Quality evaluation of cortex berberidis from different geographical origins by simultaneous high performance liquid chromatography combined with statistical methods

Purpose: To develop an effective method for evaluating the quality of Cortex berberidis from different geographical origins. Methods: A simple, precise and accurate high performance liquid chromatography (HPLC) method was first developed for simultaneous quantification of four active alkaloids (magnoflorine, jatrorrhizine, palmatine, and berberine) in Cortex berberidis obtained from Qinghai, Tibet and Sichuan Provinces of China. Method validation was performed in terms of precision, repeatability, stability, accuracy, and linearity. Besides, partial least squares discriminant analysis (PLS-DA) and one-way analysis of variance (ANOVA) were applied to study the quality variations of Cortex berberidis from various geographical origins. Results: The proposed HPLC method showed good linearity, precision, repeatability, and accuracy. The four alkaloids were detected in all samples of Cortex berberidis. Among them, magnoflorine (36.46 - 87.30 mg/g) consistently showed the highest amounts in all the samples, followed by berberine (16.00 - 37.50 mg/g). The content varied in the range of 0.66 - 4.57 mg/g for palmatine and 1.53 - 16.26 mg/g for jatrorrhizine, respectively. The total content of the four alkaloids ranged from 67.62 to 114.79 mg/g. Moreover, the results obtained by the PLS-DA and ANOVA showed that magnoflorine level and the total content of these four alkaloids in Qinghai and Tibet samples were significantly higher (p < 0.01) than those in Sichuan samples. Conclusion: Quantification of multi-ingredients by HPLC combined with statistical methods provide an effective approach for achieving origin discrimination and quality evaluation of Cortex berberidis. The quality of Cortex berberidis closely correlates to the geographical origin of the samples, with Cortex berberidis samples from Qinghai and Tibet exhibiting superior qualities to those from Sichuan.

Use of thermal imaging in sports medicine research : a short report

The assessment of skin temperature (Tsk) in athletic therapy and sports medicine research is an extremely important physiological outcome measure.Various methodsof recording Tsk, including thermistors, thermocouples and thermocrons are currently being used for research purposes. These techniques are constrained by their wires limiting the freedom of the subject, slow response times, and/or sensors falling off. Furthermore, as these products typically are directly attached to the skin and cover the measurement site, their validity may be questionable.This manuscript addresses the use and potential benefits of using thermal imaging (TI) in sport medicine research.Non-contact infrared TI offers a quick, non-invasive, portable and athlete-friendly method of assessing Tsk. TI is a useful Tsk diagnostic tool that has potential to be an integral part of sport medicine research in the future. Furthermore, as the technique is non-contact it has several advantages over existing methods of recording skin temperature

Traditional Chinese medicine research in the post-genomic era: good practice, priorities, challenges and opportunities

Background and aims: GP-TCM is the 1st EU-funded Coordination Action consortium dedicated to traditional Chinese medicine (TCM) research. This paper aims to summarise the objectives, structure and activities of the consortium and introduces the position of the consortium regarding good practice, priorities, challenges and opportunities in TCM research. Serving as the introductory paper for the GPTCM Journal of Ethnopharmacology special issue, this paper describes the roadmap of this special issue and reports how the main outputs of the ten GP-TCM work packages are integrated, and have led to consortium-wide conclusions. Materials and methods: Literature studies, opinion polls and discussions among consortium members and stakeholders. Results: By January 2012, through 3 years of team building, the GP-TCM consortium had grown into a large collaborative network involving ∼200 scientists from 24 countries and 107 institutions. Consortium members had worked closely to address good practice issues related to various aspects of Chinese herbal medicine (CHM) and acupuncture research, the focus of this Journal of Ethnopharmacology special issue, leading to state-of-the-art reports, guidelines and consensus on the application of omics technologies in TCM research. In addition, through an online survey open to GP-TCM members and non-members, we polled opinions on grand priorities, challenges and opportunities in TCM research. Based on the poll, although consortium members and non-members had diverse opinions on the major challenges in the field, both groups agreed that high-quality efficacy/effectiveness and mechanistic studies are grand priorities and that the TCM legacy in general and its management of chronic diseases in particular represent grand opportunities. Consortium members cast their votes of confidence in omics and systems biology approaches to TCM research and believed that quality and pharmacovigilance of TCM products are not only grand priorities, but also grand challenges. Non-members, however, gave priority to integrative medicine, concerned on the impact of regulation of TCM practitioners and emphasised intersectoral collaborations in funding TCM research, especially clinical trials. Conclusions: The GP-TCM consortium made great efforts to address some fundamental issues in TCM research, including developing guidelines, as well as identifying priorities, challenges and opportunities. These consortium guidelines and consensus will need dissemination, validation and further development through continued interregional, interdisciplinary and intersectoral collaborations. To promote this, a new consortium, known as the GP-TCM Research Association, is being established to succeed the 3-year fixed term FP7 GP-TCM consortium and will be officially launched at the Final GP-TCM Congress in Leiden, the Netherlands, in April 2012.

