11 research outputs found

    Quantile Regression for Location-Scale Time Series Models with Conditional Heteroscedasticity

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    This paper considers quantile regression for a wide class of time series models including ARMA models with asymmetric GARCH (AGARCH) errors. The classical mean-variance models are reinterpreted as conditional location-scale models so that the quantile regression method can be naturally geared into the considered models. The consistency and asymptotic normality of the quantile regression estimator is established in location-scale time series models under mild conditions. In the application of this result to ARMA-AGARCH models, more primitive conditions are deduced to obtain the asymptotic properties. For illustration, a simulation study and a real data analysis are provided.Comment: 37 pages, 1 figur

    Telomeres and replicative cellular aging of the human placenta and chorioamniotic membranes

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    Recent hypotheses propose that the human placenta and chorioamniotic membranes (CAMs) experience telomere length (TL)-mediated senescence. These hypotheses are based on mean TL (mTL) measurements, but replicative senescence is triggered by short and dysfunctional telomeres, not mTL. We measured short telomeres by a vanguard method, the Telomere shortest length assay, and telomere-dysfunction-induced DNA damage foci (TIF) in placentas and CAMs between 18-week gestation and at full-term. Both the placenta and CAMs showed a buildup of short telomeres and TIFs, but not shortening of mTL from 18-weeks to full-term. In the placenta, TIFs correlated with short telomeres but not mTL. CAMs of preterm birth pregnancies with intra-amniotic infection showed shorter mTL and increased proportions of short telomeres. We conclude that the placenta and probably the CAMs undergo TL-mediated replicative aging. Further research is warranted whether TL-mediated replicative aging plays a role in all preterm births

    Estimation of the fraction of COVID-19 infected people in U.S. states and countries worldwide.

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    Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, daily counts of confirmed cases and deaths have been publicly reported in real-time to control the virus spread. However, substantial undocumented infections have obscured the true size of the currently infected population, which is arguably the most critical number for public health policy decisions. We developed a machine learning framework to estimate time courses of actual new COVID-19 cases and current infections in all 50 U.S. states and the 50 most infected countries from reported test results and deaths. Using published epidemiological parameters, our algorithm optimized slowly varying daily ascertainment rates and a time course of currently infected cases each day. Severe under-ascertainment of COVID-19 cases was found to be universal across U.S. states and countries worldwide. In 25 out of the 50 countries, actual cumulative cases were estimated to be 5-20 times greater than the confirmed cases. Our estimates of cumulative incidence were in line with the existing seroprevalence rates in 46 U.S. states. Our framework projected for countries like Belgium, Brazil, and the U.S. that ~10% of the population has been infected once. In the U.S. states like Louisiana, Georgia, and Florida, more than 4% of the population was estimated to be currently infected, as of September 3, 2020, while in New York this fraction is 0.12%. The estimation of the actual fraction of currently infected people is crucial for any definition of public health policies, which up to this point may have been misguided by the reliance on confirmed cases

    Drosophila glucome screening identifies Ck1alpha as a regulator of mammalian glucose metabolism

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    Circulating carbohydrates are an essential energy source, perturbations in which are pathognomonic of various diseases, diabetes being the most prevalent. Yet many of the genes underlying diabetes and its characteristic hyperglycaemia remain elusive. Here we use physiological and genetic interrogations in D. melanogaster to uncover the ‘glucome', the complete set of genes involved in glucose regulation in flies. Partial genomic screens of ∼1,000 genes yield ∼160 hyperglycaemia ‘flyabetes' candidates that we classify using fat body- and muscle-specific knockdown and biochemical assays. The results highlight the minor glucose fraction as a physiological indicator of metabolism in Drosophila. The hits uncovered in our screen may have conserved functions in mammalian glucose homeostasis, as heterozygous and homozygous mutants of Ck1alpha in the murine adipose lineage, develop diabetes. Our findings demonstrate that glucose has a role in fly biology and that genetic screenings carried out in flies may increase our understanding of mammalian pathophysiology

    Enhanced Dendritic Actin Network Formation in Extended Lamellipodia Drives Proliferation in Growth-Challenged Rac1 \u3c sup\u3e P29S \u3c/sup\u3e Melanoma Cells

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    © 2019 Elsevier Inc. Actin assembly supplies the structural framework for cell morphology and migration. Beyond structure, this actin framework can also be engaged to drive biochemical signaling programs. Here, we describe how the hyperactivation of Rac1 via the P29S mutation (Rac1 P29S ) in melanoma hijacks branched actin network assembly to coordinate proliferative cues that facilitate metastasis and drug resistance. Upon growth challenge, Rac1 P29S -harboring melanoma cells massively upregulate lamellipodia formation by dendritic actin polymerization. These extended lamellipodia form a signaling microdomain that sequesters and phospho-inactivates the tumor suppressor NF2/Merlin, driving Rac1 P29S cell proliferation in growth suppressive conditions. These biochemically active lamellipodia require cell-substrate attachment but not focal adhesion assembly and drive proliferation independently of the ERK/MAPK pathway. These data suggest a critical link between cell morphology and cell signaling and reconcile the dichotomy of Rac1’s regulation of both proliferation and actin assembly by revealing a mutual signaling axis wherein actin assembly drives proliferation in melanoma. The RhoGTPase Rac1 is a regulator of cell morphology and proliferation. Mohan et al. report that these functions converge in Rac1 P29S -mutant melanoma cells. Under growth challenge, Rac1 P29S cells form extended lamellipodia that sequester and phospho-inactivate NF2/Merlin, resulting in sustained cell proliferation that is advantageous for metastasis and drug tolerance

    Actin-Membrane Release Initiates Cell Protrusions

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    Despite the well-established role of actin polymerization as a driving mechanism for cell protrusion, upregulated actin polymerization alone does not initiate protrusions. Using a combination of theoretical modeling and quantitative live-cell imaging experiments, we show that local depletion of actin-membrane links is needed for protrusion initiation. Specifically, we show that the actin-membrane linker ezrin is depleted prior to protrusion onset and that perturbation of ezrin's affinity for actin modulates protrusion frequency and efficiency. We also show how actin-membrane release works in concert with actin polymerization, leading to a comprehensive model for actin-driven shape changes. Actin-membrane release plays a similar role in protrusions driven by intracellular pressure. Thus, our findings suggest that protrusion initiation might be governed by a universal regulatory mechanism, whereas the mechanism of force generation determines the shape and expansion properties of the protrusion
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