dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS The continued support of the staff of the  Tandem-Alto  facility as well as the target laboratory staff is gratefully acknowledged. We thank  P. Descouvemont  and  N. Keeley  for extremely valuable discussions concerning shellmodel configurations and the link with the cluster model used in DWBA. We thank  Chris Fryer  and  Samuel Jones  for providing the trajectories used for the astrophysical simulations. AML acknowledges the support of the  Science and Technology Facilities Council (STFC  Consolidated Grant  ST/P003885/1 ). MP and TL acknowledges significant support to NuGrid from  NSF  grant  PHY-1430152  ( JINA Center  for the Evolution of the Elements) and  STFC  (through the  University of Hull 's Consolidated Grant  ST/R000840/1 ), and access to viper, the  University of Hull High Performance Computing Facility.
1A ). Moreover, all variations were rare or even private as for two HCM patients (Fig. 1A ), meaning they all have a minor allele frequency of less than 0. 01 or zero, based on 71,702 genomes from unrelated individuals of the Genome Aggregation Database (gnomAD, https://gnomad. broadinstitute. org/) (Karczewski et al. , 2020). Of note, almost all candidate mutations found in HCM patients in other studies occur at a very low frequency and ∼50% are found in a single proband or family  (Alfares et al. , 2015). In addition to their very low frequency, the majority of the variations are predicted to be damaging based on PolyPhen2  (Adzhubei et al. , 2013 , http://genetics. bwh. harvard. edu/pph2/), SIFT  (Vaser et al. , 2016 , https://sift. bii. a-star. edu. sg/) and MutationTaster  (Schwarz et al.
However, some of these only share a common domain architecture, with orthology being less evident, such as TTN with a large number of FN3 and Ig-like domains similar to the fly gene sallimus (sls), but also to bent (bt). In the case of the myomesin family, several genes with similar domain structure comprising FN3 and Ig-like domains have been reported [DIOPT database (www. flyrnai. org/cgi-bin/ DRSC_orthologs. pl), Hu et al. , 2011] , but only a single fly gene, CG14964, showed a number and an arrangement of domains close to MYOM1, MYOM2 and MYOM3 (Fig. S3A ). Of note, CG14964 is also similar to human myosin binding proteins H (MYBPH and MYBPHL), as well as myosin binding proteins C (MYBPC1, MYBPC2 and MYBPC3; Fig. S4 ). We suggest that in Drosophila, CG14964 inhabits the functional space that is occupied by several human sarcomere genes, including MYOM2 (Fig.
DISCUSSION Myofibrils mediate skeletal and cardiac muscle contraction in vertebrates and invertebrates. Their basic unit is the sarcomere with two transverse structures, the Z-disk and the M-band, which anchor thin (actin) and thick (myosin) filaments to an elastic system  (Lange et al. , 2020). Therefore, alterations of sarcomere proteins influence the contractile performance of the heart and skeletal muscle  (Hamdani et al. , 2007). In this study, we found rare deleterious genetic variations in the MYOM2 gene of HCM and TOF patients, a   Farley et al. , 2010)  and other studies  (Blandin et al. , 2013; Bruléet al. , 2010; Hornemann et al. , 2003; Obermann et al. , 1998). Drosophila orthologs are based on the DIOPT database  (Hu et al. , 2011). (B) Expression of CG14964 in adult fly hearts (arrows/arrowheads mark perinuclear space with CG14964 (red) and GAPDH1/2 (green) transcript localization).
Rare probable disease-causing mutations in HCM genes were identified neither in the 13 patients nor in the MYOM2 mutation carriers. Furthermore, mutations in MYOM1 were also excluded in these four patients. MYOM2 screening was performed using Sanger sequencing. Briefly, the 36 coding exons of MYOM2 were PCR-amplified using flanking intronic primers and directly sequenced using ABI Big Dye Terminator chemistry. The variants that were found were annotated according to the cDNA and protein reference sequence (Genbank ID NM_003970. 3; Ensembl ID, ENST00000262113. 8; and UniProtKB/Swiss-Prot P54296). The significance of the variants was further analyzed in a first step by considering the nature and location of the change, and its frequency found in large population-based datasets [such as from the Exome Aggregation Consortium  (Lek et al.
