dataesr/scientific-paragraphs-categorization huggingface dataset:
Examining data generated from normal ovarian tissue, the CNV deletion coincided with enhancer-specific histone modifications (acetylation of H3 lysine 27 (H3K27Ac) and mono-methylation of H3 lysine 4 (H3K4Me1)) and DNaseI hypersensitivity sites representative of open chromatin. By contrast, there was no evidence for these chromatin features in normal breast epithelial (HMEC) cells. Cross-reference to super-enhancers annotated in the study by Hnisz et al,  28  found the CNV deletion overlapped an enhancer, found in ovary tissue, predicted to affect the expression of EGLN2, located ~67 kb downstream of CYP2A7. Zarrei et al 7 recently published a Human CNV Map constructed from multiple studies in the Database of Genomic Variants by applying a clustering algorithm to define ~27 000 CNV regions with high stringency.
However, validation of 29 predicted CNVs throughout the genome confirmed o30% of predicted deletions, highlighting a large number of false variant calls. None of the nine rare variants (o1% allele frequency) chosen for validation was verified by qPCR or Nanostring. However, CNV calling correctly predicted 40% (8/20) deletions we tested which ranged in allele frequency from 1. 2 (OR2A1 locus) to 8. 2% (ZNF385B locus). These results confirm other published reports that indicate array-based CNV data can be unreliable without further validation using ancillary technologies, such as qPCR. 25 The accuracy may be increased by employing more stringent criteria but likely at the expense of detection sensitivity. For example, a larger number of probe markers could be used to generate a CNV call, but this approach will also reduce the spatial resolution of the array and sensitivity.
ACKNOWLEDGEMENTS LCW is funded by an  HRC   Sir Charles Hercus Health Research Fellowship. ABS is funded by an  NHMRC   Senior Research Fellowship. TO'M is funded by an  NHMRC   CJ Martin Early Career Fellowship. BCFR: This work was supported by grant  UM1 CA164920  from the  USA National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the  National Cancer Institute  or any of the collaborating centers in the  Breast Cancer Family Registry (BCFR) , nor does mention of trade names, commercial products or organizations imply endorsement by the  USA Government  or the BCFR. CIMBA: We wish to thank the patients and families, physicians, genetic counselors and research nurses involved in the CIMBA studies. The CIMBA data management and data analysis were supported by  CRUK  grants  C12292/ A11174  and  C1287/A10118.
AKG was funded by  5U01CA113916 ,  R01CA140323  and by the  Chancellors Distinguished Chair in Biomedical Sciences Professorship. GC-HBOC: The  German Consortium of Hereditary Breast and Ovarian Cancer (GC-HBOC)  is supported by the  German Cancer Aid  (grant no  110837 ,  Rita K Schmutzler ). GEMO: Genetic Modifiers of Cancer Risk in BRCA1/2 Mutation Carriers (GEMO) study: National Cancer Genetics Network «UNICANCER Genetic Group », France. We wish to pay a tribute to  Olga M Sinilnikova , who with  Dominique Stoppa-Lyonnet  initiated and coordinated GEMO until she sadly passed away on 30 June 2014, and to thank all the  GEMO collaborating groups  for their contribution to this study. GEMO Collaborating Centers are: Coordinating Centers,  Unité Mixte de Génétique Constitutionnelle des Cancers Fréquents, Hospices Civils de Lyon -Centre Léon Bérard  and  Equipe «Génétique  du cancer du sein»;  Centre de Recherche en Cancérologie de Lyon :  Olga Sinilnikova ,  Sylvie Mazoyer ,  Francesca Damiola ,  Laure Barjhoux ,  Carole Verny-Pierre ,  Mélanie Léone ,  Nadia Boutry-Kryza ,  Alain Calender  and  Sophie Giraud ; and  Service de Génétique Oncologique, Institut Curie, Paris :  Dominique Stoppa-Lyonnet ,  Marion Gauthier-Villars ,  Bruno Buecher ,  Claude Houdayer ,  Etienne Rouleau ,  Lisa Golmard ,  Agnès Collet ,  Virginie Moncoutier ,  Muriel Belotti ,  Antoine de Pauw ,  Camille Elan ,  Catherine Nogues ,  Emmanuelle Fourme  and  Anne-Marie Birot.
