Open Capture–Recapture Models with Heterogeneity: II. Jolly–Seber Model

Summary Estimation of abundance is important in both open and closed population capture–recapture analysis, but unmodeled heterogeneity of capture probability leads to negative bias in abundance estimates. This article defines and develops a suite of open population capture–recapture models using finite mixtures to model heterogeneity of capture and survival probabilities. Model comparisons and parameter estimation use likelihood‐based methods. A real example is analyzed, and simulations are used to check the main features of the heterogeneous models, especially the quality of estimation of abundance, survival, recruitment, and turnover. The two major advances in this article are the provision of realistic abundance estimates that take account of heterogenetiy of capture, and an appraisal of the amount of overestimation of survival arising from conditioning on the first capture when heterogeneity of survival is present.     

Development and evaluation of intermediate frequency magnetic field exposure system for studies of in vitro biological effects

We have developed an intermediate frequency (IF) magnetic field exposure system for in vitro studies. Since there are no previous studies on exposure to heating-frequency magnetic fields generated from an induction heating (IH) cook top, there is a strong need for such an exposure system and for biological studies of IF magnetic fields. This system mainly consists of a magnetic-field-generating coil housed inside an incubator, inside which cultured cells can be exposed to magnetic field. Two systems were prepared to allow the experiment to be conducted in a double-blind manner. The level of the generated magnetic field was set to 532 microT rms in the exposure space, 23 kHz, 80 times the value in the International Commission on Non-ionizing Radiation Protection (ICNIRP) guidelines, with a spatial field uniformity better than 3.8%. The waveforms were nearly sinusoidal. It was also confirmed that the parasitic electric field was 157 V/m rms and the induced electric field was 1.9 V/m rms. The temperature was maintained at 36.5 +/- 0.5 degrees C for 2 h. Furthermore, leaked magnetic flux density was 0.7 microT rms or lower at extremely low frequency (ELF) and IF in the stopped system when the other system was being operated, and the environmental magnetic flux density was 0.1 microT rms or lower at the center of the coils. As a result, it was confirmed that this system could be successfully used to evaluate the biological effects of exposure to IF magnetic fields.     

A new proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation

The Cox proportional hazards model has become the standard for the analysis of survival time data in cancer and other chronic diseases. In most studies, proportional hazards (PH) are assumed for covariate effects. With long-term follow-up, the PH assumption may be violated, leading to poor model fit. To accommodate non-PH effects, we introduce a new procedure, MFPT, an extension of the multivariable fractional polynomial (MFP) approach, to do the following: (1) select influential variables; (2) determine a sensible dose-response function for continuous variables; (3) investigate time-varying effects; (4) model such time-varying effects on a continuous scale. Assuming PH initially, we start with a detailed model-building step, including a search for possible non-linear functions for continuous covariates. Sometimes a variable with a strong short-term effect may appear weak or non-influential if 'averaged' over time under the PH assumption. To protect against omitting such variables, we repeat the analysis over a restricted time-interval. Any additional prognostic variables identified by this second analysis are added to create our final time-fixed multivariable model. Using a forward-selection algorithm we search for possible improvements in fit by adding time-varying covariates. The first part to create a final time-fixed model does not require the use of MFP. A model may be given from 'outside' or a different strategy may be preferred for this part. This broadens the scope of the time-varying part. To motivate and illustrate the methodology, we create prognostic models from a large database of patients with primary breast cancer. Non-linear time-fixed effects are found for progesterone receptor status and number of positive lymph nodes. Highly statistically significant time-varying effects are present for progesterone receptor status and tumour size.     

月见草油用于化妆品的研究与开发

介绍月见草的利用简况,月见草油对皮肤的活性研制营养霜过程及系列产品等。     

Upward shift of alpine plants increases floristic similarity of mountain summits

Question: Does the upward shift of species and accompanied increase in species richness, induced by climate change, lead to homogenization of Alpine summit vegetation? Location: Bernina region of the Swiss Alps. Methods: Based on a data set from previous literature we expand the analysis from species richness to beta‐diversity and spatial heterogeneity. Species compositions of mountain summits are compared using a two‐component heterogeneity concept including the mean and the variance of Sørensen similarities calculated between the summits. Non‐metric multidimensional scaling is applied to explore developments of single summits in detail. Results: Both heterogeneity components (mean dissimilarity and variance) decrease over time, indicating a trend towards more homogeneous vegetation among Alpine summits. However, the development on single summits is not strictly unidirectional. Conclusions: The upward shift of plant species leads to homogenization of alpine summit regions. Thus, increasing alpha‐diversity is accompanied by decreasing beta‐diversity. Beta‐diversity demands higher recognition by scientists as well as nature conservationists as it detects changes which cannot be described using species richness alone.     

