dataesr/scientific-paragraphs-categorization huggingface dataset:
More recently, an online challenge  [29]  was organized, where the majority of top-ranking solutions were based on convolutional neural networks. In summary, for protein localization the shift from hand-crafted features to learned representations allows for more accurate and robust pipelines. A recent perspective paper  [30]  suggests the increased use of deep learning models also for RNA localization analysis. The authors emphasize the recent successes and flexibility of neural nets with different types of input, and therefore the possibility to design a multimodal pipeline. However, the fundamental difference to existing protein localization datasets, is that RNA molecules appear as distinguishable spots, and their modeling as a texture seems therefore suboptimal. Point Cloud Models We postulate that learning to classify RNA localization patterns directly from detected spot coordinates could be an efficient approach.
Problem Statement We want to train a model, where we can provide directly the point cloud coordinates as an input and compute a continuous vector representation. This representation can then be used for classification of different RNA localization patterns. Such a deep learning model might require a large volume of annotated data to reach a satisfying performance. To generate such a large data sets, we used simulated data to train our point cloud model and then use it as a trained feature extractor. Eventually we evaluate these learned features on a real dataset. We split our dataset into train, validation and test, with 60%, 20% and 20% respectively. FISH-quant simulates point clouds from a limited number of real image templates. To avoid overfitting, we make sure simulations from the same cell template can't be assigned to different splits.
Finally, point clouds are augmented with random rotations along the up-axis, centered and divided by their absolute maximum value. This normalization step was initially performed in  [33]. Real Dataset To further validate the learned feature representation on simulated images, we use a previously published experimental dataset  [4]. These images are extracted from a smFISH study in HeLa cells targeting 27 different genes. After data cleaning, this dataset consists of 9710 individual cells, with cropped images and coordinates extracted. Cells have on average 346 RNAs and 90% of them have between 39 and 1307 transcripts. Furthermore, 810 cells have manually annotated localization patterns, as detailed in table 1, providing a ground-truth for validation. Importantly, these patterns are not mutually exclusive since cells can display several patterns at the same time, e.
Input Preparation For the simulated dataset we directly generate point clouds. In contrast, for any experimental dataset, the starting input is usually a multichannel fluorescent image including a smFISH channel. The first tasks are the detection of RNA molecules and the segmentation of cell and nucleus surfaces. These steps allow to retrieve for each cell a list of RNA coordinates we can format to be used as an input point cloud. For the experimental dataset we reuse the code and the extraction pipeline described in  [4]. Besides the original RNA point cloud, we can use an optional second input vector containing additional information as input for our model. Let X ∈ R N ×3 be the original input point cloud with N the number of RNAs. We define our second input vector as X ∈ R N ×d with d ∈ {1, 2, 3, 4, 5}.
Model Architecture We adopt the generic architecture introduced by PointNet  [33] : successive pointwise representations with increasing depth followed by a max pooling operation to keep the network invariant by input permutation. We incorporate state-ofthe-art modules to learn efficient local structures within the point cloud. As illustrated in Figure  3 , we also adapt the network to the specificity of RNA point clouds. Point-wise Block Instead of shared MLPs like PointNet, we implement a multi-head attention layer based on point transformer layer  [39]. First, we as-sign to each data point x i its 20 nearest neighbors X(i) ⊂ X, based on the euclidean distance in the feature space. We also compute a position encoding δ ij = θ(x i -x j ) for every pair within these neighborhoods, with θ a MLP.
Training on Simulated Patterns We train PointFISH on the simulated dataset. Our implementation is based on TensorFlow  [43]. We use ADAM optimizer  [44]  with a learning rate from 0. 001 to 0. 00001 and an exponential decay (decay rate of 0. 5 every 20,000 steps). Model is trained for a maximum of 150 epochs, with a batch size of 32, but early stopping criterion is implemented if validation loss does not decrease after 10 consecutive epochs. Usually, the model converges after 50 epochs. We apply a 10% dropout for the last layer and classifications are evaluated with a categorical cross entropy loss. Even if localization patterns are not necessarily exclusive, for the simulations we trained the model to predict only one pattern per cell. For this reason, we did not simulate mixed patterns and assume it could help the model to learn disentangled representations.
