Isolation, distribution and evaluation of cytotoxic and antioxidant activity of cultivable actinobacteria from the Oman Sea sediments

Screening bioactive natural products from bacteria is a determinative step in the drug discovery programs. The present study aim to isolate actinobacteria from the Oman Sea sediments for determining the effects of different culture media and treatments on the yield of the isolation process, and measure the DPPH radical scavenging and Artemia cytotoxic activity of culture extracts of the actinobacterial isolates. A total of 290 actinobacterial isolates were collected from 14 sediment samples. Heat treatment(40.68%) and M_4 medium(29.31%) exhibited the maximum isolation rates of actinobacteria. Streptomyces isolates were dominantly distributed in all of the investigated stations according to 16 S rRNA gene sequencing. The distribution pattern of Streptomyces followed a depth-dependent frequency trend, whereas the members of rare genera including Micromonospora, Nocardia Actinoplanes, Nocardiopsis, Saccharopolyspora and Crossiella were distributed in deeper stations. Approximately,25% of the examined isolates could scavenge 90% of 10~(–4) mol/L DPPH solutions at 1 250 μg/mL final concentration of their ethyl acetate culture extracts. Furthermore, the most potent extracts could scavenge DPPH radicals with IC50 ranges from 356.8 to 566.4 μg/mL. Brine shrimp cytotoxicity tests showed that 38.88% of the examined culture extracts exhibited LC_(50) lower than 1 000 μg/mL against the Artemia cells. Moreover, the most potent culture extracts exhibited LC_(50) range from 335.4 to 534.4 μg/mL. Phylogenetic analysis by 16 S rRNA gene sequence revealed that the OS 005, OS 263 and OS 157 closely related to Streptomyces djakartensis, Streptomyces olivaceus and Nocardiopsis dassonvillei respectively. These results suggested the widespread distribution of the antioxidant and cytotoxic producing actinobacteria in the Oman Sea sediments, which could be considered as promising candidates for the discovery of microbial bioactive compounds.     

Exploring the applications of 3D proximity analysis in a 3D digital cadastre

Increasing population in urban areas and limitations of suitable lands for developing houses and urban infrastructure have led to the vertical development in cities. However, these developments are managed by a cadastral system which is mainly two-dimensional and cannot efficiently represent Rights, Restrictions, and Responsibilities (RRRs) in complex scenarios. In fact, a three-dimensional cadastre is required for efficiently registering and representing RRRs. In this paper, a 3D proximity analysis was proposed and implemented to determine RRRs and associated easement rights in non-topology-based data structures. This method can be used to investigate the surrounding spaces of a subject apartment unit or storage in a high-rise. The performance of the developed method was evaluated in a large complex high-rise in Tehran, Iran. The results confirmed that the proposed method can correctly identify the neighbor spaces in complex scenarios.     

Role of the Kazerun fault system in active deformation of the Zagros fold-and-thrust belt (Iran)

Field structural and SPOT image analyses document the kinematic framework enhancing transfer of strike-slip partitioned motion from along the backstop to the interior of the Zagros fold-and-thrust belt in a context of plate convergence slight obliquity. Transfer occurs by slip on the north-trending right-lateral Kazerun Fault System (KFS) that connects to the Main Recent Fault, a major northwest-trending dextral fault partitioning oblique convergence at the rear of the belt. The KFS formed by three fault zones ended by bent orogen-parallel thrusts allows slip from along the Main Recent Fault to become distributed by transfer to longitudinal thrusts and folds. To cite this article: C. Authemayou et al., C. R. Geoscience 337 (2005).     

