病理性近视的家系研究

为了探讨我国病理性近视的遗传模式,对90个病理性近视大家系进行了分离分析。简单分离分析采用先验法和SEGRAN-B软件,进行拟合优度卡方检验,比较实际分离比与理论分离比的符合程度;复合分离分析运用SAGE-REGD软件进行孟德尔遗传模型(主基因、显性、隐性、共显性)和非孟德尔遗传模型(非传递、环境、一般)的拟合。结果显示,婚配类型为A*N的家系符合常染色体显性遗传,散发概率为13．8％,婚配类型为N*N的家系符合常染色体隐性遗传,散发概率为16．3％,但常染色体显性遗传不能除外,复合分离分析接受孟德尔遗传的显性、隐性、共显性和主基因模型,共显性模型的可能性最大,基因频率为0．21442999。因此,我国病理性近视存在常染色体显性和隐性遗传模式,并有一定比例的散发病例,具有遗传异质性。     

病理性近视与HLA的关联性研究

用ＰＣＲ－ＲＦＬＰ方法对江浙沪籍汉族５５例病理性近视眼（ＰＭ）患者的ＨＬＡⅡ类ＤＱＢ１基因的第二个外显子进行了基因分型。结果发现ＨＬＡ－ＤＱＢ１＊０２０１、＊０３０３、＊０４０１等位基因在ＰＭ患者中和正常人中的分布有显著的差异（Ｐｃ＜０．０５,ＡＦ分别为０．１６３６,０．１０９１,０．１６３６,０．１０９１ｖｓ．０．０４００,０．０３００,０．０４００,０．０２００）,可能与ＰＭ的致病相关。ＤＱＢ     

Automatic Inference of Sequence from Low-Resolution Crystallographic Data

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Automatic Background Knowledge Selection for Matching Biomedical Ontologies

Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of background knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains such as the life sciences. However, in most ontology matching systems, the background knowledge sources are either predefined by the system or have to be provided by the user. In this paper, we present a novel methodology for automatically selecting background knowledge sources for any given ontologies to match. This methodology measures the usefulness of each background knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which we call the mapping gain. We implemented this methodology in the AgreementMakerLight ontology matching framework, and evaluate it using the benchmark biomedical ontology matching tasks from the Ontology Alignment Evaluation Initiative (OAEI) 2013. In each matching problem, our methodology consistently identified the sources of background knowledge that led to the highest improvements over the baseline alignment (i.e., without background knowledge). Furthermore, our proposed mapping gain parameter is strongly correlated with the F-measure of the produced alignments, thus making it a good estimator for ontology matching techniques based on background knowledge.     

A Genome-Wide Association Analysis Identified a Novel Susceptible Locus for Pathological Myopia at 11q24.1

Myopia is one of the most common ocular disorders worldwide. Pathological myopia, also called high myopia, comprises 1% to 5% of the general population and is one of the leading causes of legal blindness in developed countries. To identify genetic determinants associated with pathological myopia in Japanese, we conducted a genome-wide association study, analyzing 411,777 SNPs with 830 cases and 1,911 general population controls in a two-stage design (297 cases and 934 controls in the first stage and 533 cases and 977 controls in the second stage). We selected 22 SNPs that showed P-values smaller than 10⁻⁴ in the first stage and tested them for association in the second stage. The meta-analysis combining the first and second stages identified an SNP, rs577948, at chromosome 11q24.1, which was associated with the disease (P = 2.22×10⁻⁷ and OR of 1.37 with 95% confidence interval: 1.21–1.54). Two genes, BLID and LOC399959, were identified within a 200-kb DNA encompassing rs577948. RT–PCR analysis demonstrated that both genes were expressed in human retinal tissue. Our results strongly suggest that the region at 11q24.1 is a novel susceptibility locus for pathological myopia in Japanese.     

Biomedical advances from tissue culture

The demonstration that the “dedifferentiation” of cells commonly observed in the early days of tissue culture was due to selective overgrowth of fibroblasts led to enrichment culture techniques (alternate animal and culture passage) designed to give a selective advantage to functionally differentiated tumor cells. These experiments resulted in the derivation of a large number of functionally differentiated clonal strains of a range of cell types. These results gave rise to the hypothesis that cells in culture accurately represent cells in vivo but without the complex in vivo environment. This concept has been strengthened with the development of hormonally defined culture media in combination with functionally differentiated clonal cell lines, which have augmented the potential of tissue culture studies. The use of hormonally defined media in place of serum-supplemented media demonstrates that hormonal responses and dependencies can be discovered in culture. Discoveries of hormonal dependencies of cancer cells has led to therapies targeting intracellular signaling pathways while discoveries of hormonal responses of pluripotent cells are helping to identify the potential application of stem cells. In these and other ways tissue culture technology will continue to contribute to solving problems of human health.     

Modeling Species Distributions from Heterogeneous Data for the Biogeographic Regionalization of the European Bryophyte Flora

The definition of biogeographic regions provides a fundamental framework for a range of basic and applied questions in biogeography, evolutionary biology, systematics and conservation. Previous research suggested that environmental forcing results in highly congruent regionalization patterns across taxa, but that the size and number of regions depends on the dispersal ability of the taxa considered. We produced a biogeographic regionalization of European bryophytes and hypothesized that (1) regions defined for bryophytes would differ from those defined for other taxa due to the highly specific eco-physiology of the group and (2) their high dispersal ability would result in the resolution of few, large regions. Species distributions were recorded using 10,000 km² MGRS pixels. Because of the lack of data across large portions of the area, species distribution models employing macroclimatic variables as predictors were used to determine the potential composition of empty pixels. K-means clustering analyses of the pixels based on their potential species composition were employed to define biogeographic regions. The optimal number of regions was determined by v-fold cross-validation and Moran’s I statistic. The spatial congruence of the regions identified from their potential bryophyte assemblages with large-scale vegetation patterns is at odds with our primary hypothesis. This reinforces the notion that post-glacial migration patterns might have been much more similar in bryophytes and vascular plants than previously thought. The substantially lower optimal number of clusters and the absence of nested patterns within the main biogeographic regions, as compared to identical analyses in vascular plants, support our second hypothesis. The modelling approach implemented here is, however, based on many assumptions that are discussed but can only be tested when additional data on species distributions become available, highlighting the substantial importance of developing integrated mapping projects for all taxa in key biogeographically areas of Europe, and the Mediterranean peninsulas in particular.     

