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Gauge theories establish the standard model of particle physics, and lattice gauge theory (LGT) calculations employing Markov Chain Monte Carlo (MCMC) methods have been pivotal in our understanding of fundamental interactions. The present limitations of MCMC techniques may be overcome by Hamiltonian-based simulations on classical or quantum devices, which further provide the potential to address questions that lay beyond the capabilities of the current approaches. However, for continuous gauge groups, Hamiltonian-based formulations involve infinite-dimensional gauge degrees of freedom that can solely be handled by truncation. Current truncation schemes require dramatically increasing computational resources at small values of the bare couplings, where magnetic field effects become important. Such limitation precludes one from `taking the continuous limit' while working with finite resources. To overcome this limitation, we provide a resource-efficient protocol to simulate LGTs with continuous gauge groups in the Hamiltonian formulation. Our new method allows for calculations at arbitrary values of the bare coupling and lattice spacing. The approach consists of the combination of a Hilbert space truncation with a regularization of the gauge group, which permits an efficient description of the magnetically-dominated regime. We focus here on Abelian gauge theories and use \\(2+1\\) dimensional quantum electrodynamics as a benchmark example to demonstrate this efficient framework to achieve the continuum limit in LGTs. This possibility is a key requirement to make quantitative predictions at the field theory level and offers the long-term perspective to utilise quantum simulations to compute physically meaningful quantities in regimes that are precluded to quantum Monte Carlo.

Gauge theories are the most successful theories for describing nature at its fundamental level, but obtaining analytical or numerical solutions often remains a challenge. We propose an experimental quantum simulation scheme to study ground state properties in two-dimensional quantum electrodynamics (2D QED) using existing quantum technology. The proposal builds on a formulation of lattice gauge theories as effective spin models in arXiv:2006.14160, which reduces the number of qubits needed by eliminating redundant degrees of freedom and by using an efficient truncation scheme for the gauge fields. The latter endows our proposal with the perspective to take a well-controlled continuum limit. Our protocols allow in principle scaling up to large lattices and offer the perspective to connect the lattice simulation to low energy observable quantities, e.g. the hadron spectrum, in the continuum theory. By including both dynamical matter and a non-minimal gauge field truncation, we provide the novel opportunity to observe 2D effects on present-day quantum hardware. More specifically, we present two Variational Quantum Eigensolver (VQE) based protocols for the study of magnetic field effects, and for taking an important first step towards computing the running coupling of QED. For both instances, we include variational quantum circuits for qubit-based hardware, which we explicitly apply to trapped ion quantum computers. We simulate the proposed VQE experiments classically to calculate the required measurement budget under realistic conditions. While this feasibility analysis is done for trapped ions, our approach can be easily adapted to other platforms. The techniques presented here, combined with advancements in quantum hardware pave the way for reaching beyond the capabilities of classical simulations by extending our framework to include fermionic potentials or topological terms.

Commercially impactful quantum algorithms such as quantum chemistry and Shor's algorithm require a number of qubits and gates far beyond the capacity of any existing quantum processor. Distributed architectures, which scale horizontally by networking modules, provide a route to commercial utility and will eventually surpass the capability of any single quantum computing module. Such processors consume remote entanglement distributed between modules to realize distributed quantum logic. Networked quantum computers will therefore require the capability to rapidly distribute high fidelity entanglement between modules. Here we present preliminary demonstrations of some key distributed quantum computing protocols on silicon T centres in isotopically-enriched silicon. We demonstrate the distribution of entanglement between modules and consume it to apply a teleported gate sequence, establishing a proof-of-concept for T centres as a distributed quantum computing and networking platform.

Transmembrane glycoproteins of enveloped viruses are targets of neutralizing antibodies and essential vaccine antigens. mRNA-LNP technology allows in vivo expression of transmembrane glycoproteins, but in vitro biophysical characterization of transmembrane antigens and analysis of post-immunization antibody responses typically rely on soluble proteins. Here, we present a platform for assembling transmembrane glycoprotein vaccine candidates into lipid nanodiscs. We demonstrate the utility of nanodiscs in HIV membrane proximal external region (MPER)-targeting vaccine development by binding assays using surface plasmon resonance (SPR), ex vivo B cell sorting with fluorescence-activated cell sorting (FACS), and by determining the structure of a prototypical HIV MPER-targeting immunogen nanodisc in complex with three broadly neutralizing antibodies (bnAbs), including MPER bnAb 10E8, to 3.5 Å by cryogenic electron microscopy (cryo-EM), providing a template for structure-based immunogen design. To demonstrate general applicability we characterize Ebola virus glycoprotein nanodiscs. Overall, the platform offers a tool for accelerating development of next-generation vaccines.

