dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Based on the motion descriptors extracted, different statistical methods have been used to analyse differences between groups. For example,  Duchateau et al. (2010)  ; 2011 ) introduced a framework to assess septal flash, which denotes an early septal inward/outward motion. From the motion atlas, they calculated the Hoteling s T-square distance between the velocity distributions of the atlas population and each individual. A higher value of abnormality corresponded to a lower p-value. Later, De Craene et al. (2012)  used statistical parametric mapping (SPM) on the velocity field obtained from the motion atlas to quantify motion abnormality. More recently,  Bai et al. (2015b )  applied SPM analysis on a larger database (10 0 0 subjects) to study the impact of gender and age on regional myocardial wall thickness.
In the context of this paper, the main motivation for multimodal feature fusion is to find a latent subspace shared by 3D MR and 3D US data to facilitate a more robust characterisation of cardiac motion using low cost 3D US data. Therefore, we focus on multi-view subspace learning here. Multi-view subspace learning techniques can be further subdivided into: supervised techniques that require full knowledge of the correspondences between the data from the different views  ( Hotelling, 1936; Wold et al. , 1984 ) ; semi-supervised techniques that require a subset of such correspondences to be known  ( Ham et al. , 2005; Georg et al. , 2008 ) ; and lastly, unsupervised techniques that do not require such knowledge  ( Wachinger et al. , 2012; Mateus et al. , 2007 ). Note that there is a close link between multiview subspace learning and manifold alignment, and often the two terms can be considered as synonyms, although typically manifold alignment approaches have tended to be semi-supervised or unsupervised (e.
Our contributions In this paper, we present a framework for building a spatiotemporal atlas from imaging data acquired from a cohort of subjects using two different modalities (MR and US), but to apply it to a new subject using only US. The technique works by applying a multi-view subspace learning algorithm  ( Sun, 2013; Xu et al. , 2013 )  to simultaneously reduce the dimensionality of the two modalities into a common latent subspace. This is the first time that such a technique has been proposed in the area of atlas formation. We evaluate our approach on a large scale monomodal data set based on 10 0 0 subjects from the UK Biobank database, and also MR and US data acquired from a cohort of 50 volunteers, and use the atlas to analyse the dominant motion patterns and the differences between motion data estimated using the two modalities.
Large scale monomodal data set For systematic validation and understanding of the robustness of the proposed dimensionality reduction algorithms, we used 10 0 0 subjects from the UK Biobank cohort. 1  From this data set, we used the multi-slice short-axis cine MR sequence (TR/ TE = 2. 7/1. 16 ms, flip angle = 80 °). Typical slice thickness was 8. 0 mm with an in-plane resolution 1. 8 mm × 1. 8 ms. The temporal resolution for all subjects was 50 frames per cycle. Further acquisition parameters can be found in  Petersen et al. (2016). Details of how we used these data to assess the dimensionality reduction algorithms can be found in Section 4. 2.
Real multimodal data set Three real MR and US data sets were used for evaluation in this paper. The first is the database used for the cardiac motion analysis challenge that was held at the 2011 MICCAI workshop "Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges" (STACOM'11)  ( Tobon-Gomez et al. , 2013 ). The STACOM'11 database includes MR and US data sets from 15 healthy volunteers acquired at the Division of Imaging Sciences and Biomedical Engineering, King's College London, United Kingdom, and the Department of Internal Medicine II -Cardiology, University of Ulm, Germany. The second data set was recently acquired at the Division of Imaging Sciences and Biomedical Engineering, King's College London, United Kingdom, and contains 9 MR and US data sets acquired from healthy volunteers.
The third data set was acquired at the Division of Cardiology, Cliniques Universitaires St-Luc, Avenue Hippocrate 10-2881, B-1200 Brussels, Belgium. This contains MR and US data acquired from 26 healthy volunteers. For all cases the MR acquisitions were performed using a 3T Philips Achieva System (Philips Healthcare, Best, The Netherlands), and the US data sets were acquired using an iE33 3D echocardiography system (Philips Medical Systems, Bothell, WA, United States) with a 1-5 MHz transthoracic matrix array transducer (xMATRIX X5. 1). Full-volume acquisition mode was used in which several smaller imaging sectors acquired over multiple cardiac cycles are combined to form a large composite volume. In particular, the three data sets contain for each subject: • cine SA : a multi-slice short-axis cine-MR sequence (TR/ TE = 3.
