The Coronaviridae are a family of viruses that cause disease in humans ranging from mild respiratory infection to potentially lethal acute respiratory distress syndrome. Finding host factors common to multiple coronaviruses could facilitate the development of therapies to combat current and future coronavirus pandemics. Here, we conducted genome-wide CRISPR screens in cells infected by SARS-CoV-2 as well as two seasonally circulating common cold coronaviruses, OC43 and 229E. This approach correctly identified the distinct viral entry factors ACE2 (for SARS-CoV-2), aminopeptidase N (for 229E) and glycosaminoglycans (for OC43). Additionally, we identified phosphatidylinositol phosphate biosynthesis and cholesterol homeostasis as critical host pathways supporting infection by all three coronaviruses. By contrast, the lysosomal protein TMEM106B appeared unique to SARS-CoV-2 infection. Pharmacological inhibition of phosphatidylinositol kinases and cholesterol homeostasis reduced replication of all three coronaviruses. These findings offer important insights for the understanding of the coronavirus life cycle and the development of host-directed therapies.

The Coronaviridae are a family of viruses that causes disease in humans ranging from mild respiratory infection to potentially lethal acute respiratory distress syndrome. Finding host factors that are common to multiple coronaviruses could facilitate the development of therapies to combat current and future coronavirus pandemics. Here, we conducted parallel genome-wide CRISPR screens in cells infected by SARS-CoV-2 as well as two seasonally circulating common cold coronaviruses, OC43 and 229E. This approach correctly identified the distinct viral entry factors ACE2 (for SARS-CoV-2), aminopeptidase N (for 229E) and glycosaminoglycans (for OC43). Additionally, we discovered phosphatidylinositol phosphate biosynthesis and cholesterol homeostasis as critical host pathways supporting infection by all three coronaviruses. By contrast, the lysosomal protein TMEM106B appeared unique to SARS-CoV-2 infection. Pharmacological inhibition of phosphatidylinositol phosphate biosynthesis and cholesterol homeostasis reduced replication of all three coronaviruses. These findings offer important insights for the understanding of the coronavirus life cycle as well as the potential development of host-directed therapies.

Despite its early promise as a diagnostic and prognostic tool, gene expression profiling remains cost-prohibitive and challenging to implement in a clinical setting. Here, we introduce a molecular computation strategy for analysing the information contained in complex gene expression signatures without the need for costly instrumentation. Our workflow begins by training a computational classifier on labelled gene expression data. This in silico classifier is then realized at the molecular level to enable expression analysis and classification of previously uncharacterized samples. Classification occurs through a series of molecular interactions between RNA inputs and engineered DNA probes designed to differentially weigh each input according to its importance. We validate our technology with two applications: a classifier for early cancer diagnostics and a classifier for differentiating viral and bacterial respiratory infections based on host gene expression. Together, our results demonstrate a general and modular framework for low-cost gene expression analysis.

High power density is required to commercialize solid oxide fuel cells for vehicular applications. In this work, high performance of metal supported solid oxide fuel cells (MS-SOFCs) is achieved via catalyst composition, electrode structure, and processing optimization. The full cell configuration consists of a dense ceramic electrolyte and porous ceramic backbones (electrodes) sandwiched between porous stainless steel metal supports. The conventional YSZ electrolyte and backbones are replaced with more conductive and thinner 10Sc1CeSZ ceramics. MS-SOFCs are co-sintered in a single step and subsequently infiltrated with nanocatalysts. Five categories of cathode catalysts are screened in full cells, including: perovskites, nickelates, praseodymium oxide, binary layered composites, and ternary layered composites. Various anode compositions are also tested. The conventional LSM cathode catalyst is replaced with more active Pr6O11 and the Ni content of the SDC-Ni anode is increased. The resulting cells achieve a peak power of 1.56, 2.0, and 2.85 W cm-2 at 700, 750, and 800 °C, respectively, with 3%H2O/H2 as fuel and 2 cathode exposed to air. Multiple cells show reproducible performance (Pmax=1.50 ± 0.06 W cm-2) and OCV (1.10 ± 0.02 V). The performance is further increased with cathode exposed to pure oxygen (2.0 W cm-2 at 700 °C).

There are many emerging and re-emerging globally prevalent viruses for which there are no licensed vaccines or antiviral medicines. Arbidol (ARB, umifenovir), used clinically for decades in several countries as an anti-influenza virus drug, inhibits many other viruses. In the current study, we show that ARB inhibits six different isolates of Zika virus (ZIKV), including African and Asian lineage viruses in multiple cell lines and primary human vaginal and cervical epithelial cells. ARB protects against ZIKV-induced cytopathic effects. Time of addition studies indicate that ARB is most effective at suppressing ZIKV when added to cells prior to infection. Moreover, ARB inhibits pseudoviruses expressing the ZIKV Envelope glycoprotein. Thus, ARB, a broadly acting anti-viral agent with a well-established safety profile, inhibits ZIKV, likely by blocking viral entry.

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pairwise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. Coherence is introduced to measure the pair-wise normalized linear synchrony and functional correlations between two EEG signals in different frequency domains, and graph analysis is further used to investigate the influence of AD on the functional connectivity of human brain. Data analysis results show that, compared with the control group, the pair-wise coherence of AD group is significantly decreased, especially for the theta and alpha frequency bands in the frontal and parieto-occipital regions. Furthermore, functional connectivity among different brain regions is reconstructed based on EEG, which exhibit obvious small-world properties. Graph analysis demonstrates that the local functional connections between regions for AD decrease. In addition, it is found that small-world properties of AD networks are largely weakened, by calculating its average path lengths, clustering coefficients, global efficiency, local efficiency, and small-worldness. The obtained results show that both pair-wise coherence and functional network can be taken as effective measures to distinguish AD patients from the normal, which may benefit our understanding of the disease.

No abstract