isks of incidence of breast cancer in a cohort of females occupationally exposed to ionizing radiation

Breast cancer (BrCa) holds the first rank place in morbidity and mortality due to malignant neoplasms among Russian women. BrCa is a multifactorial disease and ionizing radiation is among factors that cause elevated risks of developing BrCa. Our research aim was to assess relative risk (RR) of incidence of BrCa among women who were occupationally exposed to chronic ionizing radiation taking into account radiation and non-radiation factors. RR of incidence of BrCa was analyzed in a cohort of women employed at a nuclear production enterprise, namely Mayak PA, in 1948–1982. 95 % of women started working at the enterprise at their reproductive age. All those women were chronically exposed to ionizing radiation at their workplaces. A mean cumulative breast absorbed dose of external gamma-ray exposure amounted to 0.45 (standard deviation was 0.68) Gy; an average cumulative muscle absorbed dose of internal alpha-particle exposure amounted to 0.003 (0.01) Gy. According to data taken from “Clinic” medical-dosimetric database, 165 BrCa cases were detected in 157 women of the analyzed cohort (8 women had BrCa in both breasts). Our analysis involved calculating RR of incidence of BrCa in relation to known non-radiation and radiation factors. Categorical data analysis was performed without age-related and calendar period-related stratification and with them. RR was analyzed based on Poisson regression with AMFIT module in EPICURE software package. Incidence of BrCa was revealed to be associated with attained age, age of menarche, age of menopause, number of abortions, age of concomitant diseases prior to cancer diagnosis, height, body mass index, age of hiring at the Mayak PA. There was no relationship between BrCa incidence and cumulative doses of occupational chronic external gamma-ray, internal alpha-particle and neutron exposure.

Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs)

<p>The Philippines is a country where a quarter to one-third of the population is poor. Although the nation has managed to lower poverty incidence in some years, its booming population increases the poor population dramatically. This is why alleviating poverty is a pinnacle program in the country. In aid of poverty alleviation endeavor, this study focuses on assessing which programs had been effective in alleviating poverty given other family characteristics. Aside from descriptive methods, employing Generalised Linear Models (GLMs) and categorical data analysis are the focus in analysing the effects of existing intervention programs on status of improvement and income of families. In addition, varying effects of programs depending on values of other covariates are also analysed. Descriptive analysis and modeling are applied on the panel data of families. Intervention programs namely scholarship, Comprehensive Agrarian Reform Program (CARP) and government housing or other housing financing program (GHFP) have been run together with other family characteristics to describe improvement in welfare and income. Interaction effects, between access to intervention programs and other aspects of the family, have been derived to give a richer picture of the phenomenon. The study has come to conclude that the programs are indeed effective in improving lives of families, with some effects varying on some levels of other explanatory variables.</p>

Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs)

<p>The Philippines is a country where a quarter to one-third of the population is poor. Although the nation has managed to lower poverty incidence in some years, its booming population increases the poor population dramatically. This is why alleviating poverty is a pinnacle program in the country. In aid of poverty alleviation endeavor, this study focuses on assessing which programs had been effective in alleviating poverty given other family characteristics. Aside from descriptive methods, employing Generalised Linear Models (GLMs) and categorical data analysis are the focus in analysing the effects of existing intervention programs on status of improvement and income of families. In addition, varying effects of programs depending on values of other covariates are also analysed. Descriptive analysis and modeling are applied on the panel data of families. Intervention programs namely scholarship, Comprehensive Agrarian Reform Program (CARP) and government housing or other housing financing program (GHFP) have been run together with other family characteristics to describe improvement in welfare and income. Interaction effects, between access to intervention programs and other aspects of the family, have been derived to give a richer picture of the phenomenon. The study has come to conclude that the programs are indeed effective in improving lives of families, with some effects varying on some levels of other explanatory variables.</p>

Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering Over Multi Labeled Categorical Data Analysis

In data mining, clustering is the one of the efficient research concept in real time data analysis, evaluation of attribute representation in clustering is main issue in artificial intelligence related research areas. Multi labeled clustering gives high amount of valuable data, which describes the evaluation and representation of attribute be the trending concept in multi labeled categorical data analysis. Multi dimensional clustering is combined complementary data from different dimensions to provide efficient clustering results in various conditions. Different multi view clustering techniques are proposed traditionally but they can give output as single clustering with input data. Because of multiplicity, multi dimensional data can have different grouping data which are reasonable consist perspective attributes. So how to find measurable and reasonable cluster results which are represented in multi view labeled data is still challenging task, so that in this paper, we propose a novel approach i.e. Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering (OCMHAMVC) to represent data as a cluster with different categories. Based on multi labeled data, first proposed approach evaluates low dimensional data using optimized matrix factorization (OMF) method and clusters the similar labeled sample data into prototype cluster of dimensional data. After that we represent data in desirable orthonormality constrained view of data using adaptive heuristic to combine complementary data from different dimensions, also provide complexity in computational analysis of data representation. Experimental results of proposed approach applied on high amount of multi view data gives scalable and efficient performance with comparison to traditional multi view related clustering approaches.

Analysis of Categorical Data with the R Package confreq

The person-centered approach in categorical data analysis is introduced as a complementary approach to the variable-centered approach. The former uses persons, animals, or objects on the basis of their combination of characteristics which can be displayed in multiway contingency tables. Configural Frequency Analysis (CFA) and log-linear modeling (LLM) are the two most prominent (and related) statistical methods. Both compare observed frequencies (foi…k) with expected frequencies (fei…k). While LLM uses primarily a model-fitting approach, CFA analyzes residuals of non-fitting models. Residuals with significantly more observed than expected frequencies (foi…k>fei…k) are called types, while residuals with significantly less observed than expected frequencies (foi…k<fei…k) are called antitypes. The R package confreq is presented and its use is demonstrated with several data examples. Results of contingency table analyses can be displayed in tables but also in graphics representing the size and type of residual. The expected frequencies represent the null hypothesis and different null hypotheses result in different expected frequencies. Different kinds of CFAs are presented: the first-order CFA based on the null hypothesis of independence, CFA with covariates, and the two-sample CFA. The calculation of the expected frequencies can be controlled through the design matrix which can be easily handled in confreq.

Where the Research Interests of Graduate Students in China’s Christian Universities Lie

Focusing on the research landscape for graduate students of China’s Christian universities is of great significance for making sense of the path along which the theological and practical studies are conducted by contemporary China’s Christian universities and for promoting the dialogue and understanding between Chinese and foreign seminaries. In this research, thesis topics selected by graduate students majoring in theology are classified into four categories: universal theoretical research, universal practical research, theoretical research of Chinese Christianity, and practical research of Chinese Christianity. Results of coded categorical data analysis and case study show that graduate students mainly focus on universal theories without giving adequate attention to the topic of the “Sinification” of Christianity. In their universal theoretical research, graduate students examine classic Christian works and theological thoughts of important figures in a detailed and in-depth way. Universal practical studies are skewed to practices of religious reforms and teaching improvements from a multidisciplinary perspective. In the theoretical research of Sinified Christianity, researchers build upon the commensurability between traditional Chinese culture and Christian theology, including the theological thoughts of important Christian figures in China, to explore the fulfillment of cultural, national, and social identities. In the practical research of Christianity in China, empirical methodologies are widely applied, centering on the “localization” process and forms of practices taking place in churches of China. The coincidentia oppositorum between universality and particularity dictates that much tension exists with respect to the development of Christianity in China. Focusing on the accommodative process between universality and particularity is important to produce further implications for research to be conducted by China’s Christian universities.