Penciptaan Komunikasi Visual Perancangan Program Edutainment “Seri Aktivitas Alam: Gunung Meletus”

This research is the continuation of previous research. The research is included in the creation of visual communication solutions on how a process of visual communication strategy can contribute a persuasive invitation. Research aims to expose the solution in the realm of visual communication. The research applied qualitative method. It began with the development of communicators becoming a mascot, continued on the delivery of messages through the comics, and invited children as audience target for design experience with game and gimmick. Result of the research is the visual design, as well as including the process of visual communication creation. As a conclusion, creating a visual communication solution could be carried out by the same method, similar matching scope, as well as the contents adjusted with new needs

DataSheet1_Risk Score Prediction Model of Prognosis in GC Patients by Age and Gender Combined With m6A Modification Genes FTO and RBM15.pdf

Background: Gastric cancer (GC) has a high mortality rate. N6-methyladenosine (m6A) is involved in the development of GC. Age and gender are associated with GC incidence and survival. This study aimed to explore the risk score prediction model of prognosis in GC patients by age and gender combined with m6A modification genes.Methods: Data on m6A modification gene expression and clinical information downloaded from the Cancer Genome Atlas (TCGA) database were used to construct the risk score prediction model. Cox and least absolute shrinkage and selection operator (LASSO) regression were performed to identify clinical characteristics and m6A modification genes associated with prognosis. A risk score prediction model was established based on multivariate Cox regression analysis. The Gene Expression Omnibus (GEO) database was used to validate this model.Results: Most of the m6A modification genes were upregulated in GC tumor tissues compared with that in normal tissues and were correlated with clinical characteristics including grade, stage status, and T status. The risk score prediction model was established based on age, gender, FTO, and RBM15. GC patients were divided into high- or low-risk groups based on the median risk score. Patients with a high risk score had poor prognosis. Multivariate Cox regression indicated that risk score was an independent prognostic factor for GC patients. The data from GSE84437 verified the predictive value of this model.Conclusion: The risk score prediction model based on age and gender combined with m6A modification genes FTO and RBM15 was an independent prognostic factor for GC patients.</p

Autocorrelation function (ACF) and Partial ACF (PACF) plot of original and integrated the number of HFMD hospitalizations.

<p>A and B) shows ACF and PACF plot of original HFMD hospitalizations. C and D) ACF and PACF plot of integrated HFMD hospitalizations.</p

Prediction of square root transformation of the number of HFMD hospitalizations, the number of HEV71-associated and CoxA16-associated HFMD hospitalizations on the basis of a seasonal autoregressive integrated moving average model (SARIMA) model with average atmospheric temperature as the covariate for 2012.

<p>Solid line: observed values during the period, dashed line: predicted values for 2012 with and without climatic variables. A: Square root transformation of the number of HFMD hospitalizations, B: the number of HEV71-associated HFMD hospitalizations, C: the number of CoxA16-associated HFMD hospitalizations.</p

Characteristics of SARIMA models for the number of cases hospitalized with HFMD, HEV71-associated HFMD, Cox A16-associated HFMD.

<p>SARIMA: Seasonal Autoregressive Integrated Moving Average model, AR: autoregressive, MA: moving average, SAR: seasonal autoregressive, <i>R</i>²: Stationary R-squared, BIC: Bayesian information criteria, <i>P</i>: Ljung-Box test, RMSE: Root Mean Square Error.</p

Characteristics of multivariate SARIMA models using climate variables for the number of cases hospitalized with HFMD, HEV71-associated HFMD, Cox A16-associated HFMD.

<p>SARIMA: Seasonal Autoregressive Integrated Moving Average model, AR: autoregressive, MA: moving average, SAR: seasonal autoregressive. β: Coefficient, SE: Standard Error, <i>R</i>²: Stationary R-squared, BIC: Bayesian information criteria, <i>P*</i>: Ljung-Box test, RMSE: Root Mean Square Error, T{avg}-Lag2 weeks: average atmospheric temperature at lag 2 weeks, T{avg}-Lag3 weeks: average atmopheric temperature at lag 3 weeks.</p

Weekly numbers of hospitalized children with HFMD in Zhengzhou, China from January 2008 to June 2012 compared to crude meteorological variables for the same period.

<p>An alternate course is seen between temperature and the pathogens. HFMD (A), other EV(B), HEV71(C) and CoxA16(D).</p

Autocorrelation function (ACF) and Partial ACF (PACF) plot of residuals after applying a SARIMA (1, 1, 1) (1, 0, 0)₅₂ model.

<p>The x-axis gives the number of lags in weeks and, the y-axis, the value of the correlation coefficient comprised between −1 and 1. Dotted lines indicate 95% confidence interval.</p