Prediction of herbage yield in grassland: How well do Ellenberg N‐values perform?

Question: How useful are Ellenberg N‐values for predicting the herbage yield of Central European grasslands in comparison to approaches based on ordination scores of plant species composition or on soil parameters? Location: Central Germany (11°00′‐11°37’E, 50°21‐50°34’N, 500–840 m a.s.l.). Methods: Based on data from a field survey in 2001, the following models were constructed for predicting herbage yield in montane Central European grasslands: (1) Linear regression of mean Ellenberg N‐, R‐ and F‐values; (2) Linear regression of ordination scores derived from Non‐metric Multidimensional Scaling (NMDS) of vegetation data; and (3) Multiple linear regression (MLR) of soil variables. Models were evaluated by cross‐validation and validation with additional data collected in 2002. Results: Best predictions were obtained with models based on species composition. Ellenberg N‐values and NMDS scores performed equally well and better than models based on Ellenberg R‐ or F‐values. Predictions based on soil variables were least accurate. When tested with data from 2002, models based on Ellenberg N‐values or on NMDS scores accurately predicted productivity rank order of sites, but not the actual herbage yield of particular sites. Conclusions: Mean Ellenberg N‐values, which are easy to calculate, are as accurate as ordination scores in predicting herbage yield from plant species composition. In contrast, models based on soil variables may be useful for generating hypotheses about the factors limiting herbage yield, but not for prediction. We support the view that Ellenberg N‐values should be called productivity values rather than nitrogen values.     

Efficacy of a Cladosporium sp. fungus against Helicoverpa armigera (Lepidoptera: Noctuidae), other insect pests and beneficial insects of cotton

A strain of the fungus Cladosporium sp. (RM16) from an egg of Helicoverpa armigera Hübner (Lepidoptera: Noctuidae) was assessed as a potential biocontrol agent for this pest. Pathogenicity of the fungus was tested against H. armigera eggs and larvae, cotton aphids (Aphis gossypii Glover; Homoptera: Aphididae), and silverleaf whitefly type B (Bemisia tabaci Gennadius; Hemiptera: Aleyrodidae). The pathogenicity of the fungus to the predatory red and blue beetles (Dicranolaius bellulus Guérin-Méneville; Coleoptera: Melyridae), transverse ladybird beetles (Coccinella transversalis Fabricius; Coleoptera: Coccinellidae), green lacewings (Mallada signatus Schneider; Neuroptera: Chrysopidae) and damsel bugs (Nabis kinbergii Reuter; Hemiptera: Nabidae), was also assessed in the laboratory. Fungus treatment resulted in failure to hatch of up to 64% of H. armigera eggs (compared with 11% in the controls) and mortality of 54% of first instar H. armigera larvae (compared with 5% in the controls). In contrast, it was not pathogenic to later instar H. armigera larvae. Cladosporium RM16 was also efficacious against the sap-sucking insect pests of cotton that were tested. No significant harmful effect of the fungus was found on any of the four beneficial predatory insects assessed in this study. Cladosporium RM16 has the potential as biological control agent to support integrated pest management in cotton farming systems, although this needs intensive study.     

Decoding the genome beyond sequencing: the new phase of genomic research

While our understanding of gene-based biology has greatly improved, it is clear that the function of the genome and most diseases cannot be fully explained by genes and other regulatory elements. Genes and the genome represent distinct levels of genetic organization with their own coding systems; Genes code parts like protein and RNA, but the genome codes the structure of genetic networks, which are defined by the whole set of genes, chromosomes and their topological interactions within a cell. Accordingly, the genetic code of DNA offers limited understanding of genome functions. In this perspective, we introduce the genome theory which calls for the departure of gene-centric genomic research. To make this transition for the next phase of genomic research, it is essential to acknowledge the importance of new genome-based biological concepts and to establish new technology platforms to decode the genome beyond sequencing.