乐至黑山羊PRLR基因外显子10多态性与产羔数的关系研究

设计2对特异性引物对乐至黑山羊PRLR基因第10外显子进行了PCR-SSCP检测,并研究该基因与产子性能的相关性。结果表明,P1引物扩增片段不存在多态性;P2引物扩增片段存在多态性,表现为AA,AB,AD和CD 4种基因型,测序结果表明,4种基因型都在该片段第89、94、146和157位存在C→T、A→C、C→G、G→C的突变;此外AA型还在61位发生C→T的突变;AD型还在175位发生A→G的突变;CD型还在24位发生T→C的突变,96位发生C→T的突变,通过统计分析发现AD型平均产羔数优于其他3种基因型,并且与AB型差异达到显著水平(P<0.05)。因此认为PRLR基因对于乐至黑山羊产子性能有一定的影响。     

Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction

In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.     

簇毛麦端体6VS的显微切割及其专化DNA序列的克隆和分析

从簇毛麦(Haynaldia villosa (L.) Schur.)组合CA9211/RW15(6D/6V异代换系)幼胚培养SC2后代中,用原位杂交方法鉴定出T240-6为6VS端体异代换系. 以此为材料,采用微细玻璃针切割法及"单管反应"技术体系,对6VS进行切割分离及LA (Linker adaptor)-PCR扩增.扩增带在100～3 000 bp 之间,大部分集中在600～1 500 bp.利用32P标记的簇毛麦基因组为探针进行Southern杂交,证实扩增产物来源于簇毛麦.扩增产物纯化后,连接到pGEM-T载体上,构建了6VS DNA质粒文库.对文库的分析表明,文库大约有17 000个白色克隆;插入片段分布在100～1 500 bp,平均600 bp.点杂交结果表明,37%克隆有中度到强烈的杂交信号,证明含有中度或高度重复序列;63%克隆有较弱的信号或没有信号,证明为单/低拷贝序列克隆.从文库中获得8个簇毛麦特异克隆,对其中两个克隆pHVMK22和 pHVMK134进行了RFLP分析和序列分析,并利用该探针对小麦抗白粉病基因Pm21进行了检测.RFLP 结果表明,两个克隆一个为低拷贝序列克隆(pHVMK22),另一个为高度重复序列克隆,均为簇毛麦专化DNA序列.以pHVMK22为探针对抗、感病小麦(Triticum aestivum L.)品系的Southern杂交发现抗病品系有一条2 kb的特征带, 该探针可能作为检测抗病基因Pm21的探针.     

戈壁灌丛堆周边地表土壤颗粒的空间异质特征

研究戈壁地区单个灌丛及其下沙堆这一有机整体对周边土壤风蚀的抑制能力, 对加强相关地区的植被类型及其空间配置格局的防沙效应研究十分重要, 可为荒漠化监测的评价和制定科学的防治措施提供参考。该文利用数字图像处理技术, 获取吉兰泰盐湖北部戈壁上单个白刺(Nitraria tangutorum)灌丛沙堆和沙冬青(Ammopiptanthus mongolicus)灌丛沙堆周边地表不同土壤风蚀颗粒的百分含量; 并采用经典描述性统计及地统计学方法, 对各类土壤风蚀颗粒百分含量的水平空间异质性进行分析。结果表明: (1)灌丛基部和下风向是细物质积累区, 以灌丛堆为中心向外, <0.42 mm的细颗粒含量呈减少趋势; 而且细物质积累的最大值出现在白刺灌丛的迎风侧附近, 沙冬青样地则相反, 出现在灌丛的背风侧附近。在沙源物质有限的戈壁中, 白刺的防风固沙作用集中体现在灌丛附近, 其水平空间尺度范围不及沙冬青, 这亦是白刺样地粗粒化程度高于沙冬青样地的原因。(2)白刺和沙冬青灌丛附近地表中粒径>0.84 mm (不可蚀)、0.84-0.42 mm (半可蚀)及<0.42 mm (高度可蚀)颗粒的空间异质性尺度分别为17.80 m、66.63 m、8.41 m和9.82 m、15.33 m、14.91 m, 均超出了灌丛冠幅覆盖范围, 空间自相关部分比例C/(C₀ + C)在63.40%-99.96%之间, 由此推断灌丛沙堆附近的风沙流特征是造成相应尺度内土壤颗粒空间异质性的主要因子。(3)高度可蚀颗粒的空间异质性尺度略大于灌丛平均间距(8.77 m包括灌丛半径), 从防止土壤风蚀来看, 这说明研究区内的建群种灌丛间存在一定程度的相互促进关系, 有利于该区植被的稳定与发展。     

