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Featuring contributions from an award-winning, bestselling group of Black voices, past and present, this powerful poetry anthology elicits vital conversations about race, belonging, history and faith to highlight Black joy and pain.

A man in his 20s with Down syndrome presented to cardiology clinic for evaluation of a murmur. He denied symptoms. He had no prior history of invasive cardiac procedures or cardiac surgery. His examination revealed a murmur. An echocardiogram was performed and a diagnostic image is shown in figure 1.

We formulate a version of the integral Hodge conjecture for categories, prove the conjecture for two-dimensional Calabi–Yau categories which are suitably deformation equivalent to the derived category of a K3 or abelian surface, and use this to deduce cases of the usual integral Hodge conjecture for varieties. Along the way, we prove a version of the variational integral Hodge conjecture for families of two-dimensional Calabi–Yau categories, as well as a general smoothness result for relative moduli spaces of objects in such families. Our machinery also has applications to the structure of intermediate Jacobians, such as a criterion in terms of derived categories for when they split as a sum of Jacobians of curves.

Infectious endocarditis is a highly morbid infection that requires coordination of care across medical and surgical specialties, often through the use of a multidisciplinary team model. Multiple studies have demonstrated that such conferences can improve clinical outcomes. However, little is known about physicians’ impressions of these groups. We surveyed 126 (response rate of 30%) internal medicine, infectious diseases, cardiology, and cardiac surgery providers 1 year after the implementation of an endocarditis team at the University of Michigan. Ninety-eight percent of physicians felt that the endocarditis team improved communication between specialties. Additionally, over 85% of respondents agreed that the group influenced diagnostic evaluation, reduced management errors, increased access to surgery, and decreased in-hospital mortality for endocarditis patients. These results suggest that multidisciplinary endocarditis teams are valued by physicians as a tool to improve patient care and serve an important role in increasing communication between providers.

Abstract Background Clostridioides difficile infection (CDI) is a leading cause of health care–associated infection and may result in organ dysfunction, colectomy, and death. Published risk scores to predict severe complications from CDI demonstrate poor performance upon external validation. We hypothesized that building and validating a model using geographically and temporally distinct cohorts would more accurately predict risk for complications from CDI. Methods We conducted a multicenter retrospective cohort study of adults diagnosed with CDI. After randomly partitioning the data into training and validation sets, we developed and compared 3 machine learning algorithms (lasso regression, random forest, stacked ensemble) with 10-fold cross-validation to predict disease-related complications (intensive care unit admission, colectomy, or death attributable to CDI) within 30 days of diagnosis. Model performance was assessed using the area under the receiver operating curve (AUC). Results A total of 3646 patients with CDI were included, of whom 217 (6%) had complications. All 3 models performed well (AUC, 0.88–0.89). Variables of importance were similar across models, including albumin, bicarbonate, change in creatinine, non-CDI-related intensive care unit admission, and concomitant non-CDI antibiotics. Sensitivity analyses indicated that model performance was robust even when varying derivation cohort inclusion and CDI testing approach. However, race was an important modifier, with models showing worse performance in non-White patients. Conclusions Using a large heterogeneous population of patients, we developed and validated a prediction model that estimates risk for complications from CDI with good accuracy. Future studies should aim to reduce the disparity in model accuracy between White and non-White patients and to improve performance overall.

The 2008 financial crash precipitated a liquidity crisis of global proportions. With dollar funding shortages threatening the global financial system, the Federal Reserve turned to foreign central bank liquidity swaps as a key component of its crisis response. First used in the 1960s during the Bretton Woods era, foreign central bank liquidity swaps are essentially contracts between two central banks to lend each other currency. While many analysts praised the Fed’s swap lines for—at least temporarily—easing liquidity strains during the crisis, this Note argues that the Fed acted without statutory authority in establishing a network of swap lines providing aid to foreign economies. Exacerbating this tension, in 2013 the Fed converted its temporary network of swap lines into standing arrangements that select foreign central banks can draw on at any time. This extension of the Fed’s enumerated powers represents a democratically unsanctioned incursion into the realm of foreign affairs. To address this problem, this Note suggests that absent explicit legislative authority for foreign central bank liquidity swaps, the Fed should refashion its network of swap lines as an exercise of its emergency powers under Section 13(3) of the Federal Reserve Act, as amended by Dodd–Frank. While such a change is not without significant cost, doing so would imbue transparency, accountability, and legal legitimacy into the Fed’s swap network.

