Separation and purification of hyaluronic acid by glucuronic acid imprinted microbeads

The purification of hyaluronic acid (HA) is relatively significant to use in biomedical applications. The structure of HA is formed by the repetitive units of glucuronic acid and N-acetyl glucosamine. In this study, glucuronic acid-imprinted microbeads have been supplied for the purification of HA from cell culture (Streptococcus equi). Histidine-functional monomer, methacryloylamidohistidine (MAH) was chosen as the metal-complexing monomer. The glucuronic acid-imprinted poly(ethyleneglycoldimethacrylate-MAH-Copper(II)) [p(EDMA-MAH-Cu²⁺)] microbeads have been synthesized by typical suspension polymerization procedure. The template glucuronic acid has been removed by employing 5 M methanolic KOH solution. p(EDMA-MAH-Cu²⁺) microbeads have been characterized by elemental analysis, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) images and swelling studies. Moreover, HA adsorption experiments have been performed in a batch experimental set-up. Purification of HA from cell culture supernatant has been also investigated by determining the hyaluronidase activity using purified HA as substrate. The glucuronic acid imprinted p(EDMA-MAH-Cu²⁺) particles can be used many times with no significant loss in adsorption capacities. Also, the selectivity of prepared molecular imprinted polymers (MIP) has been examined. Results have showed that MIP particles are 19 times more selective for glucuronic acid than N-acetylglucose amine.     

Coagulation of Particles Through Rapid Mixing

As is often assumed, rapid mix effectively distributes the dosed coagulant in suspension to promote subsequent flocculation of suspended particles and coagulant molecules. This study determined that the role of rapid mix is considerably more complex than conventionally assumed. Particles in the raw water samples from the Banxing Water Works of Taipei City were coagulated at high shear environment, during which flocs of size 40-70 μm were produced. The produced floc interior was compacted, and residual turbidity and the amount of dissolved natural organic matter (NOM) in suspension were substantially reduced. The subsequent stage of rapid mix slightly reduces floc size, further compacts the floc interior, and expels fine particles and some adsorbed NOM from the flocs. A sudden reduction in shearing rate produced large sized flocs with not so compact structures. Moreover, this action released fine particles from flocs into the suspension and enhanced adsorption of NOM onto solid surfaces.     

Partial purification of <Emphasis Type="Italic">nigella sativa</Emphasis> L. Seed lipase and its application in hydrolytic reactions. Enrichment of γ-linolenic acid from borage oil

Selective hydrolysis of borage (Borago officinalis L.) oil was catalyzed by two lipase preparations of Nigella sativa L. seeds at 40°C in a mixture of borage oil, water, and hexane. Ammonium sulfate-precipitated lipase (Nigella PL) and lipase partially purified by DEAE-ion exchange chromatography (Nigella CPL) exhibited a negative specificity toward γ-linolenic acid (GLA). Best results were obtained in the experiments conducted with 330 U/g oil of Nigella PL and 200 U/g oil of nigella CPL. When 330 U/g oil of Nigella PL was used, after 8 h the GLA level rose from 21.9% in the starting oil to 29.6 and 41.8% in TAG and DAG fractions of the product mixtures, respectively (1.5-fold enrichment of GLA in the total unhydrolyzed acylglycerol fraction). At 200 U/g oil enzyme concentration of Nigella CPL, after 77 h maximum GLA enrichment was observed in the DAG fraction. The GLA content of the DAG increased to 34.6%, corresponding to almost 1.6-fold enrichment. The relative inability of Nigella sativa lipase(s) to hydrolyze γ-linolenoyl moieties of TAG can be used for the enrichment of this acid in the unhydrolyzed acylglycerol fractions of GLA-containing oils.     

Reynolds number-dependent permeability of wastewater sludge flocs

Most theoretical models assume constant permeability of wastewater sludge floc. This work shows that, at creeping flow limit with small intrafloc Reynolds number, the permeability of floc can not only be affected by floc structure, but also by the external flow condition. The three-dimensional structure of flocs using the fluorescence in situ hybridization (FISH) and the confocal laser scanning microscope (CLSM) was firstly probed. Then, the volumetric grid models for sludge flocs were constructed. We noted that the floc permeability could keep unchanged, increased, or decreased at increased Reynolds number (Re). Flow redistribution among channels of various sizes contributes to the noted Re-dependent permeability of flocs.     

A new perspective on data homogeneity in software cost estimation: a study in the embedded systems domain

Cost estimation and effort allocation are the key challenges for successful project planning and management in software development. Therefore, both industry and the research community have been working on various models and techniques to accurately predict the cost of projects. Recently, researchers have started debating whether the prediction performance depends on the structure of data rather than the models used. In this article, we focus on a new aspect of data homogeneity, “cross- versus within-application domain”, and investigate what kind of training data should be used for software cost estimation in the embedded systems domain. In addition, we try to find out the effect of training dataset size on the prediction performance. Based on our empirical results, we conclude that it is better to use cross-domain data for embedded software cost estimation and the optimum training data size depends on the method used.     

