异源蛋白质相互作用数据整合算法的进展

蛋白质相互作用在生物学过程和细胞功能行使中起核心作用。高通量技术的应用结合计算机预测方法的发展,使得直接和间接来源的蛋白质相互作用数据得到了大规模的增加。如何系统地整合这些数据并从中提取有用的信息是一项挑战,这也促使了许多整合算法应运而生。本文综述了八种整合蛋白质相互作用数据源的方法：投票、支持向量机、朴素贝叶斯、逻辑斯蒂回归、决策树、随机森林、基于随机森林的k-近邻法以及混合属性分类等方法。     

Divergent evolution of a bifunctional de novo protein

Primordial proteins, the evolutionary ancestors of modern sequences, are presumed to have been minimally active and nonspecific. Following eons of selective pressure, these early progenitors evolved into highly active and specific proteins. While evolutionary trajectories from poorly active and multifunctional generalists toward highly active specialists likely occurred many times in evolutionary history, such pathways are difficult to reconstruct in natural systems, where primordial sequences are lost to time. To test the hypothesis that selection for enhanced activity leads to a loss of promiscuity, we evolved a de novo designed bifunctional protein. The parental protein, denoted Syn‐IF, was chosen from a library of binary patterned 4‐helix bundles. Syn‐IF was shown previously to rescue two different auxotrophic strains of E. coli: ΔilvA and Δfes. These two strains contain deletions for proteins with very different biochemical functions; IlvA is involved in isoleucine biosynthesis, while Fes is involved in iron assimilation. In two separate experiments, Syn‐IF, was evolved for faster rescue of either ΔilvA or Δfes. Following multiple rounds of mutagenesis, two new proteins were selected, each capable of rescuing the selected function significantly faster than the parental protein. In each case, the evolved protein also lost the ability to rescue the unselected function. In both evolutionary trajectories, the original bifunctional generalist was evolved into a monofunctional specialist with enhanced activity.     

Estimating switchgrass productivity in the Great Plains using satellite vegetation index and site environmental variables

Switchgrass is being evaluated as a potential feedstock source for cellulosic biofuels and is being cultivated in several regions of the United States. The recent availability of switchgrass land cover maps derived from the National Agricultural Statistics Service cropland data layer for the conterminous United States provides an opportunity to assess the environmental conditions of switchgrass over large areas and across different geographic locations. The main goal of this study is to develop a data-driven multiple regression switchgrass productivity model and identify the optimal climate and environment conditions for the highly productive switchgrass in the Great Plains (GP). Environmental and climate variables used in the study include elevation, soil organic carbon, available water capacity, climate, and seasonal weather. Satellite-derived growing season averaged Normalized Difference Vegetation Index (GSN) was used as a proxy for switchgrass productivity. Multiple regression analyses indicate that there are strong correlations between site environmental variables and switchgrass productivity (r = 0.95). Sufficient precipitation and suitable temperature during the growing season (i.e., not too hot or too cold) are favorable for switchgrass growth. Elevation and soil characteristics (e.g., soil available water capacity) are also an important factor impacting switchgrass productivity. An anticipated switchgrass biomass productivity map for the entire GP based on site environmental and climate conditions and switchgrass productivity model was generated. Highly productive switchgrass areas are mainly located in the eastern part of the GP. Results from this study can help land managers and biofuel plant investors better understand the general environmental and climate conditions influencing switchgrass growth and make optimal land use decisions regarding switchgrass development in the GP.     

Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem

Soil organic carbon (SOC) plays an important role in soil fertility and carbon sequestration, and a better understanding of the spatial patterns of SOC is essential for soil resource management. In this study, we used boosted regression tree (BRT) and random forest (RF) models to map the distribution of topsoil organic carbon content at the northeastern edge of the Tibetan Plateau in China. A set of 105 soil samples and 12 environmental variables (including topography, climate and vegetation) were analyzed. The performance of the models was evaluated using a 10-fold cross-validation procedure. Maps of the mean values and standard deviations of SOC were generated to illustrate model variability and uncertainty. The results indicate that the BRT and RF models exhibited very similar performance and yielded similar predicted distributions of SOC. The two models explained approximately 70% of the total SOC variability. The BRT and RF models robustly predicted the SOC at low observed SOC values, whereas they underestimated high observed SOC values. This underestimation may have been caused by biased distributions of soil samples in the SOC space. Vegetation-related variables were assigned the highest importance in both models, followed by climate and topography. Both models produced spatial distribution maps of SOC that were closely related to vegetation cover. The SOC content predicted by the BRT model was clearly higher than that of the RF model in areas with greater vegetation cover because the contributions of vegetation-related variables in the two models (65% and 43%, respectively) differed significantly. The predicted SOC content increased from the northwestern to the southeastern part of the study area, average values produced by the BRT and RF models were 27.3 g kg⁻¹ and 26.6 g kg⁻¹, respectively. We conclude that the BRT and RF methods should be calibrated and compared to obtain the best prediction of SOC spatial distribution in similar regions. In addition, vegetation variables, including those obtained from remote sensing imagery, should be taken as the main environmental indicators and explicitly included when generating SOC maps in Alpine environments.     

Rapid micropreps and minipreps of Ti plasmids and binary vectors fromAgrobacterium tumefaciens

A simple and versatile procedure has been developed for the isolation of both large helper/Ti plasmids and binary vectors fromAgrobacterium tumefaciens. Using a slightly modified alkaline lysis protocol, intact plasmid can be recovered from cultures grown in standard micro-centrifuge tubes or culture tubes in sufficient yield and purity to allow for restriction analysis on ethidium bromide stained gels of the >200 kb Ti plasmid DNA. Contamination by chromosomal DNA is minimal and there is thus no need for isopycnic gradient purification. This same procedure can be combined with a high temperature treatment (37°C) and antibiotic selection to generate preparations containing binary vector DNA that are virtually free of interfering Ti plasmid DNA. Restriction patterns produced from these binary vector DNA preparations are unambiguous and therefore preliminary screening by Southern hybridization can be eliminated.     

