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Fluctuations in the strength and direction of natural selection through time are a ubiquitous feature of life on Earth. One evolutionary outcome of such fluctuations is adaptive tracking, wherein populations rapidly adapt from standing genetic variation. In certain circumstances, adaptive tracking can lead to the long-term maintenance of functional polymorphism despite allele frequency change due to selection. Although adaptive tracking is likely a common process, we still have a limited understanding of aspects of its genetic architecture and its strength relative to other evolutionary forces such as drift. Drosophila melanogaster living in temperate regions evolve to track seasonal fluctuations and are an excellent system to tackle these gaps in knowledge. By sequencing orchard populations collected across multiple years, we characterized the genomic signal of seasonal demography and identified that the cosmopolitan inversion In(2L)t facilitates seasonal adaptive tracking and shows molecular footprints of selection. A meta-analysis of phenotypic studies shows that seasonal loci within In(2L)t are associated with behavior, life history, physiology, and morphological traits. We identify candidate loci and experimentally link them to phenotype. Our work contributes to our general understanding of fluctuating selection and highlights the evolutionary outcome and dynamics of contemporary selection on inversions.

 

Polymer-encapsulated cobalt phthalocyanine (CoPc) is a model system for studying how polymer-catalyst interactions in the electrocatalytic systems influence performance for the CO2 reduction reaction. In particular, understanding how bulk electrolyte and proton concentration influences polymer protonation, and in turn how the extent of polymer protonation influences catalytic activity and selectivity, is crucial to understanding polymer-catalyst composite materials. We report a study of the dependence of bulk pH and electrolyte concentration on the fractional protonation of poly-4-vinylpyridine and related polymers with both electrochemical and spectroscopic evidence. In addition, we show that the fractional protonation of the polymer is directly related to both the activity of the catalyst and the reaction selectivity for the CO2 reduction reaction over the competitive hydrogen evolution reaction. Of particular note is that the fractional protonation of the film is related to electrolyte concentration, which suggests that the transport of counterions plays an important role in regulating proton transport within the polymer film. These insights suggest that electrolyte concentration and pH play an important in the electrocatalytic performance for polymer-catalyst composite systems, and these influences should be considered in both experimental preparation and analysis. 

Multi-scale instabilities are ubiquitous in atmospheric and oceanic flows and are essential topics in teaching geophysical fluid dynamics. Yet these topics are often difficult to teach and counter-intuitive to new learners. In this paper, we introduce our state-of-the-art Do-It Yourself Dynamics (DIYnamics) LEGO robotics kit that allows users to create table-top models of geophysical flows. Deep ocean convection processes are simulated via three experiments – upright convection, thermal wind flows, and baroclinic instability – in order to demonstrate the robust multi-scale modeling capabilities of our kit. Detailed recipes are provided to allow users to reproduce these experiments. Further, dye-visualization measurements show that the table-top experimental results adequately agree with theory. In sum, our DIYnamics setup provides students and educators with an accessible table-top framework by which to model the multi-scale behaviors, inherent in canonical geophysical flows, such as deep ocean convection. 

Temperature dependent luminescence studies were performed on one-dimensional organic–inorganic lead halide hybrid materials to obtain activation energies for non-radiative decay.

 

ABSTRACT Glaciers are rapidly receding under climate change. A melting cryosphere will dramatically alter global sea levels, carbon cycling, and water resource availability. Glaciers host rich biotic communities that are dominated by microbial diversity, and this biodiversity can impact surface albedo, thereby driving a feedback loop between biodiversity and cryosphere melt. However, the microbial diversity of glacier ecosystems remains largely unknown outside of major ice sheets, particularly from a temporal perspective. Here, we characterized temporal dynamics of bacteria, eukaryotes, and algae on the Paradise Glacier, Mount Rainier, USA, over nine time points spanning the summer melt season. During our study, the glacier surface steadily darkened as seasonal snow melted and darkening agents accumulated until new snow fell in late September. From a community-wide perspective, the bacterial community remained generally constant while eukaryotes and algae exhibited temporal progression and community turnover. Patterns of individual taxonomic groups, however, were highly stochastic. We found little support for our a priori prediction that autotroph abundance would peak before heterotrophs. Notably, two different trends in snow algae emerged—an abundant early- and late-season operational taxonomic unit (OTU) with a different midsummer OTU that peaked in August. Overall, our results highlight the need for temporal sampling to clarify microbial diversity on glaciers and that caution should be exercised when interpreting results from single or few time points. IMPORTANCE Microbial diversity on mountain glaciers is an underexplored component of global biodiversity. Microbial presence and activity can also reduce the surface albedo or reflectiveness of glaciers, causing them to absorb more solar radiation and melt faster, which in turn drives more microbial activity. To date, most explorations of microbial diversity in the mountain cryosphere have only included single time points or focused on one microbial community (e.g., bacteria). Here, we performed temporal sampling over a summer melt season for the full microbial community, including bacteria, eukaryotes, and fungi, on the Paradise Glacier, Washington, USA. Over the summer, the bacterial community remained generally constant, whereas eukaryote and algal communities temporally changed through the melt season. Individual taxonomic groups, however, exhibited considerable stochasticity. Overall, our results highlight the need for temporal sampling on glaciers and that caution should be exercised when interpreting results from single or few time points. 