里氏木霉RutC30常温常压等离子体（ARTP）诱变筛选pyr4基因缺陷型菌株及转化系统的建立

以里氏木霉Trichoderma reesei重要工业生产菌RutC30为出发菌株,通过等离子体（ARTP）诱变筛选,以5-氟乳清酸（5-FOA）和尿苷（Uridine）进行筛选,获得一株pyr4基因缺陷株RutC30ΔU3。用含有野生型里氏木霉pyr4基因的互补质粒转化突变株,可回复野生性状。经测序发现其pyr4基因在核酸序列多个位点发生突变,其中包括两个错义突变和一个移码突变,从而导致乳清酸核苷-5′-磷酸脱羧酶失活。经遗传稳定性研究分析,传代5次后仍保持良好的尿苷依赖性、去葡萄糖阻遏以及高产纤维素酶特性。经实验筛选获得了pyr4基因缺陷菌株可作为基因表达系统的受体菌株,建立了以尿苷营养缺陷为筛选标记的木霉转化系统。     

Congo red adsorption from aqueous solutions by using chitosan hydrogel beads impregnated with nonionic or anionic surfactant

The adsorption performance of CS beads impregnated with triton X-100 (TX-100) as a nonionic surfactant and sodium dodecyl sulfate (SDS) as an anionic surfactant was investigated for the removal of anionic dye (congo red) from aqueous solution. While the adsorption capacity of CS/TX-100 beads was enhanced at all concentrations of TX-100 (0.005–0.1%), the increase in the concentration of SDS above 0.01% in the CS/SDS beads gradually reduced the adsorption capacity of the beads. Equilibrium adsorption isotherm data indicated a good fit to the Sips isotherm model and a heterogeneous adsorption process. The Sips maximum adsorption capacity in dry weight of the CS/TX-100 beads was 378.79 mg/g and 318.47 mg/g for the CS/SDS beads, higher than the 223.25 mg/g of the CS beads. Modification of CS beads by impregnation with nonionic surfactant, or even anionic surfactant, at low concentrations is a possible way to enhance adsorption of anionic dye.     

Molecular basis of the differences between normal and tumor tissues of gastric cancer

To be able to describe the differences between the normal and tumor tissues of gastric cancer at a molecular level would be essential in the study of the disease. We investigated the gene expression pattern in the two types of tissues from gastric cancer by performing expression profiling of 86 tissues on 17K complementary DNA microarrays. To select for the differentially expressed genes, class prediction algorithm was employed. For predictor selection, samples were first divided into a training (n=58), and a test set (n=28). A group of 894 genes was selected by a t-test in a training set, which was used for cross-validation in the training set and class (normal or tumor) prediction in the test set. Smaller groups of 894 genes were individually tested for their ability to correctly predict the normal or tumor samples based on gene expression pattern. The expression ratios of the 5 genes chosen from microarray data can be validated by real time RT-PCR over 6 tissue samples, resulting in a high level of correlation, individually or combined. When a representative predictor set of 92 genes was examined, pathways of 'focal adhesion' (with gene components of THBS2, PDGFD, MAPK1, COL1A2, COL6A3), 'ECM-receptor interaction' pathway (THBS2, COL1A2, COL6A3, FN1) and 'TGF-beta signaling' (THBS2, MAPK1, INHBA) represent some of the main differences between normal and tumor of gastric cancer at a molecular level.