铁岭莲花湖湿地生物多样性研究进展

湿地生物多样性对于维持湿地生态功能和生态系统稳定有重要作用,湿地生物多样性研究的深入开展,对生物多样性保护和生态恢复具有重要意义,也为制定可持续利用规划提供可靠依据.简要介绍铁岭莲花湖湿地的自然概况,从植物种类的多样性、动物种类的多样性、植物群落的多样性和生物多样性评价几个方面,对现有研究成果进行评述,并对铁岭莲花湖湿地生物多样性的下一步研究进行展望.     

国外期刊亮点

正发现获取肝细胞新方法中国科学院上海生命科学研究院生物化学与细胞生物学研究所PPeennggyyuu HHuuaanngg等发现,通过表达3个肝脏转录因子,可成功将人体皮肤细胞转变为肝细胞。研究成果发表于3月6日出版的Cell-Stem Cell。此次,经过重新筛选和优化条件,研究人员成功建立了诱导人成纤维细胞重编程为肝细胞(hiHep细胞)的方法。hiHep细胞表达肝脏基因,并具有肝细胞的许多功能,包括分泌血清白蛋白、积累糖原、代谢药物、药物转运等。通过将hiHep细胞移植到肝脏特异转录因子(酪氨酸代谢缺陷)模型小鼠中,hiHep细胞可成功整合到小鼠肝脏中发挥功能。经移植后的小鼠肝功能指标明显恢复,有近40%的小鼠最终被救活。这项获得人类肝细胞的方法,向最终实现肝细胞治疗、生物人工肝等领域前进了一大步。     

生物可降解聚合物血管支架膨胀性能有限元分析

血管支架最有潜力的发展方向是生物可降解聚合物血管支架,而支架的膨胀性能直接影响血管支架的质量和应用.利用有限元方法,采用von Mises屈服和各向同性强化准则,通过与316L不锈钢和WE43镁合金两种支架材料进行对比,分析了聚左旋乳酸(PLLA)材料支架的膨胀性能.结果表明,PLLA新型血管支架具有良好的均匀膨胀性、轴向短缩性和柔顺性,但其回弹性能有待改善.     

围垦对椒江口夏季浮游植物群落结构和多样性的影响

通过对椒江口(28.67°~28.69°N,121.45°~121.62°E)浮游植物进行的3次采样调查(2007年7月、2008年7月和2010年8月),并与历史数据进行比较,共鉴定出浮游植物5门,167种,三个年份分别为101、96和117种;浮游植物细胞丰度分别为20.34×10~3个/L、266.94×10~3个/L和66.94×10~3个/L;Shannon-Wiener指数(H')分别为1.95、0.60和0.40.围垦前和围垦结束后优势种包括硅藻(Bacillariophyta)和甲藻(Pyrrophyta),如琼氏圆筛藻(Coscinodiscusjonesianus)、辐射圆筛藻(Coscinodiscus radiatus)、中肋骨条藻(Skeletonema costatum)和柔弱根管藻(Rhizosolenia delicatula)等;围垦中只有硅藻,如洛氏角毛藻(Chaetoceros lorenzia—nus)和布氏双尾藻(Ditylum brightwellii).围垦促进了浮游植物细胞丰度的大幅度提升,但改变了浮游植物群落结构,降低了其稳定性和均衡性.CCA分析表明,pH、温度、氮/磷/硅营养盐、溶解氧和化学需氧量是影响浮游植物群落结构的水质参数;影响绿藻(Chlorophyta)、硅藻、蓝藻(Cyanophyta)和甲藻的主要水质参数分别为溶解氧、石油、温度和化学需氧量.长时间尺度与短时时间尺度分析得出相似的结论,围垦前后椒江口水质、浮游植物群落结构及生物多样性均发生相应变化.     

利用芽孢杆菌混合菌群发酵生产生物有机肥的研究

以斯太贝亚叶面肥分离得到的4株芽孢杆菌(巨大芽孢杆菌M3,Bacillus oleronius O4,枯草芽孢杆菌S10,解淀粉芽孢杆菌A8)为起点,研究和改造斯太贝亚叶面肥.采用琼脂块法及钼锑抗比色法,考察4株芽孢杆菌及混合菌群在抑制植物病原真菌及降解土壤中难利用磷的情况.结果表明,混合菌群抑菌性能较其他菌株强,对梨青霉病菌的抑制率高达75.8%;混合菌群与巨大芽孢杆菌M3对土壤中难利用磷降解情况相差不大,均比其他菌株降解性强.由于斯太贝亚叶面肥含化学成分,对人体及环境会造成一定负担,故以天然无化学成分且有机质含量高的小麦秸秆为原料发酵生物有机肥,发酵后添加聚γ谷氨酸.考察生物有机肥对黄瓜幼苗感染植物病原真菌防治效果及病情指数,并考察生物有机肥对毛豆幼苗各项生长指标的影响.结果表明,研制的生物有机肥能够显著降低黄瓜幼苗感染植物病原真菌的病情指数,防治效果达55.56%;并且能够一定程度提高毛豆幼苗的株高、地上部鲜重、地上部干重.     

