Data science or “data-driven research” is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.

abstract In a study of the Bosnian-Herzegovinian (B&H) population, Y-chromosome marker frequencies for 100 individuals, generated using the PowerPlex Y23 kit, were used to perform Y-chromosome haplogroup assignment via Whit Athey's Haplogroup Predictor. This algorithm determines Y-chromosome haplogroups from Y-chromosome short tandem repeat (Y-STR) data using a Bayesian probability-based approach. The most frequent haplogroup appeared to be I2a, with a prevalence of 49%, followed by R1a and E1b1b, each accounting for 17% of all haplogroups within the population. Remaining haplogroups were J2a (5%), I1 (4%), R1b (4%), J2b (2%), G2a (1%), and N (1%). These results confirm previously published preliminary B&H population data published over 10 years ago, especially the prediction about the B&H population being a part of the Western Balkan area, which served as the Last Glacial Maximum refuge for the Paleolithic human European population. Furthermore, the results corroborate the hypothesis that this area was a significant stopping point on the “Middle East—Europe highway” during the Neolithic farmer migrations. Finally, since these results are almost completely in accordance with previously published data on B&H and neighboring populations generated by Y-chromosome single nucleotide polymorphism analysis, it can be concluded that in silico analysis of Y-STRs is a reliable method for approximation of the Y-chromosome haplogroup diversity of an examined population.

Introduction: Cysteine protease are biological catalysts which play a pivotal role in numerous biological reactions in organism. Much of the literature is inscribed to their biochemical significance, distribution and mechanism of action. Many diseases, e.g. Alzheimer’s disease, develop due to enzyme balance disruption. Understanding of cysteine protease’s disbalance is therefor a key to unravel the new possibilities of treatment. Cysteine protease are one of the most important enzymes for protein disruption during programmed cell death. Whether protein disruption is part of cell deaths is not enough clear in any cases. Thereafter, any tissue disruption, including proteolysis, generate more or less inflammation appearance. Review: This review briefly summarizes the current knowledge about pathological mechanism’s that results in AD, with significant reference to the role of cysteine protease in it. Based on the summary, new pharmacological approach and development of novel potent drugs with selective toxicity targeting cysteine protease will be a major challenge in years to come.

BACKGROUND We conducted a large international study to estimate fractions of head and neck cancers (HNCs) attributable to human papillomavirus (HPV-AFs) using six HPV-related biomarkers of viral detection, transcription, and cellular transformation. METHODS Formalin-fixed, paraffin-embedded cancer tissues of the oral cavity (OC), pharynx, and larynx were collected from pathology archives in 29 countries. All samples were subject to histopathological evaluation, DNA quality control, and HPV-DNA detection. Samples containing HPV-DNA were further subject to HPV E6*I mRNA detection and to p16(INK4a), pRb, p53, and Cyclin D1 immunohistochemistry. Final estimates of HPV-AFs were based on HPV-DNA, HPV E6*I mRNA, and/or p16(INK4a) results. RESULTS A total of 3680 samples yielded valid results: 1374 pharyngeal, 1264 OC, and 1042 laryngeal cancers. HPV-AF estimates based on positivity for HPV-DNA, and for either HPV E6*I mRNA or p16(INK4a), were 22.4%, 4.4%, and 3.5% for cancers of the oropharynx, OC, and larynx, respectively, and 18.5%, 3.0%, and 1.5% when requiring simultaneous positivity for all three markers. HPV16 was largely the most common type. Estimates of HPV-AF in the oropharynx were highest in South America, Central and Eastern Europe, and Northern Europe, and lowest in Southern Europe. Women showed higher HPV-AFs than men for cancers of the oropharynx in Europe and for the larynx in Central-South America. CONCLUSIONS HPV contribution to HNCs is substantial but highly heterogeneous by cancer site, region, and sex. This study, the largest exploring HPV attribution in HNCs, confirms the important role of HPVs in oropharyngeal cancer and drastically downplays the previously reported involvement of HPVs in the other HNCs.

UDK: 577.13:582  Plants produce a numerous and diverse secondary metabolites, organic compounds which are not essential and do not participate directly in the growth and development, but may have very important role in their adaptation and adjustment to particular environmental conditions. For humans, secondary metabolites are very important in medicine, pharmacology, food and cosmetics industries. The presence of eight types of secondary metabolites (anthocyanins, coumarins, emodins, fatty acids, phenols, saponins, steroids and tannins) in water extracts of leaves and barks of 25 broadleaf deciduous species from 15 families was qualitatively investigated by rapid phytochemical screening methods. According to literature data, in this study for the first time is determined the presence of six types of secondary metabolites in analyzed dendro species: anthocyanins in one species; both coumarins and phenols in five species; emodins in six species; saponins in eight species; and tannins in four species. Particular attention in further research should be given to Fagus sylvatica L., Populus tremula L., Quercus petraea (Matt.) Liebl., Robinia pseudoacacia L. and Sorbus aria (L.) Crantz. Since preliminary results of this study are promising it would be desirable both to identify active compounds and assess their potential antimicrobial and antioxidant activities.