Typical slice thickness was between 0. 7 mm and 1. 0 mm with an in-plane resolution ≈ 0. 65 mm × 0. 8 mm. Typical temporal resolution was between 15 and 23 frames per cycle. The US data was used for geometry and motion estimation (see Sections 3. 1 and 3. 3 ). The three real data sets described above were combined to form a single data set for the experiments described in this paper.
LV motion estimation A 3D GPU-based B-spline free-form deformation (FFD) registration was used  ( Rueckert et al. , 1999 )  to estimate LV motion between consecutive frames of the MR and US sequences. As described in  Sinclair et al. (2016)  and  Peressutti et al. (2017)  , the sequentially estimated FFD deformation fields were composed to obtain the FFD transformation from each cardiac phase to the reference ED phase. Furthermore, a 3D+ t cyclic B-spline was fitted to the composed 3D transformations in order to estimate a full cycle 3D+ t transformation  ( Unser, 1999 )  using the IRTK software. The result of the motion estimation was, for each subject n , a smooth 3D+ t transformation ψ 3 D + t ( x , t ) , where x represents the spatial coordinates of the vertices and t is the cardiac phase.
Motion reorientation For each subject k , for each modality, displacements u m,t,k = x m,t,kx m,ED,k were computed, where x m, t, k corresponds to the spatial coordinates of vertex m at cardiac phase t for subject k. With the aim of comparing the LV motions from different subjects in the atlas coordinate system, displacement data for each subject were reoriented into the atlas coordinate system under a small deformation assumption  ( Ashburner, 2007 )  using a push-forward action  ( Rao et al. , 2004; Perperidis et al. , 2005a ) : u atlas m,t,k = J φ k ( x m,ED,k ) u m,t,k (1) where J φ k ( x m,ED,k ) is the Jacobian of the anatomical transformation φ k mapping each subject's reference ED medial mesh to the atlas ED medial mesh, computed at location x m, ED, k.
Assessment of prediction errors In the second experiment we computed the prediction error for varying amounts of Gaussian noise added to the MR displacements to create the synthetic US displacements. For this experiment we fixed the number of vertices to 650 and the number of subjects to 50, which correspond to the same values used in the real database. The aim of this experiment was to assess how the variability between the MR and US data affects the evaluated algorithms. Fig. 6  a shows the variation of the prediction error whilst varying the Gaussian noise level. The x axis corresponds to the amount of Gaussian noise used ( σ M from 5 mm to 50 mm). For a small σ M (i. e. more correlated data) the E D (MR -US) is better for PCA, but then it has a steep slope, whilst the errors for PLS and CCA increase less quickly.
Similarity between MR and US data Based on the results presented so far, we considered that PLS has the better performance in terms of errors and stability. Therefore, for the remaining experiment we only use PLS. This experiment aims to find the areas where MR and US derived motion are more similar. To this end, we computed the mean point in the low dimensional space (i. e. the average motion), and then back projected this to the atlas space using the MR and US projection matrices. We then computed the Euclidean distances between the MR and US displacements on a vertex-by-vertex basis. The average such distance for each AHA segment was then computed. This helped us to visualise the areas where PLS finds greater similarities between MR and US derived motion. Fig. 11  shows the Euclidean distances between the mean MR and US motions in each AHA segment for each time point of the cardiac cycle.
Discussion and conclusion We have described a complete framework for the formation of a multimodal statistical atlas of cardiac motion, from its construction steps to the impact of different dimensionality reduction algorithms. The three algorithms proposed to build the multimodal cardiac motion atlas were: (1) PCA; (2) CCA; and (3) PLS. This is the first time that such a framework has been described, and we believe that our validation and analysis offer valuable insights into the nature and utility of multimodal MR and US data for analysing cardiac function. Each modality offers the possibility to observe the heart's motion from a distinct perspective, and sometimes offers complementary information. In clinical practice, MR is increasingly accepted as the gold standard for characterising cardiac function and anatomy, as it enables the acquisition of high contrast and high resolution sequences.