Stochastic simulation of patterns using Bayesian pattern modeling

In this paper, a Bayesian framework is introduced for pattern modeling and multiple point statistics simulation. The method presented here is a generalized clustering-based method where the patterns can live on a hyper-plane of very low dimensionality in each cluster. The provided generalizationallows a remarkable increase in variability of the model and a significant reduction in the number of necessary clusters for pattern modeling which leads to more computational efficiency compared with clustering-based methods. The Bayesian model employed here is a nonlinear model which is composed of a mixture of linear models. Therefore, the model is stronger than linear models for data modeling and computationally more effective than nonlinear models. Furthermore, the model allows us to extract features from incomplete patterns and to compare patterns in feature space instead of spatial domain. Due to the lower dimensionality of feature space, comparison in feature space results in more computational efficiency as well. Despite most of the previously employed methods, the feature extraction filters employed here are customized for each training image (TI). This causes the features to be more informative and useful. Using a fully Bayesian model, the method does not require extensive parameter setting and tunes its parameters itself in a principled manner. Extensive experiments on different TIs (either continuous or categorical) show that the proposed method is capable of better reproduction of complex geostatistical patterns compared with other clustering-based methods using a very limited number of clusters.     

P–T evolution of meta-peridotite in the Penjwin ophiolite, northeastern Iraq

The Penjwin meta-peridotite rock represents one of the five main metamorphosed ultramafic bodies in Kurdistan region, Northwest Zagros Thrust Zone. It underwent at least two successively low-retrograde metamorphic events with one progressive one which all modified the original mineralogy and texture of primary dunite and harzburgite. The primary upper mantle mineral assemblage olivine?+?orthopyroxene?+?chromian spinel is replaced by olivine?+?tremolite–actiolite?+?anthopylite?+?talc?+?ferichromite?+?Cr-chlorite assemblage of amphibolite facies. The further retrograde metamorphic amphibolite facies assemblage is replaced by lizardite–chrysotile?+?Cr-chlorite?+?syn-serpentinization Cr-magnetite of lower greenschist facies. Later at the main Zagros thrust fault, low greenschist facies underwent progressive metamorphism due to the local effect of shear stress as a result of the exhumation and obduction of Penjwin ophiolite suite over Merga Red bed series during Tertiary. Lizardite–chrysotile transformed to antigorite and producing antigorite?+?carbonate?+?syn-serpentinization Cr-magnetite?+?Cr-chlorite assemblage of upper greenschist facies. Chromian spinel is concentrically zoned as a result of multi-stages retrogressive metamorphic events, in which the Cr # (Cr/(Cr?+?Al)) increases from core to rim (0.5 to 1). Three zones can be identified from core to rim: The core is primary Al-rich and mantled by ferrichromite of amphibolite facies. The most outer zone of chromian spinel grains is represented by syn-serpentinization Cr-magnetite of greenschist facies.     

Shallow and deep aquifer hydrochemistry and chemical variability of water quality at Shariatpur district, Bangladesh

Water quality and hydrochemistry of Shariatpur district were evaluated in terms of hydrochemical composition and some important physico-chemical parameters. The groundwater of the study area is good for drinking, domestic as well as for irrigation purposes. Among the major ions, shallow tube well waters give higher concentration of Ca²⁺ which ranges from 24 to 260 mg/L. The deep tubewell waters show higher concentration of Na⁺ which varies from 74 to 582 mg/L during dry season. Among the trace elements most of the shallow aquifer samples show higher concentration of Fe²⁺, Mn²⁺ and As. Concentration of Fe²⁺ varies from 0.655 to 18.8 mg/L, and Mn²⁺ from trace to 0.868 mg/L during dry period. Hydrochemical analyses reveal significant seasonal variation in water quality of shallow aquifer. Both the shallow aquifer and the surface water of the study area are predominantly of Ca–Mg–HCO₃ type, while the deep aquifer water is mainly of Na–K–Cl–SO₄ type with slight inclination to Ca–Mg–HCO₃ type. The study area is suitable for groundwater development if comprehensive and holistic approaches towards water resource management are taken into consideration.     