Liquefaction/Solubilization of Low-Rank Turkish Coals by White-Rot Fungus (Phanerochaete chrysosporium)

Microbial coal liquefaction/solubilization of three low-rank Turkish coals (Bursa-Kestelek, Kütahya-Seyitömer and Mu?la-Yata?an lignite) was attempted by using a white-rot fungus (Phanerochaete chrysosporium DSM No. 6909); chemical compositions of the products were investigated. The lignite samples were oxidized by nitric acid under moderate conditions and then oxidized samples were placed on the agar medium of Phanerochaete chrysosporium. FTIR spectra of raw lignites, oxidized lignites and liquid products were recorded, and the acetone-soluble fractions of these samples were identified by GC-MS technique. Results show that the fungus affects the nitro and carboxyl/carbonyl groups in oxidized lignite sample, the liquid products obtained by microbial effects are the mixture of water-soluble compounds, and show limited organic solubility.     

Extracting Low-Frequency Spatio-Temporal Patterns in Ambient Power System Data Using Blind Source Separation

Wide-area techniques provide a powerful tool to extract spatio-temporal patterns from high-dimensional datasets and can be used for event detection and visualization, data fusion, stability assessment, and coherency analysis. In this paper, a novel blind source separation-based approach for extracting low-frequency spatio-temporal patterns from measured ambient power system data is proposed and a spatio-temporal visualization index is also suggested. This methodology combines a nonlinear hierarchical neural network with a Blind Source Separation (BSS) technique. The neural network allows reducing noise and removing the nonlinear relations among data (preserve dynamic features of interest), while the BSS technique permits extracting spatial and temporal patterns. In addition, the proposed approach takes advantage of the latest techniques in nonlinear estimation of non-stationary time series. Finally, application examples of the proposed framework on real test cases recorded from an actual power system by Phasor Measurement Units (PMUs) are presented. The obtained results show that the temporal patterns can be used for extracting and identifying the low-frequency oscillation modes and the spatial patterns can be used for identifying modes with the most contribution in original data. Compared to other BSS approaches, the proposed method has shown to be better for the analysis of real ambient data.     

Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation

This study presents an image segmentation system that automatically segments and labels T1-weighted brain magnetic resonance (MR) images. The method is based on a combination of unsupervised learning algorithm of the self-organizing maps (SOM) and supervised learning vector quantization (LVQ) methods. Stationary wavelet transform (SWT) is applied to the images to obtain multiresolution information for distinguishing different tissues. Statistical information of the different tissues is extracted by applying spatial filtering to the coefficients of SWT. A multidimensional feature vector is formed by combining SWT coefficients and their statistical features. This feature vector is used as input to the SOM. SOM is used to segment images in a competitive unsupervised approach and an LVQ system is used for fine-tuning. Results are evaluated using Tanimoto similarity index and are compared with manually segmented images. Quantitative comparisons of our system with the other methods on real brain MR images using Tanimoto similarity index demonstrate that our system shows better segmentation performance for the gray matter while it gives average results for white matter.     

A comparative study for estimating software development effort intervals

Software cost/effort estimation is still an open challenge. Many researchers have proposed various methods that usually focus on point estimates. Until today, software cost estimation has been treated as a regression problem. However, in order to prevent overestimates and underestimates, it is more practical to predict the interval of estimations instead of the exact values. In this paper, we propose an approach that converts cost estimation into a classification problem and that classifies new software projects in one of the effort classes, each of which corresponds to an effort interval. Our approach integrates cluster analysis with classification methods. Cluster analysis is used to determine effort intervals while different classification algorithms are used to find corresponding effort classes. The proposed approach is applied to seven public datasets. Our experimental results show that the hit rate obtained for effort estimation are around 90–100%, which is much higher than that obtained by related studies. Furthermore, in terms of point estimation, our results are comparable to those in the literature although a simple mean/median is used for estimation. Finally, the dynamic generation of effort intervals is the most distinctive part of our study, and it results in time and effort gain for project managers through the removal of human intervention.     

Activities of MFI-Supported Rhenium Catalysts for the Aromatization of Methane: Effect of Cationic Form of the Inorganic Carrier

MFI type inorganic carrier was used in two different cationic forms, hydrogen and calcium respectively. MFI-supported molybdenum and rhenium catalysts were prepared. The activities of the catalysts were compared for the aromatization reaction of methane. Higher activity values were attained with the catalysts supported on HZSM-5. Aromatics were also observed with the catalysts supported on CaZSM-5, despite their deficiency in acid sites. Highly dispersed rhenium is expected to be formed with the use of the inorganic carrier in calcium form. On the other hand, lower reaction rates were observed with rhenium supported on CaZSM-5, in spite of the improved dispersion of the active rhenium species on this catalyst. This was interpreted in terms of the critical role of the acid sites in the conversion of methane to aromatics, compared to the improved dispersion of the active metal.