Heavy metal bioaccumulation by cultivated Agaricus bisporus from artificially enriched substrates

Cultivated Agaricus bisporus possess the ability to bioaccumulate seven heavy metals (Pb, Cd, Hg, Fe, Cu, Mn, and Zn). The cultivated champignon mushroom A. bisporus was grown in soil composts of 16 different compositions. An edible mushroom, A. bisporus was also cultivated on humic compost artificially fortified with the seven heavy metals at five different concentrations (0.1 mg/kg, 0.5 mg/kg, 1.0 mg/kg, 3.0 mg/kg, and 10.0 mg/kg) and on control substrate (0 mg/kg) on a dry weight basis. The method of measuring bioaccumulation was flame atomic absorption spectrophotometry.     

A protein which masks galactose receptor mediated phage susceptibility in Lactococcus lactis subsp. lactis MPL56

A 28.5-kb plasmid, isolated from Lactococcus lactis subsp. lactis MPL56, causes complete inhibition of four lactococcal phages. Cell wall characteristics of wild-type strain MPL56 were compared with its 28.5 kb plasmid-cured, phage-sensitive derivative MPL56-22. After proteolytic enzyme treatments, adsorption of phages occurred at high levels, an example is 94.6–98.5% in MPL56 cells. Analysis of cell wall extracts of MPL56-22 by sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS–PAGE) indicated that the only difference between strains was the 55.4 kDa band in protein patterns of MPL56. Adsorption of the four phages was completely inhibited when MPL56-22 cells were subjected to SDS, Triton-X-100, HCl and NaOH treatments. Lectins that were specific for glucose/mannose and N-acetylglucosamine did not prevent adsorption of phages in cell wall extracts of MPL 56-22. However a lectin specific for galactose (MCA; Momordica charantia ) completely inhibited adsorption of these phages in cell wall extracts of MPL56-22. HPLC patterns of cell wall carbohydrates of MPL56-22 and its HCl treated preparations showed that the most prevalent difference was the galactose on untreated MPL56-22 cell wall chromatograms.     

Fuzzy portfolio selection using genetic algorithm

This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method.     

TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data

Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution (TIE) algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.     

Empirical modeling of splitting tensile strength from cylinder compressive strength of concrete by genetic programming

Compressive strength and splitting tensile strength are both mechanical properties of concrete that are utilized in structural design. This study presents gene expression programming (GEP) as a new tool for the formulations of splitting tensile strength from compressive strength of concrete. For purpose of building the GEP-based formulations, 536 experimental data have been gathered from existing literature. The GEP-based formulations are developed for splitting tensile strength of concrete as a function of age of specimen and cylinder compressive strength. In experimental parts of this study, cylindrical specimens of 150 × 300 mm and 100 × 200 mm in dimensions are utilized. Training and testing sets of the GEP-based formulations are randomly separated from the complete experimental data. The GEP-based formulations are also validated with additional 173 data of experimental results other than the data used in training and testing sets of the GEP-based formulations. All of the results obtained from the GEP-based formulations are compared with the results obtained from experimental data, the developed regression-based formulation and formulas given by some national building codes. These comparisons showed that the GEP-based formulations appeared to well agree with the experimental data and found to be quite reliable.     

Learning with rationales for document classification

We present a simple and yet effective approach for document classification to incorporate rationales elicited from annotators into the training of any off-the-shelf classifier. We empirically show on several document classification datasets that our classifier-agnostic approach, which makes no assumptions about the underlying classifier, can effectively incorporate rationales into the training of multinomial naïve Bayes, logistic regression, and support vector machines. In addition to being classifier-agnostic, we show that our method has comparable performance to previous classifier-specific approaches developed for incorporating rationales and feature annotations. Additionally, we propose and evaluate an active learning method tailored specifically for the learning with rationales framework.     

Effect of Different Surface Treatment Methods on the Friction and Wear Behavior of AISI 4140 Steel

In this study,the effects of various surface treatments on the friction and wear behavior of AISI 4140 steel have been evaluated.Sample surfaces of AISI 4140 steel were treated by quenching,carburizing,boronizing and plasma transferred arc (PTA) modification.The microstructural characteristics of surface treated steel samples were examined by optical microscopy and scanning electron microscopy (SEM).The mechanical properties of the samples including the surface roughness,microhardness,and abrasive and adhesive wear characteristics were also evaluated.Wear tests were applied by using a block-on-disc configuration under dry sliding conditions.The wear behavior and friction characteristics of the samples were determined as a function of sliding distance.Each sample group was compared with the other sample groups,and it was observed that the carburized samples demonstrated the lowest weight losses;however,PTA-treated samples demonstrated the lowest coefficient of friction in comparison to the other sample groups at the same sliding distance.     

Utilization of Electrospun Polystyrene Membranes as a Preliminary Step for Rapid Diagnosis

Recent advances in clinical practice drive deoxyribonucleic acid (DNA) as an important class of biomarker. Monitoring the change in their concentration suggests the initiation and/or progression of various disorders. However, low quantity of DNA biomarkers in body fluids requires a delicate isolation methodology that provides efficient separation and easy handling. This study describes a newer‐generation separation technology relying on electrospun fibers of sub‐micrometer diameter of a commodity polymer for DNA biomarkers in simulative serum. Fibrous polystyrene membranes are prepared by electrospinning and they are subjected to post‐modification with Au. The composite membranes may provide a convenient environment for the removal of bovine serum albumin (BSA) from BSA and DNA mixtures. The eluent can be used as an efficient tool for detection of DNA biomarkers associated with diagnosis of numerous life‐threatening diseases.

     

Polymerization of isoprene in n-heptane with triethylaluminum–titanium tetrachloride catalyst. I. Effect of Al/Ti ratio on stereoregularity,conversion, molecular weight,and molecular weight distribution

The influence of the Al/Ti ratio of the heterogeneous Ziegler–Natta catalyst system AlEt₃–TiCl₄ on the isoprene polymerization in n-heptane at 30°C in a bench scale reactor was investigated. Each batch run consisted of 50 mL isoprene, 200 mL n-heptane with 0.45 and 0.5g catalyst. Conversion of isoprene and molecular weights of polyisoprene increased with increasing Al/Ti ratio, reaching their maxima values at 1.0 and 1.2, respectively. Conversion and molecular weights decreased remarkably at a higher ratio of Al/Ti. As this ratio decreases from the value of 1.2 to 0.4, the cis1,4 content in polyisoprene decreased from 97 to 25%. When the ratio of Al/Ti increased from 1.2 to 2.2 the cis-1,4 structure of polyisoprene decreased from 97 to 85%.