Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution

In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance SR of diffusion MR brain images in terms of errors compared to ground truth. We further show that the reduced error scores produce tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use.Comment: Accepted paper at MICCAI 201

A genotyping array of 3,400 Single Nucleotide Polymorphisms (SNPs) advances the genetic analysis of the iconic tree Araucaria angustifolia, showing that the natural populations ar e more differentiated than previously reported.

Edição especial dos resumos do IUFRO World Congress, 25., 2019, Curitiba

Disentangling human error from the ground truth in segmentation of medical images

Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depends on the quality of labels. This problem is particularly pertinent in the medical image domain, where both the annotation cost and inter-observer variability are high. In a typical label acquisition process, different human experts provide their estimates of the "true'' segmentation labels under the influence of their own biases and competence levels. Treating these noisy labels blindly as the ground truth limits the performance that automatic segmentation algorithms can achieve. In this work, we present a method for jointly learning, from purely noisy observations alone, the reliability of individual annotators and the true segmentation label distributions, using two coupled CNNs. The separation of the two is achieved by encouraging the estimated annotators to be maximally unreliable while achieving high fidelity with the noisy training data. We first define a toy segmentation dataset based on MNIST and study the properties of the proposed algorithm. We then demonstrate the utility of the method on three public medical imaging segmentation datasets with simulated (when necessary) and real diverse annotations: 1) MSLSC (multiple-sclerosis lesions); 2) BraTS (brain tumours); 3) LIDC-IDRI (lung abnormalities). In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large. The experiments also show strong ability to capture the complex spatial characteristics of annotators' mistakes. Our code is available at \url{https://github.com/moucheng2017/LearnNoisyLabelsMedicalImages}

Genomic analysis in the study of drought tolerance using recombinant inbred lines and recurrent selection.

Plant breeding for drought tolerance is one of the approaches that can be applied in order to improve water use efficiency in agriculture

Engineering self-organising helium bubble lattices in tungsten

The self-organisation of void and gas bubbles in solids into a superlattices is an intriguing nanoscale phenomenon. Despite the discovery of these lattices 30 years ago, the atomistics behind the ordering mechanisms responsible for the formation of these nanostructures are yet to be fully elucidated. Here we report on the direct observation via transmission electron microscopy of the formation of bubble lattices under He+ ion bombardment. By careful control of the irradiation conditions, it has been possible to engineer the bubble size and spacing of the superlattice leading to important conclusions about the significance of vacancy supply in determining the physical characteristics of the system. Furthermore, no bubble lattice alignment was observed in the directions pointing to a key driving mechanism for the formation of these ordered nanostructures being the two-dimensional diffusion of self-interstitial atoms

Hepcidin Expression in Iron Overload Diseases Is Variably Modulated by Circulating Factors

Hepcidin is a regulatory hormone that plays a major role in controlling body iron homeostasis. Circulating factors (holotransferrin, cytokines, erythroid regulators) might variably contribute to hepcidin modulation in different pathological conditions. There are few studies analysing the relationship between hepcidin transcript and related protein expression profiles in humans. Our aims were: a. to measure hepcidin expression at either hepatic, serum and urinary level in three paradigmatic iron overload conditions (hemochromatosis, thalassemia and dysmetabolic iron overload syndrome) and in controls; b. to measure mRNA hepcidin expression in two different hepatic cell lines (HepG2 and Huh-7) exposed to patients and controls sera to assess whether circulating factors could influence hepcidin transcription in different pathological conditions. Our findings suggest that hepcidin assays reflect hepatic hepcidin production, but also indicate that correlation is not ideal, likely due to methodological limits and to several post-trascriptional events. In vitro study showed that THAL sera down-regulated, HFE-HH and C-NAFLD sera up-regulated hepcidin synthesis. HAMP mRNA expression in Huh-7 cells exposed to sera form C-Donors, HFE-HH and THAL reproduced, at lower level, the results observed in HepG2, suggesting the important but not critical role of HFE in hepcidin regulation

Reticulated origin of domesticated emmer wheat supports a dynamic model for the emergence of agriculture in the fertile crescent