, 2016)  and the Genome Aggregation Database  (Karczewski et al. , 2020) ] and our own controls [minor allele frequency below 0. 02% as recommended by Burke et al. for cardiomyopathies  (Burke et al. , 2016) ]. In a second step, the conservation of the affected amino acid and the possible functional impact of the variants [using mutation prediction tools, such as PolyPhen2  (Adzhubei et al. , 2013) , SIFT  (Vaser et al. , 2016)  and MutationTaster  (Schwarz et al. , 2010) ] were assessed.
Clinical courses of HCM patients with MYOM2 mutations and control heart tissue All four unrelated non-familiar cases were characterized by LV hypertrophy and electrocardiogram abnormalities (see detailed clinical data in Table  S1 ). They showed a symptomatic form of HOCM which led to an invasive septal reduction intervention in two of them. All patients were of European (German) ancestry. The female patient HCM-01 (carrier of mutation p. Met270Thr) presented with symptoms such as angina pectoris, dyspnea on exertion, dizziness, and palpitations at the age of 50 years [New York Heart Association (NYHA) functional class II]. Onset of disease was at the age of 31 years. She was found to have hypertrophic cardiomyopathy with outflow tract (OFT) obstruction (interventricular pressure gradient at rest of 20 mmHg, measured during cardiac catheterization).
Acknowledgements We thank  Sean Lal  and  Amy Li  ( Anatomy and Histology, School of Medical Sciences, Bosch Institute, University of Sydney, Australia ) for providing control heart tissue; and  Ante Radocaj  and  Birgit Piep  ( Medical School of Hannover, Institute of Molecular and Cell Physiology, Hannover, Germany ), as well as  Sandra Schochardt-Schuster  and  Kerstin Mika  ( Cardiovascular Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany ), for excellent technical assistance. We are deeply grateful to the patients and families for their cooperation.
The investigated demographic and clinical background data available from SFNC were documented in Table  1. In the schizophrenia group, sections of 10 individuals were taken from the left and seven from the right hemisphere. In the BD group eight were from the left and five from the right ACC, and the controls included 10 from the left and seven from the right side. Six of the 13 non-suicide schizophrenia individuals died from cardiac failure, two from overdose, two from pneumonia, one from pulmonary embolism, one from cirrhosis, and one from acute pancreatitis. Three of the eight non-suicide BD subjects died from cardiac failure, three from overdose, one from myocarditis and one from drowning. Finally, four of the schizophrenic (two hanging, two overdose) and five of the BD patients (three hanging, two overdose) had committed suicide.
Therefore, the antibody is an effective microglia/macrophages marker in human brain tissue  [27]. All sections with incomplete or insufficient immunostaining were excluded from the study. To avoid any mistake in the recognition of the microglial somata, counterstaining was omitted. Archived cresyl violet-stained coronal section series of the aMCC adjacent to the Iba1-immunostained were also obtained from the SFNC. These sections were taken as morphological reference for the identification of cortex (cortical grey matter) and medulla (cortical white matter). Photo documentation was performed using an Olympus Microscope BH-2 equipped with an Olympus DP-71 camera (Olympus Optical, Japan) and the computer-assisted software analysis Cell A (Soft imaging system GmbH, Münster, Germany). All data were exported as TIFF files into Adobe Photoshop CS5 (Adobe Imaging Systems Inc.
Cell counting Quantification of microglial cells was performed using the above mentioned Olympus Microscope BH-2, the Olympus DP-71 camera and the software analysis Cell A. For microglial counting three fields were defined for each Iba1-stained section as region of interest (ROI) localized in the areas a24a′, a24b′ and a24c′ (Fig. 1b ). Counting was performed under the microscope by focussing through the section level. Each counted field comprised the whole cortical thickness with the layers I-VI. The cortical layers were identified in the Iba1-stained sections by the very adjacent cresyl violet section (Fig. 1c ). The border between the cortical grey and white matter was identified by a drop of cellular density in the cresyl violet-stained enlarged section (Fig. 1d ). The grey matter contains numerous Nissl-stained neurons, the white matter is characterized by abundant small glial cells.
