor Alqurashi  ( Saudi Arabian Cultural Bureau ).
Western Blot Analysis Protein lysates were obtained from intervening muscle sections collected during cryosection using the Precellys 24 (Bertin Instruments, Montigny le Bretonneux, France) in RIPA buffer (ThermoFisher Scientific, Rockford, IL, USA) complemented with SDS powder (5% final) (Bio-Rad, Marnes-la-coquette, France) and protease inhibitor cocktail (ThermoFisher Scientific, Rockford, IL, USA). An amount of 25 µg of protein were loaded onto NuPAGE 3-8% Tris-Acetate Protein gels (Invitrogen, Carlsbad, CA, USA), following manufacturer instructions. Dystrophin protein was detected with NCL-DYS1 primary monoclonal antibody (NCL-DYS1; Novocastra, Newcastle, UK, dilution 1/200), as prevsiously described  [6]. Quantification was performed using the Empiria Studio software (Li-Cor, Bad Homburg, Germany) based on a standard curve made from pooled lysates from C57BL10 (WT) and mdx control for each tissue.
Immunohistochemistry Analysis Sections of 10 µm at 120 µm intervals were cut from triceps, diaphragm, and heart and examined for dystrophin expression using a rabbit polyclonal antibody Dystrophin (dilution 1:500; cat. number RB-9024-P ThermoScientific, Fremont, CA, USA), which was then detected by goat anti-rabbit IgGs Alexa Fluor 488 (dilution 1/500; A11070, Invitrogen, Eugene, OR, USA). Images were cropped, and scale bars of 100 µm were added using ImageJ software.
Statistical Analysis All in vivo data were analyzed with the GraphPad Prism8 software (San Diego, CA, USA) and expressed as means ± S. E. M. The "n" refers to the number of mice per group. Group comparisons were performed using one and two-way analyses of variance (ANOVA) with repeated-measure comparisons when needed (effects in different muscle tissues for example), followed by post-hoc Dunnett's or Sidak's multiple comparisons when appropriate. The Kruskal-Wallis test was used to compare groups that do not follow a normal distribution (assessed with the Shapiro-Wilk test). Significance levels were set at * p < 0. 05, ** p < 0. 01, *** p < 0. 001, **** p < 0. 0001.
Diffractograms were obtained with a Bruker D8 ENDEAVOR diffractometer, with CuKα radiation and LYNXEYE XE-T detector, under 40 kV and 25 mA intensity. Mineral identification was based on the COD ("Crystallography Open Database") and BGMN databases. Mineral abundances were estimated by Rietveld refinement analysis implemented in the Profex software. Solid sulfide concentrations were determined on dry bulk sediments from Lake Alberca after a wet chemical extraction using a boiling acidic Cr(II) solution, as detailed in  Gröger et al. (2009).
Acknowledgements. The authors thank  Anne-Lise Santoni ,  Elodie Cognard ,  Théophile Cocquerez , and the  GISMO platform (Biogéosciences, Université Bourgogne Franche-Comté ,  UMR CNRS 6282 , France). We thank  Céline Liorzou  and  Bleuenn Guéguen  for the analyses at the  Pôle Spectrométrie Océan (Laboratoire Géo-Océan, Brest, France ) and  Laure Cordier  for ion chromatography analyses at  IPGP (France). We thank  Nelly Assayag  and  Pierre Cadeau  for their help on the AP 2003 at IPGP. We warmly thank  Carmela Chateau-Smith  for improving the syntax and clarity of the manuscript. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-18-CE02-0013-02 ). Review statement. This paper was edited by  Tina Treude  and reviewed by two anonymous referees.
Bayesian Methods Approaches to data analysis that provide a posterior probability distribution for some parameter (e. g. treatment effect), derived from the observed data and a prior probability distribution for the parameter. The posterior distribution is then used as the basis for statistical inference. Interim Analysis Any analysis intended to compare treatment arms with respect to efficacy or safety at any time prior to the formal completion of a trial. https://doi. org/10. 1371/journal. pone. 0281674. t001 incorporating new investigational treatments into the ongoing trial and possibilities for collaboration between different consortia/sponsors. In 2018, the Innovative Medicines Initiative (IMI) put forth a call for proposals for the development of integrated research platforms to conduct platform trials to enable more patient-centric drug development.
gov and the EU clinical trials register (https://www. clinicaltrialsregister. eu). To facilitate and accelerate the identification of the most effective and promising novel treatment options for trial participants with NASH, multiple potential novel therapies, as well as combinations of novel mechanisms of action, will need to be evaluated in well-designed early clinical studies before advancing to pivotal phase 3 programs. From a platform study perspective, Phase 2b is often the preferred trial design as it generally offers a robust pipeline for most indications and the ability to make decisions more rapidly before committing to longer, more costly development. This is particularly true for NASH where there is an abundance of compounds in early development and phase 3 programs tend to run over several years.