However, MR is contraindicated for patients with cardiac pacemakers or metallic cardiovascular electronic devices, and furthermore it is a high cost technology. In contrast, US is commonly used in the clinic for assessing cardiac function due to its low cost, portability and good spatial resolution. However, it suffers from image artefacts and limited field of view. A multimodal atlas will benefit from the synergies between the motion features derived from the two modalities, and furthermore, it could be used in clinics to detect cardiovascular disease using only US data. Our results show that, using a multimodal atlas based on PLS, US-derived motion alone can be used to analyse a new subject's motion whilst exploiting the multimodal (MR+US) data of a population sample. Fig. 7  shows that the accuracy of this process (i.
E D (MR -US) ) is approximately 2. 5 mm, which is similar to the magnitude of motion tracking errors  ( Tobon-Gomez et al. , 2013 ). Note that E D (MR -US) effectively quantifies how accurate the US processing pipeline is for analysing motion compared to the gold standard of MR. A possible limitation of the proposed framework to build the multimodal atlas is the temporal normalisation step, which relies on the correct identification of cardiac timings. For instance, the beginning of the atrial systole phase may vary between healthy and diseased subjects, and might be more difficult to identify in patient data. Ideally, we would like to use valve cardiac timings as it would be more accurate. However, the spatiotemporal quality of both TAG and CINE MR data (as obtained in the clinical setting) is currently not sufficient to allow accurate detection of cardiac valve timings.
The results presented in Section 4. 3. 4 showed that the large variation of MR and US-derived motion is focused on the basal free wall segments, which correspond to the wall with higher variation from a clinical point of view. In future work we aim to use this information as prior knowledge to weight the contributions of features in the dimensionality reduction process. We used a large-scale monomodal database to compare the performances of the different algorithms under different circumstances, i. e. varying number of samples/features and similarity between modalities. Based on the results we have an approximative idea of the lower bound of the errors of the algorithms as well as how many subjects would be necessary to achieve a good  level of accuracy. However, a limitation of the proposed method to construct the synthetic US displacements is the use of a global Gaussian noise model to model the variability between modalities.
Greater realism could be achieved by using a locally varying model, but at the expense of the clarity and conciseness of the experimental results. Our current clinical data set is composed of data from two different centres, which might introduce some bias into the model. In order to minimise this bias, we acquired the data using identical machines (both MR and US). However, we did observe some variation in spatial and temporal resolution, which we presume is due to the lack of standardisation in clinical imaging protocols. This variation was noticeable both between images acquired at the same centre as well between images acquired at different centres. Nevertheless, we found no systematic differences between centres in terms of spatial and temporal resolution. In the current framework (see Fig.
Acknowledgements This work is funded by the  Kings College London & Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging  (  EP/L015226/1  ). We acknowledge financial support from the  Department of Health  via the  NIHR comprehensive Biomedical Research Centre  award to Guys & St Thomas NHS Foundation Trust with KCL and  Kings College Hospital NHS Foundation Trust. The research leading to these results has received funding from the  EU   FP7  for research, technological development and demonstration under grant agreement  VP2HF  (no 611823 ). This research has been conducted using the UK Biobank Resource under Application Number  17806.
Array data were exported as cell intensity (CEL) files for further analysis. CEL files were normalized together in the Expression Console software package (Affymetrix), using the Robust Multi-array Average (RMA) sketch algorithm (gene-level). Data were quality control assessed by using standard metrics and guidelines for the Affymetrix microarray system. Normalized data files (CHP files) were imported into Omics Explorer 3. 0 (Qlucore) for further quality control and statistical analysis. Data used for the functional analysis were selected at the statistical cut-off values of p < 0. 01 with FC >1. 1  [32]. The pathway analysis was done using the Thomson Reuters MetaCore Analytical Suite and/or the NIH Database for Annotation, Visualization and Integrated Discovery Bioinformatics Resources 6.
