Iterative Recurrence Model for Calculating Effectiveness of Defense in the Multiple Shooting Mode

Atopic dermatitis (AD) is a prevalent inflammatory skin disease characterized by its chronic nature and relapse. Ample evidence suggests that non-coding RNAs play a major role in AD pathogenesis. However, the mechanism remains unknown, particularly in AD recurrence. Dynamic morphological and cytokine changes were measured throughout the whole course of an FITC-induced AD recurrence murine model. Microarray assay and integrative analysis were performed to comprehensively explore long non-coding RNA (lncRNA), messenger RNA (mRNA), and microRNA (miRNA) networks. Our results showed that an AD recurrence model was established. Overall, 5766 lncRNAs, 4025 mRNAs, and 202 miRNAs changed after elicitation, whereas, 419 lncRNAs, 349 mRNAs, and more notably, only 23 miRNAs, were dysregulated in the remission phase. Gene ontology (GO) and KEGG pathway enrichment analyses were used to investigate the potential functions of the dysregulated genes. The altered regulation of seven miRNAs and seven lncRNAs were validated in different stages of the model. The competing endogenous RNA (ceRNA) network inferred that lncRNA humanlincRNA0490+ could compete for miR-155-5p binding, through which it might affect Pkiα expression. Altogether, our findings have provided a novel perspective on the potential roles of non-coding RNAs in AD, and suggest that specific non-coding RNAs could be new therapeutic targets against AD recurrence.

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Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models

Statistical Methods in Medical Research ◽

10.1177/0962280218805780 ◽

2018 ◽

Vol 28 (12) ◽

pp. 3591-3608 ◽

Cited By ~ 1

Author(s):

Christoph Zimmer ◽

Sequoia I Leuba ◽

Ted Cohen ◽

Reza Yaesoubi

Keyword(s):

Stochastic Systems ◽

Dynamic Models ◽

Calibration Method ◽

Compartmental Models ◽

Prediction Intervals ◽

Multiple Shooting ◽

Parameter Estimates ◽

Posterior Distributions ◽

Calibration Methods ◽

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Stochastic transmission dynamic models are needed to quantify the uncertainty in estimates and predictions during outbreaks of infectious diseases. We previously developed a calibration method for stochastic epidemic compartmental models, called Multiple Shooting for Stochastic Systems (MSS), and demonstrated its competitive performance against a number of existing state-of-the-art calibration methods. The existing MSS method, however, lacks a mechanism against filter degeneracy, a phenomenon that results in parameter posterior distributions that are weighted heavily around a single value. As such, when filter degeneracy occurs, the posterior distributions of parameter estimates will not yield reliable credible or prediction intervals for parameter estimates and predictions. In this work, we extend the MSS method by evaluating and incorporating two resampling techniques to detect and resolve filter degeneracy. Using simulation experiments, we demonstrate that an extended MSS method produces credible and prediction intervals with desired coverage in estimating key epidemic parameters (e.g. mean duration of infectiousness and R0) and short- and long-term predictions (e.g. one and three-week forecasts, timing and number of cases at the epidemic peak, and final epidemic size). Applying the extended MSS approach to a humidity-based stochastic compartmental influenza model, we were able to accurately predict influenza-like illness activity reported by U.S. Centers for Disease Control and Prevention from 10 regions as well as city-level influenza activity using real-time, city-specific Google search query data from 119 U.S. cities between 2003 and 2014.

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