In the medical field, the identification of skin cancer (Malignant Melanoma) in dermoscopy images is still a challenging task for radiologists and researchers. Due to its rapid increase, the need for decision support systems to assist the radiologists to detect it in early stages becomes essential and necessary. Computer Aided Diagnosis (CAD) systems have significant potential to increase the accuracy of its early detection. Typically, CAD systems use various types of features to characterize skin lesions. The features are often concatenated into one vector (early fusion) to represent the image. In this paper, we present a novel method for melanoma detection from images. First the lesions are segmented by combining Particle Swarm Optimization and Markov Random Field methods. Then the K-means is applied on the segmented lesions to separate them into homogeneous clusters, from which important features are extracted. Finally, an Artificial Neural Network with Radial Basis Function is applied for the detection of melanoma. The method was tested on 200 dermoscopy images. The experimental results show that the proposed method achieved higher accuracy in terms of melanoma detection, compared to alternative methods.

Veterinary antibiotics, bacteria carrying antibiotic resistance determinants located on mobile genetic elements and nutrients are spread on agricultural soil using manure as fertilizer. However, systematic quantitative studies linking antibiotic concentrations and antimicrobial resistance genes (ARGs) in manure and the environment are scarce but needed to assess environmental risks. In this microcosm study, a sandy and a loamy soil were mixed with manure spiked with streptomycin or doxycycline at five concentrations. Total-community DNA was extracted on days 28 and 92, and the abundances of ARGs (aadA, strA, tet(A), tet(M), tet(W), tet(Q), sul1, qacE/qacEΔ1) and class 1 and 2 integron integrase genes (intI1 and intI2) were determined by qPCR relative to 16S rRNA genes. Effects on the bacterial community composition were evaluated by denaturing gradient gel electrophoresis of 16S rRNA gene amplicons. Manure application to the soils strongly increased the relative abundance of most tested genes. Antibiotics caused further enrichments which decreased over time and were mostly seen at high concentrations. Strikingly, the effects on relative gene abundances and soil bacterial community composition were more pronounced in sandy soil. The concept of defining antibiotic threshold concentrations for environmental risk assessments remains challenging due to the various influencing factors. 

Abstract : The Thymus vulgaris extracts are effective as anti-inflammatory, immunomodulatory, antioxidant, antibacterial and antifungal. E. coli is a major pathogen involved in nosocomial infections and one of multi-drug resistance organisms. UTI is the most common extraintestinal E. coli infections and is caused by uropathogenic E. coli (UPEC). This study aimed to prove the Thymus vulgaris ethanol extract effect in increasing IL-10 and decreasing TNF-α and bacterial colonies in the urinary bladder. All Thymus vulgaris parts, which bought from Gharian-Libya, were dried, then macerated 3 times, and its ethanol was evaporated. Final extraction results were stored in the freezer. This study use 20 female mice divided into 5 groups; (1) positive control (infected with E. coli); (2) negative control (without infection); and treatment groups T1, T2, T3 (infected mice administrated with ethanol extract of Thymus vulgaris (ETV) 250, 500, 750 mg/kg B.wt). The bacterial colonies in urinary bladder were analyzed; the blood levels of TNF-α, IL-10 were analyzed by ELISA method. There was no visible E. coli colonies infection in urinary bladder in all treated groups. ETV increased the IL-10 and decrease TNF-α level in blood. Keywords: Thymus vulgaris, TNF-α, IL-10, Escherichia coli, in vivo..