The RAINFOR database: monitoring forest biomass and dynamics

Problem: Data from over 100 permanent sample plots which have been studied for 10–20 years need a suitable system for storage which allows simple data manipulation and retrieval for analysis. Methods: A relational database linking tree records, taxonomic nomenclature and corresponding environmental data has been built in MS Access as part of the RAINFOR project. Conclusion: The database allows flexible and long‐term use of a large amount of data: more than 100 tree plots across Amazonia, incorporating over 80 000 records of individual trees and over 300 000 total records of tree diameter measurements from successive censuses. The database is designed to enable linkages to existing soil, floristic or plant‐trait databases. This database will be a useful tool for exploring the impact of environmental factors on forest structure and dynamics at local to continental scales, and long term changes in forest ecology. As an early example of its potential, we explore the impact of different methodological assumptions on estimates of tropical forest biomass and carbon storage.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Scale sensitivity of synthetic long‐term vegetation time series derived through overlay of short‐term field records

Questions: Is change in cover of dominant species driving the velocity of succession or is it species turnover (1)? Is the length of the time‐step chosen in sampling affecting our recognition of the long‐term rate of change (2)1 Location: 74 permanent plots located in the Swiss National Park, SE Switzerland, ca. 1900 m a.s.l. Methods: We superimpose several time‐series from permanent plots to one single series solely based on compositional dissimilarity. As shown earlier (Wildi & Schütz 2000) this results in a synthetic series covering about 400 to 650 yr length. Continuous power transformation of cover‐percentage scores is used to test if the dominance or the presence‐absence of species is governing secondary succession from pasture to forest. The effect of time step length is tested by sub‐samples of the time series. Results: Altering the weight of presence‐absence versus dominance of species affects the emerging time frame, while altering time step length is uncritical. Where species turnover is fast, different performance scales yield similar results. When cover change in dominant species prevails, the solutions vary considerably. Ordinations reveal that the synthetic time series seek for shortest paths of the temporal pattern whereas in the real system longer lasting alternatives exist. Conclusions: Superimposing time series differs from the classical space‐for‐time substitution approach. It is a computation‐based method to investigate temporal patterns of hundreds of years fitting between direct monitoring (usually limited to decades) and the analysis of proxy‐data (for time spans of thousands of years and more).     

Physical and chemical variability in sugarcane fields infested with the red-striped soft scale insect,Pulvinaria Tenuivalvata (Newstead)

The red-striped soft scale insect Pulvinaria tenuivalvata (Newstead) (Hemiptera: Coccidae) started to infest sugarcane plants (Saccharum officinarum L.) in different districts in Egypt during the last decade. The percentage of infestation was recorded in El-Wakf area, Qena Governorate (Naghhamadi mill zone) Upper Egypt in some fields. There are three levels of infestation, low, intermediate and high. From these fields, samples were selected for physical and chemical studies. The results obtained show that the stalks of infested plants decreased in weight, the sugar content (glucose and sucrose) drastically reduced and as the percentage of infestation increased the percentage of glucose and sucrose content significantly decreased. The primary and secondary humidity and the cellulose content also increased in the healthy plants compared to the infested ones. All the physical character of the infested plants was significantly affected in comparison with the healthy ones.     

Influences of temporal independence of data on modelling species distributions

Modelling species distributions has been widely used to understand present and future potential distributions of species, and can provide adaptation and mitigation information as references for conservation and management under climate change. However, various methods of data splitting to develop and validate functions of the models do not get enough attention, which may mislead the interpretation of predicted results. We used the Taiwanese endemic birds to test the influences of temporal independence of datasets on model performance and prediction. Training and testing data were considered to be independent if they were collected during different survey periods (1993–2004 and 2009–2010). The results indicated no significant differences of six model performance measures (AUC, kappa, TSS, accuracy, sensitivity, and specificity) among the combinations of training and testing datasets. Both species- and grid cell-based assessments differed significantly between predictions by the annual and pooled training data. We also found an average of 85.8% similarity for species presences and absences in different survey periods. The remaining dissimilarity was mostly caused by species observed in the late survey period but not in the early one. The method of data splitting, yielding training and testing data, is critical for resulting model species distributions. Even if similar model performance exists, different methods can lead to different species distributional maps. More attention needs to be given to this issue, especially when amplifying these models to project species distributions in a changing world.     

