[This corrects the article DOI 10.1016/j.ncrna.2020.04.003.][This corrects the article DOI 10.1016/j.ncrna.2018.02.003.][This corrects the article DOI 10.1016/j.ncrna.2018.07.001.][This corrects the article DOI 10.1016/j.ncrna.2018.02.001.][This corrects the article DOI 10.1016/j.ncrna.2018.05.001.][This corrects the article DOI 10.1016/j.ncrna.2018.05.003.][This corrects the article DOI 10.1016/j.ncrna.2020.02.004.][This corrects the article DOI 10.1016/j.ncrna.2019.02.001.][This corrects the article DOI 10.1016/j.ncrna.2019.04.001.][This corrects the article DOI 10.1016/j.ncrna.2019.01.001.][This corrects the article DOI 10.1016/j.ncrna.2018.05.002.][This corrects the article DOI 10.1016/j.ncrna.2019.02.002.][This corrects the article DOI 10.1016/j.ncrna.2018.02.002.][This corrects the article DOI 10.1016/j.ncrna.2018.05.004.].[This corrects the article DOI 10.1016/j.ncrna.2019.01.002.][This corrects the article DOI 10.1016/j.ncrna.2018.04.001.][This corrects the article DOI 10.1016/j.ncrna.2020.06.002.][This corrects the article DOI 10.1016/j.ncrna.2018.08.001.][This corrects the article DOI 10.1016/j.ncrna.2019.11.002.][This corrects the article DOI 10.1016/j.ncrna.2018.02.001.][This corrects the article DOI 10.1016/j.ncrna.2018.11.001.][This corrects the article DOI 10.1016/j.ncrna.2020.02.005.][This corrects the article DOI 10.1016/j.ncrna.2018.09.001.][This corrects the article DOI 10.1016/j.ncrna.2019.01.003.][This corrects the article DOI 10.1016/j.ncrna.2020.01.001.][This corrects the article DOI 10.1016/j.ncrna.2018.11.003.][This corrects the article DOI 10.1016/j.ncrna.2018.01.001.].Breast cancer is the leading cause of cancer-related death among women. Recurrence of primary tumor and metastasis to distant body parts are major causes of breast cancer-associated mortality. The 5-year survival rate for women with metastatic breast cancer is only 25-30%. Breast cancer metastasis is a series of processes involved with EMT, invasion, loss of cell to cell adhesion, alteration in cell phenotype, extravasation, microenvironment of the tumor, and colonization to the secondary sites. Epigenetic modification is involved in the transformation of the distant stromal cell into a secondary tumor. LncRNAs, are one the key epigenetic modifiers, are the largest endogenous non-coding RNAs with approximate base-pair lengths from 200 nt to 100 kb. LncRNA plays a crucial role in breast cancer metastasis by sponging miRNA, by degrading or silencing specific mRNA, or else by targeting the enzymes and microprocessor subunits involved in the biogenesis of miRNA. LncRNA also alters the expression of several genes involved in breast cancer metastasis and modulating different cell signaling pathways. The goal of this review is to provide a better understanding of the role of lncRNA in the regulation of breast cancer metastasis. We also summarized some of the key lncRNAs that regulate the genes and signaling pathways involved in breast cancer invasion and metastasis. In late March 2020, a "Stay Home, Stay Healthy" order was issued in Washington State in response to the COVID-19 pandemic. https://www.selleckchem.com/products/BIBF1120.html On May 1, a 4-phase reopening plan began. We investigated whether adjunctive prevention strategies would allow less restrictive physical distancing to avoid second epidemic waves and secure safe school reopening. We developed a mathematical model, stratifying the population by age, infection status and treatment status to project SARS-CoV-2 transmission during and after the reopening period. The model was parameterized with demographic and contact data from King County, WA and calibrated to confirmed cases, deaths and epidemic peak timing. Adjunctive prevention interventions were simulated assuming different levels of pre-COVID physical interactions (pC_PI) restored. The best model fit estimated ~35% pC_PI under the lockdown which prevented ~17,000 deaths by May 15. Gradually restoring 75% pC_PI for all age groups between May 15-July 15 would have resulted in ~350 daily deaths by early September 2020. Maintaining <45% pC_PI was required with current testing practices to ensure low levels of daily infections and deaths. Increased testing, isolation of symptomatic infections, and contact tracing permitted 60% pC_PI without significant increases in daily deaths before November and allowed opening of schools with <15 daily deaths. Inpatient antiviral treatment was predicted to reduce deaths significantly without lowering cases or hospitalizations. We predict that widespread testing, contact tracing and case isolation would allow relaxation of physical distancing, as well as opening of schools, without a surge in local cases and deaths. We predict that widespread testing, contact tracing and case isolation would allow relaxation of physical distancing, as well as opening of schools, without a surge in local cases and deaths. Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets. Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares (LSQ) and maximum likelihood estimation (MLE) methods. We utilize parametric bootstrapping to construct confidence intervals for parameter estimates. We compare the results including RMSE, Anscombe residual, and 95% prediction interval coverage. We also evaluate the correlation between the estimates from both methods. Comparing LSQ and MLE estimates, most outbreaks have similar parameter estimates, RMSE, Anscombe, and 95% prediction interval coverage. Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase. However, for two outbreaks, there are systematic deviations in model fit to the data that explain differences in parameter estimates (e.g., residuals represent random error rather than systematic deviation). Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM, provided that the model yields a good fit to the data. Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM, provided that the model yields a good fit to the data.