Secondary metabolite as therapeutic agent from endophytic fungi Alternaria longipes strain VITN14G of mangrove plant Avicennia officinalis

Endophytic fungi, especially from mangrove plants, are rich source of secondary metabolites, which plays a major role in various pharmacological actions preferably in cancer and bacterial infections. To perceive its role in antidiabetic activity we isolated and tested the metabolites derived from a novel strain Alternaria longipes strain VITN14G obtained from mangrove plant Avicennia officinalis. The crude extract was analyzed for antidiabetic activity and subjected to column chromatography. The isolated fractions were screened in vitro for α-glucosidase and α-amylase inhibitory activities. The cytotoxicity of the isolated fractions was studied on L929 cell lines. Following which, the screened fraction 2 was allowed for structure elucidation using gas chromatography-mass spectrometry, one-dimensional, two-dimensional nuclear magnetic resonance spectroscopy, ultraviolet, and Fourier-transform infrared analysis. The binding energies of the isolated fraction 2 with glycolytic enzymes were calculated by molecular docking studies using AutoDock Vina. The isolated fraction 2 identified as 2,4,6-triphenylaniline, showed no significant difference in α-amylase inhibition rates and a significant difference of 10% in α-glucosidase inhibition rates than that of the standard drug acarbose. Further, the cytotoxicity assay of the isolated fraction 2 resulted in a cell viability of 73.96%. Supportingly, in silico studies showed 2,4,6-triphenylaniline to produce a stronger binding affinity toward the glycolytic enzyme targets. The compound 2,4,6-triphenylaniline isolated from A. longipes strain VITN14G exhibited satisfactory antidiabetic activity for type 2 diabetes in vitro, which will further be confirmed by in vivo studies. Successful outcome of the study will result in a natural substitute for existing synthetic antidiabetic drugs.     

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.     

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.     

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.     

Biosynthesis of pectinlyases in Penicillium adametzii,P. citrinum and P. janthinellum

Pectinlyase complexes of Penicillium adametzii, P. citrinum and P. janthinellum occur as multiple molecular forms distinguished by their biosynthetic control. AMP is involved in derepression of pectinlyase formation.The authors are with the Institute of Microbiology, Belarus Academy of Sciences, Zhodinskaya 2, 220141, Minsk, Republic of Belarus.     

Spatial metrics for detecting ecosystem degradation in the ridge-slough patterned landscape

Indicators of landscape condition should be selected based on their sensitivity to environmental changes and their capacity to provide early warning detection of those changes. We assessed the performance of a suite of spatial-pattern metrics selected to quantify the condition of the ridge-slough landscape in the Everglades (South Florida, USA). Spatial pattern metrics (n = 14) that describe landscape composition, geometry and hydrologic connectivity were enumerated from vegetation maps of twenty-five 2 × 2 km primary sampling units (PSUs) that span a gradient of hydrologic and ecological condition across the greater Everglades ecosystem. Metrics were assessed in comparison with field measurements from each PSU of landscape condition obtained from regional surveys of soil elevation, which have previously been shown to capture dramatic differences between conserved and degraded locations. Elevation-based measures of landscape condition included soil elevation bi-modality (BI_SE), a binary measure of landscape condition, and also the standard deviation of soil elevation (SD_SE), a continuous measure of condition. Metric performance was assessed based on the strength (sensitivity) and shape (leading vs. lagging) of the relationship between spatial pattern metrics and these elevation-based measures. We observed significant logistic regression slopes with BI_SE for only 4 metrics (slough width, ridge density, directional connectivity index – DCI, and least flow cost – LFC). More significant relationships (n = 8 metrics) were observed with SD_SE, with the strongest associations for slough density, mean ridge width, and the average length of straight flow, as well as for a suite of hydrologic connectivity metrics (DCI, LFC and landscape discharge competence – LDC). Leading vs. lagging performance, inferred from the curvature of the association obtained from the exponent of fitted power functions, suggest that only DCI was a leading metric of the loss of soil elevation variation; most metrics were indeterminate, though some were clearly lagging. Our findings support the contention that soil elevation changes from altered peat accretion dynamics precede changes in landscape pattern, and offer insights that will enable efficient monitoring of the ridge-slough landscape as part of the ongoing Everglades restoration effort.     