A comparison of statistical methods for genomic selection in a mice population

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES

Complex human diseases are a major challenge for biological research. The goal of my research is to develop effective methods for biostatistics in order to create more opportunities for the prevention and cure of human diseases. This dissertation proposes statistical technologies that have the ability of being adapted to sequencing data in family-based designs, and that account for joint effects as well as gene-gene and gene-environment interactions in the GWA studies. The framework includes statistical methods for rare and common variant association studies. Although next-generation DNA sequencing technologies have made rare variant association studies feasible, the development of powerful statistical methods for rare variant association studies is still underway. Chapter 2 demonstrates two adaptive weighting methods for rare variant association studies based on family data for quantitative traits. The results show that both proposed methods are robust to population stratification, robust to the direction and magnitude of the effects of causal variants, and more powerful than the methods using weights suggested by Madsen and Browning [2009]. In Chapter 3, I extended the previously proposed test for Testing the effect of an Optimally Weighted combination of variants (TOW) [Sha et al., 2012] for unrelated individuals to TOW &ndash F, TOW for Family &ndash based design. Simulation results show that TOW &ndash F can control for population stratification in wide range of population structures including spatially structured populations, is robust to the directions of effect of causal variants, and is relatively robust to percentage of neutral variants. In GWA studies, this dissertation consists of a two &ndash locus joint effect analysis and a two-stage approach accounting for gene &ndash gene and gene &ndash environment interaction. Chapter 4 proposes a novel two &ndash stage approach, which is promising to identify joint effects, especially for monotonic models. The proposed approach outperforms a single &ndash marker method and a regular two &ndash stage analysis based on the two &ndash locus genotypic test. In Chapter 5, I proposed a gene &ndash based two &ndash stage approach to identify gene &ndash gene and gene &ndash environment interactions in GWA studies which can include rare variants. The two &ndash stage approach is applied to the GAW 17 dataset to identify the interaction between KDR gene and smoking status.

Statistical methods for multi-marker testing in genetic association studies

As the development of genotyping and next-generation sequencing technologies, multi-marker testing in genome-wide association study and rare variant association study became active research areas in statistical genetics. This dissertation contains three methodologies for association study by exploring different genetic data features and demonstrates how to use those methods to test genetic association hypothesis. The methods can be categorized into in three scenarios: 1) multi-marker testing for strong Linkage Disequilibrium regions, 2) multi-marker testing for family-based association studies, 3) multi-marker testing for rare variant association study. I also discussed the advantage of using these methods and demonstrated its power by simulation studies and applications to real genetic data.

Evaluating statistical methods used to estimate the number of postsynaptic receptors.