Adelfa： a futuristic biodiesel fuel for DI diesel engines： a comparative experimental investigation

Biodiesel fuel is found to be a promising alter- native for the petroleum diesel based on the results published by the researchers for a decade. Biodiesel fuel is renewable and non degradable fuel. Many countries use biodiesel fuel for automotives to meet the crisis due to the depletion of the petroleum fuel and to meet the stringent emission norms. Various researches have been carried out with different bio- diesel fuels with vegetable oil as the source and appreciable results were reported. Few biodiesel fuels which have been already tested are Jatropha, Pongamia, Mahua, neem, cotton seed, etc. In this experimental work, Adelfa biodiesel blend is used as the test fuel. The emission and performance charac- teristics were compared with three other different biodiesel fuel blends. Appreciable results imply that Adelfa biodiesel （Nerium oil methyl ester） can be a futuristic biodiesel fuel, which has a good compatibility with the direct injection （DI） diesel engine without any major modification. Moreover, Adelfa can be cultivated in a non agricultural land with fewer sources of water. It is widely spread over all major countries of Asia. Experimental investigations have been carried out on a single cylinder DI diesel engine with standard engine speci- fications. In this experimental work, various Adelfa biodiesel blends is compared with reference fuel （diesel） to choose the best blend which gives a closer performance to diesel. The comparative analysis with other biodiesel fuels has also been done and results have been discussed.     

A machine learning-based method to detect fluorescent spots and an accelerated,parallel implementation of this method

Under most microscopes, the fluorescent sig- nals emitted from biological structures, such as vesicles, as well as the signals from single molecules will appear as small puncta, which contribute to a Gaussian-like distri- bution. Accurate segmentation of these spots will funda- mentally affect our interpretation of a specific biological progress. Because of the complicated backgrounds in images, many algorithms fail to identify all of the inter- esting signals; the tremendous amount of time required for algorithms to process large datasets can also decrease their utility. Here, we introduce an excellent robust detection method based on the machine learning algorithm Ada- Boost, which outperforms threshold-based segmentation, wavelets, and FDA under most situations. We also provide a GPU/multi-core CPU implementation of this algorithm; this implementation accelerates the algorithm approxi- mately 10- and 7-fold acceleration compared with a single CPU implementation. The great reduction of time should make this method a promising candidate in the processing of large datasets. Furthermore, we demonstrate the use of our algorithm on true fluorescent micrographs, and the results show that machine learning-based detection meth- ods outperform the four other previously reported methods.     

Editorial note for the special topic on ＂Nanopore Analysis＂

The concept of nanopore analysis, using the pore-forming protein a-hemolysin to detect individual nucleic acids at a single-molecule level, was first proposed in 1996. Over the past two decades, tremendous progress has been made in the nanopore field, and nanopore analysis has become a label-free and high-throughput method for probing bio- molecules and other analytes with single-molecule sensi- tivity, especially holds the promising for ＂third generation＂ DNA sequencing. However, challenges still remain in the experimental strategies and the design of whole nanopore-based instruments. Here, we proudly present a special topic dedicated to the topic of ＂Nanopore Analysis＂, with 8 reviews/articles providing up to date coverage of the experimental strategies, theoretical calcu- lations and simulations, and instrument design. Reviews and articles on the experimental strategies cover control of DNA partitioning into a nanopore, detection of target DNA, and the advantages of nanopore-based DNA sequencing. The theoretical calculations and simulations discuss the translocation behavior of DNA, and an inte- grated measurement system and data analysis software are presented for instrument design.     

Long-chain branched/cyclic alkanes in recent sediment of Lake Fuxian and their environmental implications

Lake Fuxian is a large, deep, and oligotrophic freshwater lake. In recent sediment of this lake, a series of branched and cyclic alkanes, including branched alkanes with one or two quaternary carbon atoms （C2n compounds）, alkylcycloheptane, and alkylcyclohexane, were detected in high abundance. For each homolog of long-chain branched/ cyclic alkanes, molecules with only an odd or even carbon number were present in the sediment. The abundance vari- ation of each compound homolog corresponded to those of other compound homolog in the section, indicating the same origin of these compounds. The concentrations of long-chain branched/cyclic alkanes in the sediment profile exhibited variations similar to those of phytoplankton biomarkers, such as 3-methylJalkanes, short-chain odd carbon n-alkanes, hopene, polyunsaturated fatty acids, phytol, sterols, and long-chain alkyl diols, but slightly different from those of long-chain odd carbon n-alkanes, which are ubiquitous biomarkers for higher plants. Therefore, the Can compounds and long-chain cyclic alkanes in the lake sediment were most likely produced by algae and bacteria in the water, primarily, by the heterotrophic bacteria in the moderately oxygenated waters, which flourishing might be the results of specific water environments in Lake Fuxian.     

Effects of climate change on biodiversity

1 Rise of studies on climate change＇s effects on biodiversity
Until the 1980s, climate change and biodiversity were studied as two independent disciplines for more than a century. In 1992, the Ecological Society of America＇s annual report named climate change, biodiversity, and the sustainable ecological system as the three major global environmental issues of the twenty-first century [1].