Blood-wall fluttering instability as a physiomarker of the progression of thoracic aortic aneurysms.

The diagnosis of aneurysms is informed by empirically tracking their size and growth rate. Here, by analysing the growth of aortic aneurysms from first principles via linear stability analysis of flow through an elastic blood vessel, we show that abnormal aortic dilatation is associated with a transition from stable flow to unstable aortic fluttering. This transition to instability can be described by the critical threshold for a dimensionless number that depends on blood pressure, the size of the aorta, and the shear stress and stiffness of the aortic wall. By analysing data from four-dimensional flow magnetic resonance imaging for 117 patients who had undergone cardiothoracic imaging and for 100 healthy volunteers, we show that the dimensionless number is a physiomarker for the growth of thoracic ascending aortic aneurysms and that it can be used to accurately discriminate abnormal versus natural growth. Further characterization of the transition to blood-wall fluttering instability may aid the understanding of the mechanisms underlying aneurysm progression in patients.

A Deep Learning Approach to Using Wearable Seismocardiography (SCG) for Diagnosing Aortic Valve Stenosis and Predicting Aortic Hemodynamics Obtained by 4D Flow MRI.

In this paper, we explored the use of deep learning for the prediction of aortic flow metrics obtained using 4-dimensional (4D) flow magnetic resonance imaging (MRI) using wearable seismocardiography (SCG) devices. 4D flow MRI provides a comprehensive assessment of cardiovascular hemodynamics, but it is costly and time-consuming. We hypothesized that deep learning could be used to identify pathological changes in blood flow, such as elevated peak systolic velocity ([Formula: see text]) in patients with heart valve diseases, from SCG signals. We also investigated the ability of this deep learning technique to differentiate between patients diagnosed with aortic valve stenosis (AS), non-AS patients with a bicuspid aortic valve (BAV), non-AS patients with a mechanical aortic valve (MAV), and healthy subjects with a normal tricuspid aortic valve (TAV). In a study of 77 subjects who underwent same-day 4D flow MRI and SCG, we found that the [Formula: see text] values obtained using deep learning and SCGs were in good agreement with those obtained by 4D flow MRI. Additionally, subjects with non-AS TAV, non-AS BAV, non-AS MAV, and AS could be classified with ROC-AUC (area under the receiver operating characteristic curves) values of 92%, 95%, 81%, and 83%, respectively. This suggests that SCG obtained using low-cost wearable electronics may be used as a supplement to 4D flow MRI exams or as a screening tool for aortic valve disease.

MRI-MECH: mechanics-informed MRI to estimate esophageal health.

Dynamic magnetic resonance imaging (MRI) is a popular medical imaging technique that generates image sequences of the flow of a contrast material inside tissues and organs. However, its application to imaging bolus movement through the esophagus has only been demonstrated in few feasibility studies and is relatively unexplored. In this work, we present a computational framework called mechanics-informed MRI (MRI-MECH) that enhances that capability, thereby increasing the applicability of dynamic MRI for diagnosing esophageal disorders. Pineapple juice was used as the swallowed contrast material for the dynamic MRI, and the MRI image sequence was used as input to the MRI-MECH. The MRI-MECH modeled the esophagus as a flexible one-dimensional tube, and the elastic tube walls followed a linear tube law. Flow through the esophagus was governed by one-dimensional mass and momentum conservation equations. These equations were solved using a physics-informed neural network. The physics-informed neural network minimized the difference between the measurements from the MRI and model predictions and ensured that the physics of the fluid flow problem was always followed. MRI-MECH calculated the fluid velocity and pressure during esophageal transit and estimated the mechanical health of the esophagus by calculating wall stiffness and active relaxation. Additionally, MRI-MECH predicted missing information about the lower esophageal sphincter during the emptying process, demonstrating its applicability to scenarios with missing data or poor image resolution. In addition to potentially improving clinical decisions based on quantitative estimates of the mechanical health of the esophagus, MRI-MECH can also be adapted for application to other medical imaging modalities to enhance their functionality.

Significance of Intraoperative Medication Data and Predictive Model Selection for Predicting Postoperative First-Time Atrial Fibrillation.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in clinical practice and has a well-established association with coronary artery bypass graft (CABG) surgery. Being able to predict post-operative AF (POAF) may improve surgical outcomes. This study retrospectively assembled a large cohort of 3,807 first-time CABG patients with no prior AF to study factors that contribute to occurrence of POAF, in addition to testing models that may predict its incidence. Several clinical features with established relevance to POAF were extracted from the EHR, along with a record of medications administered intra-operatively. Tests of performance with logistic regression, decision tree, and neural network predictive models showed slight improvements when incorporating medication information. Analysis of the clinical and medications data indicate that there may be effects contributing to POAF incidence captured in the medication administration records. Our results show that improved predictive performance is achievable by incorporating a record of medications administered intra-operatively.

