Road to Metastasis: The TWEAK Pathway as a Discriminant between Metastasizing and Non-Metastasizing Thick Melanomas

Cutaneous melanoma (CM) is the most aggressive form of skin cancer, and its worldwide incidence is rapidly increasing. Early stages can be successfully treated by surgery, but once metastasis has occurred, the prognosis is poor. However, some 5–10% of thick (≥2 mm) melanomas do not follow this scenario and run an unpredictable course. Little is known about the factors that contribute to metastasis in some patient with thick melanomas and the lack thereof in thick melanoma patients who never develop metastatic disease. We were therefore interested to study differential gene expression and pathway analysis and compare non-metastatic and metastatic thick melanomas. We found that the TNF-like weak inducer of apoptosis (TWEAK) pathway was upregulated in thick non-metastasizing melanomas. MAP3K14 (NIK1), BIRC2 (cIAP1), RIPK1, CASP7, CASP8, and TNF play an important role in inhibiting proliferation and invasion of tumor cells via the activation of the non-canonical NF-κB signaling pathway. In particular, this pathway sensitizes melanoma cells to TNF-alpha and activates the apoptosis module of the TWEAK pathway in thick non-metastasizing melanomas. Hence, our study suggests a potential role of the TWEAK pathway in inhibiting thick melanoma from metastasis. Exploitation of these genes and the pathway they control may open future therapeutic avenues.     

Obtaining Butter Oil Triacylglycerols Free from β-Carotene and α-Tocopherol via Activated Carbon Adsorption and Alumina-Column Chromatography Treatments

It is difficult to remove β-carotene from oils with alumina-column chromatography, because β-carotene is even less-polar than triacylglycerols (TAGs) are. The objective of this study was to obtain butter oil TAGs free from β-carotene and antioxidants via sequential treatments with activated carbon (AC) adsorption and alumina column chromatography. The AC used was prepared from waste apricots. The effects of AC dosages, temperatures and time courses on β-carotene adsorption were studied. The Langmuir and Freundlich isotherms were used to describe the adsorption of β-carotene onto AC, and it was found to be more consistent with the Freundlich isotherm with a higher R ² value (0.9784). Adsorption kinetics of β-carotene was analyzed by pseudo-first order and pseudo-second order models. The pseudo-second order model was found to explain the kinetics of β-carotene adsorption more effectively (R ² = 0.9882). The highest β-carotene reduction was achieved (from 31.9 to 1.84 mg/kg) at an AC dosage of 10 wt%, temperature of 50 °C, and adsorption time of 240 min. A considerable amount of α-tocopherol was also adsorbed during the AC treatment. Remaining portions of α-tocopherol were completely removed with alumina adsorption chromatography. The method described may be used for purification of vegetable oil TAGs, which will be used as model compounds in model oxidation studies.     

Sorption and Preconcentration of Vanadium,Chromium, Manganese,and Lead on Silica Gel Modified with (3‐Mercaptopropyl) Trimethoxysilane

Abstract

3‐mercaptopropyltrimethoxysilane, (CH₃O)₃SiCH₂CH₂CH₂SH, loaded on silica gel was used as a preconcentration sorbent for V, Cr, Mn and Pb prior to their determination by graphite furnace atomic absorption spetrometry (GFAAS). Surface characteristics and surface area of the silica gel before and after chemical modification were determined by elemental analysis. The retention and recovery of the analyte elements were studied by applying batch and column techniques. The experimental parameters, such as the effect of pH of the sample, shaking time in batch technique, flow rate of the eluent, the concentration of acid solution in the column section, and the amount of silica on retention and elution have been investigated. All elements were quantitatively (≥90%) recovered in the batch technique with R.S.D. values of 3.0 for vanadium, 1.8 for chromium, 1.7 for manganese, and 0.4 for lead. The same recoveries were obtained in the column techniques for all elements, while manganese in sea water could not be succesfully recovered. Detection limits of the method for vanadium, chromium, manganese, and lead are 1.1, 1.4, 1.3, and 0.8 ng, respectively.     

Preparation and characterization of ciprofloxacin‐loaded alginate/chitosan sponge as a wound dressing material

The aim of this study is preparation and characterization of alginate/chitosan sponges including a model antibiotic (i.e., ciprofloxacin) to use in wound and/or burn treatment. Sponges were prepared firstly by the gelation of sodium alginate followed by lyophilization, crosslinking with calcium chloride, and finally coating with chitosan. Sponges were characterized with respect to morphology, water uptake, in vitro drug release behavior, and antimicrobial activity. Investigated and evaluated parameters in all of these studies were selected as the concentration of calcium chloride, alginate viscosity, drug content, and molecular weight of chitosan. Drug release and water uptake were found to be greatly influenced by these parameters. Water uptake and drug release rate were decreased by increasing the crosslinking density, chitosan molecular weight, and alginate viscosity. In the antimicrobial tests, it was obtained that the antimicrobial activity is directly proportional with the release rates and water uptake. Morphological studies showed a highly porous structure with interconnected pores. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 1602–1609, 2006     

Lot-splitting approach of a hybrid manufacturing system under CONWIP production control: a mathematical model

This paper discusses the advantages of lot splitting in hybrid manufacturing environments where cellular and functional layouts are combined under Constant Work in Process (CONWIP) production control. The proposed model fills a research gap in the related literature by applying lot splitting and pull production simultaneously. A linear CONWIP control mathematical model that minimises the average flow time is developed in case of lot splitting. The developed model has sequence-dependent set-up times. The demand level, coefficient of variation (CV) impact and set-up time reduction effect on CONWIP production control are also investigated. The model is solved using GAMS21.6 optimisation software; the optimal backlog list, the number and size of sublots are reported. The proposed model is compared with lot production under push control in different settings as well as with two different heuristics from the literature. Experimental results indicate that in all settings, the lot splitting is more advantageous than lot production in terms of average flow time. CV has a greater effect than set-up time reduction on average flow time.     

Effects of humidity and soil organic matter on the sorption of chlorinated methanes in synthetic humic-clay complexes.

Vapor-phase sorption is the most influential process governing the transport and the fate of volatile organic compounds in soil. To understand the influence of both soil organic content and the humidity of soil on the vapor sorption is an important process for degradation processes. The single-pellet moment technique was used to investigate sorption and diffusion of trichloromethane (TCM) and carbon tetrachloride (CTC) at varying relative humidities (0-80%) of synthetic humic-clay complex pellets consisting of clay (montmorillonite) and different amounts of organic matter (humic acid). The effective diffusivities of TCM and CTC did not show a noticeable change with moisture and humic acid content. On the other hand, with increasing humic acid content of clay at 0% relative humidity, an appreciable decrease of the equilibrium sorption constants of the tracers (TCM, CTC) was found because of the blockage of some sites of the mineral surfaces and especially micropores by the humic acid. The presence of water also reduced dramatically the sorption of TCM and CTC on synthetic humic-clay complexes. Above 20% relative humidity, the sorption coefficient of TCM and CTC varied only slightly with humic acid content. It was concluded that the sorption of TCM and CTC in synthetic humic-clay complexes was strongly effected by the moisture and humic acid content.     

Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

     

Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes

With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid Grasshopper Optimization Algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and Grasshopper Optimization Algorithm. Experimental results are applied in two scenarios; the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26?% enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.