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Snežana Adamović’s book is written in simple language, so one can easily understand the complexity of the two icons of the Serbian Orthodox Church and Serbian literature: St. Bishop Nicholai Velimirovich and St. Bishop Peter II Petrović Njegoš. In addition to being composed as a diptych, spontaneously or not, with the icon of Christ the God-Man in the center, a triptych and an intercession (gr. δέησις) arise. With freedom of speech without imposing final answers and definitions, the author’s work is didactic. The goal is to reveal through these icons a Christian method of life in the quest for eternal answers to the meaning of existence. That is why the conclusion of this book is Lord Jesus Christ Himself — the visible Word of the invisible God the Father, as a fundament in the oratory of Saint Bishop Nicholai.

Introduction. Mucous membrane pemphigoid (MMP) is a rare autoimmune, chronic inflammatory disease that affects mucous membranes, most commonly the eyes and mouth, with or without skin involvement. It is a complex disease with several complications, including scarring, especially on conjunctival mucosa, that can lead to visual loss. Case report. We report four patients (two men and two women) with MMP. In all patients, the disease started between seventy and eighty years of age. The diagnosis was confirmed based on clinical appearance, histology, direct and indirect immunofluorescence studies, indirect split skin technique, and enzyme-linked immunosorbent assay (ELISA) test. The majority of lesions were on the gums and buccal mucosa; one patient had laryngeal involvement and a lesion on the umbilicus. No ocular involvement and no malignancy were detected. Direct immunofluorescence tests revealed continuous linear IgG deposition in the basal membrane zone in two patients, and they were treated with oral nicotinamide and tetracycline hydrochloride. In two patients, we detected IgG along with IgA linear deposition; they received treatment with methylprednisolone. Complete remission was achieved in all patients. Conclusion. Early diagnosis and an adequate therapeutic approach are necessary for the MMP treatment in long-term disease control and reduction of disease-related complications.

The importance of the accurate forecasting of power outputsof wind-based generation systems is increasing, as their contributions to the total system generation are rising. However, wind energy resource exhibits strong and stochastic spatio- temporal variations, which further combine with the uncertainties in WF operating regimes, i.e., numbers of wind turbines in normal operation, under curtailment, or that are faulty/disconnected. This paper presents a novel approach for efficient dealing with uncertainties in hour-ahead forecasted WF power outputs. It first applies Bayesian convolutional neural network-bidirectional long short-term memory (Bayesian CNN-BiLSTM) method, which allows for a more accurate probabilistic forecasting of wind speed, air density and wind direction, using the nearby WFs as additional input data. The WF operating regimes are also predicted using the same Bayesian CNN-BiLSTM structure. Afterwards, a high-dimensional Vine-Gaussian mixture Copula model is combined with Bayesian CNN-BiLSTM model to evaluate uncertainties in the WF outputs based on a cross-correlational conditioning of the forecasted weather variables and operating regimes. The proposed combined model is applied and validated using the actual field measurements from two WF clusters in close locations in Croatia and is also benchmarked against several other models.