Prediction of water inflow to mechanized tunnels during tunnel-boring-machine advance using numerical simulation

An accurate estimate of the groundwater inflow to a tunnel is one of the most challenging but essential tasks in tunnel design and construction. Most of the numerical or analytical solutions that have been developed ignore tunnel seepage conditions, material properties and hydraulic-head changes along the tunnel route during the excavation process, leading to inaccurate prediction of inflow rates. A method is introduced that uses MODFLOW code of GMS software to predict inflow rate as the tunnel boring machine (TBM) gradually advances. In this method, the tunnel boundary condition is conceptualized and defined using Drain package, which is simulated by dividing the drilling process into a series of successive intervals based on the tunnel excavation rates. In addition, the drain elevations are specified as the respective tunnel elevations, and the conductance parameters are assigned to intervals, depending on the TBM type and the tunnel seepage condition. The Qomroud water conveyance tunnel, located in Lorestan province of Iran, is 36 km in length. Since the Qomroud tunnel involved groundwater inrush during excavating, it is considered as a good case study to evaluate the presented method. The groundwater inflow to this tunnel during the TBM advance is simulated using the proposed method and the predicted rates are compared with observed rates. The results show that the presented method can satisfactorily predict the inflow rates as the TBM advances.     

Air pollution forecasting from sky images with shallow and deep classifiers

Air pollution is one of the most important problems in the new era. Detecting the level of air pollution from an image taken by a camera can be informative for the people who are not aware of exact air pollution level be declared daily by some organizations like municipalities. In this paper, we propose a method to predict the level of the air pollution of a location by taking an image by a camera of a smart phone then processing it. We collected an image dataset from city of Tehran. Afterward, we proposed two methods for estimation of level of air pollution. In the first method, the images are preprocessed and then Gabor transform is used to extract features from the images. At the end, two shallow classification methods are employed to model and predict the level of air pollution. In the second proposed method, a Convolutional Neural Network(CNN) is designed to receive a sky image as an input and result a level of air pollution. Some experiments have been done to evaluate the proposed method. The results show that the proposed 9 method has an acceptable accuracy in detection of the air pollution level. Our deep classifier achieved accuracy about 59.38% which is 10 about 6% higher than traditional combination of feature extraction and classification methods.     

The Miduk porphyry Cu deposit,Kerman, Iran: A geochemical analysis of the potassic zone including halogen element systematics related to Cu mineralization processes

Miduk hypogene and supergene porphyry Cu–Mo mineralization occurs within the Miocene porphyritic quartz–diorite and host Eocene plagioclase–hornblende phyric andesitic pyroclastic and flow sequence. Both the host rocks were extensively altered by hydrothermal fluids to dominantly potassic, phyllic, and argillic with interstitial to distal propylitic types.     

Prediction of Land Subsidence Under Cyclic Pumping Based on Laboratory and Numerical Simulations

In this study subsidence due to groundwater withdrawal was investigated. Kerman Province in Iran is struggling with land subsidence problem due to extensive groundwater withdrawal mainly for farming. The rate and type of groundwater withdrawal has very important impact on settlement rate. In this research, effective parameters on land subsidence caused by groundwater withdrawal were determined by laboratory tests. Sampling had done up to depth of 300 m mainly with remolded specimens from Shams-abad, Nouq plain in Kerman province. Similar to the field preconsolidation pressure was applied on specimens in the laboratory. Rate of applied stress on prepared specimens was similar to effect of oscillation of groundwater level. In order to model the actual soil behavior in the laboratory, one-dimensional consolidation device (odometer) was adopted for testing. In these tests, the effect of loading caused by seasonal oscillation of groundwater table is considered by means of cyclic loading in the testing which has great effect on rate of settlements. The results of tests show that when the water table level periodically increases and decreases the amount of settlement decrease, comparing with the case when the groundwater table drop to a constant level. In order to predict the further effects of groundwater level oscillation and actual field condition on land subsidence, a finite element model based on Biots’ three-dimensional consolidation theory was developed. After calibration of finite element model with laboratory tests, this model was used for prediction the effect of groundwater level oscillation on actual field conditions.