Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization

Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free precession (SSFP) cine sequences. However, currently available techniques lack full automation, limiting reproducibility. We propose a fully automated technique whereby a CMR image sequence is first segmented with a deep, fully convolutional neural network (CNN) architecture, and quadratic basis splines are fitted simultaneously across all cardiac frames using least squares optimization. Experiments are performed using data from 42 patients with hypertrophic cardiomyopathy (HCM) and 21 healthy control subjects. In terms of segmentation, we compared state-of-the-art CNN frameworks, U-Net and dilated convolution architectures, with and without temporal context, using cross validation with three folds. Performance relative to expert manual segmentation was similar across all networks: pixel accuracy was ~97%, intersection-over-union (IoU) across all classes was ~87%, and IoU across foreground classes only was ~85%. Endocardial left ventricular circumferential strain calculated from the proposed pipeline was significantly different in control and disease subjects (-25.3% vs -29.1%, p = 0.006), in agreement with the current clinical literature.Comment: Accepted to Functional Imaging and Modeling of the Heart (FIMH) 201

Systematic identification of edited microRNAs in the human brain

Adenosine-to-inosine (A-to-I) editing modifies RNA transcripts from their genomic blueprint. A prerequisite for this process is a double-stranded RNA (dsRNA) structure. Such dsRNAs are formed as part of the microRNA (miRNA) maturation process, and it is therefore expected that miRNAs are affected by A-to-I editing. Editing of miRNAs has the potential to add another layer of complexity to gene regulation pathways, especially if editing occurs within the miRNA–mRNA recognition site. Thus, it is of interest to study the extent of this phenomenon. Current reports in the literature disagree on its extent; while some reports claim that it may be widespread, others deem the reported events as rare. Utilizing a next-generation sequencing (NGS) approach supplemented by an extensive bioinformatic analysis, we were able to systematically identify A-to-I editing events in mature miRNAs derived from human brain tissues. Our algorithm successfully identified many of the known editing sites in mature miRNAs and revealed 17 novel human sites, 12 of which are in the recognition sites of the miRNAs. We confirmed most of the editing events using in vitro ADAR overexpression assays. The editing efficiency of most sites identified is very low. Similar results are obtained for publicly available data sets of mouse brain-regions tissues. Thus, we find that A-to-I editing does alter several miRNAs, but it is not widespread

Bringing patient advisors to the bedside: a promising avenue for improving partnership between patients and their care team

This paper presents an innovative model of care, which brings patients who have already been through a similar experience of illness (patient advisors) directly to the bedside of patients, where they are viewed as full-fledged members of the clinical team. As part of a pilot project, three patient advisors were recruited and met with patients who had sustained a traumatic amputation and were admitted to the only center of expertise in replantation of the upper limb in Canada. Several individual interviews and focus groups with patients and patient advisors have revealed very promising results. Indeed, patients have expressed tremendous appreciation for their meetings and interactions with patient advisors. They have stated feeling less isolated, having a better morale and increased hopefulness regarding the outcome of the care pathway. Patient advisors also felt a positive impact of their involvement. A larger study needs to be conducted to determine the impact of this model of care on patient adherence to treatment and on members of the health care team

Simple and efficient synthesis of 5′ pre-adenylated DNA using thermostable RNA ligase

We report a simple method of enzymatic synthesis of pre-adenylated DNA linkers/adapters for next-generation sequencing using thermostable RNA ligase from Methanobacterium thermoautotrophicum (MthRnl). Using RNA ligase for the reaction instead of the existing chemical or T4 DNA ligase-based methods allows quantitative conversion of 5′-phosphorylated single-stranded DNA (ssDNA) to the adenylated form. The MthRnl adenylation reaction is specific for ATP and either ssDNA or RNA. In the presence of Mg+2, the reaction has a pH optimum of 6.0–6.5. Unlike reactions that use T4 DNA ligase, this protocol does not require synthesis of a template strand for adenylation. The high yield of the reaction simplifies isolation and purification of the adenylated product. Conducting the adenylation reaction at the elevated temperature (65°C) reduces structural constraints, while increased ATP concentrations allow quantitative adenylation of DNA with a 3′-unprotected end

Integrating B Cell Lineage Information into Statistical Tests for Detecting Selection in Ig Sequences