SWE-BRCA collaborators are supported by the  Swedish Cancer Society. UCSF: We would like to thank  Dr Robert Nussbaum  and the following genetic counselors for participant recruitment:  Beth Crawford ,  Kate Loranger ,  Julie Mak ,  Nicola Stewart ,  Robin Lee ,  Amie Blanco  and  Peggy Conrad. And thanks to  Ms Salina Chan  for her data management. Funding was provided by the  UCSF Cancer Risk Program and Helen Diller Family Comprehensive Cancer Center. UPENN:  National Institutes of Health (NIH ;  R01-CA102776  and  R01-CA083855 ;  Breast Cancer Research Foundation ;  Susan G. Komen Foundation  for the cure,  Basser Research Center for BRCA.
). For example, the number of application available in Google play store is 1. 600. 000 and the number of applications in windows phone store is 340. 000. As mobile technologies became widespread, users will be more motivated to use more portable devices and interacting with mobile applications. Indeed, 3600 million of users are subscribed in mobile services since 2014  21. A mobile user interface (MUI) is the graphical and usually touch sensitive display on a mobile device, such as a smartphone or tablet that allows the user to interact with the device's apps, features, content and functions. User interface level represent 50% of software code which proves the importance of this level in the correctness and the effectiveness of the application  16, 18. Hellmann  8  reported that MUI-related defects have a significant impact on the end users of system.
Subjects The evaluation of the mobile user interfaces of the studied project should improve the quality of his application and enhance the satisfaction of their users. As shown in Table  2 , our work involved 20 subjects who have different age, motivation, level of education, experience and interest and using different mobile devices. All the subjects are volunteers and they have several profiles and preferences. In fact, 60% of participants were graduated (high education level), 15% have A-level (medium education level), and 25% have middle school level (low education level). The subjects having a high experience were 20%, 50% having medium experience and 30% having low experience. The data that were collected about the subjects show that 55% of participants reported having high motivation level, where 30% having medium.
By measuring oculomotor behavior while the viewer watches images, natural scenes or movies, eye tracking technology becomes a powerful tool for developing a better understanding of what the viewer's perceptual strategies are and how he / she gathers information. However, eye tracking can generate large quantities of data which need to be processed and analyzed in order to identify and extract the most relevant information regarding the viewer's oculomotor behavior. Processing of eye tracking data notably entails data exploration, organization and visualization, in order, for instance, to prepare datasets for statistical analysis and / or to communicate information to a non-expert public. In this paper, we present a visualization tool designed to analyze and visualize eye tracking data from experiments with animated characters.
Our visualization system In the following section, we first present the features of our system and then we detail how it was designed. The subsequent sections will present two use cases based on 2D and 3D media sources, respectively. The main characteristics of our visualization system are: First, it is dedicated to the visualization, aggregation and comparison of eye tracking gaze data for one or several viewers who can be split into different groups. Second, unlike most software, it is independent of both the hardware used to collect the eye movement data and of the application used to generate the visual stimuli. Third, it can be easily used by both experts and non-experts in oculomotor research. Input data. With our system, eye tracking data can be collected both with video or 3D media sources.
The user has to provide two types of information as input to our visualization system: the experimental material which is common to all viewers, and the recorded eye movement data of each individual viewer. This information has to be provided in XML format (see the example given in the supplementary material). Experimental material. The experimental material consists of the viewed material and a list of media sources descriptions (i. e. , metadata) used for the experiment. In our case, the viewed media sources represent movements of real characters in videos and virtual characters in 3D. All viewed media sources need to be semantically equivalent but they do not need to have the same kinematic features. The information describing a media source can be divided into three categories:  Basic information.
