Molecular snapshots of the Pex1/6 AAA + complex in action

The peroxisomal proteins Pex1 and Pex6 form a heterohexameric type II AAA+ ATPase complex, which fuels essential protein transport across peroxisomal membranes. Mutations in either ATPase in humans can lead to severe peroxisomal disorders and early death. We present an extensive structural and biochemical analysis of the yeast Pex1/6 complex. The heterohexamer forms a trimer of Pex1/6 dimers with a triangular geometry that is atypical for AAA+ complexes. While the C-terminal nucleotide-binding domains (D2) of Pex6 constitute the main ATPase activity of the complex, both D2 harbour essential sub-strate-binding motifs. ATP hydrolysis results in a pumping motion of the complex, suggesting that Pex1/6 function involves substrate translocation through its central channel. Mutation of the Walker B motif in one D2 domain leads to ATP hydrolysis in the neighbouring domain, giving structural insights into inter-domain communication of these unique heterohexameric AAA + assemblies

Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback

Ultrasound (US) imaging is widely employed for diagnosis and staging of peripheral vascular diseases (PVD), mainly due to its high availability and the fact it does not emit radiation. However, high inter-operator variability and a lack of repeatability of US image acquisition hinder the implementation of extensive screening programs. To address this challenge, we propose an end-to-end workflow for automatic robotic US screening of tubular structures using only the real-time US imaging feedback. We first train a U-Net for real-time segmentation of the vascular structure from cross-sectional US images. Then, we represent the detected vascular structure as a 3D point cloud and use it to estimate the longitudinal axis of the target tubular structure and its mean radius by solving a constrained non-linear optimization problem. Iterating the previous processes, the US probe is automatically aligned to the orientation normal to the target tubular tissue and adjusted online to center the tracked tissue based on the spatial calibration. The real-time segmentation result is evaluated both on a phantom and in-vivo on brachial arteries of volunteers. In addition, the whole process is validated both in simulation and physical phantoms. The mean absolute radius error and orientation error ($\pm$ SD) in the simulation are $1.16\pm0.1~mm$ and $2.7\pm3.3^{\circ}$, respectively. On a gel phantom, these errors are $1.95\pm2.02~mm$ and $3.3\pm2.4^{\circ}$. This shows that the method is able to automatically screen tubular tissues with an optimal probe orientation (i.e. normal to the vessel) and at the same to accurately estimate the mean radius, both in real-time.Comment: Accepted for publication in IEEE Transactions on Industrial Electronics Video: https://www.youtube.com/watch?v=VAaNZL0I5i

Identification of binding proteins for cholesterol absorption inhibitors as components of the intestinal cholesterol transporter

AbstractTo identify protein components of the intestinal cholesterol transporter, rabbit small intestinal brush border membrane vesicles were submitted to photoaffinity labeling using photoreactive derivatives of 2-azetidinone cholesterol absorption inhibitors. An integral membrane protein of Mr 145.3±7.5 kDa was specifically labeled in brush border membrane vesicles from rabbit jejunum and ileum. Its labeling was concentration-dependently inhibited by the presence of cholesterol absorption inhibitors whereas bile acids, D-glucose, fatty acids or cephalexin had no effect. The inhibitory potency of 2-azetidinones to inhibit photolabeling of the 145 kDa protein correlated with their in vivo activity to inhibit intestinal cholesterol absorption. These results suggest that an integral membrane protein of Mr 145 kDa is (a component of) the cholesterol absorption system in the brush border membrane of small intestinal enterocytes

Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry

Thyroid volumetry is crucial in the diagnosis, treatment, and monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D ultrasound is highly operator-dependent. This study compares 2D and tracked 3D ultrasound with an automatic thyroid segmentation based on a deep neural network regarding inter- and intraobserver variability, time, and accuracy. Volume reference was MRI. 28 healthy volunteers (24—50 a) were scanned with 2D and 3D ultrasound (and by MRI) by three physicians (MD 1, 2, 3) with different experience levels (6, 4, and 1 a). In the 2D scans, the thyroid lobe volumes were calculated with the ellipsoid formula. A convolutional deep neural network (CNN) automatically segmented the 3D thyroid lobes. 26, 6, and 6 random lobe scans were used for training, validation, and testing, respectively. On MRI (T1 VIBE sequence) the thyroid was manually segmented by an experienced MD. MRI thyroid volumes ranged from 2.8 to 16.7ml (mean 7.4, SD 3.05). The CNN was trained to obtain an average Dice score of 0.94. The interobserver variability comparing two MDs showed mean differences for 2D and 3D respectively of 0.58 to 0.52ml (MD1 vs. 2), −1.33 to −0.17ml (MD1 vs. 3) and −1.89 to −0.70ml (MD2 vs. 3). Paired samples t-tests showed significant differences for 2D (p = .140, p = .002 and p = .002) and none for 3D (p = .176, p = .722 and p = .057). Intraobsever variability was similar for 2D and 3D ultrasound. Comparison of ultrasound volumes and MRI volumes showed a significant difference for the 2D volumetry of all MDs (p = .002, p = .009, p <.001), and no significant difference for 3D ultrasound (p = .292, p = .686, p = 0.091). Acquisition time was significantly shorter for 3D ultrasound. Tracked 3D ultrasound combined with a CNN segmentation significantly reduces interobserver variability in thyroid volumetry and increases the accuracy of the measurements with shorter acquisition times

