Network-based protein structural classification.

Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on their features, and then relying on the fact that proteins with similar structures have similar functions. Existing PSC approaches rely on sequence-based or direct three-dimensional (3D) structure-based protein features. By contrast, we first model 3D structures of proteins as protein structure networks (PSNs). Then, we use network-based features for PSC. We propose the use of graphlets, state-of-the-art features in many research areas of network science, in the task of PSC. Moreover, because graphlets can deal only with unweighted PSNs, and because accounting for edge weights when constructing PSNs could improve PSC accuracy, we also propose a deep learning framework that automatically learns network features from weighted PSNs. When evaluated on a large set of approximately 9400 CATH and approximately 12 800 SCOP protein domains (spanning 36 PSN sets), the best of our proposed approaches are superior to existing PSC approaches in terms of accuracy, with comparable running times. Our data and code are available at https://doi.org/10.5281/zenodo.3787922.

Photoacoustic Imaging for Image-guided Endovenous Laser Ablation Procedures.

Accurate fiber tip tracking is a critical clinical problem during endovenous laser ablation (EVLA) of small perforating veins. Currently, ultrasound (US) imaging is the gold-standard modality for visualizing and for accurately placing the ablation fiber within the diseased vein. However, US imaging has limitations such as angular dependency and comet tail artifacts. In addition, EVLA is often performed without any real-time temperature monitoring, which could lead to an insufficient thermal dose or overheating the surrounding tissue. We propose a new technique that combines US and photoacoustic (PA) imaging for concurrent ablation fiber tip tracking and real-time temperature monitoring during EVLA procedures. Our intended implementation of PA imaging for fiber tracking requires minimal modification of existing systems, which makes this technology easy to adopt. Combining US and PA imaging modalities allows for simultaneous visualization of background anatomical structures as well as high contrast, artifact-free, and angle-independent localization of the ablation fiber tip. Preliminary data demonstrates that changes in the amplitude of the PA signal can be used to monitor the localized temperature at the tip of the ablation fiber, which will be invaluable during EVLA procedures. These improvements can enhance the physician's accuracy in performing EVLA procedures and will have a significant impact on the treatment outcomes.