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Generalizing multiple memories from a single drive: The hysteron latch
Full text linkFar-from-equilibrium systems can form memories of previous deformations or driving. In systems from sheared glassy materials to buckling beams to crumpled sheets, this behavior is dominated by return-point memory, in which revisiting a past extremum of driving restores the system to a previous state. Cyclic driving with both positive and negative strains forms multiple nested memories, as in a single-dial combination lock, while asymmetric driving (only positive strain) cannot. We study this case in a general model of hysteresis that considers discrete elements called hysterons. We show how two hysterons with a frustrated interaction can violate return-point memory, realizing multiple memories of asymmetric driving. This reveals a general principle for designing systems that store sequences of cyclic driving, whether symmetric or asymmetric. In disordered systems, asymmetric driving is a sensitive tool for the direct measurement of frustration
The role of viral interaction in household transmission of symptomatic influenza and respiratory syncytial virus
Full text linkThe role of viral interaction—where one virus enhances or inhibits infection with another virus—in respiratory virus transmission is not well characterized. This study used data from 4029 total participants from 957 households who participated in a prospective household cohort study in Southeast Michigan, U.S.A to examine how viral coinfection and cocirculation may impact transmission of symptomatic influenza and respiratory syncytial virus infections. We utilized multivariable mixed effects regression to estimate transmission risk when index cases were coinfected with multiple viruses and when viruses cocirculated within households. This analysis included 201 coinfections involving influenza A virus, 67 involving influenza B virus, and 181 involving respiratory syncytial virus. We show that exposure to symptomatic coinfected index cases was associated with reduced risk of influenza A virus and respiratory syncytial virus transmission compared to exposure to singly infected cases, while infection with another virus was associated with increased risk of acquisition of these viruses. Exposure to coinfected cases among contacts infected with other viruses was associated with increased risk of influenza B virus acquisition. These results suggest that viral interaction may impact symptomatic transmission of these viruses
Digital twins as global learning health and disease models for preventive and personalized medicine
Full text linkIneffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands of genes across multiple cell types and organs. Disease progression can vary between patients and over time, influenced by genetic and environmental factors. To address this challenge, digital twins have emerged as a promising approach, which have led to international initiatives aiming at clinical implementations. Digital twins are virtual representations of health and disease processes that can integrate real-time data and simulations to predict, prevent, and personalize treatments. Early clinical applications of DTs have shown potential in areas like artificial organs, cancer, cardiology, and hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes across multiple biological scales; (2) developing computational methods to integrate data into DTs; (3) prioritizing disease mechanisms and therapeutic targets; (4) creating interoperable DT systems that can learn from each other; (5) designing user-friendly interfaces for patients and clinicians; (6) scaling DT technology globally for equitable healthcare access; (7) addressing ethical, regulatory, and financial considerations. Overcoming these hurdles could pave the way for more predictive, preventive, and personalized medicine, potentially transforming healthcare delivery and improving patient outcomes
Simple and High-Precision Hamiltonian Simulation by Compensating Trotter Error with Linear Combination of Unitary Operations
Full text linkTrotter and linear combination of unitary (LCU) operations are two popular Hamiltonian simulation methods. The Trotter method is easy to implement and enjoys good system-size dependence endowed by commutator scaling, while the LCU method admits high-accuracy simulation with a smaller gate cost. We propose Hamiltonian simulation algorithms using LCU to compensate Trotter error, which enjoy both of their advantages. By adding few gates after the th-order Trotter formula, we realize a better time scaling than 2th-order Trotter. Our first algorithm exponentially improves the accuracy scaling of the th-order Trotter formula. For a generic Hamiltonian, the estimated gate counts of the first algorithm can be 2 orders of magnitude smaller than the best analytical bound of fourth-order Trotter formula. In the second algorithm, we consider the detailed structure of Hamiltonians and construct LCU for Trotter errors with commutator scaling. Consequently, for lattice Hamiltonians, the algorithm enjoys almost linear system-size dependence and quadratically improves the accuracy of the th-order Trotter. For the lattice system, the second algorithm can achieve 3 to 4 orders of magnitude higher accuracy with the same gate costs as the optimal Trotter algorithm. These algorithms provide an easy-to-implement approach to achieve a low-cost and high-precision Hamiltonian simulation
Autocatalytic assembly of a chimeric aminoacyl-RNA synthetase ribozyme
Full text linkAutocatalytic reactions driving the self-assembly of biological polymers are important for the origin of life, yet few experimental examples of such reactions exist. Here we report an autocatalytic assembly pathway that generates a chimeric, amino acid–bridged aminoacyl-RNA synthetase ribozyme. The noncovalent complex of ribozyme fragments initiates low-level aminoacylation of one of the fragments, which, after loop-closing ligation, generates a highly active covalently linked chimeric ribozyme. The generation of this ribozyme is increasingly efficient over time due to the autocatalytic assembly cycle that sustains the ribozyme over indefinite cycles of serial dilution. Because of its trans activity, this ribozyme also assembles ribozymes distinct from itself, such as the hammerhead, suggesting that RNA aminoacylation, coupled with nonenzymatic ligation, could have facilitated the emergence and propagation of ribozymes
Dealing With the Complexity of Effective Population Size in Conservation Practice