The aim of this paper is to map the reconfiguration and displacement of the emerging trauma of the Holocaust in the cinematic narratives of SFR Yugoslavia. The analysis of three nearly forgotten Yugoslav films of the 1960s – Killer on Leave (Mörder auf Urlaub/Ubica na odsustvu/Ubica je došao iz prošlosti, 1965, Boško Bošković), Witness Out of Hell (Bittere Kräuter/Gorke trave, 1966, Žika Mitrović) and Smoke (Dim, 1967, Slobodan Kosovalić) – follows Kansteiner’s thesis about the changes of Holocaust memorial narratives in the films shown on German television in the 1970s. Accordingly, I claim that the analyzed films position the trauma of the Holocaust as a crime committed by others, over there, and then in the past. Further, they broaden the trauma to accommodate the diversified roles of victims, perpetrators, witnesses and bystanders, and help the Germans (and other Europeans as well) come to terms with the Nazi criminal legacy and their own role. The co-productional terms allow the films to balance the memory of the Holocaust as both anti-fascist (East Germany) and cosmopolitan, multidirectional (West Germany) within the real Yugoslav/German symbolic narrative space and its intrinsic poetics (e.g., memorialization and sacralization). Cilj ovog rada je da mapira rekonfiguraciju i izmeštanje traume Holokausta u nastajanju u kinematografskim narativima SFR Jugoslavije. Analiza tri skoro zaboravljena jugoslovenska filma iz 1960-ih godina – Ubica na odsustvu (1965, Boško Bošković), Svedok iz pakla (1966, Mitrović) i Dim (1967, Slobodan Kosovalić) – prati Kanštajnerovu tezu o promenama memorijalnih narativa Holokausta u filmovima prikazivanim na nemačkoj televiziji 1970-ih godina. Shodno tome, tvrdim da analizirani filmovi traumu Holokausta pozicioniraju kao zločin koji su počinili drugi, negde, a zatim u prošlosti. Dalje, oni proširuju traumu da bi se prilagodili raznovrsnim ulogama žrtava, počinilaca, svedoka i posmatrača, i pomažu Nemcima (i drugimEvropljanima) da se pomire sa nacističkim zločinačkim nasleđem i sopstvenom ulogom. Koprodukcijski termini omogućavaju filmovima da uravnoteže sećanje na Holokaust, kao i antifašističko (Istočna Nemačka) i kosmopolitsko, višesmerno (Zapadna Nemačka) u okviru stvarnog jugoslovenskog/nemačkog simboličkog narativnog prostora i njegove unutrašnje poetike (na primer, memorijalizacija i sakralizacija).

Background: Transsexuality is explained and defined as a gender-identity disorder, characterised by very strong conviction of belonging to the opposite sex and has been associated with a distinct neuroanatomical pattern. Materials and methods: We performed a structural analysis in search of possible differences in grey matter structures based on magnetic resonance imaging scans of the brains of 26 individuals between 19 and 38 years of age. The participants were divided into two groups of 15 controls and 11 transgender individuals. The segmentation of subcortical grey matter was performed using FIRST model a model-based segmentation/registration tool, from FSL software package. Results: The results showed that the volume of the brain region called nucleus accumbens on the left side was significantly smaller in the group of transgender individuals compared to the control. It was the most important parameter which was shown to make distinction between two examined groups. Conclusions: The results also showed decreased volumes of the left thalamus, right hippocampus and right caudate nucleus.

In this paper, we present an approach to the reconstruction of signals exhibiting sparsity in a transformation domain, having some heavily disturbed samples. This sparsity-driven signal recovery exploits a carefully suited random sampling consensus (RANSAC) methodology for the selection of an inlier subset of samples. To this aim, two fundamental properties are used: a signal sample represents a linear combination of the sparse coefficients, whereas the disturbance degrade original signal sparsity. The properly selected samples are further used as measurements in the sparse signal reconstruction, performed using algorithms from the compressive sensing framework. Besides the fact that the disturbance degrades signal sparsity in the transformation domain, no other disturbance-related assumptions are made -- there are no special requirements regarding its statistical behavior or the range of its values. As a case study, the discrete Fourier transform (DFT) is considered as a domain of signal sparsity, owing to its significance in signal processing theory and applications. Numerical results strongly support the presented theory. In addition, exact relation for the signal-to-noise ratio (SNR) of the reconstructed signal is also presented. This simple result, which conveniently characterizes the RANSAC-based reconstruction performance, is numerically confirmed by a set of statistical examples., Comment: 4 figures, 15 pages

International audience; With its aim to reduce the amount of sensed data and to improve the energy efficiency, compressive sensing (CS) is recently witnessing a growing research interest in remote-sensing applications. The Fourier transform domain plays a significant role as a signal-processing tool and the sparsity domain for the CS-reconstruction methods. A generalized expression for the error in the reconstruction of nonuniformly sampled, approximately sparse, or nonsparse, noisy signals in the Fourier domain is presented in this letter. This expression holds for a wide range of practically important nonuniform signal-sampling strategies, covering the uniform and completely random sampling as the special cases. Additive noise and noise-folding effects are included in the analysis. Statistical examples and two real-world examples validate the presented theory.

