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—Early detection of malignant melanoma skin cancer is crucial for treating the disease and saving lives. Many computerized techniques have been reported in the literature to diagnose and classify the disease with satisfactory skin cancer detection performance. However, reducing the false detection rate is still challenging and preoccupying because false positives trigger the alarm and require intervention by an expert pathologist for further examination and screening. In this paper, an automatic skin cancer diagno- sis system that combines different textural and color features is proposed. New textural and color features are used in a bag-of-features approach for efficient and accurate detection. We particularly claim that the Histogram of Gradients (HG) and the Histogram of Lines (HL) are more suitable for the analysis and clas- sification of dermoscopic and standard skin images than the conventional Histogram of Oriented Gradient (HOG) and the Histogram of Oriented Lines (HOL), respectively. The HG and HL are bagged separately us- ing a codebook for each and then combined with other bagged color vector angles and Zernike moments to exploit the color information. The overall system has been assessed through intensive experiments using different classifiers on a dermoscopic image dataset and another standard dataset. Experimental results have shown the superiority of the proposed system over state-of-the-art techniques.

Leishmania pyrimidine salvage is replete with opportunities for therapeutic intervention with enzyme inhibitors or antimetabolites. Their uptake into cells depends upon specific transporters; therefore it is essential to establish whether various Leishmania species possess similar pyrimidine transporters capable of drug uptake. Here, we report a comprehensive characterization of pyrimidine transport in L. major and L. mexicana. In both species, two transporters for uridine/adenosine were detected, one of which also transported uracil and the antimetabolites 5-fluoruracil (5-FU) and 5F,2'deoxyuridine (5F,2'dUrd), and was designated uridine-uracil transporter 1 (UUT1); the other transporter mediated uptake of adenosine, uridine, 5F,2'dUrd and thymidine and was designated Nucleoside Transporter 1 (NT1). To verify the reported L. donovani model of two NT1-like genes encoding uridine/adenosine transporters, and an NT2 gene encoding an inosine transporter, we cloned the corresponding L. major and L. mexicana genes, expressing each in T. brucei. Consistent with the L. donovani reports, the NT1-like genes of either species mediated the adenosine-sensitive uptake of [3H]-uridine but not of [3H]-inosine. Conversely, the NT2-like genes mediated uptake of [3H]-inosine but not [3H]-uridine. Among pyrimidine antimetabolites tested, 5-FU and 5F,2'dUrd were the most effective antileishmanials; resistance to both analogs was induced in L. major and L. mexicana. In each case it was found that the resistant cells had lost the transport capacity for the inducing drug. Metabolomics analysis found that the mechanism of action of 5-FU and 5F-2'dUrd was similar in both Leishmania species, with major changes in deoxynucleotide metabolism. We conclude that the pyrimidine salvage system is highly conserved in Leishmania species - essential information for the development of pyrimidine-based chemotherapy.

Te output feedback eigenvalue assignment problem for discrete-time systems is considered. Te problem is formulated frst as

an unconstrained minimization problem, where a three-term nonlinear conjugate gradient method is proposed to fnd a local

solution. In addition, a cut to the objective function is included, yielding an inequality constrained minimization problem, where a

logarithmic barrier method is proposed for fnding the local solution. Te conjugate gradient method is further extended to tackle

the eigenvalue assignment problem for the two cases of decentralized control systems and control systems with time delay. Te

performance of the methods is illustrated through various test examples.

Optimistic concurrency control is widely studied in the literature due to the properties of non-blocking and deadlock free execution especially in the domain of real-time systems. In this paper we review the substantial research of optimistic concurrency control protocols. We characterize them into four categories and explore their properties. Then we investigate the general concepts and properties related to Optimistic concurrency control Finally, we demonstrate a comparison table between the varieties of these

protocols.