Training takes 6 to 8 hours to converge with a Tesla P100 GPU. A first evaluation can be performed on the simulated test dataset. With our reference PointFISH model, we obtain a general F1-score of 95% over the different patterns. The configurations of this model include the use of distance and cluster contextual inputs, a geometric affine module, an attention layer as a point-wise block and a latent dimension of 256.
Exploratory analysis of experimental data embeddings We compute the embeddings for the entire cell population studied in  [4]. All the 9170 cells can be visualized in 2D using a UMAP projection  [45]. In Figure  4  each point represents a cell. Among the 810 annotated cells, those with a unique pattern are colored according to the localization pattern observed in their RNA point cloud. The rest of the dataset is gray. Overall, PointFISH embedding discriminates well the different localization patterns. Intranuclear, nuclear edge and perinuclear cells form distinct clusters, despite their spatial overlap, as well as protrusions. We recall that the protrusion patterns was not used in simulation. Cells with foci can be found in a separated clusters as well, but also mix with nuclear and perinuclear patterns.
This confusion is not surprising as a large number of cells in the dataset present a nuclear-related foci pattern (i. e. cells have RNAs clustered in foci, which in turn are close to the nuclear envelope). Supervised Classification Because PointFISH already return meaningful embeddings, we can apply a simple classifier on top of these features to learn localization patterns. We use the 810 manually annotated cells from the real dataset. unlabelled intranuclear nuclear perinuclear protrusion foci random Fig. 4 : UMAP embedding with learned features. Each point is a cell from dataset  [4]. Manually annotated cells are colored according to their localization pattern We compare the 15 hand-crafted features selected in  [4]  with our learned embedding. Every set of features (hand-crafted or learned) is rescaled before feeding a classifier "by removing the mean and scaling to unit variance" 2.
The classifier is a SVC model  [47]. For evaluation purpose, we apply a nested cross-validation scheme. First, a test dataset is sampled (20%), then the remaining cells are used for a gridsearch to find an optimal SVC model (with another 20% validation split). Parameters grid includes the choice between a linear or a RBF kernel and the strength of the regularization. The entire process is repeated 50 times, with different test split, and F1-score for each classification task is returned. This full evaluation pipeline is implemented with scikit-learn  [46]. F1-score's distribution over 50 splits are summarized in Figure  5. Learned features match performances of hand-crafted features selected for the tasks. While the recognition of localization in protrusions is slightly worse, it is important to point out that we did not include simulations of this patterns in the training dataset.
In particular, cluster information greatly improves the recognition of the foci pattern while morphological distances boost others localization patterns. Alignment Module and Point-wise Block To measure the impact of the geometric affine module  [40] , we compare it with the TNet module implemented in PointNet  [33]. We also design a variant of TNetEdge where MLP layers extracting point-wise independent features are replaced with EdgeConv layers  [35]. Results are reported in table 3. An alignment block seems critical at the beginning of the network. In addition, geometric affine module is both more efficient (F1-score of 0. 81) and much lighter than TNet and TNetEdge. Inspired by PointNet and DGCNN, we also compare the use of their respective point-wise blocks with our multi-head attention layer.
Discussion We have presented a generic method of quantifying RNA localization patterns operating directly on the extracted point coordinates, without the need to design handcrafted features. For this, we leverage coordinates of simulated localization patterns to train a specifically designed neural network taking as input a list of points and associated features that greatly enhance generalization capabilities. We show that this method is on par with carefully designed, handcrafted feature sets. Being able to directly process list of points provides the community with a tool to integrate large datasets obtained with very different techniques on different model systems. While the actual image data might look strikingly different between such projects, they can all be summarized by segmentation maps of nuclei and cytoplasm, and a list of coordinates of RNA locations.
Having methods that operate directly on point clouds is therefore a strategic advantage. The idea of training on simulated data provides us the opportunity to query datasets with respect to new localization patterns that have not yet been observed, and for which we do not have real examples so far. In addition, this strategy allows us to control for potential confounders, such as cell morphology, or number of RNAs. Here, we provide a generic method that can leverage these simulations, without the tedious process of handcrafting new features. Of note, it is not necessary that the simulated patterns are optimized as to resemble real data: they rather serve as a pretext task. If a network is capable of distinguishing the simulated patterns, chances are high that the corresponding representation is also informative for slightly or entirely different patterns, in the same way as representations trained on ImageNet can be used for tumor detection in pathology images.