Gene assignment by means of somatic cell hybrids: a new method for the analysis of data

Summary A statistical approach to the interpretation of data from gene assignment with somatic cell hybrids is presented. The observed data are analysed under a variety of hypotheses. The fit to the hypotheses is compared by means of the likelihood obtained under a given hypothesis. Two of these hypotheses are related to fundamental questions: is a gene responsible for the enzyme observation and if so, is that gene located on a specific chromosome or could it change its position and be sometimes on chromosome j and, in another hybrid line, on chromosome k? The other hypotheses concern the assignment of the gene to just one of the chromosomes.To improve the traditional data analysis approach we considered additional information: the uncertainties and possible errors of laboratory methods in all our calculations and the length of the donor chromosomes in connection with one specific hypothesis.This method allows us to account for the reliability of the investigation methods and the nature of the hybrid lines involved. Data can be evaluated at different error probabilities within a realistic range in order to compare and discuss results.     

不同品种、不同花期的依兰花精油成分研究

为探讨依兰花精油的香气质量,我们对云南省西双版纳产依兰花精油以及引种栽培于西双版纳的泰国种、老挝种的依兰花精油的化学成分在Finnigan-4510 GC/MS/DS上用毛细管柱进行了定性和定量分析,鉴定了44个化学成分。分析结果表明,依兰油的香气与品种及精油中的酯类、醇类和酚醚的含量有关。倍半萜烯类和倍半萜醇类的含量越高,其香气质量越差。就西双版纳产依兰油的香气而言,依兰花由青转黄时得到的精油远较绿花和花蕾油为好。     

水洞沟遗址的环境与地层

水洞沟第四纪地层可与萨拉乌苏河组上段对比。水洞沟遗址包含有新石器和旧石器晚期文化,其旧石器晚期文化发生在距今20,000多年至10,000多年间的黄土状粉砂土中,即发生在末次冰期主冰期的干冷环境下,比发育在距今约35,000—50,000年间的萨拉乌苏河旧石器文化为晚。     

Processing of multi-dimensional NMR data with the new software PROSA

Summary The new program PROSA is an efficient implementation of the common data-processing steps for multi-dimensional NMR spectra. PROSA performs linear prediction, digital filtering, Fourier transformation, automatic phase correction, and baseline correction. High efficiency is achieved by avoiding disk storage of intermediate data and by the absence of any graphics display, which enables calculation in the batch mode and facilitates porting PROSA on a variety of different computer systems; including supercomputers. Furthermore, all time-consuming routines are completely vectorized. The elimination of a graphics display was made possible by the use of a new, reliable automatic phase-correction routine. CPU times for complete processing of a typical heteronuclear three-dimensional NMR data set of a protein vary between less than 1 min on a NEC SX3 supercomputer and 40 min on a Sun-4 computer system.     

慢性蜜蜂麻痹病病毒核酸与蛋白质组分的研究

从自然感染的意大利麻痹病蜂(APis mellifera)头部,经二次差速离心与蔗糖梯度离心获得纯化的慢性蜜蜂麻痹病病毒(CBPV)。纯化的CBPV制备物感染正常蜜蜂,4天后出现典型的麻痹症状,接着死亡,平均死亡率分别为95％与100％。SDS-聚丙烯酰胺凝胶电泳分析,二次差速离心初步纯化的病毒制备物含有多条蛋白带,而蔗糖密度梯度纯化的病毒制备物仅含有单一的多肽带。5％、7.5％与10％SDS-聚丙烯酰胺凝胶电泳分析,均检测出一种病毒蛋白质,分子量大约为24,200道尔顿,而且不同凝胶浓度检测的蛋白质分子量相近。慢性蜜蜂麻痹病病毒核酸也用SDS-聚丙烯酰胺凝胶电泳分析,结果表明,凝胶中有5条带,对核酸酶敏感,证明该病毒含有5个单股RNA组分。对慢性蜜蜂麻痹病病毒的基因组结构进行了讨论。