Evaluating the use of macroscale variables as proxies for local aquatic variables and to model stream fish distributions

相似文献   

Force-dependent changes in movement-related cortical potentials

The purpose of this study was to compare movement-related cortical potentials (MRCPs) associated with different levels of isometric contractions by elbow flexors. Eight healthy, right-handed male subjects participated in this study and performed different levels (10 and 50% of maximal voluntary contraction) of isometric contractions by the right elbow flexors. Electroencephalogram (EEG) signals were recorded from Fz, C3, Cz and C4 of the international 10/20 system. Motor potential (MP) amplitudes (from −200 to approximately −50 ms before force onset) for C3 associated with both force generations was significantly greater (P < 0.01) than those for C4, indicating that contralateral predominance of MRCP was observed in the right arm flexion. In Fz, the potentials of negative slope (NS′) (from −600 to approximately −200 ms) and MPs for 50% MVC were significantly greater than those of 10% MVC. In Cz, the MP associated with 50% MVC revealed a significantly greater (P < 0.05) value than that with 10% MVC. In C3 and C4, the MP associated with 50% MVC tended to be greater than that with 10% MVC, but no statistically significant differences were found. These force-dependent changes in MRCPs imply increased activation of neural circuits involved in motor preparation and initiation. It is therefore suggested that the larger potentials from Fz and Cz for 50% MVC compared with 10% MVC reflect a greater activation of supplementary motor area for the preparation of the larger force generation.     

Phleomycin resistance as a dominant selectable marker for plant cell transformation

Tobacco cells are sensitive to bleomycin and phleomycin. The Tn5 and the Streptoalloteichus hindustanus (Sh) bleomycin resistance (Ble) genes conferring resistance to these antibiotics have each been inserted into two plant expression vectors. They are flanked by the nopaline synthase (nos) or the cauliflower mosaic virus (CaMV) 35S promoters on one side, and by the nos polyadenylation signal on the other. These four chimaeric genes were introduced into the binary transformation vector pGA 492, which were thereafter mobilized into Agrobacterium tumefaciens strain LBA 4404. The resulting strains were used to transform Nicotiana tabacum cv. Xanthi nc using the leaf disc transformation procedure. In all cases, phleomycin- and bleomycin-resistant tobacco plants were regenerated from transformed cells under selective conditions; however the highest frequency of rooted plants was obtained when transformation was carried out with the Sh Ble gene under the control of the 35S promoter. Phleomycin resistance was stably transmitted to sexual offspring as a dominant nuclear trait as confirmed by Southern blotting.     

Alternative selectable markers for potato transformation using minimal T-DNA vectors

Alternative selection systems for plant transformation are especially valuable in clonal crops, such as potato (Solanum tuberosum L.), to pyramid transgenes into the same cultivar by successive transformation events. We have modified the pGPTV series of binary vectors to construct pMOA1 to pMOA5, resulting in a series of essentially identical binary vectors except for the presence of different selectable marker genes. These selectable marker genes are tightly inserted between the left and right T-DNA borders and confer resistance to kanamycin (nptII), hygromycin (hpt), methotrexate (dhfr), phosphinothricin (bar), or phleomycin (ble). The T-DNA of all the vectors is based on the minimal features necessary for plant transformation, with no extraneous DNA segments that may be unacceptable to regulatory authorities for general release of transgenic plants. A series of unique restriction sites exists between the right border and each selectable marker gene for subsequent insertion of useful genes. We have also developed improved culture procedures for potato transformation and used the pMOA1 to pMOA5 binary vectors to define stringent selection conditions for each marker gene. Combining these advances improved the frequency of recovering transformed potato plants while maintaining a low frequency of escapes. The relative efficiency of recovering transgenic potato lines with each selectable marker gene can be summarised as: kanamycin resistance>hygromycin resistance>phosphinothricin resistance>phleomycin resistance>methotrexate resistance.     

Analyzing a randomized trial on breast self-examination with noncompliance and missing outcomes

Recently, instrumental variables methods have been used to address non-compliance in randomized experiments. Complicating such analyses is often the presence of missing data. The standard model for missing data, missing at random (MAR), has some unattractive features in this context. In this paper we compare MAR-based estimates of the complier average causal effect (CACE) with an estimator based on an alternative, nonignorable model for the missing data process, developed by Frangakis and Rubin (1999, Biometrika, 86, 365-379). We also introduce a new missing data model that, like the Frangakis-Rubin model, is specially suited for models with instrumental variables, but makes different substantive assumptions. We analyze these issues in the context of a randomized trial of breast self-examination (BSE). In the study two methods of teaching BSE, consisting of either mailed information about BSE (the standard treatment) or the attendance of a course involving theoretical and practical sessions (the new treatment), were compared with the aim of assessing whether teaching programs could increase BSE practice and improve examination skills. The study was affected by the two sources of bias mentioned above: only 55% of women assigned to receive the new treatment complied with their assignment and 35% of the women did not respond to the post-test questionnaire. Comparing the causal estimand of the new treatment using the MAR, Frangakis-Rubin, and our new approach, the results suggest that for these data the MAR assumption appears least plausible, and that the new model appears most plausible among the three choices.