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Over the past few months, the outbreak of Coronavirus disease (COVID-19) has been expanding over the world. A reliable and accurate dataset of the cases is vital for scientists to conduct related research and policy-makers to make better decisions. We collect the United States COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, then compare the similarities and differences among them. To obtain reliable data for further analysis, we first examine the cyclical pattern and the following anomalies, which frequently occur in the reported cases: (1) the order dependencies violation, (2) the point or period anomalies, and (3) the issue of reporting delay. To address these detected issues, we propose the corresponding repairing methods and procedures if corrections are necessary. In addition, we integrate the COVID-19 reported cases with the county-level auxiliary information of the local features from official sources, such as health infrastructure, demographic, socioeconomic, and environmental information, which are also essential for understanding the spread of the virus. 

Background: Fluid intelligence (FI) involves abstract problem-solving without prior knowledge. Greater age-related FI decline increases Alzheimer’s disease (AD) risk, and recent studies suggest that certain dietary regimens may influence rates of decline. However, it is uncertain how long-term food consumption affects FI among adults with or without familial history of AD (FH) or APOE4 (ɛ4). Objective: Observe how the total diet is associated with long-term cognition among mid- to late-life populations at-risk and not-at-risk for AD. Methods: Among 1,787 mid-to-late-aged adult UK Biobank participants, 10-year FI trajectories were modeled and regressed onto the total diet based on self-reported intake of 49 whole foods from a Food Frequency Questionnaire (FFQ). Results: Daily cheese intake strongly predicted better FIT scores over time (FH-: β= 0.207, p < 0.001; ɛ4–: β= 0.073, p = 0.008; ɛ4+: β= 0.162, p = 0.001). Alcohol of any type daily also appeared beneficial (ɛ4+: β= 0.101, p = 0.022) and red wine was sometimes additionally protective (FH+: β= 0.100, p = 0.014; ɛ4–: β= 0.59, p = 0.039). Consuming lamb weekly was associated with improved outcomes (FH-: β= 0.066, p = 0.008; ɛ4+: β= 0.097, p = 0.044). Among at risk groups, added salt correlated with decreased performance (FH+: β= –0.114, p = 0.004; ɛ4+: β= –0.121, p = 0.009). Conclusion: Modifying meal plans may help minimize cognitive decline. We observed that added salt may put at-risk individuals at greater risk, but did not observe similar interactions among FH- and AD- individuals. Observations further suggest in risk status-dependent manners that adding cheese and red wine to the diet daily, and lamb on a weekly basis, may also improve long-term cognitive outcomes. 

Motivated by recent work involving the analysis of biomedical imaging data, we present a novel procedure for constructing simultaneous confidence corridors for the mean of imaging data. We propose to use flexible bivariate splines over triangulations to handle an irregular domain of the images that is common in brain imaging studies and in other biomedical imaging applications. The proposed spline estimators of the mean functions are shown to be consistent and asymptotically normal under some regularity conditions. We also provide a computationally efficient estimator of the covariance function and derive its uniform consistency. The procedure is also extended to the two‐sample case in which we focus on comparing the mean functions from two populations of imaging data. Through Monte Carlo simulation studies, we examine the finite sample performance of the proposed method. Finally, the proposed method is applied to analyze brain positron emission tomography data in two different studies. One data set used in preparation of this article was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

 

Fusobacterium nucleatumis implicated in accelerating colorectal cancer (CRC) and is found within metastatic CRC cells in patient biopsies. Here, we found that bacterial invasion of CRC cells and cocultured immune cells induced a differential cytokine secretion that may contribute to CRC metastasis. We used a modified galactose kinase markerless gene deletion approach and found thatF. nucleatuminvaded cultured HCT116 CRC cells through the bacterial surface adhesin Fap2. In turn, Fap2-dependent invasion induced the secretion of the proinflammatory cytokines IL-8 and CXCL1, which are associated with CRC progression and promoted HCT116 cell migration. Conditioned medium fromF. nucleatum–infected HCT116 cells caused naïve cells to migrate, which was blocked by depleting CXCL1 and IL-8 from the conditioned medium. Cytokine secretion from HCT116 cells and cellular migration were attenuated by inhibitingF. nucleatumhost-cell binding and entry using galactose sugars,l-arginine, neutralizing membrane protein antibodies, orfap2deletion.F. nucleatumalso induces the mobilization of immune cells in the tumor microenvironment. However, in neutrophils and macrophages, the bacterial-induced secretion of cytokines was Fap2 independent. Thus, our findings show thatF. nucleatumboth directly and indirectly modulates immune and cancer cell signaling and migration. Because increased IL-8 and CXCL1 production in tumors is associated with increased metastatic potential and cell seeding, poor prognosis, and enhanced recruitment of tumor-associated macrophages and fibroblasts, we propose that inhibition of host-cell binding and invasion, potentially through vaccination or novel galactoside compounds, could be an effective strategy for reducingF. nucleatum–associated CRC metastasis.

 

Abstract 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.