This study aims to obtain data on terrestrial radioactivity coming from naturally occurring (40K, 226Ra, 232Th, and 238U) and artificial (137Cs) radionuclides in surface soil (0-10 cm) of selected eighteen public-access urban areas in Krusevac city, Serbia, and to assess the corresponding health effects for citizens using those areas for recreational purposes. The specific activities of investigated radionuclides were analyzed using HPGe gamma-ray spectrometry. The mean specific activity of 40K, 226Ra, 232Th, 238U, and 137Cs was found to be 353, 39.8, 38.9, 41.0, and 5.9, respectively, in compliance with their values in other parts of Serbia and neighboring countries reported in similar researches. To evaluate the human health risk associated with radionuclides, conservative exposure assumptions and models recommended by the United States Environmental Protection Agency were employed taking into account three exposure routes: ingestion, inhalation of soil, and external irradiation. The absorbed gamma dose rate in the air due to natural radionuclides in soil was calculated. The calculated indices suggested that the radiation risk arising from natural and artificial radionuclides was not significant. The total excess lifetime cancer based on the 95 % upper confidence limit of the specific activities mean was calculated to be 5.89?10?6, lower than the tolerable risk for regulatory purposes (10?4). Among investigated radiation exposure pathways, external exposure was the most contributing one for the health risk. The results obtained for the city's parks and playgrounds suggested their safe use for recreational purposes from the radioecological point of view.

Within the Compressive Sensing (CS) paradigm, sparse signals can be reconstructed based on a reduced set of measurements. Reliability of the solution is determined by the uniqueness condition. With its mathematically tractable and feasible calculation, coherence index is one of very few CS metrics with a considerable practical importance. In this paper, we propose an improvement of the coherence based uniqueness relation for the matching pursuit algorithms. Starting from a simple and intuitive derivation of the standard uniqueness condition based on the coherence index, we derive a less conservative coherence index-based lower bound for signal sparsity. The results are generalized to the uniqueness condition of the $l_0$-norm minimization for a signal represented in two orthonormal bases., 5 pages, 1 figure

Background/Aim. The quality of life regarding oral health is a multidimensional concept that shows to which extent oral diseases and disorders impact the quality of life of each individual. One of the most frequently used questionnaires for testing the impact of oral health on the quality of life is the Oral Health Impact Profile-14 (OHIP-14). The aim of this study was to test the short form of the OHIP-14 questionnaire among professional members of the Serbian Armed Forces. Methods. A cross-sectional design was applied in this study. The reliability of the questionnaire was determined by Cronbach's coefficient alpha. The validity of the OHIP-14 questionnaire was assessed by the factor analysis. Results. A total of 1,611 participants, professional members of the Serbian Armed Forces, with a mean age of 33.5, agreed to participate in the study (the participation rate was 97.4%). The majority of the participants were males (88.8%). The overall Cronbach's alpha coefficient of the OHIP-14 scale was 0.960. The alpha reliability coefficients of all OHIP-14 subscales were between 0.732 and 0.865, indicating that the internal consistency reliability of all subscales was good. The Principal components analysis, same as inspection of the scree plot and parallel analysis supported a one-factor solution for the OHIP-14 scale. Conclusion. The OHIP-14 manual is equally reliable for deter-mining the impact of oral health on the quality of life of professional members of the Serbian Armed Forces as it is with the civilian population.

A sparsity-assisted algorithm for the detection and removal of impulsive disturbances is presented. It is assumed that the uncorrupted signal is sparse or highly concentrated in the discrete cosine transform (DCT) domain or the windowed modified DCT (MDCT) domain. To detect whether a specific signal sample is corrupted by a disturbance, the gradient of the sparsity measure is estimated in the space of the signal samples by varying the values of signal samples. Samples whose variations caused the highest estimated gradient values are considered to be far (dislocated) from the sparsity measure minimum. They are labeled as disturbed and further classified as unavailable. Subsequently, these samples are reconstructed using a compressive sensing reconstruction approach. Numerical results support the presented theory and indicate potential applicability in audio signal processing, particularly in the removal of impulsive disturbances and concealment of unavailable, disturbed or lost data.

Digital images can be considered as sparse or approximately sparse in the two-dimensional discrete cosine transform domain. According to the compressive sensing theory, these images can be recovered from a reduced set of pixels. Such reconstructions are influenced by the noise and the non-reconstructed coefficients for approximately sparse images. In the literature, this kind of reconstruction error is described by the error bound relations. In some hardware implementations, the pixels may be sensed with a low number of bits, causing quantization error. In this paper, we derive exact formula for the expected error energy in the reconstructed images, caused by the pixels quantization. It is validated trough numerical examples.

