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.     

人体和动物模型的体表物理信息地形图的研究

对人体头面、躯干、四肢、耳廓各局部几十个及整个人体等体表部位正、背面等210个部位进行超微弱冷光和温度测量,输入电子计算机,经特殊的自编程序处理,获得十分清晰的,由3000多数据构成的各个局部或人体整体的冷光和温度地形图。 对家兔左、右耳廓、胸腹部、背部都分别观察32个部位的冷光与体表温度,经计算机分析处理,每观察区域获得约由2000个数据构成的精确的冷光、温度地形分市图。并可见不同生理、病理状态及不同病程家兔体表冷光、温度等地形图呈有规律的改变。 此外,我们还编制了以体表左右相应对称部位差值为分析数据进行地形图分析的程序,用以人体和动物体表物理信息对称规律的研究。 本工作以图形的形式显示物理参量在体表的广泛的分布规律,以揭示机体内部的不同生理、病理状态。本方法定位准确、直观醒目,为研究体表信息及机体生命活动规律提供了与逐点直接测量方法相互补充的有益的新手段。     

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.     

The European carbon balance. Part 3: forests

We present a new synthesis, based on a suite of complementary approaches, of the primary production and carbon sink in forests of the 25 member states of the European Union (EU‐25) during 1990–2005. Upscaled terrestrial observations and model‐based approaches agree within 25% on the mean net primary production (NPP) of forests, i.e. 520±75 g C m^?2 yr^?1 over a forest area of 1.32 × 10⁶ km² to 1.55 × 10⁶ km² (EU‐25). New estimates of the mean long‐term carbon forest sink (net biome production, NBP) of EU‐25 forests amounts 75±20 g C m^?2 yr^?1. The ratio of NBP to NPP is 0.15±0.05. Estimates of the fate of the carbon inputs via NPP in wood harvests, forest fires, losses to lakes and rivers and heterotrophic respiration remain uncertain, which explains the considerable uncertainty of NBP. Inventory‐based assessments and assumptions suggest that 29±15% of the NBP (i.e., 22 g C m^?2 yr^?1) is sequestered in the forest soil, but large uncertainty remains concerning the drivers and future of the soil organic carbon. The remaining 71±15% of the NBP (i.e., 53 g C m^?2 yr^?1) is realized as woody biomass increments. In the EU‐25, the relatively large forest NBP is thought to be the result of a sustained difference between NPP, which increased during the past decades, and carbon losses primarily by harvest and heterotrophic respiration, which increased less over the same period.     

Nonignorable Models for Intermittently Missing Categorical Longitudinal Responses

Summary A class of nonignorable models is presented for handling nonmonotone missingness in categorical longitudinal responses. This class of models includes the traditional selection models and shared parameter models. This allows us to perform a broader than usual sensitivity analysis. In particular, instead of considering variations to a chosen nonignorable model, we study sensitivity between different missing data frameworks. An appealing feature of the developed class is that parameters with a marginal interpretation are obtained, while algebraically simple models are considered. Specifically, marginalized mixed‐effects models ( Heagerty, 1999 , Biometrics 55, 688–698) are used for the longitudinal process that model separately the marginal mean and the correlation structure. For the correlation structure, random effects are introduced and their distribution is modeled either parametrically or non‐parametrically to avoid potential misspecifications.     

Suitability evaluation of some specific crops in Souma area (Iran), using Cervatana and Almagra models

In the present study Cervatana and Almagra models from decision support system, MicroLEIS DSS, were applied to segregation of arable land surfaces from the marginal ones and suitability evaluation of wheat (Triticum aestivum), maize (Zea mays) and alfalfa (Medicago sativa) in Souma area with approximately 4100 ha extension in West Azarbaijan. Obtained results from both models are presented and discussed in this research work. Soil morphological and analytical data were collected from 35 soil profiles, representative of the study area and stored in SDBm plus database. The control or vertical section of soil for applying and running the models for annual selected crops, was calculated by soil layer generator 0.0–50 cm in depth, or between the surface and the limit of useful depth when the latter is between 0.0 and 50 cm. According to results, 80.49% of the total area was good capable for agricultural uses and 19.51% must be reforested and not dedicated to agriculture. The lands with good capability for agricultural uses is classified as highly suitable area (S2) for wheat, maize and alfalfa, but results in 822 ha for maize and in 126 ha for alfalfa refers to an excellent suitable (S1) and moderately suitable (S3) classes respectively. The most important limitation factors are soil texture and carbonate alone or together and maize — wheat — alfalfa can be selected as the best crop rotation. A simple map subsystem (ArcView GIS) was used for basic data and models result demonstration on a map.     

Plant species diversity and environmental heterogeneity: spatial scale and competing hypotheses

Questions: What is the observed relationship between plant species diversity and spatial environmental heterogeneity? Does the relationship scale predictably with sample plot size? What are the relative contributions to diversity patterns of variables linked to productivity or available energy compared to those corresponding to spatial heterogeneity? Methods: Observational and experimental studies that quantified relationships between plant species richness and within‐sample spatial environmental heterogeneity were reviewed. Effect size in experimental studies was quantified as the standardized mean difference between control (homogeneous) and heterogeneous treatments. For observational studies, effect sizes in individual studies were examined graphically across a gradient of plot size (focal scale). Relative contributions of variables representing spatial heterogeneity were compared to those representing available energy using a response ratio. Results: Forty‐one observational and 11 experimental studies quantified plant species diversity and spatial environmental heterogeneity. Observational studies reported positive species diversity‐spatial heterogeneity correlations at all points across a plot size gradient from ～1.0 × 10^?1 to ～1.0 × 10¹¹ m², although many studies reported spatial heterogeneity variables with no significant relationships to species diversity. The cross‐study effect size in experimental studies was not significantly different from zero. Available energy variables explained consistently more of the variance in species richness than spatial heterogeneity variables, especially at the smallest and largest plot sizes. Main conclusions: Species diversity was not related to spatial heterogeneity in a way predictable by plot size. Positive heterogeneity‐diversity relationships were common, confirming the importance of niche differentiation in species diversity patterns, but future studies examining a range of spatial scales in the same system are required to determine the role of dispersal and available energy in these patterns.     

Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology

Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a transformative tool in biotechnology.     

基因编辑动物模型在人类疾病研究中的应用

近年来,随着以CRISPR/Cas9为代表的多种CRISPR系统的开发和不断改进,基因编辑技术逐渐完善,并广泛应用于人类疾病动物模型的制备。基因编辑动物模型为人类疾病的发病机理、病理过程以及预防和治疗等方面的研究提供了重要的素材。目前,用于人类疾病研究的基因编辑动物模型主要有小鼠、大鼠为代表的啮齿类动物模型和以猪为代表的大动物模型。其中啮齿类动物在机体各方面与人类差别较大,且寿命短,无法对人类疾病的研究和治疗提供有效评估和长期追踪;而猪在生理学、解剖学、营养学和遗传学等各方面与人类更接近,是器官移植和人类疾病研究领域重要的动物模型。文中主要介绍了基因编辑动物模型在神经退行性疾病、肥厚心肌病、癌症、免疫缺陷类疾病和代谢性疾病等5种人类疾病研究中的应用情况,以期为人类疾病研究及相关动物模型的制备提供参考。