Genetic analysis of Turkish rice varieties (Oryza sativa L.) using seed storage proteins and RAPD markers

Due to an increasing demand for rice in Turkey, assessing and maintaining genetic variability in rice cultivars is essential for the success of rice breeding programs. Genetic polymorphisms in seed storage proteins (SSP) and RAPD markers were evaluated among different Turkish cultivars of Oryza sativa L. Twenty-one rice cultivars of Turkish origin and 11 rice cultivars from Bulgaria, France, Italy and Russia were analyzed. SDS-PAGE profiles from SSP of 20 cultivars showed similar expression patterns. Seven polymorphic and reproducible RAPD primers produced 42 bands ranging from 250 to 2,500 bp; 29 were polymorphic (69%) and 13 monomorphic (31%). Unweighted Pair-Group Method with Arithmetic Mean (UPGMA) was performed to generate a pairwise genetic distance (or similarity) matrix. A rooted dendrogram resulted in two main groups based on Jaccard’s coefficients. Sürek-95, Serhat-92, and Rocca were placed in one group, and all other cultivars in another group. Principle coordinate analysis was also conducted and the results supported UPGMA results, where Sürek-95, Serhat-92, and Rocca were distinct from all other cultivars. Our results suggest that the rice cultivars screened in this study exhibit narrow genetic diversity. In addition, RAPD-based genetic characterization can aid in both conservation and selection of desired rice cultivars in future breeding studies.     

Medium-Scale Anatolian Cities: Conceptual and Physical Routes of Urban Transformation

Banu Tomruk investigates the post-1980s transformation of the built environment of medium-scale Anatolian cities. She examines the subject through a conceptual and physical framework that places as much emphasis on populist rhetoric – ‘identity crisis’, ‘historicisation’, ‘the making of a tourist city’ – as on the built structures themselves and their realisation as standardised apartment blocks and gated communities. Copyright © 2010 John Wiley & Sons, Ltd.     

Immobilized Fe(III)-HY: an efficient and stable photo-Fenton catalyst

This article presents preparation, characterization and evaluation of an efficient heterogeneous Fe(III)-HY catalyst for photo-assisted Fenton reaction. Fe(III) ions are immobilized on HY zeolite using different loadings by impregnation, calcination and the activity of the catalyst is evaluated by the degradation of phenol. To initiate a photo-Fenton reaction, suspended Fe(III)-HY in solution is irradiated using UV light to form Fe(II)-HY necessary for the reaction to go. The effect of Fe loadings, H₂O₂ concentration, pH and quenching on photo-Fenton reaction are studied. The results obtained clearly show that 0.25 wt.% Fe(III)-HY is efficient in the degradation of phenol at pH = 6. Further the efficiency of Fe(III)-HY is compared with that of a homogeneous photo-Fenton reaction and the increased rate of reaction on Fe(III)-HY highlights the synergistic role of zeolite. Heterogeneous Fe(III)-HY in photo-Fenton reaction allows a wide range of pH for reaction against the narrow pH range in homogeneous system. The system is further subjected to evaluate its stability in solid state. Firstly the reaction solution containing Fe(III)-HY catalyst on irradiation is analyzed for Fe ions with atomic absorption spectroscopy (AAS) and also by calorimetry using 1,10-phenanthroline (o-phen) to find out any Fe leaching from the catalyst and the results show insignificant leaching of Fe (<0.3 ppm) at maximum loading of Fe under experimental conditions. Secondly, the irradiated Fe(III)-HY is complexed with o-phen and it is subjected to Fourier transform infrared spectroscopy (FT-IR), diffuse reflectance spectroscopy (DRS) and electron spectroscopy for chemical analysis (ESCA) studies to detect and confirm the oxidation state of Fe in solid state. Critical analysis of these studies clearly show that Fe(III)-HY on irradiation changed to Fe(II)-HY and it is intact with the surface during the course of the reaction. The DRS spectra further evidences complexation of Fe(II) with o-phen. The stability of the catalyst is established by recycling studies.     

Probiotic potential of lactic acid bacteria from fermented Malaysian food or milk products

This study investigated the probiotic characteristics of lactic acid bacteria (LAB) isolated from 14 Malaysian fermented food or milk products. In total, 22.3% (121 of 542) of the LAB isolated from the local fermented products exhibited antibacterial activity against Micrococcus luteus. Twelve LAB that demonstrated better antibacterial activity against M. luteus as compared to a commercial strain Lactobacillus casei strain Shirota (LABPC) were selected for further characterisation. Based on 16S rRNA gene sequence, the LAB were identified as pediococci (seven) and lactobacilli (five). All 12 LAB showed bile tolerance, but only eight were acid tolerant at pH ≥ 3.0. The highest level of adhesion to HT‐29 cells was observed among the Lactobacillus sp. LAB 1 and 10. The LAB also showed antimicrobial activity against Escherichia coli and Staphylococcus aureus through the production of organic acids. LAB isolated from Malaysian fermented food and milk products, especially fermented tapioca, contains potential probiotic candidates.     