Detecting selection in B cell immunoglobulin (Ig) sequences is critical to understanding affinity maturation, and can provide insights into antigen-driven selection in normal and pathologic immune responses. The most common sequence-based methods for detecting selection analyze the ratio of replacement (R) and silent (S) mutations using a binomial statistical analysis. However, these approaches have been criticized for low sensitivity. An alternative method is based on the analysis of lineage trees constructed from sets of clonally-related Ig sequences. Several tree shape measures have been proposed as indicators of selection that can be statistically compared across cohorts. However, we show that tree shape analysis is confounded by underlying experimental factors that are difficult to control for in practice, including the sequencing depth and number of generations in each clone. Thus, though lineage tree shapes may reflect selection, their analysis alone is an unreliable measure of in vivo selection. To usefully capture the information provided by lineage trees, we propose a new method that applies the binomial statistical method to mutations identified based on lineage tree structure. This hybrid method is able to detect selection with increased sensitivity in both simulated and experimental data sets. We anticipate that this approach will be especially useful in the analysis of large-scale Ig sequencing data sets generated by high-throughput sequencing technologies

Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images

© 2019, Springer Nature Switzerland AG. Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used

Demonstration of Patient-Specific Simulations to Assess Left Atrial Appendage Thrombogenesis Risk

Atrial fibrillation (AF) alters left atrial (LA) hemodynamics, which can lead to thrombosis in the left atrial appendage (LAA), systemic embolism and stroke. A personalized risk-stratification of AF patients for stroke would permit improved balancing of preventive anticoagulation therapies against bleeding risk. We investigated how LA anatomy and function impact LA and LAA hemodynamics, and explored whether patient-specific analysis by computational fluid dynamics (CFD) can predict the risk of LAA thrombosis. We analyzed 4D-CT acquisitions of LA wall motion with an in-house immersed-boundary CFD solver. We considered six patients with diverse atrial function, three with either a LAA thrombus (removed digitally before running the simulations) or a history of transient ischemic attacks (LAAT/TIA-pos), and three without a LAA thrombus or TIA (LAAT/TIA-neg). We found that blood inside the left atrial appendage of LAAT/TIA-pos patients had marked alterations in residence time and kinetic energy when compared with LAAT/TIA-neg patients. In addition, we showed how the LA conduit, reservoir and booster functions distinctly affect LA and LAA hemodynamics. Finally, fixed-wall and moving-wall simulations produced different LA hemodynamics and residence time predictions for each patient. Consequently, fixed-wall simulations risk-stratified our small cohort for LAA thrombosis worse than moving-wall simulations, particularly patients with intermediate LAA residence time. Overall, these results suggest that both wall kinetics and LAA morphology contribute to LAA blood stasis and thrombosis.This work was partially supported by the Comunidad de Madrid (Sinergias Y2018/BIO-4858 PREFI-CM), Cátedra Excelencia UC3M-Santander, Ministry of Education of Spain (Salvador de Madariaga program), the US NHLBI (NCAI-UCCAI-2017-06-6), the United States American Heart Association (AHA 20POST35200401), and the 2019 UCSD GEM Program. Computational time provided by XSEDE (Comet) and RES (Altamira) is gratefully acknowledged

Waveshifters and Scintillators for Ionizing Radiation Detection

Scintillation and waveshifter materials have been developed for the detection of ionizing radiation in an STTR program between Ludlum Measurements, Inc. and the University of Notre Dame. Several new waveshifter materials have been developed which are comparable in efficiency and faster in fluorescence decay than the standard material Y11 (K27) used in particle physics for several decades. Additionally, new scintillation materials useful for fiber tracking have been developed which have been compared to 3HF. Lastly, work was done on developing liquid scintillators and paint-on scintillators and waveshifters for high radiation environments

Salmonella Infection Drives Promiscuous B Cell Activation Followed by Extrafollicular Affinity Maturation

SummaryThe B cell response to Salmonella typhimurium (STm) occurs massively at extrafollicular sites, without notable germinal centers (GCs). Little is known in terms of its specificity. To expand the knowledge of antigen targets, we screened plasmablast (PB)-derived monoclonal antibodies (mAbs) for Salmonella specificity, using ELISA, flow cytometry, and antigen microarray. Only a small fraction (0.5%–2%) of the response appeared to be Salmonella-specific. Yet, infection of mice with limited B cell receptor (BCR) repertoires impaired the response, suggesting that BCR specificity was important. We showed, using laser microdissection, that somatic hypermutation (SHM) occurred efficiently at extrafollicular sites leading to affinity maturation that in turn led to detectable STm Ag-binding. These results suggest a revised vision of how clonal selection and affinity maturation operate in response to Salmonella. Clonal selection initially is promiscuous, activating cells with virtually undetectable affinity, yet SHM and selection occur during the extrafollicular response yielding higher affinity, detectable antibodies