神府东胜煤田弃土弃渣体径流产沙过程的野外试验

采用野外模拟降雨试验方法,研究了神府东胜煤田开采造成的弃土弃渣体产流产沙规律及其减水减沙效益.结果表明： 随降雨强度的增大,弃土弃渣体产流的起始时间呈递减趋势,且差异达几倍至十几倍.弃渣体比弃土体更快到达稳定流速,平均流速大小为弃土体>沙多石少弃渣体>沙少石多弃渣体.弃土弃渣体产流6 min后的径流率达到稳定,与降雨强度呈显著相关.弃土弃渣体侵蚀主要发生在产流开始后的前6 min,弃土体产流后前6 min的平均含沙量是6 min后的0.43～4.27倍,弃渣体为1.43～54.93倍.弃土体和沙多石少弃渣体径流量与降雨强度呈线性函数关系,沙少石多弃渣体呈幂函数关系.弃土体和沙少石多弃渣体的次侵蚀量与降雨强度之间分别呈指数函数和幂函数相关.弃土体侵蚀量与径流量呈线性函数关系.在降雨强度为1.0和1.5 mm·min^-1条件下,弃渣体采用鱼鳞坑及植被防护的产流滞后降雨时间为24 min,减水效益为29.5%～52.9%,减沙效益为85.7%～97.9%.     

Assessment of Key Amino-Acid Residues of CD36 in Specific Binding Interaction with an Oxidized Low-Density Lipoprotein

A stereo controlled synthesis of the biologically active neolignan, (+)-dehydrodiconiferyl alcohol (1) was achieved. This synthetic method was also efficient for preparing its enantiomer and other derivatives with biological activity.     

Identification of catalytic residues of a very large NAD-glutamate dehydrogenase from Janthinobacterium lividum by site-directed mutagenesis

We previously found a very large NAD-dependent glutamate dehydrogenase with approximately 170?kDa subunit from Janthinobacterium lividum (Jl-GDH) and predicted that GDH reaction occurred in the central domain of the subunit. To gain further insights into the role of the central domain, several single point mutations were introduced. The enzyme activity was completely lost in all single mutants of R784A, K810A, K820A, D885A, and S1142A. Because, in sequence alignment analysis, these residues corresponded to the residues responsible for glutamate binding in well-known small GDH with approximately 50?kDa subunit, very large GDH and well-known small GDH may share the same catalytic mechanism. In addition, we demonstrated that C1141, one of the three cysteine residues in the central domain, was responsible for the inhibition of enzyme activity by HgCl₂, and HgCl₂ functioned as an activating compound for a C1141T mutant. At low concentrations, moreover, HgCl₂ was found to function as an activating compound for a wild-type Jl-GDH. This suggests that the mechanism for the activation is entirely different from that for the inhibition.     

Effect of glyphosate contaminated feed on rumen fermentation parameters and in sacco degradation of grass hay and corn grain

Four rumen fistulated wethers were used to investigate the effect of glyphosate contaminated feed on rumen fermentation. The rations were based on corn silage, urea and a vitamin-mineral premix, either in the absence or presence of 0.77?g glyphosate per kg DM. Furthermore, rations were fed either with or without aromatic amino acid supplementation. During four periods of 28 days, sheep received each of the four dietary treatments according to a Latin square. After 14 days of adaptation rumen fermentation parameters (pH, ammonia, volatile fatty acids) were measured on day 15 over a five-hour period after the morning feeding. The remaining 13 days served for in sacco degradation studies with grass hay and corn grain. Ammonia (NH₃) and pH of rumen fluid were within the normal range for all dietary treatments (NH₃: 9.1 – 32.3?mmol·l^???1, pH: 6.2 – 6.7). Neither rumen fermentation parameters nor in sacco DM and NDF degradation of incubated feedstuffs were significantly affected by glyphosate, with or without aromatic amino acid supplementation. Kinetic profiles of the in sacco dry matter and NDF degradation of grass hay were almost identical for the dietary treatments.     