Fixation calculation. From the list of samples related to each capture, only fixations are used to compute visualization information. As described by  (Salvucci & Goldberg, 2000) , we consider a fixation as 'a pause over informative regions of interest' which lasts >100ms and has a spatial dispersion of gaze points with a threshold set at 1°. To extract this information from the list of samples, we use the Dispersion-Threshold Identification (I-DT) algorithm  (Stark & Ellis, 1981; Widdel, 1984). Then, from the mapping between eye coordinates and intersected body parts, we compute a new fixation-joint mapping which corresponds to the fixations which intersect an AOI area on the character. From this new mapping, we obtain a fixation time T(j) for each joint. Note that if we calculate the sum T = sum_{j in K} T(j), T is not equal to the global fixation duration of S since we exclude eye tracking coordinates that are too far away from the skeleton as well as data recorded during saccadic eye movements.
Experiment The steps usually involved in the 'production' of a motion picture in 3D format and its use in a virtual reality setup are illustrated in Figure  5. As shown, several preprocessing steps are required. The preparation of the 3D scene is decomposed into three steps: the capture of the actors (1. (a)), the creation of an animated skeleton (1. (b)) and the creation and binding of a mesh to this skeleton: 1. (d). To capture the gestures of the actors, we used the Optitrack system with 12 infrared cameras rated at 240 Hz. The actors were dressed in a suit equipped with 49 markers. The animation of the skeleton (composed of 51 joints) was automatically computed by the Motive software. To create a mesh and bind it to the animated skeleton, we used the Mixamo and MotionBuilder softwares, respectively.
Conclusion and discussion In this paper, we presented an interactive 3D tool, based on AOI data, that allows for spatio-temporal investigation of large data sets of recorded eye movements. In particular, this tool allows the user to manage and analyze the gaze pattern of several viewers on several animated sequences. This tool is independent of the hardware used to record the eye movements and of the application used to generate the visual scene / experimental stimuli, making it more flexible for general usage. Specifically, we presented a new approach to visualize the oculomotor behavior of viewers watching the movements of animated characters in dynamic sequences. This approach allows to illustrate the gaze distribution of one or several viewers, i. e. , the time spent on each part of the body on a 'heat mesh'.
2013)  or offline  (Tandeo et al. 2015b). Dealing with such situations may not be straightforward using classical model-driven assimilation schemes. Meanwhile, recent years have witnessed a proliferation of satellite data, in situ monitoring, as well as numerical simulations. Large databases of valuable information have been collected and offer a major opportunity for oceanic, atmospheric, and climate sciences. As pioneered by  Lorenz (1969) , the availability of such datasets advocates for the development of analog forecasting strategies, which make use of ''similar'' states of the dynamical system of interest to generate realistic forecasts. Analog forecasting strategies have become more and more popular in oceanic and atmospheric sciences  (Nagarajan et al. 2015; McDermott and Wikle 2016) , and have benefited from recent advances in machine learning  (Zhao and Giannakis 2014).
They have been applied to a variety of systems and application domains, including among others, rainfall nowcasting  (Atencia and Zawadzki 2015) , air quality analysis  (Delle Monache et al. 2014) , wind field downscaling  (He-Guelton et al. 2015) , climate reconstruction  (Schenk and Zorita 2012) , and stochastic weather generators  (Yiou 2014). In this work, we examine the extension of the analog forecasting paradigm for data assimilation issues. Given a representative dataset of the dynamics of the system, this extension that we call analog data assimilation (AnDA) consists of a combination of the implicit analog forecasting of the dynamics with stochastic filtering schemes, namely, ensemble Kalman and particle filtering schemes  (Evensen and Van Leeuwen 2000). This idea was first introduced in  Tandeo et al.
