Learning to Predict Error for MRI Reconstruction

In healthcare applications, predictive uncertainty has been used to assess predictive accuracy. In this paper, we demonstrate that predictive uncertainty estimated by the current methods does not highly correlate with prediction error by decomposing the latter into random and systematic errors, and showing that the former is equivalent to the variance of the random error. In addition, we observe that current methods unnecessarily compromise performance by modifying the model and training loss to estimate the target and uncertainty jointly. We show that estimating them separately without modifications improves performance. Following this, we propose a novel method that estimates the target labels and magnitude of the prediction error in two steps. We demonstrate this method on a large-scale MRI reconstruction task, and achieve significantly better results than the state-of-the-art uncertainty estimation methods.Comment: Accepted to MICCAI 202

Approximated and User Steerable tSNE for Progressive Visual Analytics

Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a well-suited technique for the visualization of several high-dimensional data. tSNE can create meaningful intermediate results but suffers from a slow initialization that constrains its application in Progressive Visual Analytics. We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets, in a real-world research scenario and for the real-time analysis of high-dimensional streams to illustrate its effectiveness for interactive data analysis

Generating High-Resolution 3D Faces and Bodies Using VQ-VAE-2 with PixelSNAIL Networks on 2D Representations

Modeling and representing 3D shapes of the human body and face is a prominent field due to its applications in the healthcare, clothes, and movie industry. In our work, we tackled the problem of 3D face and body synthesis by reducing 3D meshes to 2D image representations. We show that the face can naturally be modeled on a 2D grid. At the same time, for more challenging 3D body geometries, we proposed a novel non-bijective 3D–2D conversion method representing the 3D body mesh as a plurality of rendered projections on the 2D grid. Then, we trained a state-of-the-art vector-quantized variational autoencoder (VQ-VAE-2) to learn a latent representation of 2D images and fit a PixelSNAIL autoregressive model to sample novel synthetic meshes. We evaluated our method versus a classical one based on principal component analysis (PCA) by sampling from the empirical cumulative distribution of the PCA scores. We used the empirical distributions of two commonly used metrics, specificity and diversity, to quantitatively demonstrate that the synthetic faces generated with our method are statistically closer to real faces when compared with the PCA ones. Our experiment on the 3D body geometry requires further research to match the test set statistics but shows promising results

Genome-wide microarray analysis of tomato roots showed defined responses to iron deficiency

<p>Abstract</p> <p>Background</p> <p>Plants react to iron deficiency stress adopting different kind of adaptive responses. Tomato, a <it>Strategy I </it>plant, improves iron uptake through acidification of rhizosphere, reduction of Fe³⁺to Fe²⁺and transport of Fe²⁺into the cells. Large-scale transcriptional analyses of roots under iron deficiency are only available for a very limited number of plant species with particular emphasis for <it>Arabidopsis thaliana</it>. Regarding tomato, an interesting model species for <it>Strategy I </it>plants and an economically important crop, physiological responses to Fe-deficiency have been thoroughly described and molecular analyses have provided evidence for genes involved in iron uptake mechanisms and their regulation. However, no detailed transcriptome analysis has been described so far.</p> <p>Results</p> <p>A genome-wide transcriptional analysis, performed with a chip that allows to monitor the expression of more than 25,000 tomato transcripts, identified 97 differentially expressed transcripts by comparing roots of Fe-deficient and Fe-sufficient tomato plants. These transcripts are related to the physiological responses of tomato roots to the nutrient stress resulting in an improved iron uptake, including regulatory aspects, translocation, root morphological modification and adaptation in primary metabolic pathways, such as glycolysis and TCA cycle. Other genes play a role in flavonoid biosynthesis and hormonal metabolism.</p> <p>Conclusions</p> <p>The transcriptional characterization confirmed the presence of the previously described mechanisms to adapt to iron starvation in tomato, but also allowed to identify other genes potentially playing a role in this process, thus opening new research perspectives to improve the knowledge on the tomato root response to the nutrient deficiency.</p

Substantial impact of mobility restrictions on reducing COVID-19 incidence in Italy in 2020

Italy was the first country after China to be severely affected by the COVID-19 pandemic, in early 2020. The country responded swiftly to the outbreak with a nationwide two-step lockdown, the first one light, and the second one tight. By analysing 2020 national mobile phone movements, we assessed how lockdown compliance influenced its efficacy

Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution

Technological advances in mass spectrometry imaging (MSI) have contributed to growing interest in 3D MSI. However, the large size of 3D MSI data sets has made their efficient analysis and visualization and the identification of informative molecular patterns computationally challenging. Hierarchical stochastic neighbor embedding (HSNE), a nonlinear dimensionality reduction technique that aims at finding hierarchical and multiscale representations of large data sets, is a recent development that enables the analysis of millions of data points, with manageable time and memory complexities. We demonstrate that HSNE can be used to analyze large 3D MSI data sets at full mass spectral and spatial resolution. To benchmark the technique as well as demonstrate its broad applicability, we have analyzed a number of publicly available 3D MSI data sets, recorded from various biological systems and spanning different mass-spectrometry ionization techniques. We demonstrate that HSNE is able to rapidly identify regions of interest within these large high-dimensionality data sets as well as aid the identification of molecular ions that characterize these regions of interest; furthermore, through clearly separating measurement artifacts, the HSNE analysis exhibits a degree of robustness to measurement batch effects, spatially correlated noise, and mass spectral misalignment

The Italian national survey on coronavirus disease 2019 epidemic spread in nursing homes

Introduction: Residents in facilities such as nursing homes (NHs) are particularly vulnerable to Coronavirus disease 2019 (COVID-19). A national survey was carried out to collect information on the spreading and impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in nursing homes, and on how suspected and/or confirmed cases were managed. We carried out a survey between 25 March 2020 and 5 May 2020. Materials and methods: All Italian nursing homes either public or providing services both privately and within the NHS were included in the study. An on-line questionnaire was sent to 3292 nursing homes across all Italian regions. Nursing homes were also contacted by telephone to provide assistance in completing the questionnaire. Results: A total of 1356 nursing homes voluntarily participated to the survey, hosting a total of 100,806 residents. Overall, 9154 residents died due to any cause from February 1 to the time when the questionnaire was completed (from March 25 to May 5). Of these, 7.4% had COVID-19 and 33.8% had flu-like symptoms, corresponding to a cumulative incidence of 0.7 and 3.1, respectively. Lack of personnel, difficulty in transferring patients to hospital or other facility, isolating residents with COVID-19, number of beds and geographical area were the main factor positively associated to the presence of COVID-19 in nursing homes. Discussion: This survey showed the dissemination and impact of SARS-CoV-2 infection in Italian nursing homes and on how older and potentially chronically ill people residing in these long-term care facilities were managed