Acknowledgments This work was funded by the  ANR  ( ANR-19-CE12-0007 ) and by the  French government  under management of  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ). Furthermore, we also acknowledge  France-BioImaging  infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04 ).
Introduction The objective of the PETALE program  [1, 2]  is to provide new experimental data to constrain the stainless steel nuclear data. It consists of dosimeter activations and reactivity measurements using iron, nickel, and chromium metal reflector separately in the CROCUS reactor as presented in Figure  1. The measurement of reaction rates in a transmission experiment reduces the experimental uncertainties thanks to a strong correlation in-between the dosimeter's positions and types. The preliminary campaign presented in this paper is collaborative work between EPFL and CEA to prepare the PETALE campaign. The first objective is to test the experimental procedure and the analysis process up to the calculation-experiment comparison, including covariances in-between the dosimeters in the uncertainty propagation.
The second objective is to characterize the neutron flux in different positions of the CROCUS reactor. The campaign detailed here includes measurements in the reflector area with water replacing the PETALE device in the reflector. The measurements with the PETALE experiment are the object of the following campaign. The present preliminary campaign has been performed during the fall of 2019. The experimental setup is presented in Section 2. Specific developments have been performed to optimize the High Purity Germanium (HPGe) measurements and to propagate the nuclear data uncertainty (Sect. 3). Finally, the experimental and calculation results are compared in Section 4 for different nuclear databases.
Then using 32 random samples, the confidence interval is equal to 13% meaning that, for a propagated uncertainty of 10%, the 1σ confi- dence interval is between 8. 7 to 11. 3%, providing a good order of magnitude for a nuisance parameter. std 2 min = (n -1)std 2 χ 2 1-(α/2) , std 2 max = (n -1)std 2 χ 2 (α/2). For illustrative purposes, the spectrum variations of the first two samples are presented in Figure  6. We can see two kinds of results. The variation of the spectrum is changing according to energy: in order to represent this variation according to the 32 samples Figure  7  contains the relative standard deviation (rsd) using different nuclear databases. The second element is the direction of the variation between the samples (plain line or dashed line): in the fast range, all the regions (core center, control rod and reflector) are moving accordingly.
The reflector (blue curve) is moving in the opposite direction compared to the other regions in the thermal and epithermal ranges. This information is contained in the covariance matrix presented in Figure  8  for the 32 samples. The variation of the flux in each region being related to the others, the covariance matrix is not limited to each region separately but all together. When using the ENDF/B-VII. 1 database (Fig. 7  top) in the high energy range, the uncertainty is of around 2. 5% in the core center (blue curve) and larger than 6% in the reflector. The light red, blue and green lines are the relative standard deviation component due to the statistical uncertainty. This statistical component needs to be small enough (i. e. calculation long enough) to be negligible compared to the nuclear data uncertainty component.
In the epithermal range, the relative standard deviation is lower than at high energy with ENDF/B-VII. 1, and increases again in the thermal energy range. Comparing the tested nuclear databases ENDF/B-VII. 1, ENDF/B-VIII. 0 and JEFF-3. 3, we can see that the relative standard deviation is changing a lot, especially in the high energy range (above 1 MeV) and also in the thermal range with a reduction in the uncertainty. Thanks to these calculations, the uncertainty is propagated on the neutron spectrum. This uncertainty propagation loses the information on 'what is the origin of a specific effect, such as the larger uncertainty with ENDF/B-VII. 1 at high energy compared to the other databases. However to estimate a nuisance parameter or to perform a Bayesian Monte Carlo assimilation, this information is not required.
Associated to this standard deviation, the correlation matrix (Fig. 8 ) provides the influence of a perturbation of a specific energy bin on the other bins. The correlation matrix is a 3 × 3 block matrix, each diagonal matrice corresponds to a single spectrum location correlation, and the other matrices correspond to the influence of a perturbation in a specific position (e. g. the core center) on the other positions (e. g. the reflector). We can see here for example that, for all the nuclear databases, an increase in the thermal flux component in the core center implies a decrease (negative correlation) in the neutron flux in the reflector.
