Modeling WEPP erodibility parameters in calcareous soils in northwest Iran

Modeling soil detachment rates at the regional scale is important for better understanding of the processes of erosion and the development of erosion models. Soil erodibility is an important factor for predicting soil loss, but its direct measurement at the watershed scale is difficult, time-consuming and costly. This study used stepwise multiple-linear regression (MLR) and artificial neural networks (ANNs) to model Water Erosion Prediction Project (WEPP) soil erodibility parameters, including the baseline inter-rill erodibility (K_ib), baseline rill erodibility (K_rb) and critical shear stress (τ_cb) of cropland conditions in calcareous soils of northwest Iran. Simulated inter-rill and rill erosion experiments were conducted at 100 locations with three replications. K_ib, K_rb and τ_cb and basic soil properties were measured at each location. Auxiliary variables related to soil erodibility were derived from a Landsat 7 satellite image and a 30 m × 30 m digital elevation model (DEM). MLR and ANN models were employed to predict K_ib, K_rb and τ_cb using two groups of input variables: i) more easily measurable basic soil properties (pedo-transfer functions (PTFs)) and ii) more easily measurable basic soil properties and auxiliary data (soil spatial prediction functions (SSPFs)). The results indicated that the WEPP models performed poorly in comparison to the derived models. PTFs and SSPFs generated from ANN models provided more reliable predictions than the MLR models. ANN-based SSPF models yielded the best results (with the highest R² and lowest RMSE values) for predicting Kib and Krb. ANN-based PTF model performed reasonably well for predicting τ_cb. These results show that information from terrain attributes and remote sensing data are potential auxiliary variables for improving prediction of soil erodibility parameters.     

Interleukin-1βconverting enzyme: A novel cysteine protease required for IL-1β production and implicated in programmed cell death

Interleukin-1β converting enzyme is the first member of a new class of cysteine proteases. The most distinguishing feature of this family is a nearly absolute specificity for cleavage at aspartic acid. This enzyme has been the subject of intense research because of its role in the production of IL-1β, a key mediator of inflammation. These studies have culminated in the design of potent inhibitors and determination of its crystal structure. The structure secures the relationship of the enzyme to CED-3, the product of a gene required for programmed cell death in Caenorhabditis elegans, suggesting that members of this family function in cell death in vertebrates.     

Microbial biofilm inoculants benefit growth and yield of chrysanthemum varieties under protected cultivation through enhanced nutrient availability

Protected cultivation of ornamental flowers, as a commercial venture, becomes less profitable with excessive use of fertilizers. The present study examined the influence of microbial biofilm inoculants (Anabaena–Azotobacter, Anabaena–Trichoderma and Trichoderma–Azotobacter) on the availability of soil nutrients and structure of rhizosphere microbial communities in three varieties of chrysanthemum (var. White Star, Thai Chen Queen and Zembla). Varietal-specific responses in growth, enzyme activities, flower yield of plants and availability of soil nutrients were recorded. Dehydrogenase activity was highest in var. White Star treated with the Anabaena–Trichoderma biofilm inoculants. The Anabaena–Azotobacter inoculant enhanced the availability of nitrogen, phosphorus and micronutrients in the soil, besides 40–50% increase in soil organic carbon, as compared to carrier alone or no inoculation. PCR-DGGE profiling of the cyanobacterial communities and qPCR quantification of 16S rRNA abundance of bacteria, archaea and cyanobacteria in the rhizosphere soils, revealed the stronger influences of these inoculants, especially in var. Zembla. Principal Component Analysis (PCA) helped to illustrate that the enhanced microbe-mediated availability of soil macro-and micronutrients, except iron content (Fe), was the most influential factor facilitating improved plant growth and yield parameters. The Anabaena–Azotobacter, and Anabaena–Trichoderma biofilm inoculants, proved superior in all three chrysanthemum varieties.     

粟酒裂殖酵母N-糖酰胺酶在大肠杆菌中的表达、纯化及活性分析

根据GenBank中公布的粟酒裂殖酵母(Schizosaccharomyces pombe)N-糖酰胺酶(Png1p)cDNA序列, 设计并合成一对特异性引物, 利用RT-PCR技术从粟酒裂殖酵母中克隆出糖酰胺酶cDNA。将得到的基因克隆到表达载体pET-15b中。重组质粒转入大肠杆菌BL21(DE3)中, 经诱导表达和纯化提取后, 进行酶活测定。实验结果表明, 该酶的分子量约为39 kD, 纯化后的重组N-糖酰胺酶可以对变性处理的糖蛋白进行糖链的切除, 且这种作用需要还原剂DTT的辅助作用; N-糖酰胺酶只对错误折叠的糖蛋白有作用, 对天然的糖蛋白没有作用。等量粟酒裂殖酵母Png1p在不同温度、pH、DTT浓度和底物变性温度下对等量核糖核酸酶B(RNase B)的脱糖基化检测发现, 重组酶的最适反应温度30°C, 最适反应pH为7.0, 需要的最适DTT浓度为10 mmol/L, 底物在100°C处理10 min时酶的脱糖基化率最高。     

