Insect photoperiodism: effects of temperature on the induction of insect diapause and diverse roles for the circadian system in the photoperiodic response

This review considers the effects of temperature on insect diapause induction and the photoperiodic response, and includes constant temperature, temperature cycles, pulses and steps in daily light–dark cycles, constant darkness and in constant light, all with reference to various circadian‐based “clock” models. Although it is a comparative survey, it concentrates on two species, the flesh fly Sarcophaga argyrostoma and its pupal parasite Nasonia vitripennis, which possess radically different photoperiodic mechanisms, although both are based upon the circadian system. Particular attention is given to the effects of daily thermoperiod in darkness and to low and high temperature pulses in conjunction with a daily light–dark cycle, treatments that suggest that S. argyrostoma “measures” night length with a “clock” of the external coincidence type. However, N. vitripennis responds to seasonal changes in photoperiod with an internal coincidence device involving both “dawn” and “dusk” oscillators. Other species may show properties of both external and internal coincidence. Although the precepts of external coincidence have been well formulated and supported experimentally, those for internal coincidence remain obscure.     

The effect of resorption cavities on bone stiffness is site dependent

Resorption cavities formed during the bone remodelling cycle change the structure and thus the mechanical properties of trabecular bone. We tested the hypotheses that bone stiffness loss due to resorption cavities depends on anatomical location, and that for identical eroded bone volumes, cavities would cause more stiffness loss than homogeneous erosion. For this purpose, we used beam–shell finite element models. This new approach was validated against voxel-based FE models. We found an excellent agreement for the elastic stiffness behaviour of individual trabeculae in axial compression (R² = 1.00) and in bending (R²>0.98), as well as for entire trabecular bone samples to which resorption cavities were digitally added (R² = 0.96, RMSE = 5.2%). After validation, this new method was used to model discrete cavities, with dimensions taken from a statistical distribution, on a dataset of 120 trabecular bone samples from three anatomical sites (4th lumbar vertebra, femoral head, iliac crest). Resorption cavities led to significant reductions in bone stiffness. The largest stiffness loss was found for samples from the 4th lumbar vertebra, the lowest for femoral head samples. For all anatomical sites, resorption cavities caused significantly more stiffness loss than homogeneous erosion did. This novel technique can be used further to evaluate the impact of resorption cavities, which are known to change in several metabolic bone diseases and due to treatment, on bone competence.     

Adjusting for Nonignorable Missingness When Estimating Generalized Additive Models

Generalized additive models (GAMs) have been widely used for flexible modeling of various types of outcomes. When the outcome in a GAM is subject to missing, practical analyses often assume that missingness is missing at random (MAR). This assumption can be of suspicion when the missingness is not by design. Evaluating the potential effects of alternative nonignorable missing data mechanism on the MAR inference from a GAM can be important but often challenging due to the complicatedness of alternative nonignorable models. We apply the index approach to local sensitivity (Troxel, Ma, and Heitjan 2004 (2004). Statistica Sinica 14 , 1221–1237) to evaluate the potential changes of the GAM estimates in the neighborhood of the MAR model. The approach avoids fitting any complicated nonignorable GAM. Only MAR estimates are required to calculate the resulting sensitivity index and adjust the GAM estimates to account for nonignorable missingness. Thus the proposed approach is considerably simpler to conduct, as compared with the alternative methods. The simulation study shows that the index provides valid assessment of the local sensitivity of the GAM estimates to nonignorable missingness. We then illustrate the method using a rheumatoid arthritis clinical trial data set.     

Persistent random motion: Uncovering cell migration dynamics

In this paper we study analytically the stick-slip models recently introduced to explain the stochastic migration of free cells. We show that persistent motion of cells of many different types is compatible with stochastic reorientation models which admit an analytical mesoscopic treatment. This is proved by examining and discussing experimental data compiled from different sources in the literature, and by fitting some of these results too. We are able to explain many of the ‘apparently complex’ migration patterns obtained recently from cell tracking data, like power-law dependences in the mean square displacement or non-Gaussian behavior for the kurtosis and the velocity distributions, which depart from the predictions of the classical Ornstein-Uhlenbeck process.     

