Cancer is defined as undifferentiated and unchecked growth of cells damaging the surrounding tissue. Cancers manifest altered gene expression. Gene expression is regulated by a diverse array of non-protein-coding RNA. Aberrant expression of long non-coding RNAs (lncRNAs) has been recently found to have functional consequences in cancers. In the current study, we report CARLo-7 as the only bladder cancer-specific lncRNA from the CARLos cluster. The expression of this lncRNA correlates with bladder cancer grade. We propose that CARLo-7 has an oncogenic potential and might be regulator of cell proliferation. Furthermore, by comparison the expression of proto-oncogene MYC, which is the only well-annotated gene close to the cancer -associated linkage disequilibrium blocks of this region, does not show a pronounced change in expression between the low-and high-grade tumours. Our results indicate that CARlo-7 can act as a prognostic marker for bladder cancer. K E Y W O R D Sbladder cancer, CARLo-7 (CASC11), long non-coding RNA, MYC

Time series analysis has been applied intensively and sophisticatedly to model and forecast many problems in the biological, physical and environmental phenomena of interest. This fact accounts for the basic engineering problem in forecasting the daily peak system load to use time series analysis. ARMA and REgARMA models are among the times series models considered. ANFIS, a hybrid model from neural network is also discussed as for comparison purposes. The main interest of the forecasts consists of three days up to five days ahead predictions for daily data. The pure autoregressive model with an order 2, or AR (2) with a MAPE value of 1.27% is found to be an appropriate model for forecasting the Malaysian peak daily load for the 3 days ahead prediction. ANFIS model gives a better MAPE value when weekends' data were excluded. Regression models with ARMA errors are found to be good models for forecasting different day types. The selection of these models is depended on the smallest value of AIC statistic and the forecasting accuracy criteria.

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Efficacy of investment in educational institutes, and human capital have drastic role in economic upturn. However, at various levels human capital demonstrated regarding infrastructure of education that becomes a more relevant measure of human capital alternative to enrollment at school in different institutions. This study has taken four decades annual data to investigate the association between educational institutions and human capital on economic upswing. The data starts from 1978 to 2018. The Cob Douglas production function is used to determine the efficacy of human capital, and upswing of the economy in Pakistan. The overall results reveal that there is a significant role of human capital (educational institutions) in economic growth in the long run. It is also observed that long-term development across countries has been propelled by productivity growth at a higher scale. Economic upswing accelerates the labor productivity, if necessary, actions should be part of Government investment. The decisions, and policies in educational institutes should be positive and proof of safe flight through human capital efficacy.

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