应加强对单细胞生物分批培养生长曲线中减速期的认识和教学

根据单细胞生物分批培养过程中比生长速率(μ)的变化,其生长曲线分为延滞期、加速期、对数期、减速期、稳定期和衰亡期6个时期.与其他生长时期相比,在减速期生物的生长、基质的利用、产物的合成和基因表达谱等方面有显著的不同,并对发酵生产有着重要作用.然而,长期以来,对减速期的认识和教学相当薄弱,亟需加强对减速期的认识和教学.     

An ensemble model for predicting Rhopalosiphum padi abundance

A novel modeling method is proposed to predict the abundance of the main vector of barley yellow dwarf virus in autumn sown cereal crops. An ensemble model based on artificial neural networks (ANN) was developed to predict the number of Rhopalosiphum padi (L.) (Homoptera: Aphididae) caught in traps during the autumn flight period at Lincoln, Canterbury, New Zealand, over the period 1982–2003. Artificial neural networks were trained using historical weather data and aphid data collected from a suction trap. Model results were compared with those obtained using multiple regression (MR) models using the same independent variables. Both ANN and MR models were validated by leave‐one‐out validation, in other words, by sequentially jackknifing each year out of the data set, fitting a model to the remaining data, then using that model to predict the number of aphids for each jackknifed year. A linear ensemble of ANN models further improved the predictions and represented the trends in the number of aphids over the 22‐year period very well. The r² between the predicted and observed numbers of aphids for the ANN models changed from 0.68 to 0.83 using the linear ensemble model, but the ensemble approach did not change the prediction for the MR models. The absolute mean error (ABSME) of prediction was much lower for the ANN ensemble predictions compared to that for the MR models. The ABMSE for the ANN models dropped from 109 to 86 aphids compared to an ABMSE reduction from 245 to 220 aphids for the MR models. We discuss the potential for ensemble models for predicting insect abundance when long‐term historical data are available.     

Discordant longitudinal clines in flowering time and phytochrome C in Arabidopsis thaliana

Using seasonal cues to time reproduction appropriately is crucial for many organisms. Plants in particular often use photoperiod to signal the time to transition to flowering. Because seasonality varies latitudinally, adaptation to local climate is expected to result in corresponding clines in photoperiod-related traits. By experimentally manipulating photoperiod cues and measuring the flowering responses and photoperiod plasticity of 138 Eurasian accessions of Arabidopsis thaliana, we detected strong longitudinal but not latitudinal clines in flowering responses. The presence of longitudinal clines suggests that critical photoperiod cues vary among populations occurring at similar latitudes. Haplotypes at PHYC, a locus hypothesized to play a role in adaptation to light cues, were also longitudinally differentiated. Controlling for neutral population structure revealed that PHYC haplotype influenced flowering time; however, the distribution of PHYC haplotypes occurred in the opposite direction to the phenotypic cline, suggesting that loci other than PHYC are responsible for the longitudinal pattern in photoperiod response. Our results provide previously missing empirical support for the importance of PHYC in mediating photoperiod sensitivity in natural populations of A. thaliana. However, they also suggest that other loci and epistatic interactions likely play a role in the determination of flowering time and that the environmental factors influencing photoperiod in plants vary longitudinally as well as latitudinally.     

Genetic and ecological differentiation in the hybridizing campions Silene dioica and S. latifolia

Ecological differentiation is a major contributor to the generation and maintenance of biological diversity. We investigated habitat differentiation between and within sites in the fully cross-fertile and hybridizing Silene dioica and S. latifolia using amplified fragment length polymorphisms (AFLP) profiles and corresponding vegetation relevés around individual plants. Nineteen study sites in the Swiss Alps included pure sites and contact sites (both taxa present within 30 m). In pure sites and at contact sites, the two taxa showed consistently differentiated AFLP banding patterns across regions but few discriminating bands. This indicates that although the two taxa are weakly differentiated, current introgression has not led to genome-wide admixture. Only three putative early generation hybrids were detected at contact sites. The habitats of the two taxa differed between pure sites with S. dioica occurring in moister, colder, and less-disturbed sites than S. latifolia. However, asymmetric habitat overlap was evident within contact sites found in intermediate conditions that were more similar to S. latifolia sites. This situation might favor introgression from S. dioica into S. latifolia. Evidence for habitat-genotype associations within contact sites was weak making habitat-mediated selection against intermediate phenotypes of hybrids unlikely in the contact sites investigated. We suggest that other reproductive barriers together with dispersal limitation contribute to the rarity of early generation hybrids.     

