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Freshwater mussels are essential parts of our ecosystems to reduce water pollution. As natural bio-filters, they deactivate pollutants such as heavy metals, providing a sustainable method for water decontamination. This project will enable the use of Artificial Intelligence (AI) to monitor mussel behavior, particularly their gaping activity, to use them as bio-indicators for early detection of water contamination. In this paper, we employ advanced 3D reconstruction techniques to create detailed models of mussels to improve the accuracy of AI-based analysis. Specifically, we use a state-of-the-art 3D reconstruction tool, Neural Radiance Fields (NeRF), to create 3D models of mussel valve configurations and behavioral patterns. NeRF enables 3D reconstruction of scenes and objects from a sparse set of 2D images. To capture these images, we developed a data collection system capable of imaging mussels from multiple viewpoints. The system featured a turntable made of foam board with markers around the edges and a designated space in the center for mounting the mussels. The turntable was attached to a servo motor controlled by an ESP32 microcontroller. It rotated in a few degree increments, with the ESP32 camera capturing an image at each step. The images, along with degree information and timestamps, are stored on a Secure Digital (SD) memory card. Several components, such as the camera holder and turntable base, are 3D printed. These images are used to train a NeRF model using the Python-based Nerfstudio framework, and the resulting 3D models were viewed via the Nerfstudio API. The setup was designed to be user-friendly, making it easy for educational outreach engagements and to involve secondary education by replicating and operating 3D reconstructions of their chosen objects. We validated the accessibility and the impact of this platform in a STEM education summer program. A team of high school students from the Juntos Summer Academy at NC State University worked on this platform, gaining hands-on experience in embedded hardware development, basic machine learning principles, and 3D reconstruction from 2D images. We also report on their feedback on the activity. 

Cells interact as dynamically evolving ecosystems. While recent single-cell and spatial multi-omics technologies quantify individual cell characteristics, predicting their evolution requires mathematical modeling. We propose a conceptual framework—a cell behavior hypothesis grammar—that uses natural language statements (cell rules) to create mathematical models. This enables systematic integration of biological knowledge and multi-omics data to generate in silico models, enabling virtual “thought experiments” that test and expand our understanding of multicellular systems and generate new testable hypotheses. This paper motivates and describes the grammar, offers a reference implementation, and demonstrates its use in developing both de novo mechanistic models and those informed by multi-omics data. We show its potential through examples in cancer and its broader applicability in simulating brain development. This approach bridges biological, clinical, and systems biology research for mathematical modeling at scale, allowing the community to predict emergent multicellular behavior. 

Abstract Cells respond to physical stimuli, such as stiffness 1 , fluid shear stress 2 and hydraulic pressure 3,4 . Extracellular fluid viscosity is a key physical cue that varies under physiological and pathological conditions, such as cancer 5 . However, its influence on cancer biology and the mechanism by which cells sense and respond to changes in viscosity are unknown. Here we demonstrate that elevated viscosity counterintuitively increases the motility of various cell types on two-dimensional surfaces and in confinement, and increases cell dissemination from three-dimensional tumour spheroids. Increased mechanical loading imposed by elevated viscosity induces an actin-related protein 2/3 (ARP2/3)-complex-dependent dense actin network, which enhances Na + /H + exchanger 1 (NHE1) polarization through its actin-binding partner ezrin. NHE1 promotes cell swelling and increased membrane tension, which, in turn, activates transient receptor potential cation vanilloid 4 (TRPV4) and mediates calcium influx, leading to increased RHOA-dependent cell contractility. The coordinated action of actin remodelling/dynamics, NHE1-mediated swelling and RHOA-based contractility facilitates enhanced motility at elevated viscosities. Breast cancer cells pre-exposed to elevated viscosity acquire TRPV4-dependent mechanical memory through transcriptional control of the Hippo pathway, leading to increased migration in zebrafish, extravasation in chick embryos and lung colonization in mice. Cumulatively, extracellular viscosity is a physical cue that regulates both short- and long-term cellular processes with pathophysiological relevance to cancer biology.