Effects of temperature on the development,performance and fitness of the corn leafhopper Dalbulus maidis (DeLong) (Hemiptera: Cicadellidae): implications on its distribution under climate change

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粘性丝孢酵母的诱变及以玉米秸秆水解液为原料产油条件优化

对粘性丝胞酵母进行紫外诱变,获得一株产油率较高的菌株,较原菌株提高了1.53倍。将该菌株接种于用1%硫酸和酶水解处理并浓缩至还原糖浓度为5%的玉米秸秆水解液中培养,生长较好。通过四因素三水平正交实验,确定培养条件为初始pH值7.0、接种量1%、发酵温度30℃、发酵时间5 d时产油率最高。对最佳产油条件进行验证,测得油脂含量为21.3%。从而为利用农业废弃物大规模生产微生物油脂提供了试验数据。气相色谱-质谱联用分析仪显示油脂脂肪酸组成为棕榈酸28.36%,油酸55.86%,10-十八烯酸9.23%,硬脂酸6.70%,可以作为原料生产生物柴油。     

Type III-A CRISPR-Cas Csm Complexes: Assembly,Periodic RNA Cleavage,DNase Activity Regulation,and Autoimmunity

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Delineation of critical amino acids in activation function 1 of progesterone receptor for recruitment of transcription coregulators

The activation functions AF1 and AF2 of nuclear receptors mediate the recruitment of coregulators in gene regulation. AF1 is mapped to the highly variable and intrinsically unstructured N terminal domain and AF2 lies in the conserved ligand binding domain. The unstructured nature of AF1 offers structural plasticity and hence functional versatility in gene regulation. However, little is known about the key functional residues of AF1 that mediates its interaction with coregulators. This study focuses on the progesterone receptor (PR) and reports the identification of K464, K481 and R492 (KKR) as the key functional residues of PR AF1. The KKR are monomethylated and function cooperatively. The combined mutations of KKR to QQQ render PR isoform B (PRB) hyperactive, whereas KKR to FFF mutations abolishes as much as 80% of PR activity. Furthermore, the hyperactive QQQ mutation rescues the loss of PR activity due to E911A mutation in AF2. The study also finds that the magnitudes of the mutational effect differ in different cell types as a result of differential effects on the functional interaction with coregulators. Furthermore, KKR provides the interface for AF1 to physically interact with p300 and SRC-1, and with AF2 at E911. Intriguingly, the inactive FFF mutant interacts strikingly stronger with both SRC-1 and AF2 than wt PRB. We propose a tripartite model to describe the dynamic interactions between AF1, AF2 and SRC-1 with KKR of AF1 and E911 of AF2 as the interface. An overly stable interaction would hamper the dynamics of disassembly of the receptor complex.     

Contributions of the C-terminal domain to poly(A)-specific ribonuclease (PARN) stability and self-association

Poly(A)-specific ribonuclease (PARN) catalyzes the degradation of mRNA poly(A) tail to regulate translation efficiency and mRNA decay in higher eukaryotic cells. The full-length PARN is a multi-domain protein containing the catalytic nuclease domain, the R3H domain, the RRM domain and the C-terminal intrinsically unstructured domain (CTD). The roles of the three well-structured RNA-binding domains have been extensively studied, while little is known about CTD. In this research, the impact of CTD on PARN stability and aggregatory potency was studied by comparing the thermal inactivation and denaturation behaviors of full-length PARN with two N-terminal fragments lacking CTD. Our results showed that K⁺ induced additional regular secondary structures and enhanced PARN stability against heat-induced inactivation, unfolding and aggregation. CTD prevented PARN from thermal inactivation but promoted thermal aggregation to initiate at a temperature much lower than that required for inactivation and unfolding. Blue-shift of Trp fluorescence during thermal transitions suggested that heat treatment induced rearrangements of domain organizations. CTD amplified the stabilizing effect of K⁺, implying the roles of CTD was mainly achieved by electrostatic interactions. These results suggested that CTD might dynamically interact with the main body of the molecule and release of CTD promoted self-association via electrostatic interactions.     

Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions

Protein-DNA interactions are crucial to many cellular activities such as expression-control and DNA-repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA-binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA-binding sites; to improve the prediction by distance-dependent refinement; use these predictions to identify DNA-binding proteins. We use a support vector machines (SVM)-based approach to harness the features of the DNA-binding residues to distinguish them from non-binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA-binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post-processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA-binding residues can be used to differentiate DNA-binding proteins from non-DNA-binding proteins with an accuracy of 78%. Results presented here demonstrate that machine-learning can be applied to automated identification of DNA-binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site-directed mutagenesis and macromolecular docking.