Band 13, Heft 2

The Gram-positive bacterium Streptococcus mutans is the principal causative agent of human tooth decay, an oral disease that affects the majority of the world’s population. Although the complete S. mutans genome is known, approximately 700 proteins are still annotated as hypothetical proteins, as no three-dimensional structure or homology with known proteins exists for them. Thus, the significant portion of genomic sequences coding for unknown-function proteins makes the knowledge of pathogenicity and survival mechanisms of S. mutans still incomplete. Plasmids are found in virtually every species of Streptococcus , and some of these mediate resistance to antibiotics and pathogenesis. However, there are strains of S. mutans that contain plasmids, such as LM7 and UA140, to which no function has been assigned yet. In this work, we describe an in silico study of the structure and function of all the S. mutans proteins encoded by pLM7 and pUA140 plasmids to gain insight into their biological function. A combination of different structural bioinformatics methodologies led to the identification of plasmidic proteins potentially required for the bacterial survival and pathogenicity. The structural information obtained on these proteins can be used to select novel targets for the design of innovative therapeutic agents towards S. mutans .

The application of the fuzzy oil drop model to the analysis of protein structure is shown using two proteins. The selection of these two examples is due to their opposite character. Two proteins were selected representing very high order and very high disorder with respect to the organized uni-central hydrophobic core in proteins (one centrally localized concentration of high hydrophobicity). These two cases are to show examples of the large spectrum of variability of local organization of the hydrophobic core in proteins. The importance of the observation presented in this paper is significant with respect to large sets of proteins discussed in separate publications.

The present study attempts to explain the reason for the selective generation of an increase in intensity of Congo red (CR) fluorescence as an effect of the dye interacting with proteins and polysaccharides. This supramolecular dye, which creates ribbon-shaped micelles in aqueous solutions when excited with blue light (470 nm), presents low fluorescence with a maximum within the orange-red light range (approximately 600 nm). In the same conditions, CR-stained preparations of heat-denatured proteins, some native proteins (e.g. cell surface receptors) and cellulose show intense orange-red fluorescence when observed using a fluorescence microscope. The fluormetric measurements showed that the factors that cause the dissociation of the ribbon-shaped CR micelle – ethanol, urea, dimethyl sulfoxide (DMSO) and cholate – all contributed to a significant increase in the fluorescence intensity of the CR solutions. The fluorescence measurements of CR bound to the immunoglobulin light lambda (L λ) chain and soluble carboxymethyl cellulose (CMC) showed a fluorescence intensity which was many times higher. In the case of the denatured (65°C) immunoglobulin L λ chain, the fluorescence intensity significantly exceeded the values observed for the factors which break down the CR micelles. The dissociation of the ribbon-shaped micelles and the complexation of the monomeric CR form with polymers are two of the factors explaining the intense fluorescence of protein and polysaccharide preparations stained with CR.

The presented results cover issues related to proteins that were “never born in nature”. The paper is focused on identifying genetic information stretches of protein sequences that were not identified to be existing in nature. The aim of the work was finding traces of “never born proteins” (NBP) everywhere in completely sequenced genomes including regions not expected as carrying the genetic information. The results of analyses relate to the search of the genetic material of species from different levels of the evolutionary tree from yeast through plant organisms up to the human genome. The analysis concerns searching the genome sequences. There are presented statistical details such as sequence frequencies, their length, percent identity and similarity of alignments, as well as E value of sequences found. Computations were performed on gLite-based grid environment. The results of the analyses showed that the NBP genetic record in the genomes of the studied organisms is absent at a significant level in terms of identity of contents and length of the sequences found. Most of the found sequences considered to be similar do not exceed 50% of the length of the NBP output sequences, which confirms that the genetic record of proteins is not accidental in terms of composition of gene sequences but also as regards the place of recording in genomes of living organisms.

The human brain is able to learn language by processing written or spoken language. Recently, several deep neural networks have been successfully used for natural language generation. Although it is possible to train such networks, it remains unknown how these networks (or the brain) actually process language. A scalable method for distributed storage and recall of sentences within a neural network is presented. A corpus of 59 million words was used for training. A system using this method can efficiently identify sentences that can be considered reasonable replies to an input sentence. The system first selects a small number of seeds words which occur with low frequency in the corpus. These seed words are then used to generate answer sentences. Possible answers are scored using statistical data also obtained from the corpus. A number of sample answers generated by the system are shown to illustrate how the method works.

By defining a midline and selecting six pairs of the landmarks of the human face on digital photographs, we extracted the symmetry of the human face by means of digital techniques. As a first approach to the symmetry of the human face, the distances and the tilts from the midline, between similar landmarks, were computed and averaged, respectively. The procrustes analysis and the histogram of oriented gradients (HOG), applied on patches on the six pairs of the landmarks of the human face, were used as a second approach to the symmetry of the human face. To have a better estimation of the symmetry of the whole human face, the photographs in grayscale and color were cut on pairs of strips, equally spaced from the midline, and then the strips were compared by the HOG feature extractor. The symmetry of the human face was extracted from 89 photographs of human faces (37 females and 52 males, ages 28.67±6.65 and 35.65±12.2 years, respectively). The HOG feature extractor applied on strips for the photographs in color and grayscale provided more confident values for the symmetry of the human face, which was well correlated with the assigned value by the photographers and physiotherapists. Also, an experiment was performed to evaluate the attractiveness as a function of the human face symmetry; thus, two groups of men and women were asked to sort digital photographs of women and men according to the attractiveness of women/men on the photographs. The results show that the most selected digital photographs were those with the highest symmetry scores.

Novel, easy-automation technologies such as three-dimensional (3D) printing and reverse engineering can improve the training of medical and allied health professionals and everyday clinical practice. This paper aims at the presentation of its own concept of the repository of medical images for education and everyday clinical practice purposes. Presented concept of the repository constitutes a relatively novel solution, but its further development may lead to the novel family of commercial initiatives aiming at joining common efforts toward optimization of 3D-based technologies in everyday clinical practice and online e-learning system.