Fatores envolvidos na natureza e administração de redes sociais de estudantes universitários

No presente estudo investigamos as relações interpessoais humanas. Especificamente buscamos com ele, replicar parcialmente o trabalho de Stiller e Dunbar (2007) usando o mesmo instrumento, porém utilizando outro tipo de amostra. O objetivo principal foi verificar se as redes sociais desses estudantes estão de acordo com a Hipótese do Cérebro Social, segundo a qual seres humanos seriam capazes de manter e administrar um determinado número de relações interpessoais, por volta de 150 pessoas. Encontramos uma média de 52,53 contatos sociais, inferior ao predito pela Hipótese, despendendo com esses cerca de 25% do seu tempo. Houve correlações significativas entre as variáveis Tamanho da rede social, Freqüência, Tempo de contato, Proximidade Emocional e Coeficiente de parentesco, na rede social em geral, na rede de parentes e na rede de amigos. Em todos os casos, mesmo com a disponibilidade de tecnologias de comunicação à longa distância, os respondentes preferiram contatos face-a-face com os membros da rede social. Discutimos os resultados a partir de quatro hipóteses que não são mutuamente exclusivas. Por outro lado, foram confirmadas hipóteses secundárias, sobre a composição das redes sociais e sobre a interação entre Tamanho da rede, Freqüência e Tempo de Interações e Proximidade emocional. Estudos adicionais são necessários para esclarecer as diferenças encontradas, bem como a influência de outras variáveis que possam aumentar a compreensão das redes sociais.

Acesso ao ensino superior: a expectativa do aluno surdo do ensino médio

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Condições de acessibilidade na universidade: o ponto de vista de estudantes com deficiência

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Book Review of Cetacean Societies: Field Studies Of Whales And Dolphins Edited by I. Mann, R. C. Connor, P. L. Tyack & H. Whitehead

Studying the sociobiology and behavioral ecology of cetaceans is particularly challenging due in large part to the aquatic environment in which they live. Nevertheless, many of the obstacles traditionally associated with data gathering on tree-ranging whales, dolphins and porpoises are rapidly being overcome, and are now far less formidable. During the past several decades, marine mammal scientists equipped with innovative research methods and new technologies have taken field-based behavioral studies to a new level of sophistication. In some cases, as is true for bottlenose dolphins, killer whales, sperm whales and humpback whales, modern research paradigms in the marine environment are comparable to present-day studies of terrestrial mammal social systems. Cetacean Society stands testament to the relatively recent advances in marine mammal science, and to those scientists, past and present, whose diligence has been instrumental in shaping the discipline.

Book Review of Twilight of the Mammoths: Ice Age Extinctions and the Rewilding of America by Paul S. Martin

Applying ecological studies to the adaptations of prehistoric human hunter-gatherer groups has greatly increased our abilities to interpret effects of an ever-changing environment and our access to critical resources on these populations. The Pleistocene/Holocene transition, its climate and human genesis in the new world, draws intensive interest from a number of scientific communities. In Twilight of the Mammoths, Paul Martin adds his views, which are of no surprise, on the megafaunal extirpations during a cultural period referred to in North America as Clovis.

Harbor Porpoise (Phocoena phocoena) Abundance in Alaska: Bristol Bay to Southeast Alaska, 1991-1993

Between 1991 and 1993, Alaska harbor porpoise (Phocoena phocoena) abundance was investigated during aerial surveys throughout much of the coastal and offshore waters from Bristol Bay in the eastern Bering Sea to Dixon Entrance in Southeast Alaska. Line-transect methodology was used, and only those observations made during optimal conditions were analyzed. Survey data indicated densities of 4.48 groups/100 km2, or approximately 3,531 harbor porpoises (95% C.I. 2,206-5,651) in Bristol Bay and 0.54 groups/100 km2, or 136 harbor porpoises (95% C.I. 11-1,645) for Cook Inlet. Efforts off Kodiak Island resulted in densities of 1.85 groups/100 km2, or an abundance estimate of 740 (95% C.I. 259-2,115). Surveys off the south side of the Alaska Peninsula found densities of 2.03 groups/100 km2 and an abundance estimate of 551 (95% C.I. 423-719). Surveys of offshore waters from Prince William Sound to Dixon Entrance yielded densities of 4.02 groups/100 km’ and an abundance estimate of 3,982 (95% C.I. 2,567-6,177). Combining all years and areas yielded an uncorrected density estimate of 3.82 porpoises per 100 km2, resulting in an abundance estimate of 8,940 porpoises (CV = 13.8%) with a 95% confidence interval of 6,746-11,848. Using correction factors from other studies to adjust for animals missed by observers, the total number of Alaska harbor porpoises is probably three times this number.

