Graviton partial waves and causality in higher dimensions
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Here we propose a tomographic protocol for estimating any k-body reduced density matrix (k-RDM) of an n-mode fermionic state, a ubiquitous step in near-term quantum algorithms for simulating many-body physics, chemistry, and materials. Our approach extends the framework of classical shadows, a randomized approach to learning a collection of quantum-state properties, to the fermionic setting. Our sampling protocol uses randomized measurement settings generated by a discrete group of fermionic Gaussian unitaries, implementable with linear-depth circuits. We prove that estimating all k-RDM elements to additive precision ϵ requires on the order of ($^{n}_{k}$)k 3/2 log(n)/ϵ 2 repeated state preparations, which is optimal up to the logarithmic factor. Furthermore, numerical calculations show that our protocol offers a substantial improvement in constant overheads for k ≥ 2, as compared to prior deterministic strategies. We also adapt our method to particle-number symmetry, wherein the additional circuit depth may be halved at the cost of roughly 2–5 times more repetitions.
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High-frequency oscillations are observed in a neon plasma of a direct current magnetron discharge. At low discharge currents, we see highly coherent 60 MHz fluctuations. Above a distinct current threshold, secondary 5–10 MHz fluctuations emerge in addition to turbulent fluctuations in the 60–100 MHz range. The oscillations in the total discharge current suggest axial wave propagation. A lower-hybrid wave theory is invoked to model the high-frequency oscillations. Here, we attribute the low-frequency modes to a turbulence-driven inverse cascade process, as suggested by recent simulations.
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Electric utilities and large industrial/commercial customers, which integrate multi-MW multi-string energy storage systems (ESS), are typically at a significant risk of string failure. This results in the loss of system performance and capacity. In order to evaluate the accurate costs and benefits associated with a particular battery system, it is essential to incorporate the costs associated with battery string failure and, as a result, reduced capacity. This report analyzes how the economic losses can be estimated based on market rules and ESS availability. A framework for calculation of economic losses is developed and implemented with an illustrative string failure case for Independent System Operator (ISO-NE) forward capacity market. Further scenarios and case studies are analyzed by varying the probability of string failure as well as the capacity bid into the market. These scenarios are used to assess the variability of economic losses and the impact of string failure on the final realized benefits from the battery's market participation.
Lignins are cell wall-located aromatic polymers that provide strength and hydrophobicity to woody tissues. Lignin monomers are synthesized via the phenylpropanoid pathway, wherein CAFFEOYL SHIKIMATE ESTERASE (CSE) converts caffeoyl shikimate into caffeic acid. Here, we explored the role of the two CSE homologs in poplar (Populus tremula × P. alba). Reporter lines showed that the expression conferred by both CSE1 and CSE2 promoters is similar. CRISPR-Cas9-generated cse1 and cse2 single mutants had a wild-type lignin level. Nevertheless, CSE1 and CSE2 are not completely redundant, as both single mutants accumulated caffeoyl shikimate. In contrast, the cse1 cse2 double mutants had a 35% reduction in lignin and associated growth penalty. The reduced-lignin content translated into a fourfold increase in cellulose-to-glucose conversion upon limited saccharification. Phenolic profiling of the double mutants revealed large metabolic shifts, including an accumulation of p-coumaroyl, 5-hydroxyferuloyl, feruloyl and sinapoyl shikimate, in addition to caffeoyl shikimate. This indicates that the CSEs have a broad substrate specificity, which was confirmed by in vitro enzyme kinetics. Taken together, our results suggest an alternative path within the phenylpropanoid pathway at the level of the hydroxycinnamoyl-shikimates, and show that CSE is a promising target to improve plants for the biorefinery.
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Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. Many bioinformatic methods have been developed for predicting AMR phenotypes from whole-genome sequences and AMR genes, but recent studies have indicated that predictions can be made from incomplete genome sequence data. In order to more systematically understand this, we built random forest-based machine learning classifiers for predicting susceptible and resistant phenotypes for Klebsiella pneumoniae (1,640 strains), Mycobacterium tuberculosis (2,497 strains), and Salmonella enterica (1,981 strains). We started by building models from alignments that were based on a reference chromosome for each species. We then subsampled each chromosomal alignment and built models for the resulting subalignments, finding that very small regions, representing approximately 0.1 to 0.2% of the chromosome, are predictive. In K. pneumoniae, M. tuberculosis, and S. enterica, the subalignments are able to predict multiple AMR phenotypes with at least 70% accuracy, even though most do not encode an AMR-related function. We used these models to identify regions of the chromosome with high and low predictive signals. Finally, subalignments that retain high accuracy across larger phylogenetic distances were examined in greater detail, revealing genes and intergenic regions with potential links to AMR, virulence, transport, and survival under stress conditions. IMPORTANCE Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission. Machine learning models are capable of predicting antimicrobial resistance phenotypes from bacterial genome sequence data by identifying resistance genes, mutations, and other correlated features. They are also capable of implicating regions of the genome that have not been previously characterized as being involved in resistance. In this study, we generated global chromosomal alignments for Klebsiella pneumoniae, Mycobacterium tuberculosis, and Salmonella enterica and systematically searched them for small conserved regions of the genome that enable the prediction of antimicrobial resistance phenotypes. In addition to known antimicrobial resistance genes, this analysis identified genes involved in virulence and transport functions, as well as many genes with no previous implication in antimicrobial resistance.
Microbiomes are important contributors to many ecosystems, including ones where nutrient cycling is stimulated by aeration control. Optimizing cyclic aeration helps reduce energy needs and maximize microbiome performance during wastewater treatment; however, little is known about how most microbial community members respond to these alternating conditions.
Quantum computing is in an era of limited resources. Current hardware lacks high fidelity gates, long coherence times, and the number of computational units required to perform meaningful computation. Contemporary quantum devices typically use a binary system, where each qubit exists in a superposition of the 0 and 1 states. Furthermore, it is often possible to access the 2 or even 3 states in the same physical unit by manipulating the system in different ways. In this work, we consider automatically encoding two qubits into one four-state ququart via a compression scheme. We use quantum optimal control to design efficient proof-of-concept gates that fully replicate standard qubit computation on these encoded qubits.
18th International Symposium on Solid Oxide Fuel Cells (SOFC-XVIII), Boston, MA, May 28-June 2, 2023