Optimization in the space domain for density compensation with the nonuniform FFT.

The non-uniform Discrete Fourier Transform algorithm has shown great utility for reconstructing images from non-uniformly spaced Fourier samples in several imaging modalities. Due to the non-uniform spacing, some correction for the variable density of the samples must be made. Common methods for generating density compensation values are either sub-optimal or only consider a finite set of points in the optimization. This manuscript presents an algorithm for generating density compensation values from a set of Fourier samples that takes into account the point spread function over an entire rectangular region in the image domain. We show that the reconstructed images using the density compensation values of this method are of superior quality when compared to other standard methods. Results are shown with a numerical phantom and with magnetic resonance images of the abdomen and the knee.

Global Aortic Pulse Wave Velocity is Unchanged in Bicuspid Aortopathy With Normal Valve Function but Elevated in Patients With Aortic Valve Stenosis: Insights From a 4D Flow MRI Study of 597 Subjects.

BACKGROUND: Aortopathy is common with bicuspid aortic valve (BAV), and underlying intrinsic tissue abnormalities are believed causative. Valve-mediated hemodynamics are altered in BAV and may contribute to aortopathy and its progression. The contribution of intrinsic tissue defects versus altered hemodynamics to aortopathy progression is not known. PURPOSE: To investigate relative contributions of tissue-innate versus hemodynamics in progression of BAV aortopathy. STUDY TYPE: Retrospective. SUBJECTS: Four hundred seventy-three patients with aortic dilatation (diameter ≥40 mm; comprised of 281 BAV with varied AS severity, 192 tricuspid aortic valve [TAV] without AS) and 124 healthy controls. Subjects were 19-91 years (141/24% female). FIELD STRENGTH/SEQUENCE: 1.5T, 3T; time-resolved gradient-echo 3D phase-contrast (4D flow) MRI. ASSESSMENT: A surrogate measure for global aortic wall stiffness, pulse wave velocity (PWV), was quantified from MRI by standardized, automated technique based on through-plane flow cross-correlation maximization. Comparisons were made between BAV patients with aortic dilatation and varying aortic valve stenosis (AS) severity and healthy subjects and aortopathy patients with normal TAV. STATISTICAL TESTS: Multivariable regression, analysis of covariance (ANCOVA), Tukey's, student's (t), Mann-Whitney (U) tests, were used with significance levels P < 0.05 or P < 0.01 for post-hoc Bonferroni-corrected t/U tests. Bland-Altman and ICC calculations were performed. RESULTS: Multivariable regression showed age with the most significant association for increased PWV in all groups (increase 0.073-0.156 m/sec/year, R2  = 0.30-48). No significant differences in aortic PWV were observed between groups without AS (P = 0.20-0.99), nor were associations between PWV and regurgitation or Sievers type observed (P = 0.60, 0.31 respectively). In contrast, BAV AS patients demonstrated elevated PWV and a significant relationship for AS severity with increased PWV (covariate: age, R2  = 0.48). BAV and TAV patients showed no association between aortic diameter and PWV (P = 0.73). DATA CONCLUSION: No significant PWV differences were observed between BAV patients with normal valve function and control groups. However, AS severity and age in BAV patients were directly associated with PWV increases. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Utilizing the Wavelet Transform's Structure in Compressed Sensing.

Compressed sensing has empowered quality image reconstruction with fewer data samples than previously thought possible. These techniques rely on a sparsifying linear transformation. The Daubechies wavelet transform is commonly used for this purpose. In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result. After inclusion of this affine transformation, we modify the resulting optimization problem to comply with the form of the Basis Pursuit Denoising problem. Finally, we show theoretically that this yields a lower bound on the error of the reconstruction and present results where solving this modified problem yields images of higher quality for the same sampling patterns using both magnetic resonance and optical images.

Fast variable density Poisson-disc sample generation with directional variation for compressed sensing in MRI.

