A Comparison of Selective Classification Methods in DNA Microar¬ray Data of Cancer: Some Recommendations for Application in Health Promotion

Background: The aim of this study was to apply a new method for se¬lecting a few genes, out of thousands, as plausible markers of a disease.Methods: Hierarchical clustering technique was used along with Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers to select marker-genes of three types of breast cancer. In this method, at each step, one sub¬ject is left out and the algorithm iteratively selects some clusters of genes from the remainder of subjects and selects a representative gene from each cluster. Then, classifiers are constructed based on these genes and the accu¬racy of each classifier to predict the class of left-out subject is recorded. The classifier with higher precision is considered superior.Results: Combining classification techniques with clustering method re¬sulted in fewer genes with high degree of statistical precision. Although all classifiers selected a few genes from pre-determined highly ranked genes, the precision did not decrease. SVM precision was 100% with 22 genes instead of 50 genes while the NB resulted in higher precision of 97.95% in this case. When 20 highly ranked genes selected to be fed to the algorithm, same precision was obtained using 6 and 5 genes with SVM and NB clas¬sifiers respectively.Conclusion: Using hybrid method could be effective in choosing fewer number of plausible marker genes so that the classification precision of these markers is increased. In addition, this method enables detecting new plausible markers that their association to disease under study is not bio¬logically proved

Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks

In this paper, we propose a destination-aware adaptive traffic flow rule aggregation (DATA) mechanism for facilitating traffic flow monitoring in SDN-based networks. This method adapts the number of flow table entries in SDN switches according to the level of detail of traffic flow information that other mechanisms (e.g. for traffic engineering, traffic monitoring, intrusion detection) require. It also prevents performance degradation of the SDN switches by keeping the number of flow table entries well below a critical level. This level is not preset as a hard threshold but learned during operation by using a machine-learning based algorithm. The DATA method is implemented within a RESTful application (DATA App) which monitors and analyzes the ongoing network traffic and provides instructions to the SDN controller to adapt the traffic flow matching strategies accordingly. A thorough performance evaluation of DATA is conducted in an SDN emulation environment. The results show that---compared to the default behavior of common SDN controllers---the proposed DATA approach yields significant SDN switch performance improvements while still providing detailed traffic flow information on demand.Comment: This paper was presented at NetSys conference 2019. arXiv admin note: text overlap with arXiv:1909.0154

The study on the effective factors in Chicken meat market in Iran: An application of Vector Autoregression model

Abstract           The poultry industry is very important in Iran in comparison with other animal husbandry industries for its technical, economic and hygienic aspects. Despite the attempts made by the government to support this industry, still its products is not confidentially sufficient for growing domestic demands. Among the various factors, which influence the poultry industry, the preparation and relevant pricing of feed inputs are very critical issues.           The purpose of this study is to investigate the short-run and long-run of the effective factors in chicken meat market in Iran during The monthly data of 1993-2010 using Johansen-Juselius technique and VEC mechanism. The results showed that the price of poultry feed inputs have positive effects on price of chicken meat in both short-run and long run period. The results also indicated the high adjustment speed in the model, which reveals that chicken meat market could be regulated in the short-run period by appropriate policies

Green Biological Fabrication and Characterization of Highly Monodisperse Palladium Nanoparticles Using Pistacia Atlantica Fruit Broth

The development of green and safe processes for the synthesis of nanomaterials is one of the main aspects of nanotechnology. In this study, a biological, inexpensive and rapid process for the fabrication of palladium nanoparticles using the aqueous broth of Pistacia Atlantica fruit as a novel biomass product is reported without using extra surfactant, capping agent, and template. The synthesized palladium nanoparticles were confirmed and characterized by various spectroscopic techniques including UV-Visible spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy-dispersive X-ray spectrometer, Fourier transform infrared spectroscopy and Zeta-potential measurement. The results indicate that the spherically shaped Pd nanoparticles were successfully prepared in aqueous media in accordance with the principles of green chemistry with desired stability and crystalline in nature with face centered cubic geometry. Also, the results of transmission electron microscopy (TEM) confirmed preparation of very stable nanoparticles with the small diameter below 15 nm