Application of Random Coefficient Regression Model to Myopia Data: A Case Study

A one-year birth cohort from Northern Finland has been followed up since 1966. As a part of this study, we are in this paper concerned with analysing the progression of myopia (nearsightness) up to the age of 20 years. The random coefficient regression model was chosen for the analysis because of the large individual variation in the development of myopia. Maximum likelihood estimates for the parameters in the model were obtained via the expectation maximization (EM) algorithm. It is shown how the estimated model can be used to predict future observations for an individual using the previously recorded refractive error measurements as well as other relevant data on the patient in question.     

Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights

Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence.     

NIH小鼠纤维腺瘤流行病学及病理学诊断

目的对本单位封闭群NIH小鼠发生的皮下肿瘤进行诊断。方法统计发病率、发病体重、分娩胎次等信息,并切除肿瘤组织制作病理切片。结果该肿瘤发病率为1.32%,病理切片诊断为纤维腺瘤。结论本单位饲养繁殖的NIH小鼠发生皮下肿瘤诊断为纤维腺瘤,其发病原因需进一步研究探索。     

Mueller矩阵在生物医学检测方面应用的实验研究

Mueller矩阵是公认的能很好地表述介质偏振特性的一种方法,由于散射光偏振在生物组织无创伤诊断技术等诸多领域中的重要应用价值,对组织散射特性的Mueller矩阵的研究成为国际上组织光学的热点之一。研究设计了一种新的测量Mueller矩阵的实验装置:斜入射正接收装置,并推导出一组后续数据处理的算法。由此所获得的Mueller矩阵空间分布图的清晰度不亚于其它所报道的,并且测量方法具有结构简单、方便、准确等优点。实验结果表明:入射角影响Mueller矩阵空间分布图;随着介质浓度的增大,随机介质后向散射Mueller矩阵各元素的空间分布图样减小;同时列举了真实生物组织样品(肌肉组织)的后向散射Mueller矩阵的实测结果,由此证明各向异性生物组织的后向散射Mueller矩阵各元素的空间分布图样与纤维的走向有关。     

Autofluorescence Spectroscopy and Imaging: A Tool for Biomedical Research and Diagnosis

Native fluorescence, or autofluorescence (AF), consists in the emission of light in the UV-visible, near-IR spectral range when biological substrates are excited with light at suitable wavelength. This is a well-known phenomenon, and the strict relationship of many endogenous fluorophores with morphofunctional properties of the living systems, influencing their AF emission features, offers an extremely powerful resource for directly monitoring the biological substrate condition. Starting from the last century, the technological progresses in microscopy and spectrofluorometry were convoying attention of the scientific community to this phenomenon. In the future, the interest in the autofluorescence will certainly continue. Current instrumentation and analytical procedures will likely be overcome by the unceasing progress in new devices for AF detection and data interpretation, while a progress is expected in the search and characterization of endogenous fluorophores and their roles as intrinsic biomarkers.Key words: Endogenous-fluorophores, energeticmetabolism, functionality-monitoring     

数值诊断的一种新方法──贝叶斯定理在数值诊断中的应用研究

根据贝叶斯定理,给出数值诊断的一种新方法——评分法．该方法简单易行,便于推广,且在治疗腰椎间盘突出症应用中收到满意效果．     

Automatic Region-Based Brain Classification of MRI-T1 Data

Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, such as the selection of a suitable threshold value for the histogram-based method and the over-segmentation followed by the time-consuming merge processing in the region-based algorithm. To provide an efficient approach that not only produce better results, but also maintain low computational complexity, a new region dividing based technique is developed for image segmentation, which combines the advantages of both regions-based and histogram-based methods. The proposed method is applied to the challenging applications: Gray matter (GM), White matter (WM) and cerebro-spinal fluid (CSF) segmentation in brain MR Images. The method is evaluated on both simulated and real data, and compared with other segmentation techniques. The obtained results have demonstrated its improved performance and robustness.     

Biomedical Big Data: New Models of Control Over Access,Use and Governance

Empirical evidence suggests that while people hold the capacity to control their data in high regard, they increasingly experience a loss of control over their data in the online world. The capacity to exert control over the generation and flow of personal information is a fundamental premise to important values such as autonomy, privacy, and trust. In healthcare and clinical research this capacity is generally achieved indirectly, by agreeing to specific conditions of informational exposure. Such conditions can be openly stated in informed consent documents or be implicit in the norms of confidentiality that govern the relationships of patients and healthcare professionals. However, with medicine becoming a data-intense enterprise, informed consent and medical confidentiality, as mechanisms of control, are put under pressure. In this paper we explore emerging models of informational control in data-intense healthcare and clinical research, which can compensate for the limitations of currently available instruments. More specifically, we discuss three approaches that hold promise in increasing individual control: the emergence of data portability rights as means to control data access, new mechanisms of informed consent as tools to control data use, and finally, new participatory governance schemes that allow individuals to control their data through direct involvement in data governance. We conclude by suggesting that, despite the impression that biomedical big data diminish individual control, the synergistic effect of new data management models can in fact improve it.     

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.