Science Behind Paranormal Activities

This detail explanation is about the scientific causes behind every paranormal incident. Now-a-days documentary films on paranormal activities or researches on paranormal incidents is spreading throughout the world. But none of the researcher shows the exact cause behind those incidents. Scientists or the host of any paranormal tv shows directly said those canrsquot be explained. Yes, thatrsquos true but not completely. They should say those canrsquot be explained by normal scientific explanations. Thatrsquos why it is called lsquoParanormalrsquo. In this paper, Irsquom going to unveil the real cause behind every paranormal incident with logics and scientific explanations. People should be reminded that all those concepts of physics and metaphysics are based on assumption. All those prove has been done on the basis of several assumptions

A modified risk detection approach of biomarkers by frailty effect on multiple time to event data

Multiple indications of disease progression found in a cancer patient by loco-regional relapse, distant metastasis and death. Early identification of these indications is necessary to change the treatment strategy. Biomarkers play an essential role in this aspect. The survival chance of a patient is dependent on the biomarker, and the treatment strategy also differs accordingly, e.g., the survival prediction of breast cancer patients diagnosed with HER2 positive status is different from the same with HER2 negative status. This results in a different treatment strategy. So, the heterogeneity of the biomarker statuses or levels should be taken into consideration while modelling the survival outcome. This heterogeneity factor which is often unobserved, is called frailty. When multiple indications are present simultaneously, the scenario becomes more complex as only one of them can occur, which will censor the occurrence of other events. Incorporating independent frailties of each biomarker status for every cause of indications will not depict the complete picture of heterogeneity. The events indicating cancer progression are likely to be inter-related. So, the correlation should be incorporated through the frailties of different events. In our study, we considered a multiple events or risks model with a heterogeneity component. Based on the estimated variance of the frailty, the threshold levels of a biomarker are utilised as early detection tool of the disease progression or death. Additive-gamma frailty model is considered to account the correlation between different frailty components and estimation of parameters are performed using Expectation-Maximization Algorithm. With the extensive algorithm in R, we have obtained the threshold levels of activity of a biomarker in a multiple events scenario.Comment: 21 pages, 2 figures,7 table

Handling missingness value on jointly measured time-course and time-to-event data

Joint modeling technique is a recent advancement in effectively analyzing the longitudinal history of patients with the occurrence of an event of interest attached to it. This procedure is successfully implemented in biomarker studies to examine parents with the occurrence of tumor. One of the typical problem that influences the necessary inference is the presence of missing values in the longitudinal responses as well as in covariates. The occurrence of missingness is very common due to the dropout of patients from the study. This article presents an effective and detailed way to handle the missing values in the covariates and response variable. This study discusses the effect of different multiple imputation techniques on the inferences of joint modeling implemented on imputed datasets. A simulation study is carried out to replicate the complex data structures and conveniently perform our analysis to show its efficacy in terms of parameter estimation. This analysis is further illustrated with the longitudinal and survival outcomes of biomarkers' study by assessing proper codes in R programming language

Handling missingness value on jointly measured time-course and time-to-event data

Joint modeling technique is a recent advancement in effectively analyzing the longitudinal history of patients with the occurrence of an event of interest attached to it. This procedure is successfully implemented in biomarker studies to examine parents with the occurrence of tumor. One of the typical problem that influences the necessary inference is the presence of missing values in the longitudinal responses as well as in covariates. The occurrence of missingness is very common due to the dropout of patients from the study. This article presents an effective and detailed way to handle the missing values in the covariates and response variable. This study discusses the effect of different multiple imputation techniques on the inferences of joint modeling implemented on imputed datasets. A simulation study is carried out to replicate the complex data structures and conveniently perform our analysis to show its efficacy in terms of parameter estimation. This analysis is further illustrated with the longitudinal and survival outcomes of biomarkers' study by assessing proper codes in R programming language

Is Diabetes Pre-coded in the Brain? Role of Hypothalamus, Addiction Network and Social Cognition

The hypothalamus, the master regulator of circadian rhythm, in association with peripheral clocks, play crucial roles in glucose metabolism. Impairment in cerebral sensing, uptake and processing of glucose has been suggested in various animal and human diabetic models. Diabetes Mellitus has been largely superseded by the discovery of insulin and insulin resistance. Expanding horizons of knowledge of the roles of the hypothalamus in glucose metabolism and the overlapping neural pathways of sugar addictionwith other classically described substance and behavioral addictions networks have again thrown some light on the cerebral theory of DM pathogenesis

A Simple Model on Mothers’ Autonomy, Health Inputs, and Child Health

Using traditional health capital model of Grossman (1972) and Wagstaff (1986a) this paper attempts to fill in the theoretical missing link between mothers’ autonomy and household consumption behavior, particularly focusing on the consumption of child health inputs. It has been shown in this analysis that working mothers’ children should be of better health status. Further independent of working status of the mother, the autonomy parameter always induces consumption of more health inputs for the children. However, when autonomy is linked with mothers’ income, the basic results of the model are further strengthened. In fact, income induced autonomy may result in redefining the composite consumption good for the family as an inferior one