Our contributions are threefold. First, we present and examine various analog forecasting strategies, including locally linear ones that were not considered in previous works, and evaluate their performance for analog data assimilation. Second, in addition to the ensemble Kalman algorithms, we propose and examine a novel implementation of the analog forecasting combined with a particle filter. Finally, in the online supplemental material, we provide a unified computational framework, through both a Matlab Toolbox and a Python Library, to pave the way for practical use and future research (https://github. com/ptandeo/AnDA). The work is organized as follows. In section 2, we briefly present the general concepts of data assimilation and introduce the key ideas of analog data assimilation. Different analog forecasting strategies are introduced in section 3.
b. Data-driven data assimilation The proposed assimilation framework relies on a similar state-space formulation. The key feature is to substitute the explicit dynamical model M in (1) by a ''data driven'' dynamical model involving an analog forecasting operator, denoted by A, namely, x(t) 5 A[x(t 2 1), h(t)]. (3) Henceforth, this state-space model will be referred to as AnDA. A sequential and stochastic data assimilation scheme including filtering and smoothing, is used involving different Monte Carlo realizations of the state at each assimilation time. We sketch the proposed AnDA methodology for one realization in Fig. 1. The analog forecasting operator A requires the existence of a representative dataset of exemplars of the considered dynamics. This dataset is referred to as the catalog and denoted by C.
The reference catalog is formed by pairs of consecutive state vectors, separated by the same time lag. The second component of each pair is referred to as the successor of the first component hereafter. The catalog may be issued from observational data as well as from numerical simulations. In the last case, one can have a catalog issued from numerical simulations (based on physical equations), and wants to perform data assimilation without running the model again. This is for instance useful for operational prediction centers that do not have the computational resources to integrate a forecast model, but do have access to a large database of numerical simulations or analysis data of a large prediction center. In this respect, we discuss also the situation where the catalog comprises noisy versions of the true states (section 5d).
Hereinafter, we refer by K to the number of nearest neighbors (analogs), and by cov w the weighted covariance. The normalized kernel weight for every pair fa k [x(t)], s k [x(t)]g is given by v k [x(t)] 5 gfx(t), a k [x(t)]g å K K51 gfx(t), a k [x(t)]g. ( 5 ) Several ideas can be explored to define the analog forecasting operator A. The natural first option consists in deriving the forecast using the weighted mean of the K successors. This approach, that we call here the locally constant operator, was considered in many analog forecasting related works  (McDermott and Wikle 2016; FIG. 1. The evolution in time of one particle or member. The catalog implicitly represents the dynamics of the system from exemplars of historical datasets. The observations are shown by black asterisks, and their variance is shown by the corresponding error bar.
Zhao and Giannakis 2014; Hamilton et al. 2016) , and is also known in statistics as Nadaraya-Watson kernel regression. One can also use as analog forecasting operator the weighted mean of the anomalies between the K analogs and their successors and adding it to the state to derive the forecast. The operator, referred to as locally incremental, is seen as more physically sound and relates more closely to a finite-difference approximation of the underlying differential equations. Finally, we introduce in this work a new analog forecasting operator that makes use of local linear regression techniques based on weighted least squares estimates. This operator that we call the locally linear operator is known to make an efficient use of small datasets and to reduce biases  (Cleveland 1979). Note that the locally constant and locally incremental operators are two special cases of the locally linear operator.
b. Experimental details The considered experimental setting is as follows. To avoid divergence of the filtering methods, we use N 5 100 members/particles for the Lorenz-63 and N 5 1000 members/particles for the Lorenz-96 for both modeldriven and data-driven strategies. We use the same covariance matrix R with a noise observation variance set to 2. To avoid any spinup effect, the initial state conditions is chosen as the ground truth mean and a covariance matrix B with noise variance 0. 1. To compare the technique performances, we use the root-mean-square error (RMSE) on all the components of the state vector and for all assimilation times. As training dataset for the catalog and test dataset for RMSE computation, we use 10 3 and 100 Lorenz times, respectively. The analog forecasting operator involves two free parameters, namely, K the number of nearest neighbors and l the scale parameter of the Gaussian kernel in (4).
c. Experiments with Lorenz-96 model 1) EXPERIMENT 1 The first numerical experiment consisted only in the application of analog forecasting (without assimilation) from a catalog. We build a database using Lorenz-96 equations, then we split the samples randomly to 2/3 for training the analog forecasting operators and 1/3 for test. Finally, we compare the RMSE w. r. t. ground truth data as a function of Lorenz-96 forecast time. For local analogs, we consider n 5 2 the width of the considered component-wise neighborhood. Figure  3  shows the results of this experiment using the three choices for the analog forecasting operator A. The locally linear approach outperforms the two other approaches confirming that its forecasts are with lower bias compared to the other approaches. However, it also involves more parameters, which increases the variance of the forecasts.