While there is a lot of controversy regarding the use of non-concurrent controls  [26] , sharing only information on trial participants that could have been randomized to the arm under investigation seems uncontroversial (note that this requires data to be concurrent). As noted before, platform trials can run perpetually without limiting the number of drugs going into the trial. Any potentially successful compound in a NASH phase 2b trial would have to show either resolution of NASH without worsening of fibrosis (binary endpoint 1) and/or 1-stage fibrosis improvement without worsening of NASH (binary endpoint 2). Endpoints 1 and 2 are correlated binary endpoints and clinical studies have demonstrated a strong link between histologic resolution of steatohepatitis with improvement in fibrosis [27, After an initial inclusion of two cohorts consisting of control (usually the standard-of-care, "SOC") and "regimen" arm (which could be a monotherapy or a combination therapy), more cohorts of the same structure are entering the trial over time.
Sample sizes reflect number of trial participants with complete observations (i. e. paired biopsies) by the time of final analysis. After this number of trial participants were enrolled in a given treatment arm, enrollment to this treatment arm stops. Simulations of this trial design were performed using the cats package, which is downloadable on Github (https://github. com/el-meyer/cats) and CRAN (https://cloud. r-project. org/ web/packages/cats/index. html) and validated using the simple package (https://github. com/elmeyer/simple). For each of the distinct combinations of simulation parameters the platform trial was simulated 10000 times. Results of those 10000 simulated platform trial trajectories Table  3. Specification of important simulation parameters. Values are either fixed or varied in different simulation scenarios.
To conclude, we have found, based on our assumptions, that a NASH phase 2b platform study design employing Bayesian decision rules can demonstrate some time efficiencies when the effect sizes are large for either primary (histologic) endpoint, which is consistent with accelerating the development of transformational therapies. These time efficiencies would be in addition to those offered by a platform study in terms of accelerating start-up activities thereby creating a favorable proposition for drug developers, especially small biotechs. It is possible that once short-term (i. e. , 12-24 week) biomarkers (alone or in combination) have sufficient data to predict effiacy for histological and/or clinical outcomes, the use of a platform design may become even more powerful using a phase 2a/2b seamless design, which could offer not just time savings but reduced sample size as well.
Acknowledgments The authors are grateful to the NASH EU-PEARL investigators not included as authors for their work. The NASH EU-PEARL investigators are  Nicholas DiProspero ,  Vlad Ratziu ,  Juan M. Pericàs ,  Mette Skalshøj Kjaer ,  Quentin M. Anstee ,  Frank Tacke ,  Peter Mesenbrink ,  Jesus Rivera-Esteban ,  Franz Koenig ,  Elena Sena ,  Ramiro Manzano-Nunez ,  Joan Genescà ,  Raluca Pais ,  Leila Kara ,  Elias Laurin Meyer ,  Anna Duca ,  Timothy Kline ,  Anders Aaes-Jørgensen ,  Tania Balthaus ,  Natalie de Preville ,  Lingjiao Zhang ,  George Capuano ,  Salvatore Morello ,  Tobias Mielke ,  Sabina Hernandez Penna , and  Martin Posch. Juan M. Pericàs  led the NASH EU-PEARL investigators: juanmanuel. pericas@vallhebron. cat.
Funding:  EU-PEARL  has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  853966-2. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA and CHILDREN'S TUMOR FOUNDATION, GLOBAL ALLIANCE FOR TB DRUG DEVELOPMENT NON PROFIT ORGANISATION, SPRINGWORKS THERAPEUTICS INC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This technique includes statistical shape modelling (SSM), a method to represent the shape probability distribution by a mean shape and modes describing the shape variations  [25]. Principal component analysis (PCA) is usually the unsupervised technique used to extract the linearly independent components describing the variation of the shape in a population. PCA requires datasets containing the same number of points. Therefore, when working with computational grids, iso-topological meshes (i. e. having the same number of nodes and the same connectivity) are required. To this end, radial basis function (RBF) mesh morphing can be used to adapt a reference mesh to a new patient's anatomy  [26, 27]. SSM has been widely used in literature for many medical purposes  [28] [29] [30] [31]. A shape-based framework to identify diseased or healthy anatomical structures was described by Durrleman et al.