Calcium levels in spines play a significant role in determining the sign and magnitude of synaptic plasticity. The magnitude of calcium influx into spines is highly dependent on influx through N-methyl D-aspartate (NMDA) receptors, and therefore depends on the number of postsynaptic NMDA receptors in each spine. We have calculated previously how the number of postsynaptic NMDA receptors determines the mean and variance of calcium transients in the postsynaptic density, and how this alters the shape of plasticity curves. However, the number of postsynaptic NMDA receptors in the postsynaptic density is not well known. Anatomical methods for estimating the number of NMDA receptors produce estimates that are very different than those produced by physiological techniques. The physiological techniques are based on the statistics of synaptic transmission and it is difficult to experimentally estimate their precision. In this paper we use stochastic simulations in order to test the validity of a physiological estimation technique based on failure analysis. We find that the method is likely to underestimate the number of postsynaptic NMDA receptors, explain the source of the error, and re-derive a more precise estimation technique. We also show that the original failure analysis as well as our improved formulas are not robust to small estimation errors in key parameters.

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.

Statistical methods for polyhedral shape classification with incomplete data - application to cryo-electron tomographic images

A certain type of bacterial inclusion, known as a bacterial microcompartment, was recently identified and imaged through cryo-electron tomography. A reconstructed 3D object from single-axis limited angle tilt-series cryo-electron tomography contains missing regions and this problem is known as the missing wedge problem. Due to missing regions on the reconstructed images, analyzing their 3D structures is a challenging problem. The existing methods overcome this problem by aligning and averaging several similar shaped objects. These schemes work well if the objects are symmetric and several objects with almost similar shapes and sizes are available. Since the bacterial inclusions studied here are not symmetric, are deformed, and show a wide range of shapes and sizes, the existing approaches are not appropriate. This research develops new statistical methods for analyzing geometric properties, such as volume, symmetry, aspect ratio, polyhedral structures etc., of these bacterial inclusions in presence of missing data. These methods work with deformed and non-symmetric varied shaped objects and do not necessitate multiple objects for handling the missing wedge problem. The developed methods and contributions include: (a) an improved method for manual image segmentation, (b) a new approach to 'complete' the segmented and reconstructed incomplete 3D images, (c) a polyhedral structural distance model to predict the polyhedral shapes of these microstructures, (d) a new shape descriptor for polyhedral shapes, named as polyhedron profile statistic, and (e) the Bayes classifier, linear discriminant analysis and support vector machine based classifiers for supervised incomplete polyhedral shape classification. Finally, the predicted 3D shapes for these bacterial microstructures belong to the Johnson solids family, and these shapes along with their other geometric properties are important for better understanding of their chemical and biological characteristics.

Queensland Emergency Medicine Research Foundation: Special report

The development of any new profession is dependent on the development of a special body of knowledge which is the domain of the profession and key to this is the conduct of research. In 2007, as part of the settlement of an Enterprise Bargaining Agreement and following sustained lobbying by Emergency Physicians, the Queensland Government agreed to establish an Emergency Medicine Research Fund to foster the development of research activities in Emergency Medicine in Queensland. That fund is now managed by the Queensland Emergency Medicine Research Foundation. The aims of this article are to describe the strategic approaches taken by the Foundation and its first three years of experience, to describe the application of research funds and to foreshadow an evaluative framework for determining the strategic value of this community investment. The Foundation has developed a range of personnel and project support funding programs and competition for funding has increased. Ongoing evaluation will seek to determine the effectiveness of this funding strategy on improving the effectiveness of research performance and the clinical and organisational outcomes that may derive from that initiative.

Review of statistical methods for disease mapping

Cancer poses an undeniable burden to the health and wellbeing of the Australian community. In a recent report commissioned by the Australian Institute for Health and Welfare(AIHW, 2010), one in every two Australians on average will be diagnosed with cancer by the age of 85, making cancer the second leading cause of death in 2007, preceded only by cardiovascular disease. Despite modest decreases in standardised combined cancer mortality over the past few decades, in part due to increased funding and access to screening programs, cancer remains a significant economic burden. In 2010, all cancers accounted for an estimated 19% of the country's total burden of disease, equating to approximately $3:8 billion in direct health system costs (Cancer Council Australia, 2011). Furthermore, there remains established socio-economic and other demographic inequalities in cancer incidence and survival, for example, by indigenous status and rurality. Therefore, in the interests of the nation's health and economic management, there is an immediate need to devise data-driven strategies to not only understand the socio-economic drivers of cancer but also facilitate the implementation of cost-effective resource allocation for cancer management...