We present a fast method for generating random samples according to a variable density poisson-disc distribution. A minimum parameter value is used to create a background grid array for keeping track of those points that might affect any new candidate point; this reduces the number of conflicts that must be checked before acceptance of a new point, thus reducing the number of computations required. We demonstrate the algorithm's ability to generate variable density poisson-disc sampling patterns according to a parameterized function, including patterns where the variations in density are a function of direction. We further show that these sampling patterns are appropriate for compressed sensing applications. Finally, we present a method to generate patterns with a specific acceleration rate.

Detecting Aortic Valve-Induced Abnormal Flow with Seismocardiography and Cardiac MRI.

Cardiac MRI (CMR) techniques offer non-invasive visualizations of cardiac morphology and function. However, imaging can be time-consuming and complex. Seismocardiography (SCG) measures physical vibrations transmitted through the chest from the beating heart and pulsatile blood flow. SCG signals can be acquired quickly and easily, with inexpensive electronics. This study investigates relationships between CMR metrics of function and SCG signal features. Same-day CMR and SCG data were collected from 28 healthy adults and 6 subjects with aortic valve disease history. Correlation testing and statistical median/decile calculations were performed with data from the healthy cohort. MR-quantified flow and function parameters in the healthy cohort correlated with particular SCG energy levels, such as peak aortic velocity with low-frequency SCG (coefficient 0.43, significance 0.02) and peak flow with high-frequency SCG (coefficient 0.40, significance 0.03). Valve disease-induced flow abnormalities in patients were visualized with MRI, and corresponding abnormalities in SCG signals were identified. This investigation found significant cross-modality correlations in cardiac function metrics and SCG signals features from healthy subjects. Additionally, through comparison to normative ranges from healthy subjects, it observed correspondences between pathological flow and abnormal SCG. This may support development of an easy clinical test used to identify potential aortic flow abnormalities.

Seismocardiography and 4D flow MRI reveal impact of aortic valve replacement on chest acceleration and aortic hemodynamics.

Aortic valve replacement (AVR) is a common treatment for severe aortic valve disease, which can adversely affect blood flow in the aorta. Seismocardiography (SCG) measures physical vibrations at the exterior of the chest, which can be sensitive to altered cardiac function and flow dynamics. Magnetic resonance imaging (MRI) can image blood movement, and it can provide depiction and quantification of aortic flow. Here we present SCG and MRI measurements from before and after AVR and ascending aorta replacement, in the case of a woman with bicuspid aortic valve disease and a dilated ascending aorta. SCG measurements show elevated energy during systole indicating stenotic flow before surgery and lowered systolic energy levels after replacement with a prosthetic valve. MRI shows jetting, helical flow before surgery, and cohesive flow after.

Imaging and T2 relaxometry of short-T2 connective tissues in the knee using ultrashort echo-time double-echo steady-state (UTEDESS).

PURPOSE: To develop a radial, double-echo steady-state (DESS) sequence with ultra-short echo-time (UTE) capabilities for T2 measurement of short-T2 tissues along with simultaneous rapid, signal-to-noise ratio (SNR)-efficient, and high-isotropic-resolution morphological knee imaging. METHODS: THe 3D radial UTE readouts were incorporated into DESS, termed UTEDESS. Multiple-echo-time UTEDESS was used for performing T2 relaxometry for short-T2 tendons, ligaments, and menisci; and for Dixon water-fat imaging. In vivo T2 estimate repeatability and SNR efficiency for UTEDESS and Cartesian DESS were compared. The impact of coil combination methods on short-T2 measurements was evaluated by means of simulations. UTEDESS T2 measurements were compared with T2 measurements from Cartesian DESS, multi-echo spin-echo (MESE), and fast spin-echo (FSE). RESULTS: UTEDESS produced isotropic resolution images with high SNR efficiency in all short-T2 tissues. Simulations and experiments demonstrated that sum-of-squares coil combinations overestimated short-T2 measurements. UTEDESS measurements of meniscal T2 were comparable to DESS, MESE, and FSE measurements while the tendon and ligament measurements were less biased than those from Cartesian DESS. Average UTEDESS T2 repeatability variation was under 10% in all tissues. CONCLUSION: The T2 measurements of short-T2 tissues and high-resolution morphological imaging provided by UTEDESS makes it promising for studying the whole knee, both in routine clinical examinations and longitudinal studies. Magn Reson Med 78:2136-2148, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Improved cortical bone specificity in UTE MR Imaging.