This yielded a value of 6% for instrument variability. Overall process variability as determined by calculating the median RSD for all endogenous metabolites (that is, non-instrument standards) present in 100% of the samples gave a value of 11-12%. Raw data were extracted, peak-identified and QC processed using Metabolon's hardware and software  [35]. Metabolites were identified by automated comparison and curated by visual inspection for quality control using software developed at Metabolon  [36]. Peaks were quantified using area under the curve. The maximum percent missing data allowed was 20%. As a result, 647 and 593 metabolites were taken forward for bioanalytical analysis in kidney and liver tissues, respectively. The language and statistical environment R was employed in order to explore the relationship between the control and the treated samples.
We regressed out the batch effects to correct variation resulting from instrument inter-day tuning differences using the limma package remove-BatchEffect  [37]. Pairwise non-parametric Mann-Whitney U tests were performed and a p value was attributed to each of the metabolites. The resulting p values were adjusted by the Benjamini-Hochberg multi-test adjustment method for a high number of comparisons. Volcano plots were also constructed in order to visualize the differences in metabolite and protein expression for each of the comparisons. The aforementioned tests and plots were performed using in-house R scripts.
Transcriptomics analysis The transcriptome dataset obtained via microarray analysis was initially subjected to an unsupervised Principal Component Analysis (PCA). This analysis reduces a high-dimensional expression profile to single variables (components) retaining most of the variation. The distribution of the samples in a 3D space defined by three PCA components allows an estimation of the effects of the treatment or the detection of outliers. The results (Fig. 1a ) showed no segregation of the GM NK603 cornfed groups from the control animals, indicating that the treatment was not a major source of difference. In contrast, rats administered via drinking water with 0. 1 ppb Roundup (50 ng/L glyphosate equivalent concentration) were clearly separated in this PCA analysis from the controls and NK603 corn-fed groups (Fig.
Each sphere represents the result of a single animal. b Volcano plots of the liver and kidney transcriptome profiles. Transcript cluster expression derived from the transcriptome profile data of liver and kidneys of control and NK603 maize-fed groups, either with or without Roundup (R) application, was plotted as log 2 fold change against -log10 p values. Each dot represents a single transcript cluster damage, we next conducted a metabolome profiling of liver and kidney sections of GM NK603 (±Roundup) corn treatment groups from the same animals to ascertain any changes in metabolites that could be indicative of disease. In this study, 692 and 673 metabolites were, respectively, identified in kidney and liver tissues. In order to ascertain if Roundup residues were bioaccumulating, we also measured the presence of glyphosate (N-(phosphonomethyl) glycine) and its metabolite aminomethylphosphonic acid (AMPA).
, 2002)  and the new product provisional Landsat-8 (L8SR) ''Surface Reflectance''  (USGS, 2015) , recently developed for the U. S. Geological Survey (USGS). These algorithms were evaluated to establish the best model to mitigate the atmospheric contribution over the Landsat-8 scenes. This study seeks to reveal and quantify the seasonal changes in the SSC pattern caused by environmental factors and geomorphologic controls, and attempts to clarify their relationships. We used remote sensing data from Landsat-8 satellite over a 2. 5-year period  (2013) (2014) (2015)  with the following three objectives: to evaluate three methods of atmospheric correction to establish the best model to mitigate the atmospheric contribution over the Landsat-8 scenes; to develop a model, based on the Landsat-8 OLI images, that addresses seasonal differences and atmospheric corrections to estimate SSC in the lower Orinoco River with extreme variation; to provide spatial information of SSC through remote sensing technologies and understand its relationship to environmental factors and geomorphologic controls over the main channel characterized by an alternation of contraction and expansion zones.