泌尿道感染动物模型制作研究现状

建立与人类泌尿道感染相似的可靠动物模型,对探讨泌尿道感染发病机制和研究治疗方法十分必要。本文介绍了急性肾盂肾炎,慢性肾盂肾炎,腺性膀胱炎等泌尿道感染模型制作研究现状,并从菌种,品系等角度进行探讨。     

The expected effects of climate change on wheat development

Air temperature and the atmospheric concentrations of carbon dioxide are expected to rise. These two factor have a great potential to affect development, growth and yield of crops, including wheat. Rising air temperature may affect wheat development more than rising atmospheric CO₂ as there is not yet evidence that elevated CO₂ concentrations can directly induce changes in wheat development. In winter wheat, temperature has a complex effect on development due to its strong interaction with vernalization and photoperiod. In this paper, potential effects of rising temperature on the development of winter wheat from sowing to heading are considered in the light of this complex controlling mechanism. Data from a large series of field trials made in Romania is analysed at first and, subsequently, the IATA-Wheat Phenology model is used to calculate the impact of air warming on wheat development under different climate change scenarios. Data from the field trials showed very clearly the occurrence of a complex temperature/photoperiod/vernalization interaction for field sown crops and demostrated that the photoperiodic and vernalization responses have a key role in controlling the duration of the emergence-heading period. Temperature plays, instead, a central role in controlling seed germination and crop emergence as well as leaf inititiation and leaf appearance rate. The results of model analysis showed very well that the impact of an even or uneven distribution of warning effects may be very different. In the first case, the model predicted that the duration of the vegetative period was at least partly reduced in some years. In the second case, the model suggested that if warming will be more pronounced in winter than in spring, as predicted for some areas of the world by General Circulation Models, we may expect an increase in the duration of the vegetative phase of growth. On the contrary, in case of a spring warming but unchanged winter temperatures, we may expect a substantial decrease in the duration of the vegetative period.     

A stochastic metapopulation model accounting for habitat dynamics

A stochastic metapopulation model accounting for habitat dynamics is presented. This is the stochastic SIS logistic model with the novel aspect that it incorporates varying carrying capacity. We present results of Kurtz and Barbour, that provide deterministic and diffusion approximations for a wide class of stochastic models, in a form that most easily allows their direct application to population models. These results are used to show that a suitably scaled version of the metapopulation model converges, uniformly in probability over finite time intervals, to a deterministic model previously studied in the ecological literature. Additionally, they allow us to establish a bivariate normal approximation to the quasi-stationary distribution of the process. This allows us to consider the effects of habitat dynamics on metapopulation modelling through a comparison with the stochastic SIS logistic model and provides an effective means for modelling metapopulations inhabiting dynamic landscapes.     

Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes?

Aim Species distribution models and geographical information system (GIS) technologies are becoming increasingly important tools in conservation planning and decision‐making. Often the rich data bases of museums and herbaria serve as the primary data for predicting species distributions. Yet key assumptions about the primary data often are untested, and violation of such assumptions may have consequences for model predictions. For example, users of primary data assume that sampling has been random with respect to geography and environmental gradients. Here we evaluate the assumption that plant voucher specimens adequately sample the climatic gradient and test whether violation of this assumption influences model predictions. Location Bolivia and Ecuador. Methods Using 323,711 georeferenced herbarium collections and nine climatic variables, we predicted the distribution of 76 plant species using maximum entropy models (MAXENT) with training points that sampled the climate environments randomly and training points that reflected the climate bias in the herbarium collections. To estimate the distribution of species, MAXENT finds the distribution of maximum entropy (i.e. closest to uniform) subject to the constraint that the expected value for each environmental variable under the estimated distribution matches its empirical average. The experimental design included species that differed in geographical range and elevation; all species were modelled with 20 and 100 training points. We examined the influence of the number of training points and climate bias in training points, elevation and range size on model performance using analysis of variance models. Results We found that significant parts of the climatic gradient were poorly represented in herbarium collections for both countries. For the most part, existing climatic bias in collections did not greatly affect distribution predictions when compared with an unbiased data set. Although the effects of climate bias on prediction accuracy were found to be greater where geographical ranges were characterized by high spatial variation in the degree of climate bias (i.e. ranges where the bias of the various climates sampled by collections deviated considerably from the mean bias), the greatest influence on model performance was the number of presence points used to train the model. Main conclusions These results demonstrate that predictions of species distributions can be quite good despite existing climatic biases in primary data found in natural history collections, if a sufficiently large number of training points is available. Because of consistent overprediction of models, these results also confirm the importance of validating models with independent data or expert opinion. Failure to include independent model validation, especially in cases where training points are limited, may potentially lead to grave errors in conservation decision‐making and planning.     

