http://rulrepository.ru.ac.bd/handle/123456789/99 2026-04-07T21:46:23Z 2026-04-07T21:46:23Z Kundu, Ranjan Kumar http://rulrepository.ru.ac.bd/handle/123456789/1108 2023-08-29T08:06:32Z 2021-01-01T00:00:00Z

Factors Affecting The Technical Efficiency Of Boro Rice Producers Of Northern Rigion In Bangladesh Kundu, Ranjan Kumar Because of its significant contribution to the development of various economic-related activities and self-sufficiency in food production, agricultural production needs to increase under the existing challenges so far. Despite increased rice production, it is evident that among all varieties, Boro rice is contributing the largest share of Bangladesh as well. The satisfactory contribution in the production of Boro rice has been unearthed for the Northern regions of the country in this respect. As therefore, investigation of determinants of production risk and efficiency and /or inefficiency may have an important role based on the stochastic production framework. According to the objectives, an attempt has been made to examine the contributory impact of the assigned input variables using frontier modelling. Firm-level primary data of 350 Boro farmers have collected from Rangpur, Dinajpur, Bogura, and Natore districts using an appropriate sampling technique and choice of functional form, distributional assumptions of models. Together with different preliminary statistics related to the research, technical efficiency has computed using Stochastic Frontier Modelling for selected Upazilas and districts. Small unit efficiencies have been computed for the eight Upazilas. The Farmers' Performance Indicators on the integrated management in the efficiency ground has been investigated under the Half-Normal assumption of errors of districts as well as the pooled data. This Thesis is Submitted to the Department of Statistics , University of Rajshahi, Rajshahi, Bangladesh for The Degree of Doctor of Philosophy (PhD)

2021-01-01T00:00:00Z Alam, Md. Jahangir http://rulrepository.ru.ac.bd/handle/123456789/1092 2023-08-29T08:03:52Z 2020-01-01T00:00:00Z

Statistical Modeling for Genome Wide Association Studies Alam, Md. Jahangir Genomics is the study of the whole genome of any living organism along with its environment and it incorporates different elements from genetics (a branch of biology that generally deals with the heredity). Genomics uses “DNA sequencing methods” to generate sequences of genomes using recombinant DNA, and it utilizes Bioinformatics to assemble the sequences of whole genomes and analyze the structure and function of genomes. It differs from 'classical genetics' in that it considers an organism’s full complement of hereditary material, rather than one gene or one gene product at a time. Moreover, genomics focuses on interactions between loci and allele within the genome and other interactions such as epistasis, pleiotropy and heterosis (Figure 1.1). The availability of complete DNA sequences for entire organisms is made easy by the Genomics. Genomics was made possible by both the pioneering work of Fred Sanger and the more recent next-generation sequencing technology. Fred Sanger's group established techniques of sequencing, genome mapping, data storage, and bioinformatics analyses in the 1970s and 1980s. This work paved the way for the human genome project in the 1990s (Bentley et al., 2008) an enormous feat of global collaboration that culminated in the publication of the complete human genome sequence in 2003. Nowadays, next-generation sequence technologies have led to remarkable improvements in the speed, capacity and affordability of genome sequencing. Moreover, advances in bioinformatics have enabled hundreds of life- science databases and projects that provide support for scientific research. Information stored and organized in these databases can easily be searched, compared and analyzed…………. This Thesis is Submitted to the Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh for The Degree of Doctor of Philosophy (PhD)

2020-01-01T00:00:00Z Rahman, J.A.M. Shoquilur http://rulrepository.ru.ac.bd/handle/123456789/974 2022-12-21T04:32:53Z 2003-01-01T00:00:00Z

Prediction of Adult Stature of Japanese Boys and Girls Rahman, J.A.M. Shoquilur The longitudinal growth of individual's stature of the present study was characterized from early childhood to adulthood. The samples used here were 509 males and 311 females. A triphasic generalized logistic model (BTT model) and diphasic growth model (JPA-2 model) applied respectively on the above two sets of data through the software AUXAL for characterizing individual growth of stature. The default values of the population mean and covariance matrix in AUXAL for both the models were substituted by estimated population mean and covariance matrix based on Japanese population. The individuals without mid growth spurt for both sexes show that predicted adult stature (PAS) was significantly positive correlated with stature at onset of adolescent and adolescent growth phases, and for only girls VTO and PHV was positively correlated. The individuals with mid growth spurt for both sexes show that PAS was significantly positively correlated with statures at early childhood minimum, mid-childhood maximum, onset of adolescent and adolescent growth phases. Also positive significant correlations were found between VECM and VMC, VTO and PHV but in case of PHV and VMC, negative significant correlation was found. On the basis of JPA-2 model the mean adult stature were 171.27cm for Japanese boys and 158.51 for Japanese girls. On the basis of BTT model this study demonstrates that, on average, 46. 1 %, 39.5%, and 14.4% of the total adult stature were completed during early, middle and adolescent phase of growth, respectively, for the Japanese male population. For the female population, these percentages were 42.6%, 44.6%, and 12.8%, respectively. The distributions of predicted stature that do not have the mid growth spurt, on average, shows that the Japanese girls become taller than boys from age 1 to 5 and 10 to 12, and then again become shorter than boys. The distributions of predicted stature that have the mid growth spurt, on average, shows that the Japanese girls become taller than boys from age 8 and 11 to 12, and then again become shorter than boys. Japanese boys who do not have the mid growth spurt are, on average, 13.26 cm taller than their opposite sex. Moreover, Japanese boys who have the mid growth spurt are, on average, 13. 77 cm taller than their opposite sex. Several equations, after removing the problem of outliers and influential data points, are proposed to predict the adult stature of the Japanese based on growth parameters and statures at different ages. This Thesis is Submitted to the Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh for The Degree of Doctor of Philosophy (PhD)

