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Ipsilateral axillary adenopathy post-COVID mRNA vaccine has been widely reported and guidelines for management have been established. Isolated changes of axillary tail trabecular thickening without associated adenopathy in the breast present a diagnostic dilemma and no official guidelines have thus far been reported. This finding has been reported after COVID mRNA vaccine and has never been reported with any other vaccine. We report on a patient with such changes on screening mammography 1.5 months after the fifth dose of a COVID-mRNA vaccine and 1 week after RSV vaccine. This raises the possibility that such changes can be seen with vaccines other than the COVID mRNA series of vaccines. The main differential diagnosis includes mastitis and inflammatory breast cancer. The transient nature of this finding with spontaneous resolution at diagnostic mammography and the vaccination history helps to establish the diagnosis and exclude breast cancer.

It is well understood that a two-dimensional grid of locally interacting qubits is a promising platform for achieving fault-tolerant quantum computing. However in the near future, it may prove less challenging to develop lower-dimensional structures. In this paper, we show that such constrained architectures can also support fault tolerance; specifically we explore a 2×N array of qubits where the interactions between non-neighboring qubits are enabled by shuttling the logical information along the rows of the array. Despite the apparent constraints of this setup, we demonstrate that error correction is possible and identify the classes of codes that are naturally suited to this platform. Focusing on silicon spin qubits as a practical example of qubits believed to meet our requirements, we provide a protocol for achieving full universal quantum computation with the surface code, while also addressing the additional constraints that are specific to a silicon spin-qubit device. Through numerical simulations, we evaluate the performance of this architecture using a realistic noise model, demonstrating that both surface code and more complex quantum low-density parity-check codes efficiently suppress gate and shuttling noise to a level that allows for the execution of quantum algorithms within the classically intractable regime. This work thus brings us one step closer to the execution of quantum algorithms that outperform classical machines.

Whether it is at the fabrication stage or during the course of the quantum computation, e.g. because of high-energy events like cosmic rays, the qubits constituting an error correcting code may be rendered inoperable. Such defects may correspond to individual qubits or to clusters and could potentially disrupt the code sufficiently to generate logical errors. In this paper, we explore a novel $adaptive$ approach for surface code quantum error correction on a defective lattice. We show that combining an appropriate defect detection algorithm and a quarantine of the identified zone allows one to preserve the advantage of quantum error correction at finite code sizes, at the cost of a qubit overhead that scales with the size of the defect. Our numerics indicate that the code's threshold need not be significantly affected; for example, for a certain scenario where small defects repeatedly arise in each logical qubit, the noise threshold is $2.7\%$ (versus the defect-free case of $2.9\%$). These results pave the way to the experimental implementation of large-scale quantum computers where defects will be inevitable.

Many quantum computing platforms are based on a two-dimensional (2D) physical layout. Here we explore a concept called looped pipelines, which permits one to obtain many of the advantages of a three-dimensional (3D) lattice while operating a strictly 2D device. The concept leverages qubit shuttling, a well-established feature in platforms like semiconductor spin qubits and trapped-ion qubits. The looped-pipeline architecture has similar hardware requirements to other shuttling approaches, but can process a stack of qubit arrays instead of just one. Even a stack of limited height is enabling for diverse schemes ranging from NISQ-era error mitigation through to fault-tolerant codes. For the former, protocols involving multiple states can be implemented with a space-time resource cost comparable to preparing one noisy copy. For the latter, one can realize a far broader variety of code structures; as an example we consider layered 2D codes within which transversal cnots are available. Under reasonable assumptions this approach can reduce the space-time cost of magic state distillation by 2 orders of magnitude. Numerical modeling using experimentally motivated noise models verifies that the architecture provides this benefit without significant reduction to the code’s threshold.

There is evidence that prediction markets are useful tools to aggregate information on researchers' beliefs about scientific results including the outcome of replications. In this study, we use prediction markets to forecast the results of novel experimental designs that test established theories. We set up prediction markets for hypotheses tested in the Defense Advanced Research Projects Agency's (DARPA) Next Generation Social Science (NGS2) programme. Researchers were invited to bet on whether 22 hypotheses would be supported or not. We define support as a test result in the same direction as hypothesized, with a Bayes factor of at least 10 (i.e. a likelihood of the observed data being consistent with the tested hypothesis that is at least 10 times greater compared with the null hypothesis). In addition to betting on this binary outcome, we asked participants to bet on the expected effect size (in Cohen's d) for each hypothesis. Our goal was to recruit at least 50 participants that signed up to participate in these markets. While this was the case, only 39 participants ended up actually trading. Participants also completed a survey on both the binary result and the effect size. We find that neither prediction markets nor surveys performed well in predicting outcomes for NGS2.

Background: To address limitations in recruitment and enrollment of diverse, low-literacy patients into prostate cancer clinical trials, we evaluated the feasibility, acceptability, and efficacy of an English and Spanish, Internet-based, multilevel recruitment intervention. Methods: Intervention components included (1) a low-literacy, bilingual, automated, Internet-based clinical trial matching tool; (2) a bilingual nurse who assisted individuals with questions and enrollment; and (3) a targeted, Internet-based advertising campaign. We evaluated (a) completion of matching tool, (b) expression of interest in a clinical trial, (c) number of patients who matched to clinical trials at a single institution, (d) discussion of risks and benefits of clinical trials (via follow-up interviews), and (e) effect of the advertising on completing the matching tool. Feasibility, acceptability, and preliminary estimates of efficacy were measured through user engagement with the matching tool and subsequent qualitative interviews with these same users. Results: During the 28-week study period, 523 users provided demographic information, 263 were identified with prostate cancer, 192 (73%) matched to at least one clinical trial, and 29 (15.1%) of those who matched provided contact information. During the study period, 17 prostate cancer clinical trials were available for matching. We completed follow-up interviews with 14 of the 29 men who provided contact information. Of the 14, 85.7% discussed the risks and benefits of clinical trials with their physician, and 35.7% enrolled in a clinical trial. The Internet-based advertising campaign resulted in an increased number of matching tool completions. Conclusions: Our study demonstrates that an Internet-based clinical trial matching tool that is advertised using a targeted Internet-based campaign can provide an effective means to reach diverse, low-literacy patients. When implemented at scale and over a longer duration, such interventions may help increase trial participation among underrepresented populations. Keywords: Internet recruitment, Clinical trials participation, Prostate cancer, Underserved populations

Food choice and eating behavior affect health and longevity. Large-scale research efforts aim to understand the molecular and social/behavioral mechanisms of energy homeostasis, body weight, and food intake. Honey bees (Apis mellifera) could provide a model for these studies since individuals vary in food-related behavior and social factors can be controlled. Here, we examine a potential role of peripheral insulin receptor substrate (IRS) expression in honey bee foraging behavior. IRS is central to cellular nutrient sensing through transduction of insulin/insulin-like signals (IIS). By reducing peripheral IRS gene expression and IRS protein amount with the use of RNA interference (RNAi), we demonstrate that IRS influences foraging choice in two standard strains selected for different food-hoarding behavior. Compared with controls, IRS knockdowns bias their foraging effort toward protein (pollen) rather than toward carbohydrate (nectar) sources. Through control experiments, we establish that IRS does not influence the bees' sucrose sensory response, a modality that is generally associated with food-related behavior and specifically correlated with the foraging preference of honey bees. These results reveal a new affector pathway of honey bee social foraging, and suggest that IRS expressed in peripheral tissue can modulate an insect's foraging choice between protein and carbohydrate sources.