Trends in aortic aneurysm- and dissection-related mortality in the state of São Paulo, Brazil, 1985–2009: multiple-cause-of-death analysis

Background: Aortic aneurysm and dissection are important causes of death in older people. Ruptured aneurysms show catastrophic fatality rates reaching near 80%. Few population-based mortality studies have been published in the world and none in Brazil. The objective of the present study was to use multiple-cause-of-death methodology in the analysis of mortality trends related to aortic aneurysm and dissection in the state of Sao Paulo, between 1985 and 2009. Methods: We analyzed mortality data from the Sao Paulo State Data Analysis System, selecting all death certificates on which aortic aneurysm and dissection were listed as a cause-of-death. The variables sex, age, season of the year, and underlying, associated or total mentions of causes of death were studied using standardized mortality rates, proportions and historical trends. Statistical analyses were performed by chi-square goodness-of-fit and H Kruskal-Wallis tests, and variance analysis. The joinpoint regression model was used to evaluate changes in age-standardized rates trends. A p value less than 0.05 was regarded as significant. Results: Over a 25-year period, there were 42,615 deaths related to aortic aneurysm and dissection, of which 36,088 (84.7%) were identified as underlying cause and 6,527 (15.3%) as an associated cause-of-death. Dissection and ruptured aneurysms were considered as an underlying cause of death in 93% of the deaths. For the entire period, a significant increased trend of age-standardized death rates was observed in men and women, while certain non-significant decreases occurred from 1996/2004 until 2009. Abdominal aortic aneurysms and aortic dissections prevailed among men and aortic dissections and aortic aneurysms of unspecified site among women. In 1985 and 2009 death rates ratios of men to women were respectively 2.86 and 2.19, corresponding to a difference decrease between rates of 23.4%. For aortic dissection, ruptured and non-ruptured aneurysms, the overall mean ages at death were, respectively, 63.2, 68.4 and 71.6 years; while, as the underlying cause, the main associated causes of death were as follows: hemorrhages (in 43.8%/40.5%/13.9%); hypertensive diseases (in 49.2%/22.43%/24.5%) and atherosclerosis (in 14.8%/25.5%/15.3%); and, as associated causes, their principal overall underlying causes of death were diseases of the circulatory (55.7%), and respiratory (13.8%) systems and neoplasms (7.8%). A significant seasonal variation, with highest frequency in winter, occurred in deaths identified as underlying cause for aortic dissection, ruptured and non-ruptured aneurysms. Conclusions: This study introduces the methodology of multiple-causes-of-death to enhance epidemiologic knowledge of aortic aneurysm and dissection in São Paulo, Brazil. The results presented confer light to the importance of mortality statistics and the need for epidemiologic studies to understand unique trends in our own population.

A study of high solids anaerobic digestion of Bucknell University food waste followed by aerobic curing