We used supernetworks with datasets of nuclear gene sequences and novel markers detecting retrotransposon insertions in ribosomal DNA loci to reassess the evolutionary relationships among tetraploid wheats. We show that domesticated emmer has a reticulated genetic ancestry, sharing phylogenetic signals with wild populations from all parts of the wild range. The extent of the genetic reticulation cannot be explained by post-domestication gene flow between cultivated emmer and wild plants, and the phylogenetic relationships among tetraploid wheats are incompatible with simple linear descent of the domesticates from a single wild population. A more parsimonious explanation of the data is that domesticated emmer originates from a hybridized population of different wild lineages. The observed diversity and reticulation patterns indicate that wild emmer evolved in the southern Levant, and that the wild emmer populations in south-eastern Turkey and the Zagros Mountains are relatively recent reticulate descendants of a subset of the Levantine wild populations. Based on our results we propose a new model for the emergence of domesticated emmer. During a pre-domestication period, diverse wild populations were collected from a large area west of the Euphrates and cultivated in mixed stands. Within these cultivated stands, hybridization gave rise to lineages displaying reticulated genealogical relationships with their ancestral populations. Gradual movement of early farmers out of the Levant introduced the pre-domesticated reticulated lineages to the northern and eastern parts of the Fertile Crescent, giving rise to the local wild populations but also facilitating fixation of domestication traits. Our model is consistent with the protracted and dispersed transition to agriculture indicated by the archaeobotanical evidence, and also with previous genetic data affiliating domesticated emmer with the wild populations in southeast Turkey. Unlike other protracted models, we assume that humans played an intuitive role throughout the process.Natural Environment Research Council [NE/E015948/1]; Slovak Research and Development Agency [APVV-0661-10, APVV-0197-10]info:eu-repo/semantics/publishedVersio

Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions

<p>Abstract</p> <p>Background</p> <p>Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems.</p> <p>Results</p> <p>In this paper, we propose Deterministic Effects Propagation Networks (DEPNs) as a special Bayesian Network approach to reverse engineer signaling networks from a combination of protein expression and perturbation data. DEPNs allow to reconstruct protein networks based on combinatorial intervention effects, which are monitored via changes of the protein expression or activation over one or a few time points. Our implementation of DEPNs allows for latent network nodes (i.e. proteins without measurements) and has a built in mechanism to impute missing data. The robustness of our approach was tested on simulated data. We applied DEPNs to reconstruct the <it>ERBB </it>signaling network in <it>de novo </it>trastuzumab resistant human breast cancer cells, where protein expression was monitored on Reverse Phase Protein Arrays (RPPAs) after knockdown of network proteins using RNAi.</p> <p>Conclusion</p> <p>DEPNs offer a robust, efficient and simple approach to infer protein signaling networks from multiple interventions. The method as well as the data have been made part of the latest version of the R package "nem" available as a supplement to this paper and via the Bioconductor repository.</p

A classification of the torsion tensors on almost contact manifolds with B-metric

The space of the torsion (0,3)-tensors of the linear connections on almost contact manifolds with B-metric is decomposed in 15 orthogonal and invariant subspaces with respect to the action of the structure group. Three known connections, preserving the structure, are characterized regarding this classification.Comment: 17 pages, exposition clarified, references adde

IgE allergy diagnostics and other relevant tests in allergy, a World Allergy Organization position paper

Currently, testing for immunoglobulin E (IgE) sensitization is the cornerstone of diagnostic evaluation in suspected allergic conditions. This review provides a thorough and updated critical appraisal of the most frequently used diagnostic tests, both in vivo and in vitro. It discusses skin tests, challenges, and serological and cellular in vitro tests, and provides an overview of indications, advantages and disadvantages of each in conditions such as respiratory, food, venom, drug, and occupational allergy. Skin prick testing remains the first line approach in most instances; the added value of serum specific IgE to whole allergen extracts or components, as well as the role of basophil activation tests, is evaluated. Unproven, non-validated, diagnostic tests are also discussed. Throughout the review, the reader must bear in mind the relevance of differentiating between sensitization and allergy; the latter entails not only allergic sensitization, but also clinically relevant symptoms triggered by the culprit allergen.info:eu-repo/semantics/publishedVersio