There are two major challenges to improving interannual to decadal forecasts: (a) consistently initializing the coupled system so that variability is not dominated by initial imbalances, and (b) having a large sample of different initial conditions on which to test forecast skill. The second challenge requires consideration of time periods not only outside the recent period of intensive ocean observation, but also before the instrumental era, which increases the importance of the first challenge. Forecasts prior to the 1850s isolate internally generated sources of variability by removing the majority of anthropogenic forcing, and the sparse observational record during this time period motivates the use of paleoclimate proxy data. We address these issues by using a linear inverse model (LIM) approach and a recent proxy‐based reconstruction over the last millennium at annual resolution. The reconstruction is used to train, initialize, and validate LIM forecasts. The LIM trained on paleo‐data assimilated using a LIM trained on global climate model (GCM) simulation data outperforms a LIM trained on raw GCM data at forecast leads longer than 2 years for in‐sample experiments, and beyond 4‐year leads in most out‐of‐sample experiments validated on instrumental data. The most skillful normal mode of the paleo‐data LIM for the instrumental experiment represents a persistent pattern with a longer decay time than for the GCM‐LIM's modes, which accounts for the outperformance at longer leads. The paleo‐data LIM is consequently more sensitive to ocean initialization, which is reflected in forecasts during the instrumental era where ocean reanalyses exhibit large uncertainty.

 

While chirality imbalances are forbidden in conventional lattice systems, non-Hermiticity can effectively avoid the chiral-doubling theorem to facilitate 1D chiral dynamics. Indeed, such systems support unbalanced unidirectional flows that can lead to the localization of an extensive number of states at the boundary, known as the non-Hermitian skin effect (NHSE). Recently, a generalized (rank-2) chirality describing a 2D robust gapless mode with dispersionω = k_xk_yhas been introduced in crystalline systems. Here we demonstrate that rank-2 chirality imbalances can be established in a non-Hermitian (NH) lattice system leading to momentum-resolved chiral dynamics, and a rank-2 NHSE where there are both edge- and corner-localized skin modes. We then experimentally test this phenomenology in a 2-dimensional topolectric circuit that implements a NH Hamiltonian with a long-lived rank-2 chiral mode. Using impedance measurements, we confirm the rank-2 NHSE in this system, and its manifestation in the predicted skin modes and a highly unusual momentum-position locking response. Our investigation demonstrates a circuit-based path to exploring higher-rank chiral physics, with potential applications in systems where momentum resolution is necessary, e.g., in beamformers and non-reciprocal devices.

 

Abstract Variational quantum algorithms have the potential for significant impact on high-dimensional optimization, with applications in classical combinatorics, quantum chemistry, and condensed matter. Nevertheless, the optimization landscape of these algorithms is generally nonconvex, leading the algorithms to converge to local, rather than global, minima and the production of suboptimal solutions. In this work, we introduce a variational quantum algorithm that couples classical Markov chain Monte Carlo techniques with variational quantum algorithms, allowing the former to provably converge to global minima and thus assure solution quality. Due to the generality of our approach, it is suitable for a myriad of quantum minimization problems, including optimization and quantum state preparation. Specifically, we devise a Metropolis–Hastings method that is suitable for variational quantum devices and use it, in conjunction with quantum optimization, to construct quantum ensembles that converge to Gibbs states. These performance guarantees are derived from the ergodicity of our algorithm’s state space and enable us to place analytic bounds on its time-complexity. We demonstrate both the effectiveness of our technique and the validity of our analysis through quantum circuit simulations for MaxCut instances, solving these problems deterministically and with perfect accuracy, as well as large-scale quantum Ising and transverse field spin models of up to 50 qubits. Our technique stands to broadly enrich the field of variational quantum algorithms, improving and guaranteeing the performance of these promising, yet often heuristic, methods.