Notes

Reasoning as well as Criteria for any COVID-19 Model Composition.
Optical clearing has emerged as a powerful tool for volume imaging. Although volume imaging with immunostaining have been successful in many protocols, yet obtaining homogeneously stained thick samples remains challenging. Here, we propose a method for label-free imaging of brain slices by enhancing the regional heterogeneity of the optical properties using the tissue clearing principle. We used FxClear, a method for delipidation of brain tissue, to retain a larger proportion of lipids at the white matter (WM). Furthermore, the embedding media affected the contrasts for the lipid-rich or extracellular matrix-rich areas, depending on their chemical properties. Thus, we tailored clearing conditions for the enhancement of the refractive indices (RIs) differences between gray and WM, or several pathological features. RI differences can be imaged using conventional light microscopy or optical coherence tomography. We propose that our protocol is simple, reliable, and flexible for label-free imaging, easily implementable to routine histology laboratory.Ebola virus (EBOV) is responsible for outbreaks with case fatality rates of up to 90% and for an epidemic in West Africa with more than ten thousand deaths. EBOV glycoprotein (EBOV-GP) is the only viral surface protein and is responsible for viral entry into cells. Here, by employing pseudotyped EBOV-GP viral particles, we uncover a critical role for sphingolipids in inhibiting viral entry. Sphingosine kinase 1 (SphK1) catalyzes the phosphorylation of sphingosine to sphingosine 1-phosphate (S1P). The administration of the SphK1 activator, K6PC-5, or S1P, or the overexpression of SphK1 consistently exhibited striking inhibitory effects in EBOV-GP-driven entry in diverse cell lines. Finally, K6PC-5 markedly reduced the EBOV titer in infected cells and the de novo production of viral proteins. These data present K6PC-5 as an efficient tool to inhibit EBOV infection in endothelial cells and suggest further studies to evaluate its systemic effects.To investigate the molecular mechanisms underlying islet dysfunction and insulin resistance in diet-induced diabetes, we conducted temporal RNA sequencing of tissues responsible for insulin secretion (islets) and action (liver) every 4 weeks in mice on high-fat (HFD) or chow diet for 24 weeks, linking to longitudinal profile of metabolic characteristics. The diverse responses of α, β, and δ cells to glucose and palmitate indicated HFD-induced dynamic deterioration of islet function from dysregulation to failure. Insulin resistance developed with variable time course in different tissues. ICG001 Weighted gene co-expression network analysis and Ingenuity Pathway Analysis implicated islets and liver jointly programmed β-cell compensatory adaption via cell proliferation at early phase and irreversible islet dysfunction by inappropriate immune response at later stage, and identified interconnected molecules including growth differentiation factor 15. Frequencies of T cell subpopulation showed an early decrement in Tregs followed by increases in Th1 and Th17 cells during progression to diabetes.Autonomous experimentation (AE) accelerates research by combining automation and machine learning to perform experiments intelligently and rapidly in a sequential fashion. While AE systems are most needed to study properties that cannot be predicted analytically or computationally, even imperfect predictions can in principle be useful. Here, we investigate whether imperfect data from simulation can accelerate AE using a case study on the mechanics of additively manufactured structures. Initially, we study resilience, a property that is well-predicted by finite element analysis (FEA), and find that FEA can be used to build a Bayesian prior and experimental data can be integrated using discrepancy modeling to reduce the number of needed experiments ten-fold. Next, we study toughness, a property not well-predicted by FEA and find that FEA can still improve learning by transforming experimental data and guiding experiment selection. These results highlight multiple ways that simulation can improve AE through transfer learning.Viruses interact extensively with the host molecular machinery, but the underlying mechanisms are poorly understood. Bacteriophage T7 recruits the small protein thioredoxin of the Escherichia coli host as an essential processivity factor for the viral DNA polymerase. We challenged the phage to propagate in a host in which thioredoxin had been extensively modified to hamper its recruitment. The virus adapted to the engineered host without losing the capability to propagate in the original host, but no genetic mutations were fixed in the thioredoxin binding domain of the viral DNA polymerase. Virus adaptation correlated with mutations in the viral RNA polymerase, supporting that promiscuous thioredoxin recruitment was enabled by phenotypic mutations caused by transcription errors. These results point to a mechanism of virus adaptation that may play a role in cross-species transmission. We propose that phenotypic mutations may generally contribute to the capability of viruses to evade antiviral strategies.Seasonal disease outbreaks are perennial features of human infectious disease but the factors generating these patterns are unclear. Here we investigate seasonal and daytime variability in multiple immune parameters in 329,261 participants in UK Biobank and test for associations with a wide range of environmental and lifestyle factors, including changes in day length, outdoor temperature and vitamin D at the time the blood sample was collected. Seasonal patterns were evident in lymphocyte and neutrophil counts, and C-reactive protein CRP, but not monocytes, and these were independent of lifestyle, demographic, and environmental factors. All the immune parameters assessed demonstrated significant daytime variation that was independent of confounding factors. At a population level, human immune parameters vary across season and across time of day, independent of multiple confounding factors. Both season and time of day are fundamental dimensions of immune function that should be considered in all studies of immuno-prophylaxis and disease transmission.
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