Anaerobic digestion of food scraps has the potential to accomplish waste minimization, energy production, and compost or humus production. At Bucknell University, removal of food scraps from the waste stream could reduce municipal solid waste transportation costs and landfill tipping fees, and provide methane and humus for use on campus. To determine the suitability of food waste produced at Bucknell for high-solids anaerobic digestion (HSAD), a year-long characterization study was conducted. Physical and chemical properties, waste biodegradability, and annual production of biodegradable waste were assessed. Bucknell University food and landscape waste was digested at pilot-scale for over a year to test performance at low and high loading rates, ease of operation at 20% solids, benefits of codigestion of food and landscape waste, and toprovide digestate for studies to assess the curing needs of HSAD digestate. A laboratory-scale curing study was conducted to assess the curing duration required to reduce microbial activity, phytotoxicity, and odors to acceptable levels for subsequent use ofhumus. The characteristics of Bucknell University food and landscape waste were tested approximately weekly for one year, to determine chemical oxygen demand (COD), total solids (TS), volatile solids (VS), and biodegradability (from batch digestion studies). Fats, oil, and grease and total Kjeldahl nitrogen were also tested for some food waste samples. Based on the characterization and biodegradability studies, Bucknell University dining hall food waste is a good candidate for HSAD. During batch digestion studies Bucknell University food waste produced a mean of 288 mL CH4/g COD with a 95%confidence interval of 0.06 mL CH4/g COD. The addition of landscape waste for digestion increased methane production from both food and landscape waste; however, because the landscape waste biodegradability was extremely low the increase was small.Based on an informal waste audit, Bucknell could collect up to 100 tons of food waste from dining facilities each year. The pilot-scale high-solids anaerobic digestion study confirmed that digestion ofBucknell University food waste combined with landscape waste at a low organic loading rate (OLR) of 2 g COD/L reactor volume-day is feasible. During low OLR operation, stable reactor performance was demonstrated through monitoring of biogas production and composition, reactor total and volatile solids, total and soluble chemical oxygendemand, volatile fatty acid content, pH, and bicarbonate alkalinity. Low OLR HSAD of Bucknell University food waste and landscape waste combined produced 232 L CH4/kg COD and 229 L CH4/kg VS. When OLR was increased to high loading (15 g COD/L reactor volume-day) to assess maximum loading conditions, reactor performance became unstable due to ammonia accumulation and subsequent inhibition. The methaneproduction per unit COD also decreased (to 211 L CH4/kg COD fed), although methane production per unit VS increased (to 272 L CH4/kg VS fed). The degree of ammonia inhibition was investigated through respirometry in which reactor digestate was diluted and exposed to varying concentrations of ammonia. Treatments with low ammoniaconcentrations recovered quickly from ammonia inhibition within the reactor. The post-digestion curing process was studied at laboratory-scale, to provide a preliminary assessment of curing duration. Digestate was mixed with woodchips and incubated in an insulated container at 35 °C to simulate full-scale curing self-heatingconditions. Degree of digestate stabilization was determined through oxygen uptake rates, percent O2, temperature, volatile solids, and Solvita Maturity Index. Phytotoxicity was determined through observation of volatile fatty acid and ammonia concentrations.Stabilization of organics and elimination of phytotoxic compounds (after 10–15 days of curing) preceded significant reductions of volatile sulfur compounds (hydrogen sulfide, methanethiol, and dimethyl sulfide) after 15–20 days of curing. Bucknell University food waste has high biodegradability and is suitable for high-solids anaerobic digestion; however, it has a low C:N ratio which can result in ammonia accumulation under some operating conditions. The low biodegradability of Bucknell University landscape waste limits the amount of bioavailable carbon that it can contribute, making it unsuitable for use as a cosubstrate to increase the C:N ratio of food waste. Additional research is indicated to determine other cosubstrates with higher biodegradabilities that may allow successful HSAD of Bucknell University food waste at high OLRs. Some cosubstrates to investigate are office paper, field residues, or grease trap waste. A brief curing period of less than 3 weeks was sufficient to produce viable humus from digestate produced by low OLR HSAD of food and landscape waste.

Genetic association studies under the population stratification, family pedigree and application to genome-wide association studies

