Objective Macrophage Activation Syndrome (MAS) is a devastating cytokine storm syndrome complicating many inflammatory diseases and characterized by fever, pancytopenia, and systemic inflammation. It is clinically similar to Hemophagocytic Lymphohistiocytosis (HLH), which is caused by viral infection of a host with impaired cellular cytotoxicity. Murine models of MAS and HLH illustrate Interferon-γ (IFN-γ) as the driving stimulus for hemophagocytosis and immunopathology. We sought to understand the inflammatory contributors to a murine model of Toll-like Receptor 9 (TLR9)-induced fulminant MAS. Methods Wild-type (WT), transgenic, and cytokine-inhibited mice were treated with an IL-10 receptor blocking antibody and TLR9 agonist, and parameters of MAS were evaluated. Results Fulminant MAS was characterized by dramatic elevations in IFN-γ, IL-12, and IL-6. Serum IFN-γ correlated with enhanced IFN-γ production within some hepatic populations, but fewer IFN-γ+ cells. Surprisingly, IFN-γKO mice developed immunopathology and hemophagocytosis comparably to WT mice. However, IFN-γKO mice did not become anemic and had greater numbers of splenic erythroid precursors. IL-12 neutralization phenocopied disease in IFN-γKO mice. Interestingly, Type I interferons contributed to the severity of hypercytokinemia and weight loss, but their absence did not otherwise affect MAS manifestations. Conclusion These data demonstrate that both fulminant MAS and hemophagocytosis can arise independently of IFN-γ, IL-12, or Type I interferons. They also suggest that IFN-γ-mediated dyserythropoiesis, not hemophagocytosis, is the dominant cause of anemia in fulminant TLR9-MAS. Thus, our data establish a novel mechanism for the acute anemia of inflammation, but suggest that a variety of triggers can result in hemophagocytic disease.

Operational MMLs are associated with reductions in opioid positivity among 21- to 40-year-old fatally injured drivers and may reduce opioid use and overdose.

Summary We introduce a novel method for separating amplitude and phase variability in exponential family functional data. Our method alternates between two steps: the first uses generalized functional principal components analysis to calculate template functions, and the second estimates smooth warping functions that map observed curves to templates. Existing approaches to registration have primarily focused on continuous functional observations, and the few approaches for discrete functional data require a pre‐smoothing step; these methods are frequently computationally intensive. In contrast, we focus on the likelihood of the observed data and avoid the need for preprocessing, and we implement both steps of our algorithm in a computationally efficient way. Our motivation comes from the Baltimore Longitudinal Study on Aging, in which accelerometer data provides valuable insights into the timing of sedentary behavior. We analyze binary functional data with observations each minute over 24 hours for 592 participants, where values represent activity and inactivity. Diurnal patterns of activity are obscured due to misalignment in the original data but are clear after curves are aligned. Simulations designed to mimic the application indicate that the proposed methods outperform competing approaches in terms of estimation accuracy and computational efficiency. Code for our method and simulations is publicly available.

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Ovarian cancer is the deadliest gynecologic malignancy. Multi-omics techniques have provided a platform for improved predictive modeling of therapy response and patient outcomes. While high-grade serous carcinoma (HGSOC) tumors are immunogenic and numerous studies have defined positive correlation to immune cell infiltration, immunotherapies in clinical trials have exhibited low efficacy rates. There is a significant need to better comprehend the role and composition of immune cells in mediating ovarian cancer therapeutic response and progression. We performed multiplex IHC with an HGSOC tissue microarray (n = 127) to characterize the immune cell composition within tumors. After analyzing the composition and spatial context of T cells (CD4/CD8), macrophages (CD68), and B cells (CD19) within the tumor, we found that increased B-cell and CD4 T-cell presence correlated with overall survival. More importantly, we observed that the proximity between tumor-associated macrophages and B cells or CD4 T cells significantly correlated with overall survival. Implications: The results highlight the antitumor role of B cells and CD4 T cells, and that the spatial interactions between immune cell types are a novel predictor of therapeutic response and patient outcomes.

Motivation Multiplexed imaging is a nascent single-cell assay with a complex data structure susceptible to technical variability that disrupts inference. These in situ methods are valuable in understanding cell-cell interactions, but few standardized processing steps or normalization techniques of multiplexed imaging data are available. Results We implement and compare data transformations and normalization algorithms in multiplexed imaging data. Our methods adapt the ComBat and functional data registration methods to remove slide effects in this domain, and we present an evaluation framework to compare the proposed approaches. We present clear slide-to-slide variation in the raw, unadjusted data, and show that many of the proposed normalization methods reduce this variation while preserving and improving the biological signal. Further, we find that dividing multiplexed imaging data by its slide mean, and the functional data registration methods, perform the best under our proposed evaluation framework. In summary, this approach provides a foundation for better data quality and evaluation criteria in multiplexed imaging. Availability Source code is provided at: https://github.com/statimagcoll/MultiplexedNormalization and an R package to implement these methods is available here: https://github.com/ColemanRHarris/mxnorm. Supplementary information Supplementary data are available at Bioinformatics online.

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