A comparative study of gelatin and starch-based nano-composite films modified by nano-cellulose and chitosan for food packaging applications.

Environmental concerns have led to extensive research for replacing polymer-based food packaging with bio-nano-composites. In this study, incorporation of nano-cellulose into gelatin and starch matrices is investigated for this purpose. Chitosan is used to improve mechanical, anti-fungal and waterproof properties. Experiments are designed and analyzed using response surface methodology. Nano-Cellulose is synthesized via acid hydrolysis and incorporated in base matrices through wet processing. Also, tensile strength test, food preservation, transparency in visible and UV and water contact angle are performed on the nano-composite films. DSC/TGA and air permeability tests are also performed on the optimal films. The results show that increasing nano-cellulose composition to 10% leads to increase the tensile strength at break to 8121 MN/m2 and decrease the elongation at break. Also, increasing chitosan composition from 5% to 30% can enhance food preservation up to 15 days.

Fabrication of polyamide thin-film nanocomposite membranes with enhanced surface charge for nitrate ion removal from water resources.

Exclusion due to membrane surface charge is considered as one of the main separation mechanisms occurring in charged membranes, which can be varied through various approaches to affect membrane rejection performance. In this study, thin-film composite (TFC) polyamide (PA) membranes were fabricated via interfacial polymerization of m-phenylenediamine (m-PDA) and 2,4-diaminobenzene sulfonic acid with trimesoyl chloride (TMC) on a polysulfone sub-layer. The ability of the prepared membrane to remove nitrate ions from water resources has been investigated. In order to improve membrane permeability, zeolite-PA thin film nanocomposite (TFN) membranes were fabricated by incorporating natural zeolite nanoparticles obtained through ball milling of an Iranian natural zeolite powder in the interfacial polymerization process. The size, morphology and specific surface area of the as-obtained nanozeolite were characterized using particle size analysis, FE-SEM and BET. The functional groups, morphology and surface charge of the membrane were characterized using ATR-FTIR, SEM and zeta potential analyses. Also, field-emission scanning electron microscopy (FE-SEM) and energy dispersive X-ray spectroscopy (EDS) were used to determine the distribution of nanozeolite in TFN membranes. The influence of zeolite addition to surface roughness was accessed by atomic force microscopy. The performance of TFC and TFN membranes was evaluated in terms of pure water flux and nitrate rejection. The results showed that in case of sulfonated diamine, nitrate ions rejection was enhanced from 63% to 85% which could be attributed to surface charge enhancement. TFN permeability was almost doubled by the addition of nanozeolite.

A deep bag-of-features model for the classification of melanomas in dermoscopy images.

Deep learning and unsupervised feature learning have received great attention in past years for their ability to transform input data into high level representations using machine learning techniques. Such interest has been growing steadily in the field of medical image diagnosis, particularly in melanoma classification. In this paper, a novel application of deep learning (stacked sparse auto-encoders) is presented for skin lesion classification task. The stacked sparse auto-encoder discovers latent information features in input images (pixel intensities). These high-level features are subsequently fed into a classifier for classifying dermoscopy images. In addition, we proposed a new deep neural network architecture based on bag-of-features (BoF) model, which learns high-level image representation and maps images into BoF space. Then, we examine how using this deep representation of BoF, compared with pixel intensities of images, can improve the classification accuracy. The proposed method is evaluated on a test set of 244 skin images. To test the performance of the proposed method, the area under the receiver operating characteristics curve (AUC) is utilized. The proposed method is found to achieve 95% accuracy.

Automated colour identification in melanocytic lesions.

Colour information plays an important role in classifying skin lesion. However, colour identification by dermatologists can be very subjective, leading to cases of misdiagnosis. Therefore, a computer-assisted system for quantitative colour identification is highly desirable for dermatologists to use. Although numerous colour detection systems have been developed, few studies have focused on imitating the human visual perception of colours in melanoma application. In this paper we propose a new methodology based on QuadTree decomposition technique for automatic colour identification in dermoscopy images. Our approach mimics the human perception of lesion colours. The proposed method is trained on a set of 47 images from NIH dataset and applied to a test set of 190 skin lesions obtained from PH2 dataset. The results of our proposed method are compared with a recently reported colour identification method using the same dataset. The effectiveness of our method in detecting colours in dermoscopy images is vindicated by obtaining approximately 93% accuracy when the CIELab1 colour space is used.

Determining the minimum inhibitory concentration of Tetraclean against Candida albicans.

The purpose of the present study was to determine the minimum inhibitory concentrations of Tetraclean, chlorhexidine, hydrogen peroxide, and sodium hypochlorite against Candida albicans.Amphotericin B was used as positive control and RPMI plus 1 ml Candida suspension was used as negative control. Serial dilution method was used to determine MIC of the irrigants. Findings showed that all positive controls demonstrated complete inhibition of C. albicans at concentration of 0.78 microg mL(-1). On the other hand, all negative controls were positive for fungal growth which confirms the methodology of the study. Findings showed that the MIC of CHX was significantly lesser than other tested irrigants (p < 0.05). The MICs of other groups in an ascending order were as follows: Tetraclean, NaOCl, and H2O2. However, the difference betweenTetraclean and NaOCI was not significant (p > 0.05). It can be concluded that MIC of CHX was significantly lower than other irrigations solutions which confirms its strong antifungal activity.

Characterization of asphaltene structure using atomic force microscopy.

In this study, at the first stage, asphaltene was extracted. The roughness of asphaltene coating at different rpm was studied using an image analysis confocal microscopy. The basics of quantum mechanics and statistical thermodynamics are used to predict the potential energy and the intermolecular forces of asphaltene molecules. The functional forms for the potential energy and intermolecular forces are evaluated. Our final goal is to be able to observe and determine the surface structures of asphaltene micelles with scanning probe microscopes. So, the focus of the work on these unusual molecules is to characterize their structure, dynamics and thermodynamics and to establish the relationship between these properties and petroleum fluid behaviour. The existence of various nanostructures of asphaltene in petroleum has been extensively discussed. A set of fitted data is used to check the validity of the calculated results. The good agreement between the proposed models and the data is promising.