杜鹃花属的研究(二)

灌木,高1.5-3米。小枝适度地纤细,当年生者紫色,有鳞斑,约长4-5厘米,直径1-1.5毫米,一年生以上者淡黄色或褐色,无鳞斑。冬芽长圆锥状,鳞片覆瓦状排列,早落。叶薄纸质,狭窄或长圆状椭圆形,先端渐尖或近于钝尖状锐尖,基部楔形或钝形,长4.5-5厘米,宽1.2-2厘米,上面深绿色,有稀疏的鳞斑,下面淡绿色,有鳞斑,彼此间的距离,为其直径的2-3倍,中脉在上而略四陷,在下面稍凸起,侧脉不发育;叶柄有稀疏的鳞斑,长6-8毫米。     

转几丁质酶和葡聚糖酶双价基因棉花对土壤细菌种群多样性的影响

以转几丁质酶和葡聚糖酶双价基因棉花为研究对象,非转基因受体棉花为对照,通过比较可培养细菌数量和基于16S rRNA克隆文库细菌种群分析,评价外源双价基因的导入在苗期、蕾期、花铃期和吐絮期对棉花根际细菌群落多样性的影响。结果表明,可培养细菌的数量不受外源双价基因的影响,随着棉花生育期的交替而变化,以代谢旺盛的花铃期最多。构建的转基因和非转基因不同生育期根际土壤细菌16S rRNA文库容量为2400个克隆,涵盖了细菌的283个属。其中,Acidobacterium是最大优势类群,共包括624个克隆,其次为未知细菌种群和Flavisolibacter。比较转基因和非转基因棉花根际土壤细菌的种群结构,结果显示,同一生育期内前者种群的多样性显著低于后者,二者的共有类群随着生长发育的进行而增多。研究结果说明几丁质酶基因和葡聚糖酶基因对棉花根际细菌种群多样性有着不同程度的削减作用,但是随着种植时间的延长,该差异呈现逐渐缩小的趋势。     

党参染色体核型分析

对党参细胞染色体进行了核型分析。研究表明,党参染色体数为2 n=16。染色体的核型为2n=12m 4 sm=16。第1、2、3、4、5、8对是中间着丝粒染色体(m);第6对和第7对染色体属于近中着丝点染色体,未观察到有携带随体的染色体存在。     

自养和兼养条件下蛋白核小球藻昼夜节律的响应

【目的】研究自养和兼养两种培养方式对蛋白核小球藻(Chlorella pyrenoidosa)生长、细胞分裂和生化组分积累的影响,探讨人工培养蛋白核小球藻的昼夜节律响应机制和优化技术。【方法】小球藻自养培养采用BG11培养基,兼养培养基在BG11培养基中添加4种不同浓度(1、5、10、20 g/L)的葡萄糖,培养周期为10 d。血球板计数法测定藻细胞浓度,干重法测定藻细胞生物量。显微观察藻细胞大小和分裂情况。脂染色法测定小球藻总脂的含量,藻细胞的叶绿素、蛋白和淀粉分别采用甲醇、氢氧化钠、硝酸钙浸提后通过紫外分光光度法定量测定。【结果】葡萄糖兼养培养对蛋白核小球藻具有显著的促生长效应,最适浓度为10 g/L。10 d收获时,兼养组(10 g/L葡萄糖)藻细胞浓度和干重分别是自养组的2.57倍和6.73倍。分析一昼夜中的藻细胞增殖规律可知,第2天和第5天时自养组中增殖的新生子细胞约有76.00%在黑暗期分裂产生,而兼养组中第2天和第5天光照期的新细胞增殖量占比分别达到40.90%和67.50%。一昼夜内藻细胞大小的迁移动态监测表明,第2天自养组藻细胞的体积变化静息期为8 h,兼养组只有4 h;第5天两组藻细胞大小迁移动态的昼夜节律明显,但兼养组黑暗结束后较大细胞(D6μm)占比显著高于自养组。第8天时,兼养组藻细胞已处于稳定期,总脂和蛋白含量均显著高于自养组,藻细胞总脂和色素含量在一昼夜中相对稳定,但蛋白和淀粉含量分别在光照8 h和12 h左右达到峰值。从第2天开始,对兼养组细胞每天进行2 h光延长,收获时藻细胞浓度和干重分别比对照组提高13%和11%。【结论】葡萄糖兼养培养能大幅提高蛋白核小球藻的生物量。蛋白核小球藻生长增殖与生化组分积累均受昼夜节律调控,自养条件下藻细胞以光照期生长黑暗期增殖为主。兼养培养提高藻细胞生物量的机制在于缩短藻细胞生长静息期,在昼夜节律中加速藻细胞生长并显著提高通过细胞周期检查点的细胞比例,光照期效应尤其明显。藻细胞蛋白和淀粉含量昼夜节律明显,最佳收获时间分别在光照8 h和12 h后。     