CD33 is an immunomodulatory receptor linked to Alzheimer’s disease (AD) susceptibility via regulation of phagocytosis in microglia. Divergent features between human CD33 (hCD33) and murine CD33 (mCD33) include a unique transmembrane lysine in mCD33 and cytoplasmic tyrosine in hCD33. The functional consequences of these differences in restraining phagocytosis remains poorly understood. Using a new αmCD33 monoclonal antibody, we show that mCD33 is expressed at high levels on neutrophils and low levels on microglia. Notably, cell surface expression of mCD33 is entirely dependent on Dap12 due to an interaction with the transmembrane lysine in mCD33. In RAW264.7 cultured macrophages, BV-2 cultured microglia, primary neonatal and adult microglia, uptake of cargo — including aggregated Aβ 1–42 — is not altered upon genetic ablation of mCD33. Alternatively, deletion of hCD33 in monocytic cell lines increased cargo uptake. Moreover, transgenic mice expressing hCD33 in the microglial cell lineage showed repressed cargo uptake in primary microglia. Therefore, mCD33 and hCD33 have divergent roles in regulating phagocytosis, highlighting the importance of studying hCD33 in AD susceptibility. Abhishek Bhattacherjee et al. investigate the functional differences between human and mouse CD33, an immunomodulatory receptor linked to Alzheimer’s disease. They find that loss of mouse CD33 does not affect cargo uptake in macrophages in contrast to human CD33, which represses phagocytosis when expressed in cells or mice.

Identifying the dissemination patterns and impacts of a virus of economic or health importance during a pandemic is crucial, as it informs the public on policies for containment in order to reduce the spread of the virus. In this study, we integrated genomic and travel data to investigate the emergence and spread of the SARS-CoV-2 B.1.1.318 and B.1.525 (Eta) variants of interest in Nigeria and the wider Africa region. By integrating travel data and phylogeographic reconstructions, we find that these two variants that arose during the second wave in Nigeria emerged from within Africa, with the B.1.525 from Nigeria, and then spread to other parts of the world. Data from this study show how regional connectivity of Nigeria drove the spread of these variants of interest to surrounding countries and those connected by air-traffic. Our findings demonstrate the power of genomic analysis when combined with mobility and epidemiological data to identify the drivers of transmission, as bidirectional transmission within and between African nations are grossly underestimated as seen in our import risk index estimates. Data on geographically restricted SARS-CoV-2 variants is lacking in some regions. In this nationwide effort including 18 public health labs, the authors used genomic epidemiology and travel data to understand the origin and spread of 2 variants of interest that predominated during the second wave of the pandemic in Nigeria.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

[This corrects the article DOI: 10.1038/s42003-019-0698-6.].

Although traditional genetic assays have characterized the pattern of crossing over across the genome in Drosophila melanogaster, these assays could not precisely define the location of crossovers. Even less is known about the frequency and distribution of noncrossover gene conversion events. To assess the specific number and positions of both meiotic gene conversion and crossover events, we sequenced the genomes of male progeny from females heterozygous for 93,538 X chromosomal single-nucleotide and InDel polymorphisms. From the analysis of the 30 F1 hemizygous X chromosomes, we detected 15 crossover and 5 noncrossover gene conversion events. Taking into account the nonuniform distribution of polymorphism along the chromosome arm, we estimate that most oocytes experience 1 crossover event and 1.6 gene conversion events per X chromosome pair per meiosis. An extrapolation to the entire genome would predict approximately 5 crossover events and 8.6 conversion events per meiosis. Mean gene conversion tract lengths were estimated to be 476 base pairs, yielding a per nucleotide conversion rate of 0.86 × 10−5 per meiosis. Both of these values are consistent with estimates of conversion frequency and tract length obtained from studies of rosy, the only gene for which gene conversion has been studied extensively in Drosophila. Motif-enrichment analysis revealed a GTGGAAA motif that was enriched near crossovers but not near gene conversions. The low-complexity and frequent occurrence of this motif may in part explain why, in contrast to mammalian systems, no meiotic crossover hotspots have been found in Drosophila.