Full text linkEffective population size (Ne) is one of the most important parameters in evolutionary biology, as it is linked to the long-term survival capability of species. Therefore, Ne greatly interests conservation geneticists, but it is also very relevant to policymakers, managers, and conservation practitioners. Molecular methods to estimate Ne rely on various assumptions, including no immigration, panmixia, random sampling, absence of spatial genetic structure, and/or mutation-drift equilibrium. Species are, however, often characterized by fragmented populations under changing environmental conditions and anthropogenic pressure. Therefore, the estimation methods' assumptions are seldom addressed and rarely met, possibly leading to biased and inaccurate Ne estimates. To address the challenges associated with estimating Ne for conservation purposes, the COST Action 18134, Genomic Biodiversity Knowledge for Resilient Ecosystems (G-BiKE), organized an international workshop that met in August 2022 in Brașov, Romania. The overarching goal was to operationalize the current knowledge of Ne estimation methods for conservation practitioners and decision-makers. We set out to identify datasets to evaluate the sensitivity of Ne estimation methods to violations of underlying assumptions and to develop data analysis strategies that addressed pressing issues in biodiversity monitoring and conservation. Referring to a comprehensive body of scientific work on Ne, this meeting report is not intended to be exhaustive but rather to present approaches, workshop findings, and a collection of papers that serve as fruits of those efforts. We aimed to provide insights and opportunities to help bridge the gap between scientific research and conservation practice
Genetic, developmental, and neural changes underlying the evolution of butterfly mate preference
Full text linkMany studies have linked genetic variation to behavior, but few connect to the intervening neural circuits that underlie the arc from sensation to action. Here, we used a combination of genome-wide association (GWA), developmental gene expression, and photoreceptor electrophysiology to investigate the architecture of mate choice behavior in Heliconius cydno butterflies, a clade where males identify preferred mates based on wing color patterns. We first found that the GWA variants most strongly associated with male mate choice were tightly linked to the gene controlling wing color in the K locus, consistent with previous mapping efforts. RNA-seq across developmental time points then showed that seven genes near the top GWA peaks were differentially expressed in the eyes, optic lobes, or central brain of white and yellow H. cydno males, many of which have known functions in the development and maintenance of synaptic connections. In the visual system of these butterflies, we identified a striking physiological difference between yellow and white males that could provide an evolutionarily labile circuit motif in the eye to rapidly switch behavioral preference. Using single-cell electrophysiology recordings, we found that some ultraviolet (UV)-sensitive photoreceptors receive inhibition from long-wavelength photoreceptors in the male eye. Surprisingly, the proportion of inhibited UV photoreceptors was strongly correlated with male wing color, suggesting a difference in the early stages of visual processing that could plausibly influence courtship decisions. We discuss potential links between candidate genes and this physiological signature, and suggest future avenues for experimental work. Taken together, our results support the idea that alterations to the evolutionarily labile peripheral nervous system, driven by genetic and gene expression differences, can significantly and rapidly alter essential behaviors
Symplectic Duality for Hamiltonian Reductions; and Orthodontia for Double Grothendieck Polynomials
Full text linkThe Hamiltonian reduction of the nilpotent cone in by the torus of diagonal matrices is a Nakajima quiver variety which admits a symplectic resolution , and the corresponding BFN Coulomb branch is the affine closure of the cotangent bundle of the base affine space. We construct a surjective map of graded algebras, which the Hikita conjecture predicts to be an isomorphism. Our map is inherited from a related case of the Hikita conjecture and factors through Kirwan surjectivity for quiver varieties. We conjecture that many other Hikita maps can be inherited from that of a related dual pair. We give a new formula for double Grothendieck polynomials based on Magyar's orthodontia algorithm for diagrams. Our formula implies a similar formula for double Schubert polynomials . We also prove a curious positivity result: for vexillary permutations , the polynomial is a graded nonnegative sum of Lascoux polynomials. We conjecture that this positivity result holds for all . This conjecture would follow from a problem of independent interest regarding Lascoux positivity of certain products of Lascoux polynomials
Essays on the Econometrics of Policy Choice
Full text linkThis dissertation contains two essays on econometric methods for policy choice. The first essay develops a method for designing experiments with the objective of choosing optimal policies. An experimenter wants to choose a policy to maximize welfare subject to budget or other policy constraints. The effects of counterfactual policies are described by a structural econometric model governed by an unknown parameter. The experimenter has some pilot data, and has the opportunity to collect another wave of experimental data. The joint experimental design and policy choice problem is a dynamic optimization problem with a very high-dimensional state space. I propose a low-dimensional approximation and show it is asymptotically optimal under Bayes expected welfare. The method accommodates discrete as well as continuous treatments, such as cash transfers, prices, or tax credits, and allows targeting based on covariates. I demonstrate the method using the conditional cash transfer program Progresa, showing how to design an experiment to help choose a policy aimed at increasing graduation rates and reducing gender disparities in education. Compared to the original Progresa experiment, the optimal experiment requires only one quarter as many observations to obtain equally effective policies. The second essay studies how to choose a policy to allocate treatment to a heterogeneous population on the basis of experimental data that includes only a subset of possible treatment values. The effects of new treatments are partially identified through shape restrictions on treatment response. I propose solving an empirical minimax regret problem to estimate the policy and show it has a linear- and integer-programming formulation. I prove the maximum regret of the estimator converges to the lowest possible maximum regret at the slower of or the rate at which heterogeneous treatment effects can be estimated. In an application to designing targeted subsidies for electrical grid connections in rural Kenya, I estimate that nearly the entire population should be given a treatment not implemented in the experiment, reducing maximum regret by over 60\% relative to the policy that restricts to the original set of treatments