Design and comparison of gene-pyramiding schemes in animals

Marker-assisted gene pyramiding provides a promising way to develop new animal breeds or lines, in which genes responsible for certain favorable characters identified in different breeds or lines are incorporated. In consideration of features of animal populations, we proposed five schemes for pyramiding three genes, denoted Scheme A-E, and five schemes for pyramiding four genes, denoted Scheme F-J. These schemes are representative of the possible alternatives. We also provided an algorithm to compute the population sizes needed in each generation. We compared these schemes with respect to the total population size and the number of generations required under different situations. The results show that there is no scheme that is optimal in all cases. Among the schemes for pyramiding three genes from three lines (L1, L2 and L3), Scheme D (a three-way cross between the three lines are first performed, followed by a backcross to L1 and a subsequent intercross to obtain the desired genotype) has a significant advantage over the other schemes when the recombination rate between adjacent genes ranges from 0.1 to 0.4, while Scheme A (a two-way cross between L1 and L2 and a subsequent intercross are performed, followed by a cross with L3 and a subsequent intercross to obtain the desired genotype) is optimal when recombination rate is 0.5. Among schemes for pyramiding four genes from four lines (L1, L2, L3 and L4), Scheme I (seperately, a two-way cross between L1 and L2 (L3 and L4) followed by a backcross to L1 (L3) and a subsequent intercross are performed, then the offspring from the two sides are crossed and followed by a backcross to L1 and a subsequent intercross to obtain the desired genotype) is optimal when the recombination rate ranges from 0.1 to 0.4, while Scheme F (cross and subsequent intercross between the four lines are performed successively) is the optimal when the recombination rate is 0.5. We also disscuss how the animals' reproductive capacity, the probabilities of obtaining the desired genotypes and genetic distance between adjacent genes would affect the design of an optimal scheme.     

Recent advances in synthesis and surface modification of lanthanide-doped upconversion nanoparticles for biomedical applications

Lanthanide (Ln)-doped upconversion nanoparticles (UCNPs) with appropriate surface modification can be used for a wide range of biomedical applications such as bio-detection, cancer therapy, bio-labeling, fluorescence imaging, magnetic resonance imaging and drug delivery. The upconversion phenomenon exhibited by Ln-doped UCNPs renders them tremendous advantages in biological applications over other types of fluorescent materials (e.g., organic dyes, fluorescent proteins, gold nanoparticles, quantum dots, and luminescent transition metal complexes) for: (i) enhanced tissue penetration depths achieved by near-infrared (NIR) excitation; (ii) improved stability against photobleaching, photoblinking and photochemical degradation; (iii) non-photodamaging to DNA/RNA due to lower excitation light energy; (iv) lower cytotoxicity; and (v) higher detection sensitivity. Ln-doped UCNPs are therefore attracting increasing attentions in recent years. In this review, we present recent advances in the synthesis of Ln-doped UCNPs and their surface modification, as well as their emerging applications in biomedicine. The future prospects of Ln-doped UCNPs for biomedical applications are also discussed.     

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.