Pathogenic TNF-α drives peripheral nerve inflammation in an Aire-deficient model of autoimmunity

Immune cells infiltrate the peripheral nervous system (PNS) after injury and with autoimmunity, but their net effect is divergent. After injury, immune cells are reparative, while in inflammatory neuropathies (e.g., Guillain Barré Syndrome and chronic inflammatory demyelinating polyneuropathy), immune cells are proinflammatory and promote autoimmune demyelination. An understanding of immune cell phenotypes that distinguish these conditions may, therefore, reveal new therapeutic targets for switching immune cells from an inflammatory role to a reparative state. In an autoimmune regulator (Aire)-deficient mouse model of inflammatory neuropathy, we used single-cell RNA sequencing of sciatic nerves to discover a transcriptionally heterogeneous cellular landscape, including multiple myeloid, innate lymphoid, and lymphoid cell types. Analysis of cell-cell ligand-receptor interactions uncovered a macrophage-mediated tumor necrosis factor-α (TNF-α) signaling axis that is induced by interferon-γ and required for initiation of autoimmune demyelination. Developmental trajectory visualization suggested that TNF-α signaling is associated with metabolic reprogramming of macrophages and polarization of macrophages from a reparative state in injury to a pathogenic, inflammatory state in autoimmunity. Autocrine TNF-α signaling induced macrophage expression of multiple genes (Clec4e, Marcksl1, Cxcl1, and Cxcl10) important in immune cell activation and recruitment. Genetic and antibody-based blockade of TNF-α/TNF-α signaling ameliorated clinical neuropathy, peripheral nerve infiltration, and demyelination, which provides preclinical evidence that the TNF-α axis may be effectively targeted to resolve inflammatory neuropathies

Suppressing quantum errors by scaling a surface code logical qubit

Practical quantum computing will require error rates that are well below what is achievable with physical qubits. Quantum error correction offers a path to algorithmically-relevant error rates by encoding logical qubits within many physical qubits, where increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low in order for logical performance to improve with increasing code size. Here, we report the measurement of logical qubit performance scaling across multiple code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, both in terms of logical error probability over 25 cycles and logical error per cycle ($2.914\%\pm 0.016\%$ compared to $3.028\%\pm 0.023\%$). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a $1.7\times10^{-6}$ logical error per round floor set by a single high-energy event ($1.6\times10^{-7}$ when excluding this event). We are able to accurately model our experiment, and from this model we can extract error budgets that highlight the biggest challenges for future systems. These results mark the first experimental demonstration where quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.Comment: Main text: 6 pages, 4 figures. v2: Update author list, references, Fig. S12, Table I

Measurement-induced entanglement and teleportation on a noisy quantum processor

Measurement has a special role in quantum theory: by collapsing the wavefunction it can enable phenomena such as teleportation and thereby alter the "arrow of time" that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space-time that go beyond established paradigms for characterizing phases, either in or out of equilibrium. On present-day NISQ processors, the experimental realization of this physics is challenging due to noise, hardware limitations, and the stochastic nature of quantum measurement. Here we address each of these experimental challenges and investigate measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping, to avoid mid-circuit measurement and access different manifestations of the underlying phases -- from entanglement scaling to measurement-induced teleportation -- in a unified way. We obtain finite-size signatures of a phase transition with a decoding protocol that correlates the experimental measurement record with classical simulation data. The phases display sharply different sensitivity to noise, which we exploit to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realize measurement-induced physics at scales that are at the limits of current NISQ processors

Non-Abelian braiding of graph vertices in a superconducting processor

Indistinguishability of particles is a fundamental principle of quantum mechanics. For all elementary and quasiparticles observed to date - including fermions, bosons, and Abelian anyons - this principle guarantees that the braiding of identical particles leaves the system unchanged. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well developed mathematical description of non-Abelian anyons and numerous theoretical proposals, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. While efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasi-particles, superconducting quantum processors allow for directly manipulating the many-body wavefunction via unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons, we implement a generalized stabilizer code and unitary protocol to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of employing the anyons for quantum computation and utilize braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and - through the future inclusion of error correction to achieve topological protection - could open a path toward fault-tolerant quantum computing

Development and validation of a rabbit model of Pseudomonas aeruginosa non-ventilated pneumonia for preclinical drug development