Finally, we obtain a standard deviation for each dosimeter, plus a corresponding covariance matrix shown in Figure  9. All the measured dosimeters are settled here on the same x-axis, allowing a global overview of the propagated uncertainty. The first line is the relative standard deviation, each color corresponds to a type of dosimeter for a better understanding. For a given dosimeter, for example, the nickel in orange, three different values are visible with ENDF/B-VII. 1: the first (∼2%) corresponds to the core center, the second (∼4%) to the control rod location and the third (∼6%) to the reflector position. Gold and nickel dosimeters take a larger part of the axis due to the axial measurement all along the plastic bars and then a larger number of measurements. Depending on the nuclear database, the propagated uncertainty can be very different.
12 ). Finally, Figure  13  presents the "calculation/ experiment" in percents for the different tested nuclear databases. We can see a good global agreement for the dosimeter sensitive to the thermal range on the left and an underestimation of the reaction rate for the high energy sensitive dosimeters on the right. For the ENDF/B-VII. 1 nuclear data library, the average value is better and, thanks to the nuclear data uncertainties, the results are compatible with a C/E = 1. However for JEFF-3. 3 and ENDF/B-VIII. 0 libraries, the average results are reduced and the strong reduction in the nuclear data uncertainties makes the C/E incompatible with 1. A remaining uncertainty component to add to the analysis is the fission neutron emission spectrum which is not sampled yet and might explain a part of the discrepancies at high energy.
The gaseous chemical scheme used in CHIMERE is MELCHIOR-2  [37]. The aerosol module of CHIMERE is not considered in the simulations and inversions, as the adjoint of this module is not available  [44]. For the comparison between the observations and the model during the inversion process (Figure  1 ), super-observations at the model resolution (0. 5° × 0. 5°) are considered. As the number of OMI observations lying in the model grid-cell is generally small and will not allow one to decrease observation errors by averaging, we choose to use the median of corresponding OMI NO2 columns. The averaging kernel A and the observation errors associated with the median columns are considered too. The observation errors are used to fill the R matrix. To compare with OMI super-observations, simulated NO2 concentrations in the same grid cells as the super observations are selected.
The hourly-simulated concentrations are interpolated in time and OMI's pressure levels. OMI averaging kernels are applied to these NO2 vertical profiles using Equation (  2   The gaseous chemical scheme used in CHIMERE is MELCHIOR-2  [37]. The aerosol module of CHIMERE is not considered in the simulations and inversions, as the adjoint of this module is not available  [44]. For the comparison between the observations and the model during the inversion process (Figure  1 ), super-observations at the model resolution (0. 5 • × 0. 5 • ) are considered. As the number of OMI observations lying in the model grid-cell is generally small and will not allow one to decrease observation errors by averaging, we choose to use the median of corresponding OMI NO 2 columns. The averaging kernel A and the observation errors associated with the median columns are considered too.
For example, the latter is the case in Daqing for January and December 2015, where no OMI observation data are available over the location. At Daqing, Baotou, Henan, YRD, and PRD, posterior emissions decrease compared to 2010 throughout both years, with the largest reduction in YRD and PRD during summertime, 20% and 26%, respectively. At Wuhan, Chongqing, Hunan, and Anhui, there are no significant changes in the NOx emissions between 2010, 2015 and 2019 in the first half of the year, but a decrease compared to 2010 occurs mainly in the second half of the years, with the highest reduction observed in Wuhan (22% in 2015 and 41% in 2019) in August. In the northern locations, particularly around the Yellow Sea, such as Dalian, BT, and Shijiazhuang, the 2015 emissions are increased during spring compared to 2010.
