DNA标记技术在螨类系统学研究中的应用

长期以来,螨类主要依靠其形态特征进行系统学研究。DNA标记是指能反映生物个体或物种间基因组中某种差异特征的DNA片段。近年来,DNA标记技术在螨类系统学研究中得到越来越广泛的应用。本文综述了随机扩增多态性RAPD、限制性内切酶片段长度多态性RFLP、微卫星SSR、核酸序列扩增、扩增片段长度多态性AFLP和直接扩增片段长度多态性DALP等6种DNA标记技术在螨类系统学研究中的应用现状及前景。     

Mechanisms driving understory evergreen herb distributions across slope aspects: as derived from landscape position

In the Northern Hemisphere, the surface of south-facing slopes orients toward the sun and thus receives a greater duration and intensity of solar irradiation, resulting in a relatively warmer, drier microclimate and seasonal environmental extremes. This creates potentially detrimental conditions for evergreen plants which must endure the full gamut of conditions. I hypothesize that (1) increased southerly aspect will correlate negatively with evergreen understory plant distributions; (2) derived environmental variables (summer and winter light and heat load) will predict variance in evergreen distributions as well as topographic position (aspect, slope, and elevation) and (3) winter light will best predict evergreen understory plant distributions. In order to test these hypotheses, survey data were collected characterizing 10 evergreen understory herb distributions (presence, abundance, and reproduction) as well as the corresponding topographical information across north- and south-facing slopes in the North Carolina mountains and Georgia piedmont. The best predictive models were selected using AIC, and Bayesian hierarchical generalized linear models were used to estimate the strength of the retained coefficients. As predicted, evergreen understory herbs occurred and reproduced less on south-facing than north-facing slopes, though slope and elevation also had robust predictive power, and both discriminated well between evergreen species. While the landscape variables explained where the plants occurred, winter light and heat load provided the best explanation why they were there. Evergreen plants likely are limited on south-facing slopes by low soil moisture combined with high temperatures in summer and high irradiance combined with lower temperatures in winter. The robust negative response of the understory evergreen herbs to increased winter light also suggested that the winter rather than the summer (or growing season) environment provided the best predictive power for understory evergreen distributions, which has substantive implications for predicting responses to global climate change.     

Relationship between benthic insects (Ephemeroptera, Plecoptera, Coleoptera, Trichoptera) and temperature in small and medium-sized streams in Germany: A multivariate study

Benthic insect communities (Ephemeroptera, Plecoptera, Coleoptera, Trichoptera) were studied together with water temperature and environmental parameters in streams between June 2000 and June 2001. The sampling area consisted of 20 sites in small and medium-sized streams located in the lower mountainous area of Central Europe. Temperature was recorded nearly continuously and several physicochemical and environmental variables were assessed. Macroinvertebrates were sampled both in spring and summer. Data-sets of species abundance and occurrence were analysed using multivariate techniques and were correlated to the thermal and environmental conditions of the streams. The temperature preferences of the species were compared to published data-sets on their autecological characteristics. Up to 29% of the variability in the Ephemeroptera, Plecoptera, Trichoptera and Coleoptera community was explained by summer temperature variation in the data-sets for both small and medium-sized streams. A smaller, but significant part of the variability in species distribution was explained by conductivity, substratum type, and the percent coverage of local riparian forest. Compared to small streams, temperature was less important for the macroinvertebrate composition in medium-sized streams. This result is likely due to the more tolerant, eurythermic species composition in larger streams. A total of 33 Ephemeroptera, Plecoptera, Coleoptera and Trichoptera taxa were positively correlated and 28 taxa were negatively correlated to summer temperature patterns. The temperature preferences of taxa considered in this study were related to species traits, such as egg dormancies and life cycle plasticity.     

