The aim of our study was to investigate HER-2 and TOP2A gene status and their correlation with Bcl-2, p53, Ki67, ssDNA, and clinicopathological parameters in four molecular subtypes of breast cancer. Seventy-four paraffin-embedded samples are immunohistochemically studied for the expression of estrogen receptor (ER), progesterone receptor (PR), HER-2, p53, Bcl-2, ssDNA, and Ki67, while HER-2 and TOP2A gene status by fluorescence in situ hybridization was investigated in 60 samples. Luminal A and B subtypes were characterized with small tumor size, intermediate histological grade, negative lymph node, and metastatic status, while triple negative and HER-2 positive subtypes were associated with larger tumor size, poorly differentiated tumors, and positive lymph node status. p53, Ki67, and ssDNA expression was higher in triple negative and HER-2 positive than in luminal subtypes, while ER, PR, and Bcl-2 dominated in luminal subtypes. HER-2 gene status was higher in luminal B and HER-2 positive than in luminal A and triple negative subtypes, while TOP2A gene status was similar. HER-2 gene status positively correlated with TOP2A gene status, HER-2 receptor, and histological grade, while negative correlation characterized relationship between HER-2 gene status and ER, PR, and Bcl-2. The shortened overall survival period characterized patients from triple negative breast cancer subtype (18.7 months). HER-2 and TOP2A gene amplification showed a tendency to be associated with larger tumor size, positive lymph node status, high level of apoptotic and proliferative indexes, and low level of p53 and Bcl-2 expression, which all together indicate group of patients with similar outcome during the progression of the disease.

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One of the main concerns with Particle Swarm Optimization (PSO) is to increase or maintain diversity during search in order to avoid premature convergence. In this study, a Performance Class-Based learning PSO (PCB-PSO) algorithm is proposed, that not only increases and maintains swarm diversity but also improves exploration and exploitation while speeding up convergence simultaneously. In the PCB-PSO algorithm, each particle belongs to a class based on its fitness value and particles might change classes at evolutionary stages or search step based on their updated position. The particles are divided into an upper, middle and lower. In the upper class are particles with top fitness values, the middle are those with average while particles in the bottom class are the worst performing in the swarm. The number of particles in each group is predetermined. Each class has a unique learning strategy designed specifically for a given task. The upper class is designed to converge towards the best solution found, Middle class particles exploit the search space while lower class particles explore. The algorithm’s strength is its flexibility and robustness as the population of each class allows us to prioritize a desired swarm behavior. The Algorithm is tested on a set of 8 benchmark functions which have generally proven to be difficult to optimize. The algorithm is able to be on par with some cutting edge PSO variants and outperforms other swarm and evolutionary algorithms on a number of functions. On complex multimodal functions, it is able to outperform other PSO variants showing its ability to escape local optima solutions.

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Problematika rada usmerena je na interkulturne izazove sa kojima se projektni menadžeri susreću pri upravljanju globalnim projektnim timovima. Pokazano je koji su to izazovi, u kojoj meri je važno poznavanje drugih kultura i na koje načine se mogu prevazići problemi koji se često javljaju. Cilj empirijskog istraživanja jeste da se vidi kako odabrani članovi srpskih i američkih globalnih timova doživljavaju i prevazilaze interkulturalne komunikacione probleme i kako oni utiču na performanse globalnog tima.