Sector Imaging Radar for Enhanced Vision

SIREV (Sector Imaging Radar for Enhanced Vision) is an innovative radar system which can supply high-quality images of a sector in front of an aircraft under almost every weather condition. In contrast to synthetic aperture radar (SAR) systems, no relative motion between sensor and targets is required in SIREV. The high frame repetition frequency in SIREV allows to detect even very rapid changes in the imaged scenes. Besides a map of the earth’s surface, the complex-valued radar images can be further processed to supply additional information about the topography and objects in the field of view. A fast processing algorithm has been developed for SIREV which allows a very accurate, phase preserving and efficient image formation. Raw data were obtained by a first demonstration flight using a helicopter as a platform and the data processing results show good agreement with the theory. Motion errors of the platform could be extracted from the range compressed raw data avoiding the need of an inertial navigation system. After the image formation, coherent and incoherent image averaging processes have been applied to improve the image quality. The evaluation of the computational effort shows that a real-time hardware realization can be carried out using off-the-shelf digital components

RSV: robotic sonography for thyroid volumetry

In nuclear medicine, radioiodine therapy is prescribed to treat diseases like hyperthyroidism. The calculation of the prescribed dose depends, amongst other factors, on the thyroid volume. This is currently estimated using conventional 2D ultrasound imaging. However, this modality is inherently user-dependant, resulting in high variability in volume estimations. To increase reproducibility and consistency, we uniquely combine a neural network-based segmentation with an automatic robotic ultrasound scanning for thyroid volumetry. The robotic acquisition is achieved by using a 6 DOF robotic arm with an attached ultrasound probe. Its movement is based on an online segmentation of each thyroid lobe and the appearance of the US image. During post-processing, the US images are segmented to obtain a volume estimation. In an ablation study, we demonstrated the superiority of the motion guidance algorithms for the robot arm movement compared to a naive linear motion, executed by the robot in terms of volumetric accuracy. In a user study on a phantom, we compared conventional 2D ultrasound measurements with our robotic system. The mean volume measurement error of ultrasound expert users could be significantly decreased from 20.85+/-16.10% to only 8.23+/-3.10% compared to the ground truth. This tendency was observed even more in non-expert users where the mean error improvement with the robotic system was measured to be as high as $85\%$ which clearly shows the advantages of the robotic support.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Human Laryngeal Mucus from the Vocal Folds: Rheological Characterization by Particle Tracking Microrheology and Oscillatory Shear Rheology.

Mucus consistency affects voice physiology and is connected to voice disorders. Nevertheless, the rheological characteristics of human laryngeal mucus from the vocal folds remain unknown. Knowledge about mucus viscoelasticity enables fabrication of artificial mucus with natural properties, more realistic ex-vivo experiments and promotes a better understanding and improved treatment of dysphonia with regard to mucus consistency. We studied human laryngeal mucus samples from the vocal folds with two complementary approaches: 19 samples were successfully applied to particle tracking microrheology (PTM) and five additional samples to oscillatory shear rheology (OSR). Mucus was collected by experienced laryngologists from patients together with demographic data. The analysis of the viscoelasticity revealed diversity among the investigated mucus samples according to their rigidity (absolute G' and G″). Moreover some samples revealed throughout solid-like character (G' &gt; G″), whereas some underwent a change from solid-like to liquid-like (G' &lt; G″). This led to a subdivision into three groups. We assume that the reason for the differences is a variation in the hydration level of the mucus, which affects the mucin concentration and network formation factors of the mucin mesh. The demographic data could not be correlated to the differences, except for the smoking behavior. Mucus of predominant liquid-like character was associated with current smokers