Conclusions and perspectives The present paper demonstrates the potential of datadriven schemes for data assimilation. We propose and evaluate efficient yet simple data-driven forecasting strategies that can be coupled with classical stochastic filters (viz. , the ensemble Kalman filter/smoother and the particle filter). We set a unified framework that we call the analog data assimilation (AnDA). The key features of the AnDA are twofold: (i) it relies on a datadriven representation of the state dynamics, and (ii) it does not require online evaluations of dynamical models based on physical equations. The relevance of the AnDA is tangible when the dynamical system of interest demands tremendous and time-consuming physical modeling efforts and/or uncertainties are difficult to assess. In cases when large observational or modelsimulated datasets of the considered system are available, AnDA can both support or compete with classical data assimilation schemes.
As a proof concept, we demonstrate the relevance of the proposed methodology to retrieve the chaotic behavior of the Lorenz-63 and Lorenz-96 models. We performed numerical experiments to evaluate critical aspects of the method, especially the relevant combinations of analog forecasting strategies and of stochastic filters as well as the exploitation of noisy and noise-free catalogs. All the reported experiments were carried out using the AnDA Python/Matlab library (https://github. com/ ptandeo/AnDA), which includes the Lorenz-63 and Lorenz-96 systems. In the spirit of reproducible research, the user can conduct the different experiments shown in this paper. Overall, the reported results demonstrate the relevance of the proposed analog data assimilation methods, even with highly damaged catalogs.
They suggest that AnEnKS combined with locally incremental or locally linear analog forecasting leads to the best reconstruction performance, the locally incremental version being the most robust to noisy settings. Moreover, the flexibility of the analog data assimilation demonstrates the potential for the identification of hidden underlying dynamics from a series of partial observations. The main pillar of our data-driven approach is the catalog. As such, analog data assimilation deeply relates to the quality and representativity of the catalog. In our experiments, we assumed that we were provided with large-scale catalogs of complete states of the system of interest. While catalogs built from numerical simulations fulfill this assumption, observational datasets (e. g. , satellite remote sensing or in situ data) typically involve missing data, which may require specific strategies to be dealt with in the building of the catalogs.
This is regarded as the key explanation for the much better performance reported for the local analog data assimilation for Lorenz-96 dynamics using catalogs of about a million of exemplars (Fig. 4 ). For real-world applications to high-dimensional systems, for instance to ocean and atmosphere dynamics, the combination of such local analog strategies to multiscale decompositions  (Mallat 1989 ) arise as a promising research direction as illustrated in  Fablet et al. (2017). Such multiscale decompositions are expected to enhance the spatial redundancy, with a view to building the requested catalogs of millions to hundreds of millions of exemplars (for an intrinsic dimensionality between 4 and 7, see the appendix) from observation or simulation datasets over a few decades. Another important aspect that controls the effective size of the catalog is the evolution of the system in time.
The more nonlinear the dynamics, the greater the number of requested exemplars in the global catalog to learn the forecast operator and the spread of the prediction. We believe that this study opens new research avenues for the analysis, reconstruction, and understanding of the dynamics of geophysical systems using datadriven techniques. Such techniques will benefit from the increasing availability of large-scale historical observational and/or simulated datasets. Beyond the wide range of possible applications, future research should further investigate methodological issues. First of all, our study demonstrates the relevance of the analog particle filter, but as mentioned in section 5, the AnPF suffers from degeneracy and sample impoverishment. We may point out that complementary experiments with particle smoother schemes (not shown in this paper) resulted in numerical instabilities.