Delineation of prognostic biomarkers in prostate cancer

Prostate cancer is the most frequently diagnosed cancer in American men. Screening for prostate-specific antigen (PSA) has led to earlier detection of prostate cancer, but elevated serum PSA levels may be present in non-malignant conditions such as benign prostatic hyperlasia (BPH). Characterization of gene-expression profiles that molecularly distinguish prostatic neoplasms may identify genes involved in prostate carcinogenesis, elucidate clinical biomarkers, and lead to an improved classification of prostate cancer. Using microarrays of complementary DNA, we examined gene-expression profiles of more than 50 normal and neoplastic prostate specimens and three common prostate-cancer cell lines. Signature expression profiles of normal adjacent prostate (NAP), BPH, localized prostate cancer, and metastatic, hormone-refractory prostate cancer were determined. Here we establish many associations between genes and prostate cancer. We assessed two of these genes-hepsin, a transmembrane serine protease, and pim-1, a serine/threonine kinase-at the protein level using tissue microarrays consisting of over 700 clinically stratified prostate-cancer specimens. Expression of hepsin and pim-1 proteins was significantly correlated with measures of clinical outcome. Thus, the integration of cDNA microarray, high-density tissue microarray, and linked clinical and pathology data is a powerful approach to molecular profiling of human cancer.     

Large-scale discovery and genotyping of single-nucleotide polymorphisms in the mouse

Single-nucleotide polymorphisms (SNPs) have been the focus of much attention in human genetics because they are extremely abundant and well-suited for automated large-scale genotyping. Human SNPs, however, are less informative than other types of genetic markers (such as simple-sequence length polymorphisms or microsatellites) and thus more loci are required for mapping traits. SNPs offer similar advantages for experimental genetic organisms such as the mouse, but they entail no loss of informativeness because bi-allelic markers are fully informative in analysing crosses between inbred strains. Here we report a large-scale analysis of SNPs in the mouse genome. We characterized the rate of nucleotide polymorphism in eight mouse strains and identified a collection of 2,848 SNPs located in 1,755 sequence-tagged sites (STSs) using high-density oligonucleotide arrays. Three-quarters of these SNPs have been mapped on the mouse genome, providing a first-generation SNP map of the mouse. We have also developed a multiplex genotyping procedure by which a genome scan can be performed with only six genotyping reactions per animal.     

A centrosomal mechanism involving CDK5RAP2 and CENPJ controls brain size

Autosomal recessive primary microcephaly is a potential model in which to research genes involved in human brain growth. We show that two forms of the disorder result from homozygous mutations in the genes CDK5RAP2 and CENPJ. We found neuroepithelial expression of the genes during prenatal neurogenesis and protein localization to the spindle poles of mitotic cells, suggesting that a centrosomal mechanism controls neuron number in the developing mammalian brain.     

Transition metals in an experimental tumour system.

In this report we present an analysis by atomic absorption spectrometry of some of the transition metals Fe, Zn, Cu, Mn, Co, and Ni in tissues and nuclear fractions in an experimental tumour system.     

大荷载无人机-机器人高压输电线协同检修新方法研究

输电线路带电检修机器人是一种辅助和代替人工进行输电线路带电作业检修的一种智能化装备,其对于提高作业效率、作业可靠性、作业安全性具有重要实际意义,然而机器人在上下线过程中耗费大量的人力物力,大大降低了整体作业效率,机器人上下线问题已经成为制约机器人实用化的一个重要瓶颈。针对上述问题,提出了大荷载无人机(unmanned aerial vehical, UAV)和机器人的输电线协同检修新方法,该系统分为无人机、机器人和联接件,联接件用于联接无人机和机器人,通过电磁铁磁力的控制实现无人机与机器人系统的对接与分离,同时,通过对机器人机械臂系统的运动学建模与分析,得到了作业末端套筒所能到达的运动空间,从而进一步优化机械臂的结构参数,最后,通过硬件系统、软件系统、机械系统的集成设计,开发了机器人物理样机系统,在四分裂输电线路上进行引流板拧紧作业实验。实验结果表明:改系统能够顺利完成四分裂引流板作业任务,具有较强工程实用性,研究结果对于输电线路智能运维管理具有重要理论意义和实际应用价值。     

Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci

We carried out a genome-wide association study of type-2 diabetes (T2D) in individuals of South Asian ancestry. Our discovery set included 5,561 individuals with T2D (cases) and 14,458 controls drawn from studies in London, Pakistan and Singapore. We identified 20 independent SNPs associated with T2D at P < 10(-4) for testing in a replication sample of 13,170 cases and 25,398 controls, also all of South Asian ancestry. In the combined analysis, we identified common genetic variants at six loci (GRB14, ST6GAL1, VPS26A, HMG20A, AP3S2 and HNF4A) newly associated with T2D (P = 4.1 × 10(-8) to P = 1.9 × 10(-11)). SNPs at GRB14 were also associated with insulin sensitivity (P = 5.0 × 10(-4)), and SNPs at ST6GAL1 and HNF4A were also associated with pancreatic beta-cell function (P = 0.02 and P = 0.001, respectively). Our findings provide additional insight into mechanisms underlying T2D and show the potential for new discovery from genetic association studies in South Asians, a population with increased susceptibility to T2D.     

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.