BackgroundNew drugs targeting antimicrobial resistant pathogens, including Pseudomonas aeruginosa, have been challenging to evaluate in clinical trials, particularly for the non-ventilated hospital-acquired pneumonia and ventilator-associated pneumonia indications. Development of new antibacterial drugs is facilitated by preclinical animal models that could predict clinical efficacy in patients with these infections.MethodsWe report here an FDA-funded study to develop a rabbit model of non-ventilated pneumonia with Pseudomonas aeruginosa by determining the extent to which the natural history of animal disease reproduced human pathophysiology and conducting validation studies to evaluate whether humanized dosing regimens of two antibiotics, meropenem and tobramycin, can halt or reverse disease progression.ResultsIn a rabbit model of non-ventilated pneumonia, endobronchial challenge with live P. aeruginosa strain 6206, but not with UV-killed Pa6206, caused acute respiratory distress syndrome, as evidenced by acute lung inflammation, pulmonary edema, hemorrhage, severe hypoxemia, hyperlactatemia, neutropenia, thrombocytopenia, and hypoglycemia, which preceded respiratory failure and death. Pa6206 increased >100-fold in the lungs and then disseminated from there to infect distal organs, including spleen and kidneys. At 5 h post-infection, 67% of Pa6206-challenged rabbits had PaO2 <60 mmHg, corresponding to a clinical cut-off when oxygen therapy would be required. When administered at 5 h post-infection, humanized dosing regimens of tobramycin and meropenem reduced mortality to 17-33%, compared to 100% for saline-treated rabbits (P<0.001 by log-rank tests). For meropenem which exhibits time-dependent bactericidal activity, rabbits treated with a humanized meropenem dosing regimen of 80 mg/kg q2h for 24 h achieved 100% T>MIC, resulting in 75% microbiological clearance rate of Pa6206 from the lungs. For tobramycin which exhibits concentration-dependent killing, rabbits treated with a humanized tobramycin dosing regimen of 8 mg/kg q8h for 24 h achieved Cmax/MIC of 9.8 ± 1.4 at 60 min post-dose, resulting in 50% lung microbiological clearance rate. In contrast, rabbits treated with a single tobramycin dose of 2.5 mg/kg had Cmax/MIC of 7.8 ± 0.8 and 8% (1/12) microbiological clearance rate, indicating that this rabbit model can detect dose-response effects.ConclusionThe rabbit model may be used to help predict clinical efficacy of new antibacterial drugs for the treatment of non-ventilated P. aeruginosa pneumonia

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant

Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: A comparative risk assessment

Background: High blood pressure, blood glucose, serum cholesterol, and BMI are risk factors for cardiovascular diseases and some of these factors also increase the risk of chronic kidney disease and diabetes. We estimated mortality from cardiovascular diseases, chronic kidney disease, and diabetes that was attributable to these four cardiometabolic risk factors for all countries and regions from 1980 to 2010. Methods: We used data for exposure to risk factors by country, age group, and sex from pooled analyses of population-based health surveys. We obtained relative risks for the effects of risk factors on cause-specific mortality from meta-analyses of large prospective studies. We calculated the population attributable fractions for each risk factor alone, and for the combination of all risk factors, accounting for multicausality and for mediation of the effects of BMI by the other three risks. We calculated attributable deaths by multiplying the cause-specific population attributable fractions by the number of disease-specific deaths. We obtained cause-specific mortality from the Global Burden of Diseases, Injuries, and Risk Factors 2010 Study. We propagated the uncertainties of all the inputs to the final estimates. Findings: In 2010, high blood pressure was the leading risk factor for deaths due to cardiovascular diseases, chronic kidney disease, and diabetes in every region, causing more than 40% of worldwide deaths from these diseases; high BMI and glucose were each responsible for about 15% of deaths, and high cholesterol for more than 10%. After accounting for multicausality, 63% (10·8 million deaths, 95% CI 10·1-11·5) of deaths from these diseases in 2010 were attributable to the combined effect of these four metabolic risk factors, compared with 67% (7·1 million deaths, 6·6-7·6) in 1980. The mortality burden of high BMI and glucose nearly doubled from 1980 to 2010. At the country level, age-standardised death rates from these diseases attributable to the combined effects of these four risk factors surpassed 925 deaths per 100 000 for men in Belarus, Kazakhstan, and Mongolia, but were less than 130 deaths per 100 000 for women and less than 200 for men in some high-income countries including Australia, Canada, France, Japan, the Netherlands, Singapore, South Korea, and Spain. Interpretation: The salient features of the cardiometabolic disease and risk factor epidemic at the beginning of the 21st century are high blood pressure and an increasing effect of obesity and diabetes. The mortality burden of cardiometabolic risk factors has shifted from high-income to low-income and middle-income countries. Lowering cardiometabolic risks through dietary, behavioural, and pharmacological interventions should be a part of the global response to non-communicable diseases. Funding: UK Medical Research Council, US National Institutes of Health. © 2014 Elsevier Ltd

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

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant