Alternating lysis and lysogeny is a winning strategy in bacteriophages due to Parrondo's paradox.

SignificanceBacteriophages, the most widespread reproducing biological entity on Earth, employ two strategies of virus-host interaction: lysis of the host cell and lysogeny whereby the virus genome integrates into the host genome and propagates vertically with it. We present a population model that reveals an effect known as Parrondo's paradox in game theory: Alternating between lysis and lysogeny is a winning strategy for a bacteriophage, even when each strategy individually is at a disadvantage compared with a competing bacteriophage. Thus, evolution of bacteriophages appears to optimize the ratio between the lysis and lysogeny propensities rather than the phage burst size in any individual phase. This phenomenon is likely to be relevant for understanding evolution of other host-parasites systems.

Simulation of Interaction-Induced Chiral Topological Dynamics on a Digital Quantum Computer.

Chiral edge states are highly sought after as paradigmatic topological states relevant to both quantum information processing and dissipationless electron transport. Using superconducting transmon-based quantum computers, we demonstrate chiral topological propagation that is induced by suitably designed interactions, instead of flux or spin-orbit coupling. Also different from conventional 2D realizations, our effective Chern lattice is implemented on a much smaller equivalent 1D spin chain, with sequences of entangling gates encapsulating the required time-reversal breaking. By taking advantage of the quantum nature of the platform, we circumvented difficulties from the limited qubit number and gate fidelity in present-day noisy intermediate-scale quantum era quantum computers, paving the way for the quantum simulation of more sophisticated topological states on very rapidly developing quantum hardware.

Extending the Lifespan of Multicellular Organisms via Periodic and Stochastic Intercellular Competition.

Resolution of the intrinsic conflict between the reproduction of single cells and the homeostasis of a multicellular organism is central to animal biology and has direct impact on aging and cancer. Intercellular competition is indispensable in multicellular organisms because it weeds out senescent cells, thereby increasing the organism's fitness and delaying aging. In this Letter, we describe the growth dynamics of multicellular organisms in the presence of intercellular competition and show that the lifespan of organisms can be extended and the onset of cancer can be delayed if cells alternate between competition (a fair strategy) and noncompetitive growth, or cooperation (a losing strategy). This effect recapitulates the weak form of the game-theoretic Parrondo's paradox, whereby strategies that are individually fair or losing achieve a winning outcome when alternated. We show in a population model that periodic and stochastic switching between competitive and cooperative cellular strategies substantially extends the organism lifespan and reduces cancer incidence, which cannot be achieved simply by optimizing the competitive ability of the cells. These results indicate that cells could have evolved to optimally mix competitive and cooperative strategies, and that periodic intercellular competition could potentially be exploited and tuned to delay aging.

Synaptic Pruning in Schizophrenia: Does Minocycline Modulate Psychosocial Brain Development?

Recent studies suggest that the tetracycline antibiotic minocycline, or its cousins, hold therapeutic potential for affective and psychotic disorders. This is proposed on the basis of a direct effect on microglia-mediated frontocortical synaptic pruning (FSP) during adolescence, perhaps in genetically susceptible individuals harboring risk alleles in the complement component cascade that is involved in this normal process of CNS circuit refinement. In reviewing this field, it is argued that minocycline is actually probing and modulating a deeply evolved and intricate system wherein psychosocial stimuli sculpt the circuitry of the "social brain" underlying adult behavior and personality. Furthermore, this system can generate psychiatric morbidity that is not dependent on genetic variation. This view has important ramifications for understanding "pathologies" of human social behavior and cognition as well as providing long-sought potential mechanistic links between social experience and susceptibility to mental and physical disease.

Paradoxical Survival: Examining the Parrondo Effect across Biology.

Parrondo's paradox, in which losing strategies can be combined to produce winning outcomes, has received much attention in mathematics and the physical sciences; a plethora of exciting applications has also been found in biology at an astounding pace. In this review paper, the authors examine a large range of recent developments of Parrondo's paradox in biology, across ecology and evolution, genetics, social and behavioral systems, cellular processes, and disease. Intriguing connections between numerous works are identified and analyzed, culminating in an emergent pattern of nested recurrent mechanics that appear to span the entire biological gamut, from the smallest of spatial and temporal scales to the largest-from the subcellular to the complete biosphere. In analyzing the macro perspective, the pivotal role that the paradox plays in the shaping of biological life becomes apparent, and its identity as a potential universal principle underlying biological diversity and persistence is uncovered. Directions for future research are also discussed in light of this new perspective.

Black Swans of CRISPR: Stochasticity and Complexity of Genetic Regulation.

Recent waves of controversies surrounding genetic engineering have spilled into popular science in Twitter battles between reputable scientists and their followers. Here, a cautionary perspective on the possible blind spots and risks of CRISPR and related biotechnologies is presented, focusing in particular on the stochastic nature of cellular control processes.

Realization of higher-order topological lattices on a quantum computer.

Programmable quantum simulators may one day outperform classical computers at certain tasks. But at present, the range of viable applications with noisy intermediate-scale quantum (NISQ) devices remains limited by gate errors and the number of high-quality qubits. Here, we develop an approach that places digital NISQ hardware as a versatile platform for simulating multi-dimensional condensed matter systems. Our method encodes a high-dimensional lattice in terms of many-body interactions on a reduced-dimension model, thereby taking full advantage of the exponentially large Hilbert space of the host quantum system. With circuit optimization and error mitigation techniques, we measured on IBM superconducting quantum processors the topological state dynamics and protected mid-gap spectra of higher-order topological lattices, in up to four dimensions, with high accuracy. Our projected resource requirements scale favorably with system size and lattice dimensionality compared to classical computation, suggesting a possible route to useful quantum advantage in the longer term.

Designing non-Hermitian real spectra through electrostatics.

Non-hermiticity presents a vast newly opened territory that harbors new physics and applications such as lasing and sensing. However, only non-Hermitian systems with real eigenenergies are stable, and great efforts have been devoted in designing them through enforcing parity-time (PT) symmetry. In this work, we exploit a lesser-known dynamical mechanism for enforcing real-spectra, and develop a comprehensive and versatile approach for designing new classes of parent Hamiltonians with real spectra. Our design approach is based on a new electrostatics analogy for modified non-Hermitian bulk-boundary correspondence, where electrostatic charge corresponds to density of states and electric fields correspond to complex spectral flow. As such, Hamiltonians of any desired spectra and state localization profile can be reverse-engineered, particularly those without any guiding symmetry principles. By recasting the diagonalization of non-Hermitian Hamiltonians as a Poisson boundary value problem, our electrostatics analogy also transcends the gain/loss-induced compounding of floating-point errors in traditional numerical methods, thereby allowing access to far larger system sizes.

Generalized Solutions of Parrondo's Games.

In game theory, Parrondo's paradox describes the possibility of achieving winning outcomes by alternating between losing strategies. The framework had been conceptualized from a physical phenomenon termed flashing Brownian ratchets, but has since been useful in understanding a broad range of phenomena in the physical and life sciences, including the behavior of ecological systems and evolutionary trends. A minimal representation of the paradox is that of a pair of games played in random order; unfortunately, closed-form solutions general in all parameters remain elusive. Here, we present explicit solutions for capital statistics and outcome conditions for a generalized game pair. The methodology is general and can be applied to the development of analytical methods across ratchet-type models, and of Parrondo's paradox in general, which have wide-ranging applications across physical and biological systems.

Predator Dormancy is a Stable Adaptive Strategy due to Parrondo's Paradox.

Many predators produce dormant offspring to escape harsh environmental conditions, but the evolutionary stability of this adaptation has not been fully explored. Like seed banks in plants, dormancy provides a stable competitive advantage when seasonal variations occur, because the persistence of dormant forms under harsh conditions compensates for the increased cost of producing dormant offspring. However, dormancy also exists in environments with minimal abiotic variation-an observation not accounted for by existing theory. Here it is demonstrated that dormancy can out-compete perennial activity under conditions of extensive prey density fluctuation caused by overpredation. It is shown that at a critical level of prey density fluctuations, dormancy becomes an evolutionarily stable strategy. This is interpreted as a manifestation of Parrondo's paradox: although neither the active nor dormant forms of a dormancy-capable predator can individually out-compete a perennially active predator, alternating between these two losing strategies can paradoxically result in a winning strategy. Parrondo's paradox may thus explain the widespread success of quiescent behavioral strategies such as dormancy, suggesting that dormancy emerges as a natural evolutionary response to the self-destructive tendencies of overpredation and related biological phenomena.

Data-driven computational method for determining accurate analytical field solutions on arbitrary-geometry spectrometers.

Not all workable spectrometer systems can be represented by an exact analytical field; furthermore, while traditional design methods typically involve approximating known analytical fields with appropriate electrode/magnet configurations, modern simulation-based approaches do away with analytical representations altogether. Nonetheless, analytical solutions offer several advantages, including the ease of accurately evaluating focusing properties, and the prospect of optimization through mathematical analysis. In this paper, we propose an original and novel data-driven computational method for determining highly-accurate analytical field solutions, applicable to energy analyzers of arbitrary geometry and electrode/magnet configuration. The method encompasses a statistical analysis on a sample numerical field, from which appropriate eigenvalue bases are identified for the construction of an approximate series solution. The proposed method is demonstrated on three instruments-the parallel radial mirror analyzer, radial mirror analyzer, and parallel cylindrical mirror analyzer-illustrating its excellent versatility and accuracy in both the derived analytical fields ( < 1% mean error) and relative energy resolution predictions. We also demonstrate the potential application of automated optimization on the analytical fields through an adaptive Levenberg-Marquardt algorithm, exploiting the dimension-reducing properties of the method to aid in efficiency. Our proposed method is general enough to be applied across other fields of applications.

