翠竹及菲白竹LEA3同源基因的克隆及序列分析

竹类植物在中国有着非常悠久的栽培历史,并被广泛应用于园林、造林、庭院以及食品等行业。本研究以翠竹和菲白竹为材料提取其叶片总DNA,采用PCR方法获得翠竹SpLEA3基因与菲白竹SfLEA3-1、SfLEA3-2基因;其中SpLEA3全长804 bp,编码195个氨基酸,GC含量为68.4%;SfLEA3-1全长803 bp,编码195个氨基酸,GC含量为68.2%;SfLEA3-2全长557 bp,编码144个氨基酸,GC含量为67.8%。通过ProtParamy等生物软件分析,SpLEA3、SfLEA3-1和SfLEA3-2与贵州悬竹LEA3基因同源性高达61%以上,且3个基因编码蛋白均属于亲水性蛋白。源自2个竹种LEA3基因均包含一个完整的开放阅读框,其编码的氨基酸含有4~6个由11个氨基酸组成的保守基元序列。本研究不仅为深入了解竹类植物抗旱的分子机理研究提供了基础数据,也为竹类植物的抗旱育种后续研究提供了科学依据。     

Effect of CHOP expression knock-down on apoptosis of renal tubular epithelial cells

AIM:To study the effect of C/EBP homologous protein (CHOP) on the apoptosis of renal tubular epithelial HK2 cells. METHODS:The serum mRNA levels of CHOP in the patients with acute kidney injury and healthy controls were detected by qPCR. In vitro, renal tubular epithelial HK2 cells were divided into control group, negative group (transfected with negative control siRNA), si-CHOP group (transfected with CHOP siRNA), and induced by transforming growth factor-β1 (TGF-β1). The viability of the cells was measured by MTT assay, and the apoptotic rate was analyzed by flow cytometry. The protein levels of nuclear antigen Ki-67, proliferating cell nuclear antigen (PCNA), caspase-3 and cleaved caspase-3 were determined by Western blot. RESULTS:Compared with the healthy controls, the serum mRNA levels of CHOP in the patients with acute kidney injury were increased significantly (P<0.05). Transfection with CHOP siRNA significantly decreased the expression of CHOP in the renal tubular epithelial HK2 cells (P<0.05). Knock-down of CHOP expression by siRNA significantly increased the viability of renal tubular epithelial HK2 cells (P<0.05), decreased the apoptotic rate (P<0.05), increased the expression of Ki-67 and PCNA (P<0.05), and down-regulated the protein level of cleaved caspase-3 (P<0.05). CONCLUSION:The serum mRNA levels of CHOP were increased in the patients with acute kidney injury. Knock-down of CHOP expression inhibits the apoptosis of renal tubular epithelial cells by regulating the expression of proliferation-and apoptosis-related proteins.     

Mapping the current and future distributions of Onosma species endemic to Iran

Climate change may cause shifts in the natural range of species especially for those that are geographically restricted and/or endemic species. In this study, the spatial distribution of five endemic and threatened species belonging to the genus Onosma (including O. asperrima, O. bisotunensis, O. kotschyi, O. platyphylla, and O. straussii) was investigated under present and future climate change scenarios: RCP2.6 (RCP, representative concentration pathway; optimistic scenario) and RCP8.5 (pessimistic scenario) for the years 2050 and 2080 in Iran. Analysis was conducted using the maximum entropy (MaxEnt) model to provide a basis for the protection and conservation of these species. Seven environmental variables including aspect, depth of soil, silt content, slope, annual precipitation, minimum temperature of the coldest month, and annual temperature range were used as main predictors in this study. The model output for the potential habitat suitability of the studied species showed acceptable performance for all species (i.e., the area under the curve (AUC)>0.800). According to the models generated by MaxEnt, the potential current patterns of the species were consistent with the observed areas of distributions. The projected climate maps under optimistic and pessimistic scenarios (RCP2.6 and RCP8.5, respectively) of 2050 and 2080 resulted in reductions and expansions as well as positive range changes for all species in comparison to their current predicted distributions. Among all species, O. bisotunensis showed the most significant and highest increase under the pessimistic scenario of 2050 and 2080. Finally, the results of this study revealed that the studied plant species have shown an acute adaptability to environmental changes. The results can provide useful information to managers to apply appropriate strategies for the management and conservation of these valuable Iranian medicinal and threatened plant species in the future.     

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R2 adj), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH≥15.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.     

Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis(Diptera: Tephritidae) in China using MaxEnt ecological niche modeling

Bactrocera bryoniae and Bactrocera neohumeralis are highly destructive and major biosecurity/quarantine pests of fruit and vegetable in the tropical and subtropical regions in the South Pacific and Australia. Although these pests have not established in China, precautions must be taken due to their highly destructive nature. Thus, we predicted the potential geographic distribution of B. bryoniae and B. neohumeralis across the world and in particular China by ecological niche modeling of the Maximum Entropy(Max Ent) model with the occurrence records of these two species. Bactrocera bryoniae and B. neohumeralis exhibit similar potential geographic distribution ranges across the world and in China, and each species was predicted to be able to distribute to over 20% of the globe. Globally, the potential geographic distribution ranges for these two fruit fly species included southern Asia, the central and the southeast coast of Africa, southern North America, northern and central South America, and Australia. While within China, most of the southern Yangtze River area was found suitable for these species. Notably, southern China was considered to have the highest risk of B. bryoniae and B. neohumeralis invasions. Our study identifies the regions at high risk for potential establishment of B. bryoniae and B. neohumeralis in the world and in particular China, and informs the development of inspection and biosecurity/quarantine measures to prevent and control their invasions.     