This dissertation has three separate parts: the first part deals with the general pedigree association testing incorporating continuous covariates; the second part deals with the association tests under population stratification using the conditional likelihood tests; the third part deals with the genome-wide association studies based on the real rheumatoid arthritis (RA) disease data sets from Genetic Analysis Workshop 16 (GAW16) problem 1. Many statistical tests are developed to test the linkage and association using either case-control status or phenotype covariates for family data structure, separately. Those univariate analyses might not use all the information coming from the family members in practical studies. On the other hand, the human complex disease do not have a clear inheritance pattern, there might exist the gene interactions or act independently. In part I, the new proposed approach MPDT is focused on how to use both the case control information as well as the phenotype covariates. This approach can be applied to detect multiple marker effects. Based on the two existing popular statistics in family studies for case-control and quantitative traits respectively, the new approach could be used in the simple family structure data set as well as general pedigree structure. The combined statistics are calculated using the two statistics; A permutation procedure is applied for assessing the p-value with adjustment from the Bonferroni for the multiple markers. We use simulation studies to evaluate the type I error rates and the powers of the proposed approach. Our results show that the combined test using both case-control information and phenotype covariates not only has the correct type I error rates but also is more powerful than the other existing methods. For multiple marker interactions, our proposed method is also very powerful. Selective genotyping is an economical strategy in detecting and mapping quantitative trait loci in the genetic dissection of complex disease. When the samples arise from different ethnic groups or an admixture population, all the existing selective genotyping methods may result in spurious association due to different ancestry distributions. The problem can be more serious when the sample size is large, a general requirement to obtain sufficient power to detect modest genetic effects for most complex traits. In part II, I describe a useful strategy in selective genotyping while population stratification is present. Our procedure used a principal component based approach to eliminate any effect of population stratification. The paper evaluates the performance of our procedure using both simulated data from an early study data sets and also the HapMap data sets in a variety of population admixture models generated from empirical data. There are one binary trait and two continuous traits in the rheumatoid arthritis dataset of Problem 1 in the Genetic Analysis Workshop 16 (GAW16): RA status, AntiCCP and IgM. To allow multiple traits, we suggest a set of SNP-level F statistics by the concept of multiple-correlation to measure the genetic association between multiple trait values and SNP-specific genotypic scores and obtain their null distributions. Hereby, we perform 6 genome-wide association analyses using the novel one- and two-stage approaches which are based on single, double and triple traits. Incorporating all these 6 analyses, we successfully validate the SNPs which have been identified to be responsible for rheumatoid arthritis in the literature and detect more disease susceptibility SNPs for follow-up studies in the future. Except for chromosome 13 and 18, each of the others is found to harbour susceptible genetic regions for rheumatoid arthritis or related diseases, i.e., lupus erythematosus. This topic is discussed in part III.

Determining the roles of dendritic cells and ICAM-1 in the transpresentation of IL-15 to CD8 T cells

The maintenance and generation of memory CD8 T cells is dependent on the cytokine IL-15. IL-15 is delivered by a novel mechanism termed transpresentation: IL-15 is presented by a cell expressing IL-15Ralpha to the CD8 T cell which responds via IL-2Rbeta/gammac. The identity of what cells transpresent IL-15 to support the survival and homeostatic proliferation of memory CD8 T cells is unknown. Using a transgenic mouse model that limits IL-15 transpresentation to DCs, I have demonstrated that DCs transpresent IL-15 to CD8 T cells. DCs transpresent IL-15 to CD8 T cells during the contraction of an immune response and also drive homeostatic proliferation of memory CD8 T cells. Additionally, I identified a role for ICAM-1 in promoting homeostatic proliferation. Wt memory CD8 T cells displayed impaired homeostatic proliferation in ICAM-1-/- hosts but not in models of acute IL-15-driven proliferation. In this way, the role of ICAM-1 in IL-15 transpresentation resembles the role for ICAM-1 in antigenpresentation: where antigen or IL-15 is limited, adhesion molecules are important for generating maximal responses. In vitro cultures between CD8 T cells and bone marrowdifferentiated DCs (BMDC) activated with a TLR agonist established a model of proliferation and signaling in CD8 T cells that was dependent on IL-15 transpresentation and required ICAM-1 expression by BMDCs. Regarding the expression of IL-15, I demonstrated that in normal mice it is undetectable without stimulation but is elevated in lymphopenic mice, suggesting a role for T cells in regulating IL-15 expression. Overall, these studies have identified many novel aspects of the interaction between DCs and CD8 T cells that were previously unknown. The study of adhesion molecules in IL-15 transpresentation describes a novel role for these well-known adhesion molecules and it will be interesting for future studies to further characterize this relationship for other IL-15-dependent cell types.

Determining the roles of dendritic cells and ICAM-1 in the transpresentation of IL-15 to CD8 T cells