Statistical analyses Normal distribution of the data was confirmed by the Kolmogorov-Smirnov and the Shapiro-Wilk test. All results are shown as group means [± standard error of the mean (SEMs)]. Between-group differences were examined using univariate analysis of variance (ANOVA), followed by post hoc Tukey test (α < 0. 05) with a Bonferroni correction for multiple comparison (see Table  1 ). There was homogeneity of the error variances, as assessed by Levine's test (p > 0. 05). The statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS Inc. , Chicago, Illinois) for windows. p values < 0. 05 were considered to be statistically significant.
However, LSTM are not able to model very long sequences, and their training is hardware intensive. Owing to these shortcomings, LSTMs are being replaced by CNNs. The reason for the great results of CNN methods for time series analysis (1D data processing in general) is not well understood. One possibility may lie in the universal function approximation capacity of deep neural networks  [5, 6]  rather than its biological semblance. The research in this area is primarily led by its success rather than its understanding. An important point to mention is that the performance of CNN is largely driven by the availability of very large labeled datasets. This probably explains their tremendous success in facial recognition tasks. Google's FaceNet  [7]  and Facebook's DeepFace  [8]  architectures are trained on 400 million facial images, a significant proportion of world's population.
These companies are easily equipped with gigantic labeled facial images data as these are 'tagged' by their respective users. In the said problem, deep networks reach almost 100% accuracy, even surpassing human capabilities. However, when it comes to tasks that require expert labeling, such as facial recognition from sketches (requiring forensic expertise)  [8]  or ischemic attack detection from EEG (requiring medical expertise)  [9] , the accuracies become modest. Indeed, such tasks require expert labeling that is difficult to acquire, thus limiting the size of available labeled dataset. The same is believed by a number of machine learning researchers, including Hinton himself, are wary of supervised learning. In an interview with Axios,  1 Hinton mentioned his 'deep suspicion' on backpropagation, the workhorse behind all supervised deep neural networks.
For example, biomedical signals like ECG and EEG come from multiple leads; financial data from stocks are recorded with different inputs (open, close, low, high and net asset value), demand forecasting problems in smartgrids come with multiple types of data (power consumption, temperature, humidity, occupancy, etc. ). In all such cases, the final goal is to perform prediction/classification task from such multivariate time series. We propose to address such problem as one of feature fusion. The information from each of the sources will be processed by the proposed deep CTL pipeline, and the generated deep features will be finally fused by an unsupervised fully connected layer. This is the second major contribution of this work -an unsupervised fusion framework with deep CTL. The resulting features can be used for different applicative tasks.
CNN for Time Series Analysis Let us briefly review and discuss CNN based methods for time series analysis. For a more detailed review, the interested reader can peruse  [22]. We mainly focus on studies on stock forecasting as it will be our use case for experimental validation. The traditional choice for processing time series with neural network is to adopt a recurrent neural network (RNN) architecture. Variants of RNN like long-short term memory (LSTM)  [38]  and gated recurrent unit (GRU)  [39]  have been proposed. However, due to the complexity of training such networks via backpropagation through time, they have been progressively replaced with 1D CNN  [11]. For example, in  [12] , a generic time series analysis framework was built based on LSTM, with assessed performance on the UCR time series classification datasets  [14].
The later study from the same group  [13] , based on 1D CNN, showed considerable improvement over the prior model on the same datasets. There are also several studies that convert 1D time series data into a matrix form so as to be able to use 2D CNNs  [15] [16] [17]. Each column of the matrix corresponds to a subset of the 1D series within a given time window and the resulting matrix is processed as an image. The 2D CNN model has been especially popular in stock forecasting. In  [17] , the said techniques have been used on stock prices for forecasting. A slightly different input is used in  [18] : instead of using the standard stock variables (open, close, high, low and NAV), it uses high frequency data for forecasting major points of inflection in the financial market. In another work  [19] , a similar approach is used for modeling Exchange Traded Fund (ETF).
