Short, partially complementary, single-stranded (ss)DNA strands can form nanostructures with a wide variety of shapes and mechanical properties. It is well known that semiflexible, linear dsDNA can undergo an isotropic to nematic (IN) phase transition and that sufficiently bent structures can form a biaxial nematic phase. Here, we use numerical simulations to explore how the phase behavior of linear DNA constructs changes as we tune the mechanical properties of the constituent DNA by changing the nucleotide sequence. The IN-phase transition can be suppressed in so-called DNA “nunchakus”: structures consisting of two rigid dsDNA arms, separated by a sufficiently flexible spacer. In this paper, we use simulations to explore what phase behavior to expect for different linear DNA constructs. To this end, we first performed numerical simulations exploring the structural properties of a number of different DNA oligonucleotides using the oxDNA package. We then used the structural information generated in the oxDNA simulations to construct more coarse-grained models of the rod-like, bent-core, and nunchaku DNA. These coarse-grained models were used to explore the phase behavior of suspensions of the various DNA constructs. The approach explored in this paper makes it possible to “design” the phase behavior of DNA constructs by a suitable choice of the constituent nucleotide sequence.

COVID-19 data exhibit various biases, not least a significant weekly periodic oscillation observed globally in case and death data. There has been significant debate over whether this may be attributed to weekly socialising and working patterns, or is due to underlying biases in the reporting process. We characterise the weekly biases globally and demonstrate that equivalent biases also occur in the current cholera outbreak in Haiti. By comparing published COVID-19 time series to retrospective datasets from the United Kingdom (UK) that are not subject to the same reporting biases, we demonstrate that this dataset does not contain any weekly periodicity, and hence the weekly trends observed both in the UK and globally may be fully explained by biases in the testing and reporting processes. These conclusions play an important role in forecasting healthcare demand and determining suitable interventions for future infectious disease outbreaks.

Standard-of-care treatment regimes have long been designed to for maximal cell kill, yet these strategies often fail when applied to treatment-resistant tumors, resulting in patient relapse. Adaptive treatment strategies have been developed as an alternative approach, harnessing intra-tumoral competition to suppress the growth of treatment resistant populations, to delay or even prevent tumor progression. Following recent clinical implementations of adaptive therapy, it is of significant interest to optimise adaptive treatment protocols. We propose the application of deep reinforcement learning models to provide generalised solutions within adaptive drug scheduling, and demonstrate this framework can outperform the current adaptive protocols, extending time to progression by up to a quarter. This strategy is robust to varying model parameterisations, and the underlying tumor model. We demonstrate the deep learning framework can produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing a novel, analytically-derived optimal treatment strategy with no knowledge of the underlying mathematical tumor model. This approach is highly relevant beyond the simple, analytically-tractable tumor model considered here, demonstrating the capability of deep learning frameworks to help inform and develop treatment strategies in complex settings. Finally, we propose a pathway to integrate mechanistic modelling with DRL to tailor generalist treatment strategies to individual patients in the clinic, generating personalised treatment schedules that consistently outperform clinical standard-of-care protocols.