Isolation and Identification of Pathogens Causing Marigold (Tagetes erecta L.) Black Spot in Beijing,China

Black spot leads to great marigold losses worldwide. The disease is characterized by black spots on leaves and stems in its early stages, and the whole plant has black rot at the advanced stage. In this report, 6 of 217 Alternaria strains isolated from lesions of marigold plants in Beijing were randomly selected. The morphological characteristics and a pathogenic tree based on two protein‐coding genes (gpd and alt a 1) indicated that Alternaria tagetica is the causal agent of marigold black spot in Beijing. All six Alternaria strains could successfully re‐infect marigold, but they could not infect carrot or zinnia by either spore spray in a greenhouse or planting experiments in the epidemic area. This is the first report of the A. tagetica pathogen being isolated from marigold in Beijing.     

唾液酸酶Neu1及其相关信号通路研究进展

唾液酸广泛存在于生物体内,常存在于糖复合物的糖链末端,对组织器官特别是脑神经的发育至关重要,并且与多种疾病的发生和发展过程密切相关.唾液酸被唾液酸酶水解后,可改变糖复合物的构象从而调控相关因子的生物学功能.在目前发现的4种唾液酸酶中,对Neu1的研究较为深入.研究表明,Neu1的剪切底物具有多样性,其与细胞表面受体的结构和功能调节密切相关.随着研究的深入,Neu1逐渐被认为是唾液酸介导的调控疾病发生过程中的重要因子,Neu1在人类疾病中的作用比预想的还要深远.本篇综述了在已有总结的唾液酸酶性质和生理病理学功能的基础上,概述了近年来Neu1的研究进展,并对其作用及与不同细胞表面受体的相互作用机制做了总结.     

Parametric analysis of the biomechanical response of head subjected to the primary blast loading – a data mining approach

Traumatic brain injury due to primary blast loading has become a signature injury in recent military conflicts and terrorist activities. Extensive experimental and computational investigations have been conducted to study the interrelationships between intracranial pressure response and intrinsic or ‘input’ parameters such as the head geometry and loading conditions. However, these relationships are very complicated and are usually implicit and ‘hidden’ in a large amount of simulation/test data. In this study, a data mining method is proposed to explore such underlying information from the numerical simulation results. The heads of different species are described as a highly simplified two-part (skull and brain) finite element model with varying geometric parameters. The parameters considered include peak incident pressure, skull thickness, brain radius and snout length. Their interrelationship and coupling effect are discovered by developing a decision tree based on the large simulation data-set. The results show that the proposed data-driven method is superior to the conventional linear regression method and is comparable to the nonlinear regression method. Considering its capability of exploring implicit information and the relatively simple relationships between response and input variables, the data mining method is considered to be a good tool for an in-depth understanding of the mechanisms of blast-induced brain injury. As a general method, this approach can also be applied to other nonlinear complex biomechanical systems.