PURPOSE: Methods for direct visualization of compact bone using MRI have application in several "MR-informed" technologies, such as MR-guided focused ultrasound, MR-PET reconstruction and MR-guided radiation therapy. The specificity of bone imaging can be improved by manipulating image sensitivity to Bloch relaxation phenomena, facilitating distinction of bone from other tissues detected by MRI. METHODS: From Bloch equation dynamics, excitation pulses suitable for creating specific sensitivity to short-T2 magnetization from cortical bone are identified. These pulses are used with UTE subtraction demonstrate feasibility of MR imaging of compact bone with positive contrast. RESULTS: MR images of bone structures are acquired with contrast similar to that observed in x-ray CT images. Through comparison of MR signal intensities with CT Hounsfield units of the skull, the similarity of contrast is quantified. The MR technique is also demonstrated in other regions of the body that are relevant for interventional procedures, such as the shoulder, pelvis and leg. CONCLUSION: Matching RF excitation pulses to relaxation rates improves the specificity to bone of short-T2 contrast. It is demonstrated with a UTE sequence to acquire images of cortical bone with positive contrast, and the contrast is verified by comparison with x-ray CT. Magn Reson Med 77:684-695, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Accelerated model-based proton resonance frequency shift temperature mapping using echo-based GRAPPA reconstruction.

PURPOSE: To develop an acceleration method for MR temperature estimation using model-based proton resonance frequency (PRF) shift method. MATERIALS AND METHODS: Images of 16 different echo times (TE) were acquired in one RF excitation using a multi-echo gradient-recalled echo (GRE) sequence. Fully sampled k-space data were retrospectively under-sampled at a net reduction factor between two and three using the proposed under-sampling strategy. K-spaces of three different TEs were combined together to perform the proposed reconstruction method called Echo-based GRAPPA. Ex vivo goose liver cooling experiment and in vivo breast imaging experiment were performed to investigate the accuracy of Echo-based GRAPPA. Conventional GRAPPA reconstruction was implemented for comparison using the same sampling pattern. RESULTS: The goose liver imaging experiment shows that the reconstruction-induced temperature RMSE of a selected region of interest (ROI) is less than 1.4 °C for Echo-based GRAPPA at a net reduction factor of 2.3. The breast imaging experiment shows that the mean temperature error of water-fat mixed ROIs is 2.3 °C at a net reduction factor of 2.7. Conventional GRAPPA shows larger temperature RMSE than Echo-based GRAPPA. CONCLUSION: The proposed method can accelerate the MR temperature estimation using model-based PRF at a net reduction factor between two and three with a reconstruction-induced temperature error less than 3°C in water-fat mixed ROIs.

Engineered protein nano-compartments for targeted enzyme localization.

Compartmentalized co-localization of enzymes and their substrates represents an attractive approach for multi-enzymatic synthesis in engineered cells and biocatalysis. Sequestration of enzymes and substrates would greatly increase reaction efficiency while also protecting engineered host cells from potentially toxic reaction intermediates. Several bacteria form protein-based polyhedral microcompartments which sequester functionally related enzymes and regulate their access to substrates and other small metabolites. Such bacterial microcompartments may be engineered into protein-based nano-bioreactors, provided that they can be assembled in a non-native host cell, and that heterologous enzymes and substrates can be targeted into the engineered compartments. Here, we report that recombinant expression of Salmonella enterica ethanolamine utilization (eut) bacterial microcompartment shell proteins in E. coli results in the formation of polyhedral protein shells. Purified recombinant shells are morphologically similar to the native Eut microcompartments purified from S. enterica. Surprisingly, recombinant expression of only one of the shell proteins (EutS) is sufficient and necessary for creating properly delimited compartments. Co-expression with EutS also facilitates the encapsulation of EGFP fused with a putative Eut shell-targeting signal sequence. We also demonstrate the functional localization of a heterologous enzyme (ß-galactosidase) targeted to the recombinant shells. Together our results provide proof-of-concept for the engineering of protein nano-compartments for biosynthesis and biocatalysis.

Optimized compatible set of BioBrick™ vectors for metabolic pathway engineering.

The BioBrick™ paradigm for the assembly of enzymatic pathways is being adopted and becoming a standard practice in microbial engineering. We present a strategy to adapt the BioBrick™ paradigm to allow the quick assembly of multi-gene pathways into a number of vectors as well as for the quick mobilization of any cloned gene into vectors with different features for gene expression and protein purification. A primary BioBrick™ (BB-eGFP) was developed where the promoter/RBS, multiple cloning sites, optional protein purification affinity tags and reporter gene were all separated into discrete regions by additional restriction enzymes. This primary BB-eGFP then served as the template for additional BioBrick™ vectors with different origins of replication, antibiotic resistances, inducible promoters (arabinose, IPTG or anhydrotetracycline), N- or C-terminal Histidine tags with thrombin cleavage, a LacZα reporter gene and an additional origin of mobility (oriT). All developed BioBricks™ and BioBrick™ compatible vectors were shown to be functional by measuring reporter gene expression. Lastly, a C(30) carotenoid pathway was assembled as a model enzymatic pathway to demonstrate in vivo functionality and compatibility of this engineered vector system.

