Optical one-way quantum computing with a simulated valence-bond solid

One-way quantum computation proceeds by sequentially measuring individual spins (qubits) in an entangled many-spin resource state. It remains a challenge, however, to efficiently produce such resource states. Is it possible to reduce the task of generating these states to simply cooling a quantum many-body system to its ground state? Cluster states, the canonical resource for one-way quantum computing, do not naturally occur as ground states of physical systems. This led to a significant effort to identify alternative resource states that appear as ground states in spin lattices. An appealing candidate is a valence-bond-solid state described by Affleck, Kennedy, Lieb, and Tasaki (AKLT). It is the unique, gapped ground state for a two-body Hamiltonian on a spin-1 chain, and can be used as a resource for one-way quantum computing. Here, we experimentally generate a photonic AKLT state and use it to implement single-qubit quantum logic gates.Comment: 11 pages, 4 figures, 8 tables - added one referenc

Silencing Early Viral Replication in Macrophages and Dendritic Cells Effectively Suppresses Flavivirus Encephalitis

West Nile (WN) and St. Louis encephalitis (SLE) viruses can cause fatal neurological infection and currently there is neither a specific treatment nor an approved vaccine for these infections. In our earlier studies, we have reported that siRNAs can be developed as broad-spectrum antivirals for the treatment of infection caused by related viruses and that a small peptide called RVG-9R can deliver siRNA to neuronal cells as well as macrophages. To increase the repertoire of broad-spectrum antiflaviviral siRNAs, we screened 25 siRNAs targeting conserved regions in the viral genome. Five siRNAs were found to inhibit both WNV and SLE replication in vitro reflecting broad-spectrum antiviral activity and one of these was also validated in vivo. In addition, we also show that RVG-9R delivers siRNA to macrophages and dendritic cells, resulting in effective suppression of virus replication. Mice were challenged intraperitoneally (i.p.) with West Nile virus (WNV) and treated i.v. with siRNA/peptide complex. The peritoneal macrophages isolated on day 3 post infection were isolated and transferred to new hosts. Mice receiving macrophages from the anti-viral siRNA treated mice failed to develop any disease while the control mice transferred with irrelevant siRNA treated mice all died of encephalitis. These studies suggest that early suppression of viral replication in macrophages and dendritic cells by RVG-9R-mediated siRNA delivery is key to preventing the development of a fatal neurological disease

Experimental investigation of the uncertainty principle in the presence of quantum memory

Heisenberg's uncertainty principle provides a fundamental limitation on an observer's ability to simultaneously predict the outcome when one of two measurements is performed on a quantum system. However, if the observer has access to a particle (stored in a quantum memory) which is entangled with the system, his uncertainty is generally reduced. This effect has recently been quantified by Berta et al. [Nature Physics 6, 659 (2010)] in a new, more general uncertainty relation, formulated in terms of entropies. Using entangled photon pairs, an optical delay line serving as a quantum memory and fast, active feed-forward we experimentally probe the validity of this new relation. The behaviour we find agrees with the predictions of quantum theory and satisfies the new uncertainty relation. In particular, we find lower uncertainties about the measurement outcomes than would be possible without the entangled particle. This shows not only that the reduction in uncertainty enabled by entanglement can be significant in practice, but also demonstrates the use of the inequality to witness entanglement.Comment: 8 pages, 4 figures, comments welcom

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

An open challenge to advance probabilistic forecasting for dengue epidemics

This is the final version. Available on open access from the National Academy of Sciences via the DOI in this recordData Availability: Data deposition: The data are available at https://github.com/cdcepi/dengue-forecasting-project-2015 (DOI: https://doi.org/10.5281/zenodo.3519270).A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue

Processing of joint molecule intermediates by structure-selective endonucleases during homologous recombination in eukaryotes

Homologous recombination is required for maintaining genomic integrity by functioning in high-fidelity repair of DNA double-strand breaks and other complex lesions, replication fork support, and meiotic chromosome segregation. Joint DNA molecules are key intermediates in recombination and their differential processing determines whether the genetic outcome is a crossover or non-crossover event. The Holliday model of recombination highlights the resolution of four-way DNA joint molecules, termed Holliday junctions, and the bacterial Holliday junction resolvase RuvC set the paradigm for the mechanism of crossover formation. In eukaryotes, much effort has been invested in identifying the eukaryotic equivalent of bacterial RuvC, leading to the discovery of a number of DNA endonucleases, including Mus81–Mms4/EME1, Slx1–Slx4/BTBD12/MUS312, XPF–ERCC1, and Yen1/GEN1. These nucleases exert different selectivity for various DNA joint molecules, including Holliday junctions. Their mutant phenotypes and distinct species-specific characteristics expose a surprisingly complex system of joint molecule processing. In an attempt to reconcile the biochemical and genetic data, we propose that nicked junctions constitute important in vivo recombination intermediates whose processing determines the efficiency and outcome (crossover/non-crossover) of homologous recombination

Statistical physics of vaccination

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination–one of the most important preventive measures of modern times–is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research