[39]. Liang et al. [40]  used regression methods combined with machine learning models to investigate the relationship between shape features of the thoracic aorta and rupture risk previously derived from numerical simulation. Meyrignac et al. [41]  combined abdominal aortic lumen volume and parameters derived from numerical simulation such as wall shear stress (WSS) with regression models in order to predict the abdominal aneurysm growth. In this paper, using the geometric decomposition techniques presented in our previous work  [42] , we first derive a set of local shape features using a more extensive dataset than the one previously exploited. After, we perform statistical shape analysis to extract global shape features from principal component analysis and partial least squares. Local shape features are geometric measurements of specific segments or curves related to the aneurysm domain whereas global shape features describe the overall shape and dimension of the aneurysm and are extracted using all information related to the studied population.
Materials and methods The principal purpose of this paper is to compare the prediction performances of regression techniques applied to assess the ascending aortic aneurysm growth rate through local or global shape features. The workflow, shown in Fig. 1 , consists of the following steps: (1) selection of a longitudinal dataset consisting in patients with ascending aortic aneurysm; (2) image segmentation to extract 3D geometries; (3) geometric decomposition of the aortic domain, (4) determination of local shape features; (5) computation of the aneurysm growth rate; (6) mesh morphing application to obtain iso-topological surface meshes; (7) implementation of statistical shape analysis based on unsupervised principal components analysis to extract global shape features due to the geometrical variability; (8) execution of statistical shape analysis based on supervised partial least squares to extract global shape features related to the growth of the aorta; (9) assessment of the relationship between local-global shape features and growth rate through regression models.
Dataset We included patients affected by ascending aortic aneurysm with two 3D exams separated by at least 6 months. The dataset was composed of subjects with both bicuspid and tricuspid aortic valve and was provided by three French centres: the University Hospital of Rennes, the University Hospital of Dijon and the University Hospital of Toulouse. The ethical standards were respected in performing this study. Images from both CT and MRI-Angiography were used, with a maximum voxel size of 1 mm  1 mm  1 mm. To increase the likelihood of producing reliable and reproducible results, additional exclusion criteria were set: (1) patients under 25 years of age, (2) aneurysms related to systemic inflammatory diseases, (3) prior aortic valve surgery, (4) acute aortic syndrome, (5) congenital tissue disorders such as Marfan syndrome and (6) images damaged by artefacts.
In total,  = 70 patients were included, 50 of whom had already been used in our previous work related to the identification of local shape features  [42]. 47 (67. 1%) patients had double ECG-gated acquisitions. The longitudinal dataset consisted of 120 (85. 7%) CT-Scans and 20 (14. 3%) MRI-Angiographies. 85 acquisitions (60. 7%) were performed with contrast agent injection and 35 (39. 3%) without.
Growth rate assessment Although the diameter threshold for elective surgery may not have been met, patients experiencing rapid AsAA growth should be carefully and frequently monitored for preventive surgical purposes  [47]. For these reasons, we can deduce that the risk of aneurysm rupture is closely related to the risk of aneurysm growth  [48]. By using longitudinal data and naming  ′  the diameter  related to the first acquisition of the -patient and  ′′  the maximum diameter of the model from the follow-up exam, we derived the aneurysm growth rate GR  by dividing the difference in maximum diameters by the time gap   , measured in months, between the two acquisitions: GR 𝑖 = 𝐷 ′′ 𝑖 -𝐷 ′ 𝑖 𝛥𝜏 𝑖 (4) All the growth rates were computed and stored in the vector Y: 𝐘 = ( GR 1 , GR 2 , … , GR 𝑁 ) ∈ R 𝑁 (5) The study about the correlation between the local shape features here proposed and the aneurysm growth rate has already been performed in a previous work  [42].
Thus, equation (  7 ) can be written as:  (10)  and simplifying: 𝐂 = 1 𝑁 -1 ( 𝐔𝐒𝐕 𝑇 ) ( 𝐔𝐒𝐕 𝑇 ) 𝑇 𝐂 = 1 𝑁 -1 𝐔𝐒 2 𝐔 𝑇 (11) This demonstrates that the singular values of the data matrix are related to the eigenvalues of the covariance matrix: 𝜆 𝑗 = 1 𝑁 -1 𝑠 2 𝑗 ( 12 ) Once the template or mean shape  has been extracted, each patient shape x belonging to the dataset can be reconstructed using the first  shape modes: x𝒊 (𝒘) = x + 𝝓𝒘 ( 13 ) where  is the vector containing the shape feature weights for the i-patient which can be derived from: 𝒘 = 𝝓 𝑇 (𝒙 𝒊 -x) (14) Assuming the data follows a normal distribution, each feature weight   is conventionally bounded within a certain range of the standard deviation: -𝜉 𝑙𝑖𝑚 √ 𝜆 𝑗 ≤ 𝑤 𝑗 ≤ 𝜉 𝑙𝑖𝑚 √ 𝜆 𝑗 ( 15 ) where   is usually assumed equal to 3. In other words,   is a scalar value providing the geometrical influence of each shape mode on the final deformed model.