Enhancement of survival and electricity production in an engineered bacterium by light-driven proton pumping.

Microorganisms can use complex photosystems or light-dependent proton pumps to generate membrane potential and/or reduce electron carriers to support growth. The discovery that proteorhodopsin is a light-dependent proton pump that can be expressed readily in recombinant bacteria enables development of new strategies to probe microbial physiology and to engineer microbes with new light-driven properties. Here, we describe functional expression of proteorhodopsin and light-induced changes in membrane potential in the bacterium Shewanella oneidensis strain MR-1. We report that there were significant increases in electrical current generation during illumination of electrochemical chambers containing S. oneidensis expressing proteorhodopsin. We present evidence that an engineered strain is able to consume lactate at an increased rate when it is illuminated, which is consistent with the hypothesis that proteorhodopsin activity enhances lactate uptake by increasing the proton motive force. Our results demonstrate that there is coupling of a light-driven process to electricity generation in a nonphotosynthetic engineered bacterium. Expression of proteorhodopsin also preserved the viability of the bacterium under nutrient-limited conditions, providing evidence that fulfillment of basic energy needs of organisms may explain the widespread distribution of proteorhodopsin in marine environments.

Light-energy conversion in engineered microorganisms.

Increasing interest in renewable resources by the energy and chemical industries has spurred new technologies both to capture solar energy and to develop biologically derived chemical feedstocks and fuels. Advances in molecular biology and metabolic engineering have provided new insights and techniques for increasing biomass and biohydrogen production, and recent efforts in synthetic biology have demonstrated that complex regulatory and metabolic networks can be designed and engineered in microorganisms. Here, we explore how light-driven processes may be incorporated into nonphotosynthetic microbes to boost metabolic capacity for the production of industrial and fine chemicals. Progress towards the introduction of light-driven proton pumping or anoxygenic photosynthesis into Escherichia coli to increase the efficiency of metabolically-engineered biosynthetic pathways is highlighted.

Characterization of three homologs of the large subunit of the magnesium chelatase from Chlorobaculum tepidum and interaction with the magnesium protoporphyrin IX methyltransferase.

Green bacteria synthesize several types of (bacterio)chlorophylls for the assembly of functional photosynthetic reaction centers and antenna complexes. A distinctive feature of green bacteria compared with other photosynthetic microbes is that their genomes contain multiple homologs of the large subunit (BchH) of the magnesium chelatase which is a three-subunit enzyme complex (BchH, BchD, and BchI) that inserts magnesium into protoporphyrin IX as the first committed step of (bacterio)chlorophyll biosynthesis. There is speculation that the additional BchH homologs may regulate the biosynthesis of each type of chlorophyll, although the biochemical properties of the different magnesium chelatase complexes from a single species of green bacteria have not yet been compared. In this study, we investigated the activities of all three chelatase complexes from the green sulfur bacterium Chlorobaculum tepidum and interactions with the next enzyme in the pathway, magnesium protoporphyrin IX methyltransferase (BchM). Although all three chelatase complexes insert magnesium into protoporphyrin IX, the activities range by a factor of 10(5). Further, there are differences in the interactions between the BchH homologs and BchM; two of the subunits increase the methyltransferase activity by 30-60%, and the third decreases it by 30%. Expression of the chelatase complexes alone and together with BchM in Escherichia coli overproducing protoporphyrin IX suggests that the chelatase is the rate-limiting enzyme. We observed that BchM uses protoporphyrin IX without bound metal as a substrate. Our results conflict with expectations generated by previous gene inactivation studies and suggest a complex regulation of chlorophyll biosynthesis in green bacteria.

Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics.

Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample set were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias to some degree, assigned ranks among the techniques revealed that for most LC-FTICR-MS analyses linear regression normalization ranked either first or second. However, the lack of a definitive trend among the techniques suggested the need for additional investigation into adapting normalization approaches for label-free proteomics. Nevertheless, this study serves as an important step for evaluating approaches that address systematic biases related to relative quantification and label-free proteomics.