Constructing software for analysis of neuron, glial and endothelial cell numbers and density in histological Nissl-stained rodent brain tissue

Cell number, density and volume of white and gray matter in brain structures are not constant values. Cellular alterations in brain areas might coincide with neurological and psychiatric pathologies as well as with changes in brain functionality during selection experiments, pharmacological treatment or aging. Several softwares were created to facilitate quantitative analysis of brain tissues, however results obtained from these softwares require multiple manual settings making the computing process complex and time-consuming. This study attempts to establish half automated software for fast, ergonomic and an accurate analysis of cellular density, cell number and cellular surface in morphologically different brain areas: cerebral cortex, pond and cerebellum. Images of brain sections of bank voles stained with standard cresyl-violet technique (Nissl staining), were analyzed in designed software. Results were compared with other commercially available tools regarding number of steps to be done by user and number of parameters possible to measure

A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe

Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adjacent populations of common origin. Machine learning (ML) techniques seem to be especially well suited for analyzing large datasets obtained using MPS. The Slavic populations constitute about a third of the population of Europe and inhabit a large area of the continent, while being relatively closely related in population genetics terms. In this proof-of-concept study, various ML techniques were used to classify DNA samples from Slavic and non-Slavic individuals. The primary objective of this study was to empirically evaluate the feasibility of discerning the genetic provenance of individuals of Slavic descent who exhibit genetic similarity, with the overarching goal of categorizing DNA specimens derived from diverse Slavic population representatives. Raw sequencing data were pre-processed, to obtain a 1200 character-long binary vector. A total of three classifiers were used—Random Forest, Support Vector Machine (SVM), and XGBoost. The most-promising results were obtained using SVM with a linear kernel, with 99.9% accuracy and F1-scores of 0.9846–1.000 for all classes

Konstrukcja systemów eksploracji danych dla obrazów rastrowych

The following article deals with possibilities for retrieving information from raster images stored in a database. An author’s approach for the possibility of a support system construction for the process of exploration of such data is proposed. The following article describes construction and implementation aspects.Poniższy artykuł porusza tematykę możliwości wydobywania wiedzy zawartej w obrazach rastrowych, magazynowanych w bazie danych. Zaproponowano autorskie podejście do możliwości konstrukcji systemu wspomagającego proces eksploracji takich danych. Omówione zostały aspekty konstrukcyjne oraz implementacyjne

The Data Exploration System for Image Processing Based on Server-Side Operations

Part 4: Pattern Recognition and Image ProcessingInternational audienceIn this paper the possibilities for construction of an ad hoc search system to examine large-sized raster image data sets, e.g. rock images or medical images, for analysis of its characteristic parameters are presented. A new solution for image exploration based on any attributes extracted with computer image analysis by using extensions for server-side operations is proposed