Each of the five inputs is processed by a separate 1D processing pipeline. Each of the pipelines produces a flattened output (Fig. 1 ). The flattened outputs are then concatenated and fed into the Transform Learning layer acting as the fully connected layer (Fig. 2 ) for fusion. While our processing pipeline ends here (being unsupervised), the benchmark techniques are supervised and have an output node. The node is binary (buy / sell) for classification and real valued for regression. More precisely, we will compare with two state-of-the-art time series analysis models, namely TimeNet  [12]  and ConvTimeNet  [13]. In the former, the processing individual processing pipelines are based on LSTM and in the later they use 1D CNN. We make use of a real dataset from the National Stock Exchange (NSE) of India.
The dataset contains information of 150 symbols between 2014 and 2018; these stocks were chosen after filtering out stocks that had less than three years of data. The companies available in the dataset are from various sectors such as IT (e. g. , TCS, INFY), automobile (e. g. , HEROMOTOCO, TATAMOTORS), bank (e. g. , HDFCBANK, ICICIBANK), coal and petroleum (e. g. , OIL, ONGC), steel (e. g. , JSWSTEEL, TATASTEEL), construction (e. g. , ABIRLANUVO, ACC), public sector units (e. g. , POWERGRID, GAIL). The detailed architectures for each tested techniques, namely DeConFuse, ConvTimeNet and TimeNet are presented in the Table  2. For DeConFuse, TimeNet and ConvTimeNet, we have tuned the architectures to yield the best performance and have randomly initialized the weights for each stock's training.
More recently,  [8]  considered a harmonic oscillator involving another set of variables, including mean curvature. Revisiting the theory allows to show that the various approaches are equivalent in the limit of small perturbations but we aim at deriving a model valid with large deformations. We thus need to propose the proper set of kinetic and potential energies related to the droplet deformations and the proper variables in order to describe such physics. We propose to rely on a DNS of a single droplet performed with the code ARCHER and a post-processing library Mercur(v)e, which is designed for mean geometrical variable evaluation and analysis preserving topological invariants. The results of this simulation suggest a specific choice for both the constraints to be applied on the small scale dynamics and the small scale kinetic and potential energies defined as function of the proper variable, which is the surface variation compared to the spherical minimal surface of the inclusions.
The system of PDEs is eventually derived and involves both large scale and small scales dynamics as well as their coupling through convection and dissipation. The paper is organized as follows: we first briefly recall the main results obtained by an harmonic analysis of the motion of an ellipsoidal droplet performed in  [11]  in the limit of an infinitely small perturbation and extend the derivations to obtain analytical expressions for several geometric variables involved in the dynamics beyond the kinetic energy and the potential energy. We then introduce the DNS results obtained with the code ARCHER and post-processed by the library Mercur(v)e in order to estimate the evolution of geometric variables and associated potential energies. We identify the proper variable for the potential energy to remain harmonic in the large deformation regime, thus identifying the best candidate to use the SAP.
Two-phase Interface Model and Discretization Strategy of the code ARCHER The code ARCHER is developed at the CORIA laboratory. It has been successfully used for simulating the atomization of a liquid-jet under a realistic diesel injection configuration  [19]. The two-phase medium considered in ARCHER is composed by two incompressible fluids separated by a sharp interface. The position of the interface and the description of its geometrical characteristics is ensured by a Coupled Level Set/Volume of Fluid (CLSVOF) method: a Level Set function is used for evaluating the normal and the curvature of the interface while the Volume of Fluid approach ensures the mass conservation of each component. A key element of the numerical strategy implemented in ARCHER relies on solving a Poisson equation for the dynamic pressure that is performed using a MultiGrid preconditioned Conjugate Gradient algorithm (MGCG)  [20]  coupled with a Ghost-Fluid method  [21]  to take into account the pressure jump due to the presence of surface tension.
Conclusion In the present work, we have recalled and extended the analytical study of  [11]  within the framework of a spherical liquid droplet subject to an axisymmetric ellipsoidal deformation. We have in particular verified that the system is experiencing a harmonic oscillator behavior with identified frequency and potential damping and have shown that in the small perturbation limit, several candidate in terms of small scale variables can be envisioned since most of the variable experience a quasi-harmonic dynamics in that limit. In order to discriminate the proper variables, we have conducted a series of DNS configurations with variable deformation amplitudes relying on the code ARCHER for high-fidelity simulations. The evolution of the geometrical characteristics of the droplet surface have been analyzed thanks to a post-processing library Mercur(v)e introduced in  [18].