Only the first  of the  eigenvectors were selected to account for a predetermined percentage of the variance and synthetically represent each aortic shape in the dataset. can be chosen by computing the compactness (CN) and finding the number of shape modes for which the variance curve reaches 80%, 90%, 95% or 99%. CN is defined as the sum of variances normalized by the whole cumulative variance: CN(𝑀) = ∑ 𝑀 𝑗=1 𝜆 𝑗 ∑ 𝑁 𝑗=1 𝜆 𝑗 ( 16 ) The CN curve shows how many PCA modes are needed to describe a certain amount of variation in the dataset. A second parameter to assess the SSM quality is the generalization (GE). It is used to estimate the SSM capability to represent unseen data and is computed as the average sum of square errors of a leave-one-out (LOO) procedure  [54]. Each time, in fact, one patient is excluded and a new statistical shape model is built using the  -1 remaining ascending aortic shapes.
Results The population mean age at the baseline acquisition was 62. 7 ± 15. 5 years. 21 women (30%) and 49 men (70%) were included in the dataset. The mean time between two consecutive acquisitions was 18 ± 16 months, with a range between 6 and 98 months. At the baseline, the maximum diameter was 49. 4 ± 4. 1 mm whereas at the follow-up it was 51. 9 ± 4. 6 mm. The full dataset presented a median growth rate of 0. 118 mm/month with an interquartile range IQR = 0. 133 mm/month. The local shape features reported in terms of mean value and standard deviation were:  = 2. 336 ± 0. 323,  = 0. 496 ± 0. 052 and  = 1. 213 ± 0. 072. Mesh morphing was applied on all geometries, ensuring a correct correspondence between the pseudo-landmarks computed on the template model and the pseudo-landmarks identified on the target geometry after the first step and a perfect matching after the second.
The higher frequency of male patients agrees with what is observed for the aneurysm disease  [60]. The obtained growth rate results are consistent with what has been reported in literature  [61, 62]. Concerning the initial grid, we chose the patient's model reporting the median diameter to reduce the mesh degradation after mesh morphing  [63]. This starting model is a reasonable compromise to reach the aneurysms with the smallest and largest diameter in the dataset. Like the maximally inscribed sphere method, the geometric decomposition allowed to derive the maximum diameter along the vessel centerline but it additionally ensured the possibility of identifying sections perpendicular to the centerline which enable the creation of the splines. The number of pseudo-landmarks acting as Source Points to perform the first morphing step was chosen in order to achieve good results in terms of computational grid quality  [51].
A fundamental requirement for building accurate statistical shape models is the oneto-one correspondence between the positions of the landmarks on each geometry of the dataset. Since it is very complex to identify landmarks for the isolated ascending tract, the proposed morphing method allows associating points of the splines resulting from the geometric decomposition of the initial template to the same ones computed on the target aorta. This ensured a better control of the grid distortion than using purely distance-based methods in which iterative energies stabilization methods and recursive smoothing techniques, i. e. not driven by statistical information, are usually performed  [23]. The SSA was carried out on the ascending tract and not on the entire aorta because a detailed and restricted correspondence between ascending aortic aneurysm shape and growth rate was sought.
This demonstrated the valuable ability of the SSM to represent a wide population. The Ftest results using the PCA components give high importance to shape modes determining the aneurysm location, its size and the tortuosity of the ascending tract. This agrees with the correlation study between local shape features and growth reported in our previous work  [42]  and with the shape representation provided by the PLS modes. Only three global shape features were selected because it was the number for which the root mean square prediction error was the lowest. Using a different number of shape features for the regression, RMSE   went from 0. 089 mm/month to 0. 142 mm/month and RMSE   went from 0. 066 mm/month to 0. 121 mm/month. Based on this dataset, the value of R 2  and RMSE  and the representation of real versus predicted response values for local shape features in Fig.
6  indicate that the SVM regression method is highly inaccurate in predicting the growth rate, in particular for patients whose growth is very rapid, especially if the prediction error is compared to the median GR of the dataset. Results improve when using global shape features. The RMSE was in fact lowered using SVM with a combination of PCA-based shape features and was further reduced by approximately 56% from RMSE  using PLS regression. Better results when using global shape features compared to local shape features were already reported by Liang et al. [40]  for the classification of patients whose aneurysms might burst according to numerical simulation results. In addition, it is worth observing that partial dependencies plots (Fig. 7 ) for local shape features highlight a major dependence on  than  and even more than  , an aspect already emerged in our previous work  [42] , in which the linear correlation between local shape features and growth rate using the reduced dataset was stronger for the first index.