A systematic review of software fault prediction studies

This paper provides a systematic review of previous software fault prediction studies with a specific focus on metrics, methods, and datasets. The review uses 74 software fault prediction papers in 11 journals and several conference proceedings. According to the review results, the usage percentage of public datasets increased significantly and the usage percentage of machine learning algorithms increased slightly since 2005. In addition, method-level metrics are still the most dominant metrics in fault prediction research area and machine learning algorithms are still the most popular methods for fault prediction. Researchers working on software fault prediction area should continue to use public datasets and machine learning algorithms to build better fault predictors. The usage percentage of class-level is beyond acceptable levels and they should be used much more than they are now in order to predict the faults earlier in design phase of software life cycle.     

Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem

Software quality engineering comprises of several quality assurance activities such as testing, formal verification, inspection, fault tolerance, and software fault prediction. Until now, many researchers developed and validated several fault prediction models by using machine learning and statistical techniques. There have been used different kinds of software metrics and diverse feature reduction techniques in order to improve the models’ performance. However, these studies did not investigate the effect of dataset size, metrics set, and feature selection techniques for software fault prediction. This study is focused on the high-performance fault predictors based on machine learning such as Random Forests and the algorithms based on a new computational intelligence approach called Artificial Immune Systems. We used public NASA datasets from the PROMISE repository to make our predictive models repeatable, refutable, and verifiable. The research questions were based on the effects of dataset size, metrics set, and feature selection techniques. In order to answer these questions, there were defined seven test groups. Additionally, nine classifiers were examined for each of the five public NASA datasets. According to this study, Random Forests provides the best prediction performance for large datasets and Naive Bayes is the best prediction algorithm for small datasets in terms of the Area Under Receiver Operating Characteristics Curve (AUC) evaluation parameter. The parallel implementation of Artificial Immune Recognition Systems (AIRS2Parallel) algorithm is the best Artificial Immune Systems paradigm-based algorithm when the method-level metrics are used.     

Decentralized routing controller design using overlapping decompositions

The use of overlapping decompositions in designing routing controllers for large-scale networks is considered and a decentralized dynamic routing controller design strategy is proposed. The strategy is based on the overlapping decompositions method and can be used for large-scale networks which have more than two overlapping subnetworks and/or have more than one node in the overlapping part. The controller obtained by the proposed strategy is decentralized in the sense that all on-line computations can be done locally at the individual nodes without any information transfer from the other nodes. The controller satisfies all the routing control constraints and it avoids any looping. The controller also clears the queues in finite time in the absence of external arrivals and it keeps the queue lengths bounded in the presence of external arrival rates which do not exceed a certain maximum rate. In order to illustrate the proposed controller's performance, the controller is applied to an example network and some simulation results are presented in a number of cases. Furthermore, a centrally designed controller is also applied to the same network for the same cases, in order to obtain a basis for comparison.     

Investigation of oxygen diffusion into contact lenses using electron spin resonance spectroscopy

Oxygen permeability is the most important parameter of contact lenses, as lack of oxygen causes corneal edema and threatens the vision of the patient. This study was unique in that it used an electron spin resonance (ESR) technique to determine the oxygen diffusion coefficient (D) of contact lenses. Although there are many methods and techniques for investigating oxygen diffusion into contact lenses, ESR was used for the first time in this study. The ESR technique is based on the scavenging of radicals produced in lenses by oxygen. As a contact lens is not a paramagnetic substance, it cannot give an ESR spectrum. But it does produce an ESR spectrum after γ irradiation. When a vacuum‐irradiated contact lens is exposed to air, the radicals trapped in the lens are transformed into peroxide radicals by the addition of molecular oxygen to the free radicals, and the ESR spectrum begins to change with time. This effect can be used as a tool to measure oxygen uptake in irradiated contact lenses. The oxygen diffusion coefficient of a contact lens was determined from changes in ESR signal intensity varying with time. The diffusion coefficients of oxygen for a contact lens were determined for rapid decay [(1.5 + 0.4) × 10^?8 cm²/s] and slow decay [(1.3 + 0.3) × 10^?9 cm²/s] in this study. These values are in agreement with the D values given in the literature for polymeric materials used for contact lenses. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 2937–2941, 2006     

A CMOS GSM IF-sampling circuit with reduced in-channel aliasing

A complex intermediate frequency (IF) sampling technique with intrinsic rejection of even-order aliasing channels is demonstrated. The circuit subsamples in-phase and quadrature IF signals and uses a discrete-time analog delay and an adder to notch out the undesired aliasing frequencies. A chip designed in 0.25-/spl mu/m CMOS technology demonstrates 27-dB antialiasing rejection for a 377-MHz IF GSM signal with 52-MHz sampling rate and 70-dB dynamic range.