Historical demographic profiles and genetic variation of the East African Butana and Kenana indigenous dairy zebu cattle

Butana and Kenana breeds from Sudan are part of the East African zebu Bos indicus type of cattle. Unlike other indigenous zebu cattle in Africa, they are unique due to their reputation for high milk production and are regarded as dairy cattle, the only ones of their kind on the African continent. In this study, we sequenced the complete mitochondrial DNA (mtDNA) D‐loop of 70 animals to understand the maternal genetic variation, demographic profiles and history of the two breeds in relation to the history of cattle pastoralism on the African continent. Only taurine mtDNA sequences were identified. We found very high mtDNA diversity but low level of maternal genetic structure within and between the two breeds. Bayesian coalescent‐based analysis revealed different historical and demographic profiles for the two breeds, with an earlier population expansion in the Butana vis a vis the Kenana. The maternal ancestral populations of the two breeds may have diverged prior to their introduction into the African continent, with first the arrival of the ancestral Butana population. We also reveal distinct demographic history between the two breeds with the Butana showing a decline in its effective population size (N_e) in the recent past ~590 years. Our results provide new insights on the early history of cattle pastoralism in Sudan indicative of a large ancient effective population size.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Does a reduction in the price of rhino horn prevent poaching?

Rhino poaching around the world has increased inordinately, to the extent that concerns exist over the possible survival of the species. An open access rhino poaching model is developed for South African rhino. The model is a hybrid dynamical model, as both a system dynamics model as well as a Bayesian network model are developed. The system dynamics model is used to estimate the unknown parameter values (through optimisation) and also to determine the intervals for the parameters. These intervals are then used in the Bayesian Belief Network model to assess uncertainty. Hybrid approaches improve the ability to validate models compared with conventional modelling. The resultant model indicates that reducing the price of rhino horn would not be effective at curbing poaching, unless poacher costs are also increased. However, increasing poacher costs is not a realistic policy option since these costs are largely beyond the control of decision-makers. The insensitivity of price to poaching effort has implications for methods proposed to reduce the value of rhinos, such as introducing synthetic rhino horn and the de-horning of rhinos.     

Legacy effects and memory loss: how contingencies moderate the response of rocky intertidal biofilms to present and past extreme events

Understanding how historical processes modulate the response of ecosystems to perturbations is becoming increasingly important. In contrast to the growing interest in projecting biodiversity and ecosystem functioning under future climate scenarios, how legacy effects originating from historical conditions drive change in ecosystems remains largely unexplored. Using experiments in combination with stochastic antecedent modelling, we evaluated how extreme warming, sediment deposition and grazing events modulated the ecological memory of rocky intertidal epilithic microphytobenthos (EMPB). We found memory effects in the non‐clustered scenario of disturbance (60 days apart), where EMPB biomass fluctuated in time, but not under clustered disturbances (15 days apart), where EMPB biomass was consistently low. A massive grazing event impacted on EMPB biomass in a second run of the experiment, also muting ecological memory. Our results provide empirical support to the theoretical expectation that stochastic fluctuations promote ecological memory, but also show that contingencies may lead to memory loss.     

A Bayesian scoring rule on clustered event data for familial risk assessment – An example from colorectal cancer screening

Colorectal cancer screening is well established. The identification of high risk populations is the key to implement effective risk‐adjusted screening. Good statistical approaches for risk prediction do not exist. The family's colorectal cancer history is used for identification of high risk families and usually assessed by a questionnaire. This paper introduces a prediction algorithm to designate a family for colorectal cancer risk and discusses its statistical properties. The new algorithm uses Bayesian reasoning and a detailed family history illustrated by a pedigree and a Lexis diagram. The algorithm is able to integrate different hereditary mechanisms that define complex latent class or random factor structures. They are generic and do not reflect specific genetic models. This is comparable to strategies in complex segregation analysis. Furthermore, the algorithm can integrate different statistical penetrance models for right censored event data. Computational challenges related to the handling of the likelihood are discussed. Simulation studies assess the predictive quality of the new algorithm in terms of ROC curves and corresponding AUCs. The algorithm is applied to data of a recent study on familial colorectal cancer risk. Its predictive performance is compared to that of a questionnaire currently used in screening for familial colorectal cancer. The results of the proposed algorithm are robust against different inheritance models. Using the simplest hereditary mechanism, the simulation study provides evidence that the algorithm improves detection of families with high cancer risk in comparison to the currently used questionnaire. The applicability of the algorithm goes beyond the field of colorectal cancer.     

Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping

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