A hybrid genetic-Levenberg Marquardt algorithm for automated spectrometer design optimization.

Advancements in computational tools have driven increasingly automated, simulation-centric approaches in the design and optimization of spectroscopic electron-optical systems. These augmented methodologies accelerate the optimization process, and can yield better-performing instruments. While classical gradient-based methods had been explored, modern alternatives such as genetic algorithms have rarely been applied. In this paper, we propose a novel fully-automated hybrid optimization method for use on electron-optical systems. An adaptive switching scheme between a Levenberg-Marquardt and a genetic sub-algorithm enables the simultaneous exploitation of the computational efficiency of the former and the robustness of the latter. The hybrid algorithm is demonstrated on two test examples-the parallel cylindrical mirror analyzer, and the first-order focusing parallel magnetic sector analyzer-and is found to outperform both the Levenberg-Marquardt and genetic algorithms individually. Our work is significant as a versatile tool for parallel energy spectrometer design, and can greatly aid the development of mechanically-complex parallel energy analyzers, which are expected to be of utility to the semiconductor industry in the near future.

Approximate perturbative solutions of quasispecies model with recombination.

Despite the major roles played by genetic recombination in ecoevolutionary processes, limited progress has been made in analyzing realistic recombination models to date, due largely to the complexity of the associated mechanisms and the strongly nonlinear nature of the dynamical differential systems. In this paper, we consider a many-loci genomic model with fitness dependent on the Hamming distance from a reference genome, and adopt a Hamilton-Jacobi formulation to derive perturbative solutions for general linear fitness landscapes. The horizontal gene transfer model is used to describe recombination processes. Cases of weak selection and weak recombination with simultaneous mutation and selection are examined, yielding semianalytical solutions for the distribution surplus of O(1/N) accuracy, where N is the number of nucleotides in the genome.

Acute Health Impacts of the Southeast Asian Transboundary Haze Problem-A Review.

Air pollution has emerged as one of the world's largest environmental health threats, with various studies demonstrating associations between exposure to air pollution and respiratory and cardiovascular diseases. Regional air quality in Southeast Asia has been seasonally affected by the transboundary haze problem, which has often been the result of forest fires from "slash-and-burn" farming methods. In light of growing public health concerns, recent studies have begun to examine the health effects of this seasonal haze problem in Southeast Asia. This review paper aims to synthesize current research efforts on the impact of the Southeast Asian transboundary haze on acute aspects of public health. Existing studies conducted in countries affected by transboundary haze indicate consistent links between haze exposure and acute psychological, respiratory, cardiovascular, and neurological morbidity and mortality. Future prospective and longitudinal studies are warranted to quantify the long-term health effects of recurrent, but intermittent, exposure to high levels of seasonal haze. The mechanism, toxicology and pathophysiology by which these toxic particles contribute to disease and mortality should be further investigated. Epidemiological studies on the disease burden and socioeconomic cost of haze exposure would also be useful to guide policy-making and international strategy in minimizing the impact of seasonal haze in Southeast Asia.

Solution of the Crow-Kimura model with changing population size and Allee effect.

The Crow-Kimura model is commonly used in the modeling of genetic evolution in the presence of mutations and associated selection pressures. We consider a modified version of the Crow-Kimura model, in which population sizes are not fixed and Allee saturation effects are present. We demonstrate the evolutionary dynamics in this system through an analytical approach, examining both symmetric and single-peak fitness landscape cases. Especially interesting are the dynamics of the populations near extinction. A special version of the model with saturation and degradation on the single-peak fitness landscape is investigated as a candidate of the Allee effect in evolution, revealing reduction tendencies of excessively large populations, and extinction tendencies for small populations. The analytical solutions for these dynamics are presented with accuracy O(1/N), where N is the number of nucleotides in the genome.

Computational Imaging Prediction of Starburst-Effect Diffraction Spikes.

When imaging bright light sources, rays of light emanating from their centres are commonly observed; this ubiquitous phenomenon is known as the starburst effect. The prediction and characterization of starburst patterns formed by extended sources have been neglected to date. In the present study, we propose a novel trichromatic computational framework to calculate the image of a scene viewed through an imaging system with arbitrary focus and aperture geometry. Diffractive light transport, imaging sensor behaviour, and implicit image adjustments typical in modern imaging equipment are modelled. Characterization methods for key optical parameters of imaging systems are also examined. Extensive comparisons between theoretical and experimental results reveal excellent prediction quality for both focused and defocused systems.