Constructing China's greenways naturally

Roads are a prominent feature of many landscapes, and high road densities create correspondingly high ecological impacts by altering landscape patterns, interrupting ecological flows, increasing erosion, fragmenting habitat, and facilitating the spread of invasive species. Here, we describe the construction of “near-natural greenways” that produce environmentally and socially harmonious road systems that meet the needs of both the environment and the socioeconomic development in China and that satisfy environmental, ecological, educational, traffic safety, economic, and sustainable developmental goals. In the last decade, China has embarked upon the implementation of a network of near-natural greenways with the vision of linking economic development with nature conservation by means of improved rehabilitation of existing road systems and improved construction of new roads. Ecological science, and especially landscape ecology, will play an important role in the planning and implementation of future near-natural greenways in China and around the world.     

乳酸链球菌nisk基因与细菌性双成分调节子的研究

本研究通过对乳酸链球菌亚种Ｎ８的Ｎｉｓｉｎｚ结构基因下游基因区的分子克隆、序列分析和ＯＲＦ的检测,揭示出一个属于细菌性双成份调节子系统的基因,ｎｉｓＫ,其起始区略与ｎｉｓＲ末端重叠,并发现ＮｉｓＲ和ｎｉｓＫ同被转录一个多顺反子ｍＲＮＡ。在ｎｉｓＫ编码区后,有一个ρ非依赖性终止子。     

Examination of the quality of various force fields and solvation models for the equilibrium simulations of GA88 and GB88

Elucidating the relationship between sequence and conformation is essential for the understanding of functions of proteins. While sharing 88 % sequence identity and differing by only seven residues, GA88 and GB88 have completely different structures and serve as ideal systems for investigating the relationship between sequence and function. Benefiting from the continuous advancement of the computational ability of modern computers, molecular dynamics (MD) simulation is now playing an increasingly important role in the study of proteins. However, the reliability of MD simulations is limited by the accuracy of the force fields and solvent model approximations. In this work, several AMBER force fields (AMBER03, AMBER99SB, AMBER12SB, AMBER14SB, AMBER96) and solvent models (TIP3P, IGB5, IGB7, IGB8) have been employed in the simulations of GA88 and GB88. The statistical results from 19 simulations show that GA88 and GB88 both adopt more compact structures than the native structures. GB88 is more stable than GA88 regardless of the force fields and solvent models utilized. Most of the simulations overestimated the salt bridge interaction. The combination of AMBER14SB force field and IGB8 solvent model shows the best overall performance in the simulations of both GA88 and GB88. AMBER03 and AMBER12SB also yield reasonable results but only in the TIP3P explicit solvent model.     

MicroRNA-150 Predicts a Favorable Prognosis in Patients with Epithelial Ovarian Cancer,and Inhibits Cell Invasion and Metastasis by Suppressing Transcriptional Repressor ZEB1

MicroRNA (miR)-150 has been reported to be dramatically downregulated in human epithelial ovarian cancer (EOC) tissues and patients’ serum compared to normal controls. This study aimed to investigate clinical significance and molecular mechanisms of miR-150 in EOC. In the current study, quantitative real-time PCR analysis showed that miR-150 was significantly downregulated in human EOC tissues compared to normal tissue samples. Then, we demonstrated the significant associations of miR-150 downregulation with aggressive clinicopathological features of EOC patients, including high clinical stage and pathological grade, and shorter overall and progression-free survivals. More importantly, the multivariate analysis identified miR-150 expression as an independent prognostic biomarker in EOC. After that, luciferase reporter assays demonstrated that Zinc Finger E-Box Binding Homeobox 1 (ZEB1), a crucial regulator of epithelial-to-mesenchymal transition (EMT), was a direct target of miR-150 in EOC cells. Moreover, we found that the ectopic expression of miR-150 could efficiently inhibit cell proliferation, invasion and metastasis by suppressing the expression of ZEB1. Furthermore, we also observed a significantly negative correlation between miR-150 and ZEB1 mRNA expression in EOC tissues (rs = –0.45, P<0.001). In conclusion, these findings offer the convincing evidence that aberrant expression of miR-150 may play a role in tumor progression and prognosis in patients with EOC. Moreover, our data reveal that miR-150 may function as a tumor suppressor and modulate EOC cell proliferation, and invasion by directly and negatively regulating ZEB1, implying the re-expression of miR-150 might be a potential therapeutic strategy for EOC.     

Physiological Heterothallism and Sexuality in Euascomycetes: A Partial History

Copyright 1998 Academic Press.     

FragGeneScan: predicting genes in short and error-prone reads

The advances of next-generation sequencing technology have facilitated metagenomics research that attempts to determine directly the whole collection of genetic material within an environmental sample (i.e. the metagenome). Identification of genes directly from short reads has become an important yet challenging problem in annotating metagenomes, since the assembly of metagenomes is often not available. Gene predictors developed for whole genomes (e.g. Glimmer) and recently developed for metagenomic sequences (e.g. MetaGene) show a significant decrease in performance as the sequencing error rates increase, or as reads get shorter. We have developed a novel gene prediction method FragGeneScan, which combines sequencing error models and codon usages in a hidden Markov model to improve the prediction of protein-coding region in short reads. The performance of FragGeneScan was comparable to Glimmer and MetaGene for complete genomes. But for short reads, FragGeneScan consistently outperformed MetaGene (accuracy improved ∼62% for reads of 400 bases with 1% sequencing errors, and ∼18% for short reads of 100 bases that are error free). When applied to metagenomes, FragGeneScan recovered substantially more genes than MetaGene predicted (>90% of the genes identified by homology search), and many novel genes with no homologs in current protein sequence database.