Generating Protein Structures for Pathway Discovery Using Deep Learning.

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today. One common aspect of this challenge is pathway discovery, where the start and end states of a scientific phenomenon are known or can be approximated, but the mechanistic details in between are unknown. Here, we propose a representation-learning-based solution that uses interpolation and extrapolation in an abstract representation space to synthesize potential transition states, which are automatically validated using MD simulations. The new simulations of the synthesized transition states are subsequently incorporated into the representation learning, leading to an iterative framework for targeted path sampling. Our approach is demonstrated by recovering the transition of a RAS-RAF protein domain (CRD) from membrane-free to interacting with the membrane using coarse-grain MD simulations.

Exploring Classification of Topological Priors with Machine Learning for Feature Extraction.

In many scientific endeavors, increasingly abstract representations of data allow for new interpretive methodologies and conceptualization of phenomena. For example, moving from raw imaged pixels to segmented and reconstructed objects allows researchers new insights and means to direct their studies toward relevant areas. Thus, the development of new and improved methods for segmentation remains an active area of research. With advances in machine learning and neural networks, scientists have been focused on employing deep neural networks such as U-Net to obtain pixel-level segmentations, namely, defining associations between pixels and corresponding/referent objects and gathering those objects afterward. Topological analysis, such as the use of the Morse-Smale complex to encode regions of uniform gradient flow behavior, offers an alternative approach: first, create geometric priors, and then apply machine learning to classify. This approach is empirically motivated since phenomena of interest often appear as subsets of topological priors in many applications. Using topological elements not only reduces the learning space but also introduces the ability to use learnable geometries and connectivity to aid the classification of the segmentation target. In this paper, we describe an approach to creating learnable topological elements, explore the application of ML techniques to classification tasks in a number of areas, and demonstrate this approach as a viable alternative to pixel-level classification, with similar accuracy, improved execution time, and requiring marginal training data.

Dual atrial rhythms: a case report of an unusual cause of pacemaker syndrome.

BACKGROUND: Atrial dissociation (AD) is described as the existence of two simultaneous electrically isolated atrial rhythms. Theoretically, detection of dual atrial rhythms with a sufficiently high rate by pacemaker can lead to automatic mode switching and associated pacemaker syndrome. Such a clinical observation has not been reported before in the literature. CASE SUMMARY: An 87-year-old female with Ebstein's anomaly status post-tricuspid valve annuloplasty and tricuspid valve replacement and a dual-chamber pacemaker presented with congestive heart failure 1 week after undergoing atrial lead revision. Interrogation of her dual-chamber pacemaker revealed two atrial rhythms: sinus or atrial-paced rhythm and electrically isolated atrial tachycardia (AT). Sensing of both atrial rhythms by the pacemaker led to automatic mode switching, which manifested as ventricular paced rhythm with retrograde P waves on electrocardiogram. Adjusting the atrial lead sensitivity to a level higher than the sensing amplitude of AT restored atrial paced and ventricular sensed rhythm, which resulted in resolution of heart failure symptoms. DISCUSSION: Regardless of the cause of AD, there must be electrical insulation between the two rhythms for their independent coexistence in the atria. Atrial dissociation can lead to pacemaker syndrome from automatic mode switching. If the sensing amplitude during sinus rhythm is significantly larger than that of AT, adjusting the atrial lead sensitivity would solve the issue, as in the present case. Otherwise, atrial lead revision, pharmacotherapy, or AT ablation should be considered.