Acknowledgement The support of the  French Government Space Agency (CNES)  and the  French Aerospace Lab (ONERA)  through a PhD grant for  P. Cordesse  is gratefully acknowledged. We also thank the PhD grant of  Ruben Di Battista  from  École polytechnique  and the  Direction générale de l'Armement (DGA). Simulations have been successfully conducted using the  ARCHER code of CORIA laboratory.
Methods Using data from the Renal Epidemiology and Information Network registry, we estimated age-adjusted RRT incidence rates during 2010-2014 for the 282 townships of the area. A hierarchical Bayesian Poisson model was used to examine the association between incidence rates and 18 contextual variables describing population health status, socioeconomic level and health services characteristics. Relative risks (RRs) and 95% credible intervals (95% CrIs) for each variable were estimated for a 1-SD increase in incidence rate.
As chronic kidney disease (CKD) screening is a part of primary care and general practitioners (GPs) play a gatekeeping role for CKD patients, we considered the local potential accessibility (LPA) indicator  [18] , which measures real primary-care availability, taking into account both supply and demand in township municipalities. For geographic accessibility to secondary care, we used indicators of travel time to the closest nephrologist and dialysis unit. Travel times by car to reach the closest nephrologist or the closest dialysis unit (in minutes) were computed for each municipality of the study area by using a distance matrix based on the national French road dataset [BD TOPO 2015, French national geographical and forest information institute (IGN)], weighted by road classification, topology and demographic context such as population density and land use (INSEE and CORINE Land Cover inventory).
These calculations used the geolocated data of municipality town halls and nephrologist services or dialysis centers. Therefore, distance calculations assumed that inhabitants of a municipality were located at the municipality's town hall. We computed mean travel times for the inhabitants of each township, corresponding to the average travel time for all municipalities belonging to the township and weighted by the number of inhabitants of each municipality. Finally, we considered three indicators illustrating possible differences in clinical practices in dialysis used in previous French studies: the median estimated glomerular filtration rate (eGFR) at RRT start, the percentage of incident patients 85 years and older and the percentage of incident patients who died within 3 months  [4, 19].
Ethics statement This retrospective study was approved by the French Biomedicine Agency in 2015 and included patients' data that were deidentified directly in the REIN database before extraction for the analysis. The French REIN registry was approved by the French data protection agency [Commission Nationale de l'Informatique et des Libertés (CNIL)] in 2010. REIN is registered with the CNIL under the following number: 903188 Version 3.
Covariables whose 95% CrI for the RR did not include 1 were considered significantly associated with the risk in townships. Finally, stepwise regression was performed by forward selection, adding successively the variables giving the best improvement of the deviance information criterion (DIC)  [22]. In the final model, the DIC change after the deletion of spatial components was measured to assess the fraction of spatial variability embedded into retained contextual variables. Analyses were performed with SAS v9. 4 and the R packages DCluster  [23]  and R2Win-BUGS  [24]. The Bayesian model inference was estimated in WinBUGS  [25]  with Markov chain Monte Carlo (MCMC) methods. Vague distributions were chosen for prior distributions, and sensitivity analyses were conducted to assess the effect on parameter estimations.
Acknowledgments Membership of the Vigie Study Group:  Dr Carole Ayav  ( CHRU Nancy ),  Dr Abdelkeder Bemrah  ( Centre hospitalier Chalon/Saone ),  Dr Francois Chantrel  ( Centre hospitalier, Mulhouse ),  Pr Cecile Courivaud  ( CHU Besancon ),  Dr Maxime Desmarets  ( CHU Besancon ),  Pr Luc Frimat  ( CHRU Nancy ),  Dr Isabelle Kazes  ( CHU Reims ),  Pr Elisabeth Monnet  ( CHU Besancon , lead author). We thank all registry participants, especially the nephrologists and the professionals who collected the data and performed the quality control analysis. We are grateful to  Anne-Laure Parmentier ,  Berenger Martin ,  Guillaume Boiteux  and  Laura Smales  for their technical assistance.