For example, the latter is the case in Daqing for January and December 2015, where no OMI observation data are available over the location. At Daqing, Baotou, Henan, YRD, and PRD, posterior emissions decrease compared to 2010 throughout both years, with the largest reduction in YRD and PRD during summertime, 20% and 26%, respectively. At Wuhan, Chongqing, Hunan, and Anhui, there are no significant changes in the NO x emissions between 2010, 2015 and 2019 in the first half of the year, but a decrease compared to 2010 occurs mainly in the second half of the years, with the highest reduction observed in Wuhan (22% in 2015 and 41% in 2019) in August. In the northern locations, particularly around the Yellow Sea, such as Dalian, BT, and Shijiazhuang, the 2015 emissions are increased during spring compared to 2010.
Cardiac magnetic resonance All patients underwent CMR with 2D LGE imaging for visualization of myocardial infarct tissue. All exams were performed on a 1. 5 T clinical MR system (Ingenia; Philips Healthcare, Best, The Netherlands). Images were reviewed at the MUMC on a dedicated workstation (Sectra IDS7, Linköping, Sweden), and segmental LGE transmurality was determined by visual assessment of an experienced cardiologist. At the UMCU, images were analyzed offline using Philips ISP9 software (Philips Healthcare, Best, The Netherlands). Using the RV insertion points to the interventricular septum as anatomical landmarks, the heart was subdivided into 16 segments according to the model of the American Heart Association (AHA)  22  , excluding the apical cap. The LGE was quantitatively assessed using the full width at half maximum (FWHM) method, providing a percentage for each of the analyzed segments and the total infarct size (global %) of the whole LV.
Myocardial strain imaging Using QLAB advanced quantification software 13 for Philips ultrasound systems (MUMC) or EchoPac version 201 for GE (UMCU), good quality two-chamber, three-chamber and four-chamber echocardiographic acquisitions were used for speckle tracking to obtain 18-segment LV longitudinal strain. Regions of interest were automatically tracked and manually adjusted to both the endo-and epicardial border following the standard recommendations  23. The first frame in which the mitral valve was closed was manually selected as zero-strain reference.
Computational model: the CircAdapt model of the human heart and circulation The CircAdapt model of the human heart and circulation is a closed-loop lumped-parameter model which simulates beat-to-beat hemodynamics and mechanics of the heart and blood vessels  11. The pulmonary and systemic circulation are modeled using a three-element model of resistive wave impedance, compliance and peripheral resistance  24. Cardiac walls are modelled as spherical shells, and the left and right ventricular walls are coupled through the interventricular septum using the TriSeg module  25. Simulation of myocardial active and passive tissue mechanics is based on the three-element Hill contraction model. When walls have homogeneous tissue properties, the model includes a total number of 𝐷 = 100 parameters eligible for personalization.
Measured strains were averaged in the apex-to-base dimension, and 𝑛 𝑠𝑒𝑔 = 6 was used to calculate the strain-and strain rate-based components of the cost function as well as the total cost function value (equations (  4 ), (  5 ), (  1 )). To cover all patients in one set of simulations, 𝑡𝐶𝑦𝑐𝑙𝑒 was added as a parameter to allow for variation in heart rate. For each patient dataset 𝑠, the best 𝑁 𝑏𝑒𝑠𝑡 simulations were selected according to 𝜒 𝐼𝐴 2 , which was based on the original 𝜒 2 with an additional term describing deviation of cycle time with doubled weight to increase importance: 𝜒 𝐼𝐴 2 = 𝜒 𝑉 𝐸𝐷 2 + 𝜒 𝐸𝐹 2 + 𝜒 𝜀 2 + 𝜒 𝜀2 + 2 • 𝜒 𝑡 𝑐𝑦𝑐𝑙𝑒 2 4 + 𝑛 𝑠𝑒𝑔 • 2 , (11) with: 𝜒 𝑡 𝑐𝑦𝑐𝑙𝑒 2 = ( 𝑡𝐶𝑦𝑐𝑙𝑒 -𝑡 𝑐𝑦𝑐𝑙𝑒,𝑚𝑒𝑎 𝜎 𝑡 𝑐𝑦𝑐𝑙𝑒 ) 2 (12) where 𝜎 𝑡 𝑐𝑦𝑐𝑙𝑒 = 50 𝑚𝑠, and 𝑡𝐶𝑦𝑐𝑙𝑒 and 𝑡 𝑐𝑦𝑐𝑙𝑒,𝑚𝑒𝑎 are the modeled and measured cycle time, respectively.