2UPR-RPU过约束并联机构刚度性能评价

运用螺旋理论和应变能方法研究了具有2R1T三自由度的2UPR-RPU过约束并联机构的静弹性刚度性能,模型考虑了杆件和关节的柔度。首先,基于螺旋理论得到分支的约束螺旋系;其次,基于材料力学得到分支中杆件的应变能,通过映射分支约束螺旋系到铰空间得到关节的应变能,通过汇总杆件、关节的应变能和卡氏定理得到与约束螺旋系对应的分支紧凑刚度矩阵;最后,通过虚功原理得到机构的总体刚度矩阵。采用有限元商业软件建立了有限元模型,并与理论模型进行对比,验证了理论模型的正确性。定义弹性元件存储的应变能与总应变能之比作为应变能因子指标,给出了应变能因子指标在规则工作空间的四维切片分布图,从应变能的角度定量评价了各弹性元件对机构刚度性能的影响程度,给出了不同载荷作用下的全局应变能因子指标。本研究为定位对机构刚度性能影响最大的弹性元件提供了新的思路。     

基于Simulink的液压系统建模与仿真

针对农业机械中液压系统结构复杂、研发周期长等问题,通过分析农业机械中液压回路各个组成元件特性,基于Simulink构建元件动态数学模型。对液压回路进行研究,分析了液压回路在不同元件参数下的动态性能,为优化液压回路系统提供参考。     

植物碱基错配修复系统中Muts蛋白家族研究进展

DNA错配修复(mismatch repair,MMR)是DNA损伤修复的一个重要途径,主要用于细胞中DNA合成和遗传重组时发生损伤过程中出现的单个或少数碱基的缺失、插入以及错配的修复,它对维持基因组稳定性和DNA复制保真度至关重要。原核生物和真核生物中都有非常保守的MMR系统。在人体DNA修复系统的研究中,发现癌症的发生与Muts蛋白家族功能缺陷密切相关。类似的在拟南芥和水稻中功能缺陷的Muts蛋白家族(同源蛋白Muts homolog,MSH)会产生增变基因表型,这将为植物的基因功能分析和育种利用奠定基础。基于此,本文综述了近年来有关植物DNA错配修复及相关基因功能的研究进展,特别是植物MMR功能缺陷导致基因突变和微卫星不稳定造成的性状畸形进行了描述,对Muts蛋白家族各个亚基功能做了分类说明,并对植物中如何利用MSH产生的增变基因突变和如何利用突变育种研究进行了展望。     

The pelagic habitat analysis module for ecosystem‐based fisheries science and management

We have developed a set of tools that operate within an aquatic geographic information system to improve the accessibility, and usability of remote‐sensed satellite and computer‐modeled oceanographic data for marine science and ecosystem‐based management. The tools form the Pelagic Habitat Analysis Module (PHAM), which can be applied as a modeling platform, an investigative aid in scientific research, or utilized as a decision support system for marine ecological management. Applications include fisheries, marine biology, physical and biological oceanography, and marine spatial management. The GIS provides a home for diverse data types and automated tools for downloading remote sensed and global circulation model data. Within the GIS environment, PHAM provides a framework for seamless interactive four‐dimensional visualization, for matching between disparate data types, for flexible statistic or mechanistic model development, and for dynamic application of user developed models for habitat, density, and probability predictions. Here we describe PHAM in the context of ecosystem‐based fisheries management, and present results from case study projects which guided development. In the first, an analysis of the purse seine fishery for tropical tuna in the eastern Pacific Ocean revealed oceanographic drivers of the catch distribution and the influence of climate‐driven circulation patterns on the location of fishing grounds. To support management of the Common Thresher Shark (Alopias vulpinus) in the California Current Ecosystem, a simple empirical habitat utilization model was developed and used to dynamically predict the seasonal range expansion of common thresher shark based on oceanographic conditions.     

Artificial neural network modeling of microbial community structures in the Atlantic Forest of Brazil

Microbial communities vary across the landscape in forest soils, but prediction of their biomass and composition is a difficult challenge due to the large numbers of variables that influence their community structures. Here we examine the use of artificial neural network (ANN) models for extraction of patterns among soil chemical variables and microbial community structures in forest soils from three regions of the Atlantic Forest of Brazil. At each location, variations in soil chemical properties and FAME profiles of microbial community structures were mapped at 20 × 20 m intervals within 10 ha parcels. Geostatistical analyses showed that spatial variability in soil physical and chemical variables could be mapped at scale distances of 20 m, but that FAME profiles representing the microbial communities were highly variable and had no spatial dependence at the same scale in most cases. RDA analysis showed that FAME signatures representing different microbial groups were positively associated with soil pH, OM, P and base cations concentrations, whereas microbial biomass was negatively associated with the same environmental factors. In contrast, ANN models revealed clear relationships between microbial community structures at each parcel location, and generated verifiable predictions of variations in FAME profiles in relation to soil pH, texture, and the relative abundances of base cations. The results suggest that ANN modeling provides a useful approach for describing the relationships between microbial community structures and soil properties in tropical forest soils that were not able to be captured using geostatistical and RDA analyses.