A Data Analytics Approach for Revealing Influencing Factors of HPV-Related Cancers From Population-Level Statistics Data

Human papillomavirus (HPV) is considered as one of the major causes of multiple cancers, including cervical, anal, and vaginal cancers. Some studies analyzed the infection patterns of cancers caused by HPV using individual clinical test data, which is resource and time expensive. In order to facilitate the understanding of cancers caused by HPV, we propose to use data analytics methods to reveal the influencing factors from the population-level statistics data, which is available more easily. Particularly, we demonstrate the effectiveness of data analytics approach by introducing a predictive analytics method in studying the risk factors of cervix cancer in the United States. Besides accurate prediction of the number of infections, the predictive analytics method discovers the population statistic factors that most affect the cervical cancer infection pattern. Furthermore, we discuss the potential directions in developing more advanced data analytics approaches in studying cancers caused by HPV.

Dissection of the impact of prioritized QTL-linked and -unlinked SNP markers on the accuracy of genomic selection1

Background Use of genomic information has resulted in an undeniable improvement in prediction accuracies and an increase in genetic gain in animal and plant genetic selection programs in spite of oversimplified assumptions about the true biological processes. Even for complex traits, a large portion of markers do not segregate with or effectively track genomic regions contributing to trait variation; yet it is not clear how genomic prediction accuracies are impacted by such potentially nonrelevant markers. In this study, a simulation was carried out to evaluate genomic predictions in the presence of markers unlinked with trait-relevant QTL. Further, we compared the ability of the population statistic FST and absolute estimated marker effect as preselection statistics to discriminate between linked and unlinked markers and the corresponding impact on accuracy. Results We found that the accuracy of genomic predictions decreased as the proportion of unlinked markers used to calculate the genomic relationships increased. Using all, only linked, and only unlinked marker sets yielded prediction accuracies of 0.62, 0.89, and 0.22, respectively. Furthermore, it was found that prediction accuracies are severely impacted by unlinked markers with large spurious associations. FST-preselected marker sets of 10 k and larger yielded accuracies 8.97 to 17.91% higher than those achieved using preselection by absolute estimated marker effects, despite selecting 5.1 to 37.7% more unlinked markers and explaining 2.4 to 5.0% less of the genetic variance. This was attributed to false positives selected by absolute estimated marker effects having a larger spurious association with the trait of interest and more negative impact on predictions. The Pearson correlation between FST scores and absolute estimated marker effects was 0.77 and 0.27 among only linked and only unlinked markers, respectively. The sensitivity of FST scores to detect truly linked markers is comparable to absolute estimated marker effects but the consistency between the two statistics regarding false positives is weak. Conclusion Identification and exclusion of markers that have little to no relevance to the trait of interest may significantly increase genomic prediction accuracies. The population statistic FST presents an efficient and effective tool for preselection of trait-relevant markers.

Change-Point Analysis: An Effective Technique for Detecting Abrupt Change in the Homicide Trends in a Democratic South Africa

South Africa is considered the murder capital of the world. The challenge for the South African government is to attract foreign investment to boost the economy in a country plagued by homicide. In this study, a change-point analysis was used to pinpoint significant changes in the murder trends in each of the nine provinces in South Africa from 2005 to 2015. This analysis will assist authorities to gain a better understanding of the big picture view in order to mitigate against this crime. Two methods were used in the analysis, namely, CUSUM and Bootstrap. CUSUM was used to analyse data trends, and Bootstrap was used to calculate the occurrence of change points based on the confidence level. The results of the analysis clearly show the abrupt shifts in murder data across the provinces of South Africa. In addition, we used the South African population statistic dataset from 2005 to 2015 to evaluate the relationship between population of the nine provinces and contextualise the murder crime rates year to year and province to province.

The generalized Price equation: forces that change population statistics

The Price equation partitions the change in the expected value of a population measure. The first component describes the partial change caused by altered frequencies. The second component describes the partial change caused by altered measurements. In biology, frequency changes often associate with the direct effect of natural selection. Measure changes reflect processes during transmission that alter trait values. More broadly, the two components describe the direct forces that change population composition and the altered frame of reference that changes measured values. The classic Price equation is limited to population statistics that can expressed as the expected value of a measure. Many statistics cannot be expressed as expected values, such as the harmonic mean and the family of rescaled diversity measures. We generalize the Price equation to any population statistic that can be expressed as a function of frequencies and measurements. We obtain the generalized partition between the direct forces that cause frequency change and the altered frame of reference that changes measurements.

Researcher Teacher Program: Achievements and Shortcomings

This paper reports the results of a study entitled "Pathology and Researcher Teacher Program promotion and development approaches”. The aim of this study is to identify achievements, the cooperation and participation of teachers and principals in school activities in Researcher Teacher Plan and awareness of the shortcomings and obstacles in the implementation of this program. The research method is descriptive. The population statistic in this study involves two groups of researcher and non-researcher teachers and administrators. The sample consisted of 500 members who are estimated according to the population size on the basis of Kerjcie and Morgan table. Moreover, to select desired sample, multi-stage sampling method is used. Totally, 19 districts region of Tehran are divided into 5 categories and one region is randomly selected of each category and also from each region girls and boy’s school in primary school, secondary and high schools are chosen randomly and a number of researcher and non-researcher teachers and administrators of schools are selected randomly as samples size. Two types of questionnaire are used for data collection. In order to study the form and content tools, the opinions of 10 experts and Cronbach’s alpha is used to calculate reliability. The results showed that obstacles and shortcomings measurement has are significant level of P<0.5 and alpha coefficient is obtained 0.715 and questionnaire to assess the qualitative achievements of the plan has a significant level P<0.5 and Alpha as 0.587. Results showed achievements for researcher teacher is far greater than non- researcher teachers and administrators. Furthermore, researcher teachers and administrators have participated more than non-researcher ones in holding briefings, workshops and council meetings teachers but no significant differences appears in participation between the two groups of teachers in workshops and council meetings there. Non-Researcher teachers and administrators state that obstacles and shortcomings of non-researcher teacher’s plans are more than researcher teachers and administrators. However, both group almost are deployed similarly the first six obstacles as priorities 1 to 6.