This study aimed to morphometrically examine the development of glomeruli and tubules in the kidney cortex of human foetuses at different gestational ages (GAs). We also investigated the expression of the proliferation marker Ki-67 and apoptosis-related markers Bcl-2 and Bax during nephrogenesis using immunohistochemistry. Kidney samples from 38 human foetuses of both sexes with GA ranging from 13 to 40 weeks were analysed. The average area, perimeter, and Feret's diameter of the glomeruli gradually decreased up to a period of 29–32 weeks of gestation and subsequently increased until a period of 37–40 weeks. There was a weak negative correlation with GA. In contrast, the areal density of glomeruli increased up to a period of 21–24 weeks and then gradually decreased until a period of 37–40 weeks, showing a moderate negative correlation with GA. The average area of renal tubules slightly decreased until a period of 21–24 weeks of gestation and then gradually increased until a period of 36–40 weeks, showing a moderate positive correlation with GA. The average areal density of renal tubules increased significantly until a period of 21–24 weeks of gestation, remained relatively constant until a period of 33–36 weeks, and then increased significantly at 36–40 weeks. There was a strong positive correlation with GA. Our results showed that Ki-67 was expressed in numerous cells of the metanephric mesenchyme, pretubular aggregates, renal vesicles, comma-shaped bodies, and early S-shaped bodies. During subsequent development the expression of Ki-67 was markedly reduced. Similarly, Bcl-2 was strongly expressed in induced nephrogenic progenitor cells, pretubular aggregates, renal vesicles, and comma-shaped bodies. As vascularisation and maturation of the nephron proceeded, Bcl-2 staining became less intense and limited. Weak Bax expression was observed in individual scattered cells within segments of the nephrons at all developmental stages.

Generative Adversarial Networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some recent GANs (e.g., InfoGAN), are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images are with different properties due to the imaging mechanism. Despite the success of InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties, thus editing specific properties usually relies on empirical trials, unreliable and time-consuming. In this paper, we show that latent codes are disentangled to affect the properties of SAR images in a non-linear manner. By introducing some property estimators for latent codes, we are able to provide a completely analytical nonlinear model to decompose the entangled causality between latent codes and different properties. The qualitative and quantitative experimental results further reveal that the properties can be calculated by latent codes, inversely, the satisfying latent codes can be estimated given desired properties. In this case, properties can be manipulated by latent codes as we expect., Comment: 13 pages, 14 figures

Background One of the early signs of aging is loss of jawline contour. Not all cases require surgical intervention and soft-tissue augmentation with injectable fillers may restore the profile and youthful appearance of the jawline. Objective To demonstrate the effectiveness and safety of calcium hydroxylapatite with lidocaine [CaHA (+); Radiesser (+)] to improve the contour of jawline after deep (subdermal and/or supraperiosteal) injection. Methods Healthy eligible patients with moderate or severe ratings on the Merz Jawline Assessment Scale (MJAS) were randomized 2:1 to treatment with CaHA (+) or to control. Patients in the control group remained untreated until week 12, then received delayed treatment. Touch-ups were allowed in both groups, and re-treatment was allowed in the treatment group only. Effectiveness was evaluated on the MJAS, patient and investigator Global Aesthetic Improvement Scales, and FACE-Qt questionnaires. Adverse events were recorded over a 60-week period. Results Treatment response rate (g1-point MJAS improvement) was 93/123 (75.6%) for the treatment group and 5/57 (8.8%) for the control/delayed-treatment group at week 12. The difference between response rates was statistically significant (Pl0.0001), showing superiority of treatment over control. Satisfaction with aesthetic improvement was reported by patients and treating investigators throughout the study. A total of 76/113 (67.3%) patients who responded to treatment 12 weeks after initial injection also demonstrated persistent improvement 48 weeks after initial treatment. The study demonstrated a favorable safety profile, with no reported unexpected adverse events. Conclusions CaHA (+) is a safe and effective treatment for improving the contour of the jawline. J Drugs Dermatol. 2021;20(11): 1231-1238. doi:10.36849/JDD.6442.