Heterogeneous genomic differentiation between walking-stick ecotypes: "isolation by adaptation" and multiple roles for divergent selection

Genetic differentiation can be highly variable across the genome. For example, loci under divergent selection and those tightly linked to them may exhibit elevated differentiation compared to neutral regions. These represent "outlier loci" whose differentiation exceeds neutral expectations. Adaptive divergence can also increase genome-wide differentiation by promoting general barriers to neutral gene flow, thereby facilitating genomic divergence via genetic drift. This latter process can yield a positive correlation between adaptive phenotypic divergence and neutral genetic differentiation (described here as "isolation-by-adaptation"). Here, we examine both these processes by combining an AFLP genome scan of two host plant ecotypes of Timema cristinae walking-sticks with existing data on adaptive phenotypic divergence and ecological speciation in these insects. We found that about 8% of loci are outliers in multiple population comparisons. Replicated comparisons between population-pairs using the same versus different host species revealed that 1-2% of loci are subject to host-related selection specifically. Locus-specific analyses revealed that up to 10% of putatively neutral (nonoutlier) AFLP loci exhibit significant isolation-by-adaptation. Our results suggest that selection may affect differentiation directly, via linkage, or by facilitating genetic drift. They thus illustrate the varied and sometimes nonintuitive contributions of selection to heterogeneous genomic differentiation.     

PHYTOPHAGOUS INSECT–MICROBE MUTUALISMS AND ADAPTIVE EVOLUTIONARY DIVERSIFICATION

Adaptive diversification is a process intrinsically tied to species interactions. Yet, the influence of most types of interspecific interactions on adaptive evolutionary diversification remains poorly understood. In particular, the role of mutualistic interactions in shaping adaptive radiations has been largely unexplored, despite the ubiquity of mutualisms and increasing evidence of their ecological and evolutionary importance. Our aim here is to encourage empirical inquiry into the relationship between mutualism and evolutionary diversification, using herbivorous insects and their microbial mutualists as exemplars. Phytophagous insects have long been used to test theories of evolutionary diversification; moreover, the diversification of a number of phytophagous insect lineages has been linked to mutualisms with microbes. In this perspective, we examine microbial mutualist mediation of ecological opportunity and ecologically based divergent natural selection for their insect hosts. We also explore the conditions and mechanisms by which microbial mutualists may either facilitate or impede adaptive evolutionary diversification. These include effects on the availability of novel host plants or adaptive zones, modifying host-associated fitness trade-offs during host shifts, creating or reducing enemy-free space, and, overall, shaping the evolution of ecological (host plant) specialization. Although the conceptual framework presented here is built on phytophagous insect–microbe mutualisms, many of the processes and predictions are broadly applicable to other mutualisms in which host ecology is altered by mutualistic interactions.     

Analysis of growth patterns in purebred Kambing Katjang goat and its crosses with the German Fawn

The objective of this study was to investigate growth patterns of goats utilizing data from a crossbreeding program involving the exotic German Fawn (GF) and the indigenous Kambing Katjang (KK) goats. Growth curve models and growth curve parameters were compared and analyzed for different genotypes and litter types. A total of 20,393 weight–age data from 208 female goats belonging to various crossbreeding genotypes were individually fitted to four growth curve models (Brody, Bertalanffy, Gompertz and Logistic). The goodness of fit was highest in the Brody model in most cases. A comparison of R² among genotypes showed that they were highest for KK. There were no significant differences of genotypes for estimated mature weight in the Brody model. The estimated mature weights for KK were significantly lower (P < 0.05) than for GF × KK (F₁), backcrosses with 75% GF genes (BC) and F₁ × F₁ (F₂) in the other models. The correlations between estimated mature weights and the maturing rates were lowest for BC. The genotype significantly (P < 0.01) affected the age at the constant degree of maturity (67% and 90% of mature weight) in all models. The BC genotype was the youngest at maturity and KK the oldest. All models well expressed the growth pattern of the target animals when they were older than 2.5 years of age. The results from the present study showed that the growth pattern may be altered by crossbreeding of KK with the GF breed.     

Combining multiple biomarker models in logistic regression

Summary .   In medical research, there is great interest in developing methods for combining biomarkers. We argue that selection of markers should also be considered in the process. Traditional model/variable selection procedures ignore the underlying uncertainty after model selection. In this work, we propose a novel model-combining algorithm for classification in biomarker studies. It works by considering weighted combinations of various logistic regression models; five different weighting schemes are considered in the article. The weights and algorithm are justified using decision theory and risk-bound results. Simulation studies are performed to assess the finite-sample properties of the proposed model-combining method. It is illustrated with an application to data from an immunohistochemical study in prostate cancer.