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1. Characterizing the spatiotemporal evolution of paramagnetic colloids in time-varying magnetic fields with Minkowski functionals

   https://doi.org/10.1039/D0SM01100B  

   Hilou, Elaa; Joshi, Kedar; Biswal, Sibani Lisa (January 2020, Soft Matter)

   Phase separation processes are widely utilized to assemble complex fluids into novel materials. These separation processes can be thermodynamically driven due to changes in concentration, pressure, or temperature. Phase separation can also be induced with external stimuli, such as magnetic fields, resulting in novel nonequilibrium systems. However, how external stimuli influence the transition pathways between phases has not been explored in detail. Here, we describe the phase separation dynamics of superparamagnetic colloids in time-varying magnetic fields. An initially homogeneous colloidal suspension can transition from a continuous colloidal phase with voids to discrete colloidal clusters, through a bicontinuous phase formed via spinodal decomposition. The type of transition depends on the particle concentration and magnitude of the applied magnetic field. The spatiotemporal evolution of the microstructure during the nucleation and growth period is quantified by analyzing the morphology using Minkowski functionals. The characteristic length of the colloidal systems was determined to correlate with system variables such as magnetic field strength, particle concentration, and time in a power-law scaling relationship. Understanding the interplay between particle concentration and applied magnetic field allows for better control of the phases observed in these magnetically tunable colloidal systems. 

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2. A Generative Approach to Testing the Performance of Physiological Control Algorithms

   https://doi.org/10.1115/1.4065934  

   Tivay, Ali; Bighamian, Ramin; Hahn, Jin-Oh; Scully, Christopher G (July 2024, ASME Letters in Dynamic Systems and Control)

   Abstract Physiological closed-loop control algorithms play an important role in the development of autonomous medical care systems, a promising area of research that has the potential to deliver healthcare therapies meeting each patient's specific needs. Computational approaches can support the evaluation of physiological closed-loop control algorithms considering various sources of patient variability that they may be presented with. In this article, we present a generative approach to testing the performance of physiological closed-loop control algorithms. This approach exploits a generative physiological model (which consists of stochastic and dynamic components that represent diverse physiological behaviors across a patient population) to generate a select group of virtual subjects. By testing a physiological closed-loop control algorithm against this select group, the approach estimates the distribution of relevant performance metrics in the represented population. We illustrate the promise of this approach by applying it to a practical case study on testing a closed-loop fluid resuscitation control algorithm designed for hemodynamic management. In this context, we show that the proposed approach can test the algorithm against virtual subjects equipped with a wide range of plausible physiological characteristics and behavior and that the test results can be used to estimate the distribution of relevant performance metrics in the represented population. In sum, the generative testing approach may offer a practical, efficient solution for conducting preclinical tests on physiological closed-loop control algorithms. 

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3. Multiple infection of cells changes the dynamics of basic viral evolutionary processes

   https://doi.org/10.1002/evl3.95  

   Wodarz, Dominik; Levy, David_N; Komarova, Natalia_L (February 2019, Evolution Letters)

   Abstract The infection of cells by multiple copies of a given virus can impact viral evolution in a variety of ways, yet some of the most basic evolutionary dynamics remain underexplored. Using computational models, we investigate how infection multiplicity affects the fixation probability of mutants, the rate of mutant generation, and the timing of mutant invasion. An important insight from these models is that for neutral and disadvantageous phenotypes, rare mutants initially enjoy a fitness advantage in the presence of multiple infection of cells. This arises because multiple infection allows the rare mutant to enter more target cells and to spread faster, while it does not accelerate the spread of the resident wild-type virus. The rare mutant population can increase by entry into both uninfected and wild-type-infected cells, while the established wild-type population can initially only grow through entry into uninfected cells. Following this initial advantageous phase, the dynamics are governed by drift or negative selection, respectively, and a higher multiplicity reduces the chances that mutants fix in the population. Hence, while increased infection multiplicity promotes the presence of neutral and disadvantageous mutants in the short-term, it makes it less likely in the longer term. We show how these theoretical insights can be useful for the interpretation of experimental data on virus evolution at low and high multiplicities. The dynamics explored here provide a basis for the investigation of more complex viral evolutionary processes, including recombination, reassortment, as well as complementary/inhibitory interactions. 

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4. Conformance verification for neural network models of glucose-insulin dynamics

   https://doi.org/10.1145/3365365.3382210  

   Kushner, Taisa; Sankaranarayanan, Sriram; Breton, Marc (April 2020, Hybrid Systems: Computation and Control)

   Neural networks present a useful framework for learning complex dynamics, and are increasingly being considered as components to closed loop predictive control algorithms. However, if they are to be utilized in such safety-critical advisory settings, they must be provably "conformant" to the governing scientific (biological, chemical, physical) laws which underlie the modeled process. Unfortunately, this is not easily guaranteed as neural network models are prone to learn patterns which are artifacts of the conditions under which the training data is collected, which may not necessarily conform to underlying physiological laws. In this work, we utilize a formal range-propagation based approach for checking whether neural network models for predicting future blood glucose levels of individuals with type-1 diabetes are monotonic in terms of their insulin inputs. These networks are increasingly part of closed loop predictive control algorithms for "artificial pancreas" devices which automate control of insulin delivery for individuals with type-1 diabetes. Our approach considers a key property that blood glucose levels must be monotonically decreasing with increasing insulin inputs to the model. Multiple representative neural network models for blood glucose prediction are trained and tested on real patient data, and conformance is tested through our verification approach. We observe that standard approaches to training networks result in models which violate the core relationship between insulin inputs and glucose levels, despite having high prediction accuracy. We propose an approach that can learn conformant models without much loss in accuracy. 

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5. Boosting quantum amplitude exponentially in variational quantum algorithms

   https://doi.org/10.1088/2058-9565/acf4ba  

   Kyaw, Thi Ha; Soley, Micheline B; Allen, Brandon; Bergold, Paul; Sun, Chong; Batista, Victor S; Aspuru-Guzik, Alán (October 2023, Quantum Science and Technology)

   Abstract We introduce a family of variational quantum algorithms, which we coin as quantum iterative power algorithms (QIPAs), and demonstrate their capabilities as applied to global-optimization numerical experiments. Specifically, we demonstrate the QIPA based on a double exponential oracle as applied to ground state optimization of theH₂molecule, search for the transmon qubit ground-state, and biprime factorization. Our results indicate that QIPA outperforms quantum imaginary time evolution (QITE) and requires a polynomial number of queries to reach convergence even with exponentially small overlap between an initial quantum state and the final desired quantum state, under some circumstances. We analytically show that there exists an exponential amplitude amplification at every step of the variational quantum algorithm, provided the initial wavefunction has non-vanishing probability with the desired state and that the unique maximum of the oracle is given by ${\lambda }_1>0$, while all other values are given by the same value $0<{\lambda }_2<{\lambda }_1$(hereλcan be taken as eigenvalues of the problem Hamiltonian). The generality of the global-optimization method presented here invites further application to other problems that currently have not been explored with QITE-based near-term quantum computing algorithms. Such approaches could facilitate identification of reaction pathways and transition states in chemical physics, as well as optimization in a broad range of machine learning applications. The method also provides a general framework for adaptation of a class of classical optimization algorithms to quantum computers to further broaden the range of algorithms amenable to implementation on current noisy intermediate-scale quantum computers. 

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