(2013) , where the authors observed that, during the hydrological cycle of the Orinoco River, the presence of hysteresis between discharge and SSC and the two peaks of SSC (Fig. 2 ) invalidates the use of relations (SSC = f(Q)) to calculate the sediment discharge. The HYBAM observation service (http://www. so-hybam. org/index. php/eng/Data) collected 266 surface water samples each 10 days for SSC determination between January 2007 and December 2015 at the Ciudad Bolivar gauging station (88 9 0 1. 79 00 N, 638 32 0 25. 26 00 W, 8 m a. s. l. ) from which 33 were acquired after June 2013. It is on this date that Landsat-8 satellite began operating. A second set of 15 SSC data (in situ) were used to test the SSC retrieval model. The data retrieved from the HYBAM sampling at the Ciudad Bolivar station coincides with the acquisition of OLI cloud-free images during the year 2016.
Landsat-8 OLI data Landsat-8 (L-8) was launched on February 11, 2013 and normal operations commenced on May 30, 2013. This satellite has a ground track repeat cycle of 16 days, which crosses the equator at 10:00 a. m. The Operational Land Imager (OLI) on L-8 is a nine-band push broom scanner with a swath width of 185 km and eight channels at 30 m, and one panchromatic channel at 15 m spatial resolution. Compared to the Thematic Mapper (L4-5/TM) and the Enhanced Thematic Mapper Plus (L-7/ETM + ) previous Landsat missions, L-8/OLI offers higher signal-to-noise ratios (SNR)-mainly because of longer integration times on the push broom scanner-and an improved quantization (12 bits instead of 8 bits for radiometric digitization). This new sensor shows great potential, in terms of radiometric and spatial accuracy for monitoring turbid waters (Case 2)  (Gerace et al.
, 2013). The Ciudad Bolivar station is located at the overlap of Landsat-8 frames (path/row: 1/54-2/54, with an overlapping of approximately $ 22 km). This situation results in an increased revisit frequency of 3-4 images per month instead of 1 or 2 per month (Fig. 1 ). After a preliminary analysis of the 81 images without cloud cover, 27 scenes were selected. We examined the correspondence of 27 in situ SSC (HYBAM dataset) with their corresponding satellite images to derive empirical relationships between SSC (mgÁl À1 ) and reflectance in both path/row at the Ciudad Bolivar station. The foundation of remote sensing approach for SSC estimation is that the amount of sediment in water directly affects the reflectance of solar radiation in the VISIBLE and NIR portions of the spectrum. We decided to evaluate only the VIS and NIR bands of Landsat-8 OLI (Band 1-coastal/ aerosol: 433-453 nm, Band 2-blue: 450-515 nm, Band 3green: 525-600 nm, Band 4-red: 630-680 nm and Band 5-NIR: 845-885 nm).
Spatial-temporal representativeness of SSC sampling on the study section A total of 33 samples from June 2013 to December 2015 (HYBAM dataset) were collected monthly at the river water surface in the middle of the channel at the Ciudad Bolivar station. Out of 33 samples, only 27 were realized during Landsat-8 acquisition day or very close to. The 500ml water samples were transported to the laboratory and filtered using 0. 45-mm cellulose acetate filters to assess the SSC. After filtration, the filters are dried for 24 h at 60 8C and weighed. SSC is computed as the difference between the weights of the filter after and before filtration divided by the volume of water collected  (Martinez et al. , 2015). Ten-river sediment discharge assessments were realized from 2007 to 2010 using the sampling protocol  (Filizola and Guyot, 2004) , which uses a point sampler at various depths in selected vertical profiles within the sampling transect.
Based on a larger dataset of sediment discharge measurements acquired at the same station,  Laraque et al. (2013)  demonstrate that one sample taken close to the water's surface in the middle of the transect is representative of the average SSC across the whole of the river's reach. According to  Laraque et al. (2013) , this particular situation was also reported by other authors (Lopez and Perez-Hernandez, 1999;  Meade, 1994; Mora, 2011) , and can be explained by the fluvial geomorphology affecting the course of the river upstream (presence of closed meanders, a tectonic threshold, fluvial islands, etc. ), which generates rapid and strong vortexes in the river's reach, dominated by a ''funnel effect''. Indeed, the width of the channel changes rapidly from 8 km to 1 km upstream in the Ciudad Bolivar ''estrecho'', inducing a SSC homogenization in the water column, which is dominated by a rate fine distribution with a particle size distribution D 50 = 8 to 10 mm  (Laraque et al.