Land use and climate influences on waterbirds in the Prairie Potholes

Aim We examined the influences of regional climate and land‐use variables on mallard (Anas platyrhynchos), blue‐winged teal (Anas discors), ruddy duck (Oxyura jamaicensis) and pied‐billed grebe (Podilymbus podiceps) abundances to inform conservation planning in the Prairie Pothole Region of the United States. Location The US portion of Bird Conservation Region 11 (US‐BCR11, the Prairie Potholes), which encompasses six states within the United States: Montana, North Dakota, South Dakota, Nebraska, Minnesota and Iowa. Methods We used data from the North American Breeding Bird Survey (NABBS), the National Land Cover Data Set, and the National Climatic Data Center to model the effects of environmental variables on waterbird abundance. We evaluated land‐use covariates at three logarithmically related spatial scales (1000, 10,000 and 100,000 ha), and constructed hierarchical spatial count models a priori using information from published habitat associations. Model fitting was performed using a hierarchical modelling approach within a Bayesian framework. Results Models with the same variables expressed at different scales were often in the best model subset, indicating that the influence of spatial scale was small. Both land‐use and climate variables contributed strongly to predicting waterbird abundance in US‐BCR11. The strongest positive influences on waterbird abundance were the percentage of wetland area across all three spatial scales, herbaceous vegetation and precipitation variables. Other variables that we included in our models did not appear to influence waterbirds in this study. Main conclusions Understanding the relationships of waterbird abundance to climate and land use may allow us to make predictions of future distribution and abundance as environmental factors change. Additionally, results from this study can suggest locations where conservation and management efforts should be focused.     

A dynamic, architectural plant model simulating resource-dependent growth

BACKGROUND AND AIMS: Physiological and architectural plant models have originally been developed for different purposes and therefore have little in common, thus making combined applications difficult. There is, however, an increasing demand for crop models that simulate the genetic and resource-dependent variability of plant geometry and architecture, because man is increasingly able to transform plant production systems through combined genetic and environmental engineering. MODEL: GREENLAB is presented, a mathematical plant model that simulates interactions between plant structure and function. Dual-scale automaton is used to simulate plant organogenesis from germination to maturity on the basis of organogenetic growth cycles that have constant thermal time. Plant fresh biomass production is computed from transpiration, assuming transpiration efficiency to be constant and atmospheric demand to be the driving force, under non-limiting water supply. The fresh biomass is then distributed among expanding organs according to their relative demand. Demand for organ growth is estimated from allometric relationships (e.g. leaf surface to weight ratios) and kinetics of potential growth rate for each organ type. These are obtained through parameter optimization against empirical, morphological data sets by running the model in inverted mode. Potential growth rates are then used as estimates of relative sink strength in the model. These and other 'hidden' plant parameters are calibrated using the non-linear, least-square method. KEY RESULTS AND CONCLUSIONS: The model reproduced accurately the dynamics of plant growth, architecture and geometry of various annual and woody plants, enabling 3D visualization. It was also able to simulate the variability of leaf size on the plant and compensatory growth following pruning, as a result of internal competition for resources. The potential of the model's underlying concepts to predict the plant's phenotypic plasticity is discussed.