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Neural image representations offer the possibility of high fidelity, compact storage, and resolution-independent accuracy, providing an attractive alternative to traditional pixel- and grid-based representations. However, coordinate neural networks fail to capture discontinuities present in the image and tend to blur across them; we aim to address this challenge. In many cases, such as rendered images, vector graphics, diffusion curves, or solutions to partial differential equations, the locations of the discontinuities are known. We take those locations as input, represented as linear, quadratic, or cubic Bézier curves, and construct a feature field that is discontinuous across these locations and smooth everywhere else. Finally, we use a shallow multi-layer perceptron to decode the features into the signal value. To construct the feature field, we develop a new data structure based on a curved triangular mesh, with features stored on the vertices and on a subset of the edges that are marked as discontinuous. We show that our method can be used to compress a 100, 000²-pixel rendered image into a 25MB file; can be used as a new diffusion-curve solver by combining with Monte-Carlo-based methods or directly supervised by the diffusion-curve energy; or can be used for compressing 2D physics simulation data. 

The human body represents a collection of interacting systems that range in scale from nanometers to meters. Investigations from a systems perspective focus on how the parts work together to enact changes across spatial scales, and further our understanding of how systems function and fail. Here, we highlight systems approaches presented at the 2022 Summer Biomechanics, Bio-engineering, and Biotransport Conference in the areas of solid mechanics; fluid mechanics; tissue and cellular engineering; biotransport; and design, dynamics, and rehabilitation; and biomechanics education. Systems approaches are yielding new insights into human biology by leveraging state-of-the-art tools, which could ultimately lead to more informed design of therapies and medical devices for preventing and treating disease as well as rehabilitating patients using strategies that are uniquely optimized for each patient. Educational approaches can also be designed to foster a foundation of systems-level thinking. 

The voltage-sensing domain (VSD) is a four-helix modular protein domain that converts electrical signals into conformational changes, leading to open pores and active enzymes. In most voltage-sensing proteins, the VSDs do not interact with one another, and the S1–S3 helices are considered mainly scaffolding, except in the voltage-sensing phosphatase (VSP) and the proton channel (Hv). To investigate its contribution to VSP function, we mutated four hydrophobic amino acids in S1 to alanine (F127, I131, I134, and L137), individually or in combination. Most of these mutations shifted the voltage dependence of activity to higher voltages; however, not all substrate reactions were the same. The kinetics of enzymatic activity were also altered, with some mutations significantly slowing down dephosphorylation. The voltage dependence of VSD motions was consistently shifted to lower voltages and indicated a second voltage-dependent motion. Additionally, none of the mutations broke the VSP dimer, indicating that the S1 impact could stem from intra- and/or intersubunit interactions. Lastly, when the same mutations were introduced into a genetically encoded voltage indicator, they dramatically altered the optical readings, making some of the kinetics faster and shifting the voltage dependence. These results indicate that the S1 helix in VSP plays a critical role in tuning the enzyme’s conformational response to membrane potential transients and influencing the function of the VSD. 

The goal of tissue decellularization is to efficiently remove unwanted cellular components, such as DNA and cellular debris, while retaining the complex structural and molecular milieu within the extracellular matrix (ECM). Decellularization protocols to date are centered on customized tissue-specific and lab-specific protocols that involve consecutive manual steps which results in variable and protocol-specific ECM material. The differences that result from the inconsistent protocols between decellularized ECMs affect consistency across batches, limit comparisons between results obtained from different laboratories, and could limit the transferability of the material for consistent laboratory or clinical use. The present study is the first proof-of-concept towards the development of a standardized protocol that can be used to derive multiple ECM biomaterials (powders and hydrogels) via a previously established automated system. The automated decellularization method developed by our group was used due to its short decellularization time (4 hours) and its ability to reduce batch-to-batch variability. The ECM obtained using this first iteration of a unified protocol was able to produce ECM hydrogels from skin, lung, muscle, tendons, cartilage, and laryngeal tissues. All hydrogels formed in this study were cytocompatible and showed gelation and rheological properties consistent with previous ECM hydrogels. The ECMs also showed unique proteomic composition. The present study represents the first step towards developing standardized protocols that can be used on multiple tissues in a fast, scalable, and reproducible manner. 

Phosphates and polyphosphates play ubiquitous roles in biology as integral structural components of cell membranes and bone, or as vehicles of energy storage via adenosine triphosphate and phosphocreatine. The solution phase space of phosphate species appears more complex than previously known. We present nuclear magnetic resonance (NMR) and cryogenic transmission electron microscopy (cryo-TEM) experiments that suggest phosphate species including orthophosphates, pyrophosphates, and adenosine phosphates associate into dynamic assemblies in dilute solutions that are spectroscopically “dark.” Cryo-TEM provides visual evidence of the formation of spherical assemblies tens of nanometers in size, while NMR indicates that a majority population of phosphates remain as unassociated ions in exchange with spectroscopically invisible assemblies. The formation of these assemblies is reversibly and entropically driven by the partial dehydration of phosphate groups, as verified by diffusion-ordered spectroscopy (DOSY), indicating a thermodynamic state of assembly held together by multivalent interactions between the phosphates. Molecular dynamics simulations further corroborate that orthophosphates readily cluster in aqueous solutions. This study presents the surprising discovery that phosphate-containing molecules, ubiquitously present in the biological milieu, can readily form dynamic assemblies under a wide range of commonly used solution conditions, highlighting a hitherto unreported property of phosphate’s native state in biological solutions. 

Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit natural random variation. Many different types of noise exist, each produced by a separate algorithm. In this paper, we present a single generative model which can learn to generate multiple types of noise as well as blend between them. In addition, it is capable of producing spatially-varying noise blends despite not having access to such data for training. These features are enabled by training a denoising diffusion model using a novel combination of data augmentation and network conditioning techniques. Like procedural noise generators, the model's behavior is controllable via interpretable parameters plus a source of randomness. We use our model to produce a variety of visually compelling noise textures. We also present an application of our model to improving inverse procedural material design; using our model in place of fixed-type noise nodes in a procedural material graph results in higher-fidelity material reconstructions without needing to know the type of noise in advance. Open-sourced materials can be found at https://armanmaesumi.github.io/onenoise/ 

We introduce the task of spotting temporally precise, fine-grained events in video (detecting the precise moment in time events occur). Precise spotting requires models to reason globally about the full-time scale of actions and locally to identify subtle frame-to-frame appearance and motion differences that identify events during these actions. Surprisingly, we find that top performing solutions to prior video understanding tasks such as action detection and segmentation do not simultaneously meet both requirements. In response, we propose E2E-Spot, a compact, end-to-end model that performs well on the precise spotting task and can be trained quickly on a single GPU. We demonstrate that E2E-Spot significantly outperforms recent baselines adapted from the video action detection, segmentation, and spotting literature to the precise spotting task. Finally, we contribute new annotations and splits to several fine-grained sports action datasets to make these datasets suitable for future work on precise spotting.