Identification of soil quality indicators for assessing the effect of different tillage practices through a soil quality index in a semi-arid environment

Tillage is known to potentially affect soil quality in various ways. In this study, a soil quality index (SQI) was developed by quantifying several soil attributes either sensitive or insensitive to physical disturbance, using factor analysis as a dimension reduction technique, in order to discriminate different tillage systems. Soil properties including physical (MWD), chemical (pH, organic C, total N, available P and POM contents) and microbial (MBC, MBN, PCM, PNM and three enzymes) parameters were measured to establish a minimum data set (MDS) for the assessment of overall SQI. The soil attributes were determined on samples (0–20 cm depth) collected under moldboard (MP) and disk (DP) plows as conventional tillage (CT), and rotary (RP) and chisel (CP) plows as reduced tillage (RT) systems with a similar plant C input rate and cover crop over a period of six years (2005–2011) in a semi-arid calcareous soil (Calcixerepts) from Central Iran. Results indicated a clear difference in soil quality among the tillage systems with a significant increase of SQI under RT over time, particularly under CP practices. Although RT improved most soil microbial attributes, not all attributes contributed to SQI because of their close interrelationship. The final SQI consisted only of geometric mean of microbial activity (GMA, the square root of the product of PCM and PNM) and geometric mean of enzyme activity (GME, the cube root of the product of enzyme activities). Soil GME and GMA were found to be as key indicators contributing 55% and 36% to SQI, respectively. Therefore, the GME and GMA were the most important indicators effectively discriminating tillage systems, and could be used to monitor the enhancement of soil quality under RT in this semiarid environment. The influence of tillage year on SQI was greater than that of tillage practices. In conclusion, RT systems were characterized by a higher value of SQI, suggesting a good recovery of soil capacity and functions after abandoning CT in the studied area. Smallholder farmers should therefore be aware of the potential for high soil quality in future as a result of continuing RT systems, especially with surface tillage using CP practices.     

Physical and Microbiological Analysis of Sandstone Deterioration in the Argentine Jesuit Missions

Mainly cyanobacteria, but also algae and fungi, were identified on the Jesuit missions San Ignacio Miní, Loreto, Santa María and Santa Ana, in NE Argentina. Many organisms were deeply pigmented, indicative of stressful conditions. San Ignacio samples of itacurú rock showed higher colonization and biodiversity, related to higher porosity allowing entry of water and nutrients. Sandstone from other locations was arenitous, containing 95% silicates. Physicochemical analyses suggested mobilization of ions, particularly Fe, from within the stone to the surface. This intense deteriorating colonization, together with climatic weathering, transforms the sandstone to a “protosoil,” for growth of bryophytes and higher plants.     

Energy budgeting and emergy synthesis of rainfed maize–wheat rotation system with different soil amendment applications

Soil is a non-renewable resource and its preservation is essential for food security, ecosystem services and our sustainable future. Simultaneously, it is a major challenge to substitute non-renewable fossil based resources with renewable resources to reduce environmental load. In order to check soil erosion vis-a-vis degradation of sloppy lands of rainfed maize–wheat rotation system, fertilization with organic manure supplemented with inorganic fertilizers is required. In order to address these issues, substitution of 50% NPK through four organic manures viz. farmyard manure (FYM), vermicompost (VC), poultry manure (PM) and in situ green manuring (GM) of sunnhemp (Crotalaria juncea L.) were evaluated against 100% NPK through inorganic fertilizers and through FYM for energy budgeting and emergy synthesis during 2009–2014. Integrated use of FYM along with 50% NPK fertilizers could maintain the highest energy ratio (7.3), human energy profitability (142.4), energy productivity (0.22 kg MJ⁻¹), and energy profitability (6.3 MJ ha⁻¹) over other treatments. However, GM and inorganic fertilizers on equal NPK basis maintained the highest energy intensiveness (24.61 MJ US $⁻¹) and exhibited higher emergy yield ratio (2.66) and lower emergy investment ratio (0.60) and environmental loading ratio (3.74) which resulted into higher environmental sustainability index (0.71) over other treatments. Fertilization with organic manure (FYM) alone could not compete with other fertilized options to energy budgeting and emergy synthesis except specific energy. The study demonstrated that innovative integrated nutrient management of chemical fertilizers and organic manures particularly FYM for energy budgeting and GM for emergy synthesis may be considered as feasible and environment-friendly options for soil conservation, thereby benefiting a 50% saving on costly chemical fertilizers in non-OPEC countries which import most of its phosphorus and potassium fertilizers.