Background Surveillance of cerebral palsy in Europe (SCPE) presents the first population-based results on neuroimaging findings in children with cerebral palsy (CP) using a magnetic resonance imaging classification system (MRICS). Method MRIs of children with CP born between 1999 and 2009 from 18 European countries were analyzed. MRICS identifies patterns of brain pathology according to timing during brain development which was analyzed with respect to CP subtypes and gestational age. Results MRIs or written reports from 3,818 children were available. The main clinical characteristics were similar to the 5,415 without such data. Most frequent was predominant white matter injury (49%), followed by predominant gray matter injury (21%). Maldevelopments were found in 11% of cases. Miscellaneous findings were present in 8.5% and normal findings in 10.6%. MRI patterns of children with unilateral spastic, bilateral spastic, and dyskinetic CP were mainly lesional (77, 71, and 59%, respectively), whereas children with ataxic CP had more maldevelopments, miscellaneous, and normal findings (25, 21, and 32%, respectively). In children born preterm, predominant white matter injury was most prevalent (80% in children born Conclusion Analysis of MRI in the European CP database identified CP as a mainly lesional condition on a large population basis, maldevelopments were relatively uncommon. An exception was ataxic CP. Children born preterm mostly presented with a lesion typical for their gestational age (GA) at birth. The decreasing prevalence of CP in this group suggests that progress in perinatal and neonatal medicine may lead to a reduction of these lesions.

The area of Data Analytics on graphs promises a paradigm shift, as we approach information processing of new classes of data which are typically acquired on irregular but structured domains (such as social networks, various ad-hoc sensor networks). Yet, despite the long history of Graph Theory, current approaches tend to focus on aspects of optimisation of graphs themselves rather than on eliciting strategies relevant to the objective application of the graph paradigm, such as detection, estimation, statistical and probabilistic inference, clustering and separation from signals and data acquired on graphs. In order to bridge this gap, we first revisit graph topologies from a Data Analytics point of view, to establish a taxonomy of graph networks through a linear algebraic formalism of graph topology (vertices, connections, directivity). This serves as a basis for spectral analysis of graphs, whereby the eigenvalues and eigenvectors of graph Laplacian and adjacency matrices are shown to convey physical meaning related to both graph topology and higher-order graph properties, such as cuts, walks, paths, and neighborhoods. Through a number of carefully chosen examples, we demonstrate that the isomorphic nature of graphs enables both the basic properties of data observed on graphs and their descriptors (features) to be preserved throughout the data analytics process, even in the case of reordering of graph vertices, where classical approaches fail. Next, to illustrate the richness and flexibility of estimation strategies performed on graph signals, spectral analysis of graphs is introduced through eigenanalysis of mathematical descriptors of graphs and in a generic way. Finally, benefiting from enhanced degrees of freedom associated with graph representations, a framework for vertex clustering and graph segmentation is established based on graph spectral representation (eigenanalysis) which demonstrates the power of graphs in various data association tasks, from image clustering and segmentation trough to low-dimensional manifold representation. The supporting examples demonstrate the promise of Graph Data Analytics in modeling structural and functional/semantic inferences. At the same time, Part I serves as a basis for Part II and Part III which deal with theory, methods and applications of processing Data on Graphs and Graph Topology Learning from data.

The existence and uniqueness conditions are a prerequisite to ensure the reliable reconstruction of sparse signals from reduced sets of measurements within the compressive sensing (CS) paradigm. However, despite their underpinning role in practical applications, the existing uniqueness relations are either computationally prohibitive to implement [the restricted isometry property (RIP)] or involve mathematical tools that are beyond the standard background of engineering graduates (the coherence index). This may introduce conceptual and computational obstacles in the development of engineering intuition, design of suboptimal practical solutions, and understanding of theoretical and practical limitations of the CS framework.

