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Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

The ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to make a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coasts. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April and begin to abate by July, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model also showed how small changes in variables can make large differences in outcomes and highlights the importance of healthcare preparedness during pandemics.

Cedar virus (CedV) is a nonpathogenic member of the Henipavirus (HNV) genus of emerging viruses, which includes the deadly Nipah (NiV) and Hendra (HeV) viruses. CedV forms syncytia, a hallmark of henipaviral and paramyxoviral infections and pathogenicity. However, the intrinsic fusogenic capacity of CedV relative to NiV or HeV remains unquantified. HNV entry is mediated by concerted interactions between the attachment (G) and fusion (F) glycoproteins. Upon receptor binding by the HNV G head domain, a fusion-activating G stalk region is exposed and triggers F to undergo a conformational cascade that leads to viral entry or cell-cell fusion. Here, we first demonstrated quantitatively that CedV is inherently significantly less fusogenic than NiV at equivalent G and F cell surface expression levels. We then generated and tested six headless CedV G mutants of distinct stalk C-terminal lengths, surprisingly revealing highly hyperfusogenic cell-cell fusion phenotypes 3 to 4-fold greater than wild-type CedV levels. Additionally, similarly to NiV, a headless HeV G mutant yielded a less pronounced hyperfusogenic phenotype compared to wild-type HeV. Further, coimmunoprecipitation and cell-cell fusion assays revealed heterotypic NiV/CedV functional G/F bidentate interactions, as well as evidence of HNV G head domain involvement beyond receptor binding or G stalk exposure. All evidence points to the G head/stalk junction being key to modulating HNV fusogenicity, supporting the notion that head domains play several distinct and central roles in modulating stalk domain fusion promotion. Further, this study exemplifies how CedV may help elucidate important mechanistic underpinnings of HNV entry and pathogenicity. IMPORTANCE The Henipavirus genus in the Paramyxoviridae family includes the zoonotic Nipah (NiV) and Hendra (HeV) viruses. NiV and HeV infections often cause fatal encephalitis and pneumonia, but no vaccines or therapeutics are currently approved for human use. Upon viral entry, Henipavirus infections yield the formation of multinucleated cells (syncytia). Viral entry and cell-cell fusion are mediated by the attachment (G) and fusion (F) glycoproteins. Cedar virus (CedV), a nonpathogenic henipavirus, may be a useful tool to gain knowledge on henipaviral pathogenicity. Here, using homotypic and heterotypic full-length and headless CedV, NiV, and HeV G/F combinations, we discovered that CedV G/F are significantly less fusogenic than NiV or HeV G/F, and that the G head/stalk junction is key to modulating cell-cell fusion, refining the mechanism of henipaviral membrane fusion events. Our study exemplifies how CedV may be a useful tool to elucidate broader mechanistic understanding for the important henipaviruses.