2003-01-01T00:00:00Z Hasan, Mohammad Nazmol http://rulrepository.ru.ac.bd/handle/123456789/817 2022-08-31T06:40:33Z 2019-01-01T00:00:00Z

Exploring Toxicogenomic Biomarkers using Statistical Models Hasan, Mohammad Nazmol Toxicogenomics studies combines toxicology with several omics technologies (genornics, transcriptomics, proteomics and metabolomics) to assess the risk of toxins (small molecules, peptides or proteins) and chemical agents (drugs, gasoline, alcohol, pesticides, fuel oil and cosmetics) in organism. Through integration of these omics technologies with bioinformatics, toxicogenomics can be used to suggest the molecular mechanism of toxicity. This can reduce the cost in terms of time, labor, compound synthesis and animal use which are main limitations of traditional toxicology work. There are three main objectives of toxicogenomics studies as well as drug discovery and development. 1) To explore the toxicogenomic biomarkers and toxicity of the doses of chemical compounds (DCCs). 2) Exploration of co-clusters between correlated genes and DCCs. and 3) Detection of significant gene and DCCs interactions. In this thesis, we have addressed all of these objectives in absence and presence outlying observations in the toxicogenomic dataset. There are some online computational tools that can explore the toxicogenomic biomarker genes based on t-test and Mann-Whitney U test. However, these tools cannot identify the significant DCCs that regulate the expression pattern of biomarker genes. To overcome this problem we have described one-way ANOVA together with and tukeys' HSD test (post-hoc test) ( chapter 2) to explore toxicogenomic biomarker genes and the significant toxic DCCs. The biomarker genes identified by the ANOV A approach are functionally annotated and found statistically significant for the respective pathway. The tukeys' HSD test identified toxic DCCs have also been validated by the existing literature. Besides this, according to the characteristics of toxicogenomic data exploration of co-clusters between genes and DCCs is another important objective of toxicogenomic studies. Hierarchical clustering (HC) is very popular and widely applied data analysis tool; it search interesting groups of objects in a dataset based on any combination of distance (euclidean, maximum, manhattan, canberra, minkowski) and HC (ward.D, ward.D2, single, complete, average, mcquitty, median, centroid) methods. However, these distance or clustering methods do not perform equally in grouping objects for all types of dataset. Even the performance of some of these combinations is very poor in some specific field of study. In this thesis ( chapter 3) we have selected more suitable HC methods ward.D or ward.D2 in combination with distance methods euclidean, manhattan or minkowski for clustering genes and DCCs of toxicogenomic data. Furthermore, in chapter 3 we have proposed an algorithm for co-clustering between genes and DCCs based HC approach. Though the selected HC clustering approaches together with the proposed co-clustering algorithm can co-cluster the genes and DCCs, these approaches are very sensitive to outlying observations. Therefore, we robustify the selected HC approaches in chapter 4. We observed that the proposed robust HC (RHC) approaches outperform over the classical HC approaches. The gene-DCCs co-clustering results based on RHC using the co-clustering algorithm (proposed in chapter 3) have been validated by the existing literature. Nonetheless, the classical clustering approaches (e.g. k-means, fuzzy, HC, etc.) including our proposed RHC use one-way (gene or DCCs) information for clustering/co­clustering. Thus, these clustering approaches is not flexible and effective for co­clustering genes and DCCs. On the other hand, probabilistic hidden variable model (PHVM) uses two-way (gene and DCCs) information simultaneously for co-clustering between genes and DCCs. Therefore, this approach is more effective and suitable for co­clustering. However, the PHVM approach is not robust against outliers. To overcome this limitation of PHVM in this research (chapter 5) we have proposed logistic PHVM called as LPHVM for robust co-clustering between genes and DCCs discover toxicogenomic biomarkers and their regulatory DCCs. We have observed that the proposed LPHVM approach perform better compare to PHVM and classical co-clustering approaches based on the results of the simulated and real data analysis. In order to answering toxicogenomic questions researchers are being suggested different suitable offline and online computational tools, since a single computational algorithm cannot always produce the answers of the toxicogenomic questions. This is because; large scale datasets have been generated by the complex toxicogenomic experiments. For example, the co-clustering approaches based on HC and hidden variable models sometimes cannot separate the significant up-regulatory (UpR) (the gene is up-regulated by the influence of the DCC) and down-regulatory (DnR) (the gene is down-regulated by the influence of the DCC) gene-DCC interactions from the equal-regulatory (EqR) (thereis no DCC effect on that gene) interactions within a co-cluster. On the other hand, separation of UpR and DnR gene-DCC interactions from the EqR interactions is the cornerstone in toxicogenomics studies as well as in drug discovery and development. Therefore, in chapter 6 we have proposed MRC and LMRC for the detection of significant gene-DCC interactions, so far of our knowledge which have not been considered yet in toxicogenomic studies as well as in drug discovery and development. However, LMRC produces robust results compare to MRC in presence of outlying observations in the data otherwise they perform equally. This Thesis is Submitted to the Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh for The Degree of Doctor of Philosophy (PhD)

2019-01-01T00:00:00Z