The maintenance and generation of memory CD8 T cells is dependent on the cytokine IL-15. IL-15 is delivered by a novel mechanism termed transpresentation: IL-15 is presented by a cell expressing IL-15Ralpha to the CD8 T cell which responds via IL-2Rbeta/gammac. The identity of what cells transpresent IL-15 to support the survival and homeostatic proliferation of memory CD8 T cells is unknown. Using a transgenic mouse model that limits IL-15 transpresentation to DCs, I have demonstrated that DCs transpresent IL-15 to CD8 T cells. DCs transpresent IL-15 to CD8 T cells during the contraction of an immune response and also drive homeostatic proliferation of memory CD8 T cells. Additionally, I identified a role for ICAM-1 in promoting homeostatic proliferation. Wt memory CD8 T cells displayed impaired homeostatic proliferation in ICAM-1-/- hosts but not in models of acute IL-15-driven proliferation. In this way, the role of ICAM-1 in IL-15 transpresentation resembles the role for ICAM-1 in antigenpresentation: where antigen or IL-15 is limited, adhesion molecules are important for generating maximal responses. In vitro cultures between CD8 T cells and bone marrowdifferentiated DCs (BMDC) activated with a TLR agonist established a model of proliferation and signaling in CD8 T cells that was dependent on IL-15 transpresentation and required ICAM-1 expression by BMDCs. Regarding the expression of IL-15, I demonstrated that in normal mice it is undetectable without stimulation but is elevated in lymphopenic mice, suggesting a role for T cells in regulating IL-15 expression. Overall, these studies have identified many novel aspects of the interaction between DCs and CD8 T cells that were previously unknown. The study of adhesion molecules in IL-15 transpresentation describes a novel role for these well-known adhesion molecules and it will be interesting for future studies to further characterize this relationship for other IL-15-dependent cell types.

Exceptional or just well connected? Political entrepreneurs and brokers in policy making

Policy brokers and policy entrepreneurs are assumed to have a decisive impact on policy outcomes. Their access to social and political resources is contingent on their influence on other agents. In social network analysis (SNA), entrepreneurs are often closely associated with brokers, because both are agents presumed to benefit from bridging structural holes; for example, gaining advantage through occupying a strategic position in relational space. Our aim here is twofold. First, to conceptually and operationally differentiate policy brokers from policy entrepreneurs premised on assumptions in the policy-process literature; and second, via SNA, to use the output of core algorithms in a cross-sectional analysis of political brokerage and political entrepreneurship. We attempt to simplify the use of graph algebra in answering questions relevant to policy analysis by placing each algorithm within its theoretical context. In the methodology employed, we first identify actors and graph their relations of influence within a specific policy event; then we select the most central actors; and compare their rank in a series of statistics that capture different aspects of their network advantage. We examine betweenness centrality, positive and negative Bonacich power, Burt’s effective size and constraint and honest brokerage as paradigmatic. We employ two case studies to demonstrate the advantages and limitations of each algorithm for differentiating between brokers and entrepreneurs: one on Swiss climate policy and one on EU competition and transport policy.

Linking livelihood strategies with a regional level landscape mosaic to explore trade-offs between forest conservation and human wellbeing in a Madagascar biodiversity hotspot

The north-eastern escarpment of Madagascar contains the island’s last remaining large-scale humid forest massifs surrounded by diverse small-scale agricultural mosaics. There is high deforestation mainly caused by shifting cultivation practiced by local land users to produce upland rice for subsistence. Today, large protected areas restrict land users’ access to forests to collect wood and other forest products. Moreover, they are no more able to expand their cultivated land, which leads to shorter shifting cultivation cycles and decreasing plot sizes for irrigated rice and cash crop cultivation. Cash crop production of clove and vanilla is exposed to risks such as extreme inter-annual price fluctuations, pests and cyclones. In the absence of work opportunities, agricultural extension services and micro-finance schemes people are stuck in a poverty trap. New development strategies are needed to mitigate the trade-offs between forest conservation and human well-being. As landscape composition and livelihood strategies vary across the region, these strategies need to be spatially differentiated to avoid implementing generic solutions, which do not fit the local context. However, up to date, little is known about the spatial patterns of shifting cultivation and other land use systems at the regional level. This is mainly due to the high spatial and temporal dynamics inherent to shifting cultivation, which makes it difficult to monitor the dynamics of this land use system with remote sensing methods. Furthermore, knowledge about land users’ livelihood strategies and the risks and opportunities they face stems from very few local case studies. To overcome this challenge, firstly, we used remote sensing data and a landscape mosaic approach to delineate the main landscape types at the regional level. Secondly, we developed a land user typology based on socio-ecological data from household surveys in 45 villages spread throughout the region. Combining the land user typology with the landscape mosaic map allowed us to reveal spatial patterns of the interaction between landscapes and people and to better understand the trade-offs between forest conservation and local wellbeing. While shifting cultivation systems are being transformed into more intensive permanent agricultural systems in many countries around the globe, Madagascar seems to be an exception to this trend. Linking land cover information to human-environmental interactions over large areas is crucial to designing policies and to inform decision making for a more sustainable development of this resource-rich but poverty-prone context.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.