Humans rely on a finely tuned ability to recognize and adapt to socially relevant patterns in their everyday face-to-face interactions. This allows them to anticipate the actions of others, coordinate their behaviors, and create shared meaning-to communicate. Social robots must likewise be able to recognize and perform relevant social patterns, including interactional synchrony, imitation, and particular sequences of behaviors. We use existing empirical work in the social sciences and observations of human interaction to develop nonverbal interactive capabilities for a robot in the context of shadow puppet play, where people interact through shadows of hands cast against a wall. We show how information theoretic quantities can be used to model interaction between humans and to generate interactive controllers for a robot. Finally, we evaluate the resulting model in an embodied human-robot interaction study. We show the benefit of modeling interaction as a joint process rather than modeling individual agents.

Annular pancreas is a rare embryonal abnormality. Its manifestation in adulthood is often pinpointed with a substantial delay, which is most often attributed to pancreatitis, biliary pathology or dyspepsia. We present a case of a 28-year-old woman who had exacerbating symptoms of high bowel obstruction from 20th week of pregnancy, progressing after premature delivery. Diagnostic work-up revealed partial annular pancreas compressing the duodenum. Despite attempts of conservative treatment, her state deteriorated to such an extent that surgery was indicated and gastrojejunal bypass created. Her postoperative recovery was uneventful. In cases in which symptoms of high bowel obstruction in pregnancy persist and prostration occurs, we suggest close monitoring and a more thorough diagnostic approach. The question remains whether annular pancreas presents a cause of pathologic findings, a cofactor, or a mere accidental diagnosis in the development of superposed pathologies.

CONFLICT OF INTEREST: NONE DECLARED SUMMARY Introduction Agency for healthcare quality and accreditation in Federation of Bosnia and Herzegovina (AKAZ) is authorized body in the field of healthcare quality and safety improvement and accreditation of healthcare institutions. Beside accreditation standards for hospitals and primary health care centers, AKAZ has also developed accreditation standards for family medicine teams. Methods Software development was primarily based on Accreditation Standards for Family Medicine Teams. Seven chapters / topics: (1. Physical factors; 2. Equipment; 3. Organization and Management; 4. Health promotion and illness prevention; 5. Clinical services; 6. Patient survey; and 7. Patient’s rights and obligations) contain 35 standards describing expected level of family medicine team’s quality. Based on accreditation standards structure and needs of different potential users, it was concluded that software backbone should be a database containing all accreditation standards, self assessment and external assessment details. In this article we will present the development of standardized software for self and external evaluation of quality of service in family medicine, as well as plans for the future development of this software package. Conclusion Electronic data gathering and storing enhances the management, access and overall use of information. During this project we came to conclusion that software for self assessment and external assessment is ideal for accreditation standards distribution, their overview by the family medicine team members, their self assessment and external assessment.

In this paper we describe CB513 a non-redundant dataset, suitable for development of algorithms for prediction of secondary protein structure. A program was made in Borland Delphi for transforming data from our dataset to make it suitable for learning of neural network for prediction of secondary protein structure implemented in MATLAB Neural-Network Toolbox. Learning (training and testing) of neural network is researched with different sizes of windows, different number of neurons in the hidden layer and different number of training epochs, while using dataset CB513.