Silver nanoparticles in the presence of Ca2+ as a selective and sensitive probe for the colorimetric detection of cysteine

A sensitive and selective cysteine colorimeric sensor based on the interaction between cysteine, silver nanoparticles (AgNPs) and calcium ions has been developed. In the presence of Ca2+ and NaCl, cysteine could rapidly induce the aggregation of AgNPs, thereby resulting in a yellow-to-red color change. The presence of 10 mM NaCl in the samples decreases the electrostatic repulsion and accelerates the aggregation of AgNPs. Results demonstrated that other amino acids cannot change the color of AgNPs solution in the same conditions, probably due to the absence of the thiol groups, suggesting the selectivity of the proposed method toward cysteine. The color change was monitored by naked eye and UV-Vis spectrophotometry. The ratio of absorption at 524 to 396 nm (A(524)/A(396)) is linearly dependent on the cysteine concentration in the range of 0.25-10 mu M

Silver nanoparticles-tragacanth gel as a green membrane for effective extraction and determination of capecitabine

A novel eco-friendly and effective electromembrane extraction method combining high-performance liquid chromatography with UV detection was developed for the enrichment and determination of capecitabine. Tragacanth-silver nanoparticles conjugated gel was prepared by dissolving the tragacanth powder in synthesized silver nanoparticles solution and was used as a green membrane in electromembrane extraction. The porosity and presence of silver nanoparticles in the gel were characterized by field emission scanning electron microscopy. This new electromembrane extraction approach uses neither organic solvent nor carrier agents to extract the target analyte. The best electromembrane extraction efficiency was obtained by using 4.0 mm membrane gel thickness containing 2.5% w/v of tragacanth gum, donor phase pH = 5.0, acceptor phase pH = 3.0, applied voltage 50 V, extraction time 20 min, and agitation rate 500 rpm. During method validation under the optimized conditions, good linearity dynamic range between 1 and 500 ng/mL with the coefficient of determination (R-2) = 0.998 was obtained. Limit of detection and Limit of quantitation were estimated to be 0.84 and 1.0 ng/mL, respectively. Finally, the applicability of this method in real samples was confirmed by an acceptable performance in extraction and determination of capecitabine in human plasma samples

Silver nanoparticles as a cyanide colorimetric sensor in aqueous media

The interaction between aqueous colloidal silver nanoparticles (AgNPs) and cyanide ions was studied using UV-Vis absorption and scanning electron microscopy (SEM) techniques. It was found that AgNPs were oxidized by dissolved oxygen in the presence of cyanide ions, resulting in a considerable decrease in the intensity of the surface plasmon resonance (SPR) absorption band of AgNPs. So, we propose a simple, cost effective, rapid, sensitive and selective colorimetric sensor for the detection of cyanide using AgNPs in aqueous media. There is a linear relationship between the absorbance intensity of AgNPs and the concentration of cyanide ions over the range of 16.7 mu mol L-1-133.3 mu mol L-1 at 394 nm. The proposed method has been successfully used for the determination of cyanide in water samples

Highly selective Hg2+ colorimetric sensor using green synthesized and unmodified silver nanoparticles

The reaction between biologically green synthesized silver nanoparticles (Ag NPs) and mercury (II) ions was introduced as a new and high potential calorimetric sensor for the selective recognition and monitoring of mercuric ions in aqueous samples. The green synthesized silver nanoparticles were characterized with surface plasmon resonance (SPR) ultraviolet spectroscopy (UV-vis), SEM and X-ray diffraction analysis (XRD) techniques. The fresh biologically synthesized silver nanoparticles are yellowish-brown in color due to the intense SPR absorption band. In the presence of Hg2+, the yellow Ag NPs solution was turned to colorless, accompanying the broadening and blue shifting of SPR band. The sensitivity and selectivity of green prepared Ag NPs toward other representative transition-metal ions, alkali metal ions and alkaline earth metal ions were studied. Also the effect of the concentration of Hg2+ to the Ag NPs was considered and the LOD for mercury (II) ion was 2.2 x 10(-6) mol L-1. The proposed method has been successfully used for the determination of mercury (II) ions in various water samples. (C) 2011 Elsevier B.V. All rights reserved