Symbiotic Ocean Modeling Using Physics-Controlled Echo State Networks

We introduce a “symbiotic” ocean modeling strategy that leverages data-driven and machine learning methods to allow high- and low-resolution dynamical models to mutually benefit from each other. In this work we mainly focus on how a low-resolution model can be enhanced within a symbiotic model configuration. The broader aim is to enhance the representation of unresolved processes in low-resolution models, while simultaneously improving the efficiency of high-resolution models. To achieve this, we use a grid-switching approach together with hybrid modeling techniques that combine linear regression-based methods with nonlinear echo state networks. The approach is applied to both the Kuramoto–Sivashinsky equation and a single-layer quasi-geostrophic ocean model, and shown to simulate short-term and long-term behavior better than either purely data-based methods or low-resolution models. By maintaining key flow characteristics, the hybrid modeling techniques are also able to provide higher quality initial conditions for high-resolution models, thereby improving their efficiency.</p

Symbiotic Ocean Modeling Using Physics-Controlled Echo State Networks

We introduce a “symbiotic” ocean modeling strategy that leverages data-driven and machine learning methods to allow high- and low-resolution dynamical models to mutually benefit from each other. In this work we mainly focus on how a low-resolution model can be enhanced within a symbiotic model configuration. The broader aim is to enhance the representation of unresolved processes in low-resolution models, while simultaneously improving the efficiency of high-resolution models. To achieve this, we use a grid-switching approach together with hybrid modeling techniques that combine linear regression-based methods with nonlinear echo state networks. The approach is applied to both the Kuramoto–Sivashinsky equation and a single-layer quasi-geostrophic ocean model, and shown to simulate short-term and long-term behavior better than either purely data-based methods or low-resolution models. By maintaining key flow characteristics, the hybrid modeling techniques are also able to provide higher quality initial conditions for high-resolution models, thereby improving their efficiency.</p

Recommendations for a practical implementation of circulating tumor DNA mutation testing in metastatic non-small-cell lung cancer

BACKGROUND: Liquid biopsy (LB) is a rapidly evolving diagnostic tool for precision oncology that has recently found its way into routine practice as an adjunct to tissue biopsy (TB). The concept of LB refers to any tumor-derived material, such as circulating tumor DNA (ctDNA) or circulating tumor cells that are detectable in blood. An LB is not limited to the blood and may include other fluids such as cerebrospinal fluid, pleural effusion, and urine, among others. PATIENTS AND METHODS: The objective of this paper, devised by international experts from various disciplines, is to review current challenges as well as state-of-the-art applications of ctDNA mutation testing in metastatic non-small-cell lung cancer (NSCLC). We consider pragmatic scenarios for the use of ctDNA from blood plasma to identify actionable targets for therapy selection in NSCLCs. RESULTS: Clinical scenarios where ctDNA mutation testing may be implemented in clinical practice include complementary tissue and LB testing to provide the full picture of patients’ actual predictive profiles to identify resistance mechanism (i.e. secondary mutations), and ctDNA mutation testing to assist when a patient has a discordant clinical history and is suspected of showing intertumor or intratumor heterogeneity. ctDNA mutation testing may provide interesting insights into possible targets that may have been missed on the TB. Complementary ctDNA LB testing also provides an option if the tumor location is hard to biopsy or if an insufficient sample was taken. These clinical use cases highlight practical scenarios where ctDNA LB may be considered as a complementary tool to TB analysis. CONCLUSIONS: Proper implementation of ctDNA LB testing in routine clinical practice is envisioned in the near future. As the clinical evidence of utility expands, the use of LB alongside tissue sample analysis may occur in the patient cases detailed here

Symbiotic Ocean Modeling Using Physics-Controlled Echo State Networks

We introduce a “symbiotic” ocean modeling strategy that leverages data-driven and machine learning methods to allow high- and low-resolution dynamical models to mutually benefit from each other. In this work we mainly focus on how a low-resolution model can be enhanced within a symbiotic model configuration. The broader aim is to enhance the representation of unresolved processes in low-resolution models, while simultaneously improving the efficiency of high-resolution models. To achieve this, we use a grid-switching approach together with hybrid modeling techniques that combine linear regression-based methods with nonlinear echo state networks. The approach is applied to both the Kuramoto–Sivashinsky equation and a single-layer quasi-geostrophic ocean model, and shown to simulate short-term and long-term behavior better than either purely data-based methods or low-resolution models. By maintaining key flow characteristics, the hybrid modeling techniques are also able to provide higher quality initial conditions for high-resolution models, thereby improving their efficiency. Key Points We propose a symbiotic ocean modeling framework in which models of different complexities benefit from each other Unresolved processes are represented through hybrid machine learning methods using data from the symbiotic framework Hybrid correction strategies with imperfect physics as control input improve the representation of key long-term flow propertie