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.     

甲状腺髓样癌遗传学研究进展

甲状腺髓样癌(medullary thyroid carcinoma,MTC)是起源于甲状腺C细胞或滤泡旁细胞的恶性肿瘤,分遗传型髓样癌和散发型髓样癌两种.MTC主要由RET原癌基因突变引起,对患者进行基因测序分析能在基因水平诊断MTC,从而为患者早期行预防性手术治疗提供依据.从甲状腺髓样癌临床分型、分子遗传基础及动物模型不同层面进行综述,有利于进一步了解疾病致病机制和开展药物实验性治疗研究.     

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。     

被视为恐龙的鸟

尽管于 2 0世纪 2 0年代在亚洲地表层首次发现长有羽毛的恐龙和著名的“龙骨突位点” ,关于鸟类起源的争论仍没有休止。来自化石的证据表明 ,鸟类在进化分支上应归于兽脚类的特殊分支。本文主要阐明完好无损的化石揭示的鸟和非鸟类恐龙的亲密关系和鸟类羽毛及鸟类出现以前的羽毛的起源证据 ,分析体型缩小对飞行进化的重要意义及从新的角度论述鸟类如何飞上了天     

Competition between near and far dispersers in spatially structured habitats

Competitive interactions and invasibility between short- and long-distance dispersal was investigated in a population on a heterogeneous landscape with spatial correlations in habitat types, and where the driving interaction between individuals is competition for space. Stochastic spatially explicit simulations were used, along with differential equation models based on pair approximations. Conditions under which either dispersal strategy can successfully invade the other were determined, as a function of the amount and clustering of suitable habitat and the relative costs involved in the two dispersal strategies. Long-distance dispersal, which reduces intraspecific competition, is sometimes advantageous even where aggregation of suitable habitat would otherwise favor short-distance dispersal, although certain habitat distributions can lead to either strategy being dominant. Coexistence is also possible on some landscapes, where the spatial structure of the populations partitions suitable sites according to the number of suitable neighboring sites. Mutual competitive exclusion, where whichever strategy is established first cannot be invaded, is also possible. All of these results are observed even when there is no intrinsic difference in the two strategies' costs, such as mortality or competitive abilities.