The area of Data Analytics on graphs deals with information processing of data acquired on irregular but structured graph domains. The focus of Part I of this monograph has been on both the fundamental and higher-order graph properties, graph topologies, and spectral representations of graphs. Part I also establishes rigorous frameworks for vertex clustering and graph segmentation, and illustrates the power of graphs in various data association tasks. Part II embarks on these concepts to address the algorithmic and practical issues related to data/signal processing on graphs, with the focus on the analysis and estimation of both deterministic and random data on graphs. The fundamental ideas related to graph signals are introduced through a simple and intuitive, yet general enough case study of multisensor temperature field estimation. The concept of systems on graph is defined using graph signal shift operators, which generalize the corresponding principles from traditional learning systems. At the core of the spectral domain representation of graph signals and systems is the Graph Fourier Transform (GFT), defined based on the eigendecomposition of both the adjacency matrix and the graph Laplacian. Spectral domain representations are then used as the basis to introduce graph signal filtering concepts and address their design, including Chebyshev series polynomial approximation. Ideas related to the sampling of graph signals, and in particular the challenging topic of data dimensionality reduction through graph subsampling, are presented and further linked with compressive sensing. The principles of time-varying signals on graphs and basic definitions related to random graph signals are next reviewed. Localized graph signal analysis in the joint vertex-spectral domain is referred to as the vertex-frequency analysis, since it can be considered as an extension of classical time-frequency analysis to the graph serving as signal domain. Important aspects of the local graph Fourier transform (LGFT) are covered, together with its various forms including the graph spectral and vertex domain windows and the inversion conditions and relations. A link between the LGFT with a varying spectral window and the spectral graph wavelet transform (SGWT) is also established. Realizations of the LGFT and SGWT using polynomial (Chebyshev) approximations of the spectral functions are further considered and supported by examples. Finally, energy versions of the vertex-frequency representations are introduced, along with their relations with classical timefrequency analysis, including a vertex-frequency distribution that can satisfy the marginal properties. The material is supported by illustrative examples.

Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications

Graphs are irregular structures that naturally represent the multifaceted data attributes; however, traditional approaches have been established outside signal processing and largely focus on analyzing the underlying graphs rather than signals on graphs. Given the rapidly increasing availability of multisensor and multinode measurements, likely recorded on irregular or ad hoc grids, it would be extremely advantageous to analyze such structured data as "signals on graphs" and thus benefit from the ability of graphs to incorporate spatial sensing awareness, physical intuition, and sensor importance, together with the inherent "local versus global" sensor association. The aim of this lecture note is, therefore, to establish a common language between graph signals that are observed in irregular signal domains and some of the most fundamental paradigms in digital signal processing (DSP), such as spectral analysis, system transfer function, digital filter design, parameter estimation, and optimal denoising.

This paper examines the impact of external quality assurance evaluations conducted by the Institutional Evaluation Programmes (IEP). IEP is an international quality assurance agency offering improv...

The detection of the gravitational-wave (GW) events from the noisy measured data requires reliable filtering techniques. This paper provides the performance analysis of the intersection of confidence intervals (ICI) and local polynomial approximation (LPA) based techniques when applied to the filtering of the GW signals in the real- life, low signal-to-noise- ratio (SNR) conditions. The techniques’ sensitivities to the parameter changes are studied by evaluating the performance indices values. The analysis shows that the proper selection of the algorithm parameters is important in obtaining the optimal filtering performance, with the technique based on the relative intersection of confidence intervals (RICI) having an additional parameter that may compensate for the sub-optimally selected ICI parameter. Moreover, the evaluation of the results obtained by applying the optimally tuned filtering techniques suggests that the RICI-based technique provides better performance in filtering the GW signals, even compared to the tested wavelet-based techniques.

Machine Learning(ML) is an important scientific area and applied science reality. It is being used by numerous industries, its use is vast and now it seems endless. In this paper we use ML methods to analyze students' grades for similar subjects, and trying to find the best model for prediction of targeted subject grade. The success of the model, or lack of it, can pinpoint to specific conclusions regarding other aspect of students performance and organization of the subject.

Starting from the realistic assumption that digital images are highly concentrated in the domain of the two-dimensional discrete cosine transform (2D-DCT), we have exploited principles from the compressive sensing (CS) theory to build up a blind denoising framework for the detection and removal of outliers. Namely, we have developed two techniques, both of which use the fact that pixels are linear combinations of sparse coefficients, and that the disturbances degrade the concentration of the image. One of the techniques directly uses minimization of concentration measure of the image to detect the positions of corrupted pixels, which is followed by compressive sensing reconstruction. The second technique uses RANSAC-based automatic selection of pixels that are likely not corrupted, and are further considered as available measurements. The selection is performed based on the CS principles. In this paper, we present a short comparative analysis of these techniques based on numerical experiments.