Each year in the United States, Clostridioides difficile causes nearly 500,000 gastrointestinal infections that range from mild diarrhea to severe colitis and death. The ability to identify patients at increased risk for severe disease or mortality at the time of diagnosis of C. difficile infection (CDI) would allow clinicians to effectively allocate disease modifying therapies. In this study, we developed models consisting of only a small number of serum biomarkers that are capable of predicting both 30-day all-cause mortality and adverse outcomes of patients at time of CDI diagnosis. We were able to validate these models through experimental mouse infection. This provides evidence that the biomarkers reflect the underlying pathophysiology and that our mouse model of CDI reflects the pathogenesis of human infection. Predictive models can not only assist clinicians in identifying patients at risk for severe CDI but also be utilized for targeted enrollment in clinical trials aimed at reduction of adverse outcomes from severe CDI. Clostridioides difficile infection (CDI) can result in severe disease and death, with no accurate models that allow for early prediction of adverse outcomes. To address this need, we sought to develop serum-based biomarker models to predict CDI outcomes. We prospectively collected sera ≤48 h after diagnosis of CDI in two cohorts. Biomarkers were measured with a custom multiplex bead array assay. Patients were classified using IDSA severity criteria and the development of disease-related complications (DRCs), which were defined as ICU admission, colectomy, and/or death attributed to CDI. Unadjusted and adjusted models were built using logistic and elastic net modeling. The best model for severity included procalcitonin (PCT) and hepatocyte growth factor (HGF) with an area (AUC) under the receiver operating characteristic (ROC) curve of 0.74 (95% confidence interval, 0.67 to 0.81). The best model for 30-day mortality included interleukin-8 (IL-8), PCT, CXCL-5, IP-10, and IL-2Rα with an AUC of 0.89 (0.84 to 0.95). The best model for DRCs included IL-8, procalcitonin, HGF, and IL-2Rα with an AUC of 0.84 (0.73 to 0.94). To validate our models, we employed experimental infection of mice with C. difficile . Antibiotic-treated mice were challenged with C. difficile and a similar panel of serum biomarkers was measured. Applying each model to the mouse cohort of severe and nonsevere CDI revealed AUCs of 0.59 (0.44 to 0.74), 0.96 (0.90 to 1.0), and 0.89 (0.81 to 0.97). In both human and murine CDI, models based on serum biomarkers predicted adverse CDI outcomes. Our results support the use of serum-based biomarker panels to inform Clostridioides difficile infection treatment. IMPORTANCE Each year in the United States, Clostridioides difficile causes nearly 500,000 gastrointestinal infections that range from mild diarrhea to severe colitis and death. The ability to identify patients at increased risk for severe disease or mortality at the time of diagnosis of C. difficile infection (CDI) would allow clinicians to effectively allocate disease modifying therapies. In this study, we developed models consisting of only a small number of serum biomarkers that are capable of predicting both 30-day all-cause mortality and adverse outcomes of patients at time of CDI diagnosis. We were able to validate these models through experimental mouse infection. This provides evidence that the biomarkers reflect the underlying pathophysiology and that our mouse model of CDI reflects the pathogenesis of human infection. Predictive models can not only assist clinicians in identifying patients at risk for severe CDI but also be utilized for targeted enrollment in clinical trials aimed at reduction of adverse outcomes from severe CDI.

We study the derived categories of coherent sheaves on Gushel–Mukai varieties. In the derived category of such a variety, we isolate a special semiorthogonal component, which is a K3 or Enriques category according to whether the dimension of the variety is even or odd. We analyze the basic properties of this category using Hochschild homology, Hochschild cohomology, and the Grothendieck group. We study the K3 category of a Gushel–Mukai fourfold in more detail. Namely, we show this category is equivalent to the derived category of a K3 surface for a certain codimension 1 family of rational Gushel–Mukai fourfolds, and to the K3 category of a birational cubic fourfold for a certain codimension 3 family. The first of these results verifies a special case of a duality conjecture which we formulate. We discuss our results in the context of the rationality problem for Gushel–Mukai varieties, which was one of the main motivations for this work.

Remote attestation is a technology for establishing trust in a remote computing system. Copland is a domain-specific language for specifying attestation protocols that operate in diverse, layered measurement topologies. In this work, we formally define and verify the Copland Virtual Machine for executing Copland protocols alongside a dual generalized appraisal procedure. Together these components provide a principled pipeline to execute and bundle arbitrary Copland-based attestations, then unbundle and evaluate the resulting evidence for measurement content and cryptographic integrity. All artifacts are implemented as monadic, functional programs in the Coq proof assistant and verified with respect to the Copland reference semantics. Finally, we leverage formal properties of component implementations and their surrounding security architecture to aid in the design and analysis of attestation scenarios in the context of an active adversary attempting to subvert attestation. These components lay the foundation for a verified end-to-end attestation stack.