产丁二酸工程菌的构建及其厌氧发酵

为了考察过量表达苹果酸酶对于E.coli NZN111(ldhA::Kan pfl::Cam)厌氧发酵产丁二酸的影响, 将连接有苹果酸酶基因sfcA的表达载体pTrc99a-sfcA转化进NZN111中, 构建了重组NZN111(pTrc99a-sfcA)。0.5 mmol/L IPTG诱导8 h后, 测定的苹果酸酶比酶活为30.67 u/mg, 比受体菌提高了140倍。采用两阶段发酵模式, 结果表明: 过量表达的苹果酸酶在NZN111体内催化了从丙酮酸到苹果酸的逆向反应, 丁二酸是发酵过程中积累的主要有机酸, 且当加入0.7 mmol/L IPTG诱导, 初始葡萄糖糖浓度为18.5 g/L时, 选择对数生长期后期的菌种以10%的接种量转入厌氧发酵, 发酵结束时发酵液中丁二酸的浓度为12.84 g/L, 对葡萄糖的收率为69.43%, 乙酸为0.58 g/L, 二者浓度比为22:1, 没有检测到甲酸和乳酸。构建的菌种具有高产丁二酸和副产物极少的优点, 在同类菌种中处于先进水平。     

COI序列:影响动物分类学与生态学的DNA barcode

DNA barcode是一段特殊的、可用于物种鉴定的DNA序列.目前在动物中最常用的DNA barcode是细胞色素C氧化酶1号基因(COI)的部分序列.随着标准数据库的建立,基于COI基因的DNA barcode在动物分类学和生态学中得到了广泛应用.但是,采用COI基因作为DNA barcode所隐含的涉及线粒体的进化历史、遗传特性和物种成种时间的默认前提,并非完全成立,由此引发了许多问题.本文阐述了基于COI基因的DNA barcode对分类学和生态学的影响,目前存在的问题,以及可能的研究方向.     

植物挥发性气体(VOCs)研究进展

植物挥发性气体(VOCs)在植物一植食性昆虫-天敌三级营养关系、植物间信息传递及适应性改变上都发挥着重要作用.植物释放VOCs具特异性、系统性、时序性与节律性等特点,VOCs主要在寄主选择行为、产卵行为、求偶行为、引来昆虫夭敌干涉等方面影响植食性昆虫.VOCs-介导的植物间信息传递作用包括4个过程:"释放者"植物合成及释放气体、气体在空气中的运输、气体在植物表面的吸附及"接收者"植株对气体信号的感知.收集VOCs的方法主要有吸附-溶剂洗脱法和吸附-热脱附法.     

Inhibition of carbonic anhydrases from the extremophilic bacteria Sulfurihydrogenibium yellostonense (SspCA) and S. azorense (SazCA) with a new series of sulfonamides incorporating aroylhydrazone-, [1,2,4]triazolo[3,4-b][1,3,4]thiadiazinyl- or 2-(cyanophenylmethylene)-1,3,4-thiadiazol-3(2H)-yl moieties

A series of new sulfonamides was prepared starting from 2-oxo-N′-(4-sulfamoylphenyl)-propanehydrazonoyl chloride, a sulfanilamide derivative, which was reacted with aroylhydrazides, amines, or thiols. A library of derivatives incorporating aroylhydrazone, [1,2,4]triazolo[3,4-b][1,3,4]thiadiazinyl- or 2-(cyanophenyl-methylene)-1,3,4-thiadiazol-3(2H)-yl moieties was thus synthesized. The new compounds were investigated as inhibitors of four α-carbonic anhydrases (CAs, EC 4.2.1.1), the human (h) isoforms hCA I and II, and the bacterial ones recently isolated from the extremophilic bacteria Sulfurihydrogenibium yellostonense (SspCA) and Sulfurihydrogenibium azorense (SazCA). Low nanomolar activity was observed against hCA II (K_Is of 0.56–17.1 nM) whereas hCA I was less inhibited by these compounds (K_Is of 86.4 nM–32.8 μM). The bacterial CAs were also effectively inhibited by these derivatives (K_Is in the range of 0.77–234 nM against SazCA, and of 6.2–89.1 against SspCA, respectively), with several low nanomolar/subnanomolar inhibitors detected against both of them. As SspCA and SazCA are among the most thermostable and catalytically active CAs, it is of interest to find modulators of their activity for potential biotechnologic applications.     

Developing pedotransfer functions to estimate the S-index for indicating soil quality

Indicating soil quality usually requires many soil properties of which the measurements are time consuming. Therefore, it is desirable to developing simple and effective indices for reflecting soil quality based on soil properties that can be readily obtained. The soil physical quality index, S-index, derived from the slope at the inflection point of the water retention curve (particularly the Van-Genuchten equation), is a comprehensive index for indicating soil properties. By comparing the S-index with a widely used soil quality index (SQI), this study used 298 samples to determine soil chemical and physical properties for calculating SQI, and found that the correlation coefficient between the S-index and SQI was 0.88, indicating that the S-index can represent soil quality well. An artificial neural network (ANN) model and a linear regression (LR) model were proposed for estimating S-index. Results showed that the ANN model was better than LR model in estimating S-index. Particularly, the ANN model with the soil bulk density and soil organic carbon (scenario A1) as inputs, had the highest R² of 0.807, while the LR model get the highest R² (predicted v.s. observed) of 0.75 with the combination of soil organic carbon, soil bulk density, total nitrogen and available nitrogen. This study is helpful for extending the applications of S-index.