If acquired using multiple sensors, non-stationary multicomponent signals can be decomposed into individual components by exploiting interdependences of signals from different channels. Earlier, we have proposed a decomposition approach being able to extract individual non-stationary signal components even in the challenging cases when their domains of support overlap in the time, frequency or joint time-frequency (TF) domains. The approach is based upon the eigenvalue analysis of the multichannel autocorrelation matrix and minimizations of concentration measures calculated using TF representations. In this paper, we investigate the influence of the number of sensors (channels) and external noise variance to the outcome of the decomposition process.

The search for an effective and non-toxic radioprotector is ongoing. We tested a novel, natural aminothiol-based radioprotector, GL2011, that was applied 30 min, 3 h or 6 h after the exposure of male albino Wistar rats to a 6.7 Gy mild dose of gamma radiation. The molecular signatures of radioprotection were investigated with Raman microspectroscopy of brainstem tissue samples. Morphological changes and activation of astrocytes and microglia were assessed by immunohistochemistry. Global markers of neuroinflammation were followed by ELISA to monitor blood plasma levels of proinflammatory (IL-6 and TNF-?) and anti-inflammatory (IL-10) cytokines. A thirty-day follow-up determined survival of unprotected animals 37.5%. A survival increase was observed after radioprotection (75%, irrespective of the time of application). Raman spectra revealed a slightly deleterious effect of radiation on nucleic acids in surviving animals that was mitigated with the radioprotector, as GL2011 preserved the morphology of both astrocytes and microglia, with reduced microglial infiltration. Cytokine assessment revealed an immunomodulatory effect of the novel radioprotector. The overall results point out the positive effects of a single dose of GL2011 applied at different times. The molecular and cellular changes in the brainstem indicate that the radioprotector applied after radiation conferred better protection, which underlines its translation to cure radiation accidents.

Simulation is an imitation of the operation of a real process or system over time that is applied for a variety of purposes, including entertainment, education, training, system evaluation, and research. Medical simulation is an artificial presentation of real clinical situations, which is applied in education. Medical simulation allows the acquisition of clinical skills without the risk of harming the patient. Medical simulations have been developed and refined over the years-simulation models, cadavers, actors and robots have found wide application in medical training. Of more sophisticated simulation technologies, Virtual and Augmented Realities are used. The presence of science in the digital world is necessary in order to market the proven knowledge acquired in an adequate manner. The traditional teaching process, despite serious and thorough research, seems non-inspirational, and it is important that educators and teachers keep up with the times and provide students with the latest teaching and work methods.

Aim: To report on prevalence of cerebral palsy (CP), severity rates, and types of brain lesions in children born preterm 2004 to 2010 by gestational age groups.Methods: Data from 12 population-based registries of the Surveillance of Cerebral Palsy in Europe network were used. Children with CP were eligible if they were born preterm (Results: The sample comprised 2,273 children. 25.8% were born from multiple pregnancies. About 2-thirds had a bilateral spastic CP. 43.5% of children born ≤27 weeks had a high impairment index compared to 37.0 and 38.5% in the two other groups. Overall prevalence significantly decreased (incidence rate ratio per year: 0.96 [0.92–1.00[) in children born 32–36 weeks. We showed a decrease until 2009 for children born 28–31 weeks but an increase in 2010 again, and a steady prevalence (incidence rate ratio per year = 0.97 [0.92–1.02] for those born ≤27 weeks. The prevalence of the most severely affected children with CP revealed a similar but not significant trend to the overall prevalence in the corresponding GA groups. Predominant white matter injuries were more frequent in children born Conclusion: Prevalence of CP in preterm born children continues to decrease in Europe excepting the extremely immature children, with the most severely affected children showing a similar trend.

The reconstruction of noisy wideband underwater sonar images using the improved techniques of compressive sensing is analyzed in this paper. The received signal is considered with different kinds of additive noise, with some of the samples being affected by a high-impulsive noise. The sonar images are assumed to be sparse, which makes the compressive sensing (CS) theory suitable for their processing and reconstruction. The goal is to localize and reconstruct targets using a combination of the CS technique and a sonar signal sample selection method (RANSAC). Numerical examples confirm the robustness of the proposed method.

AIM The aim of the study was to examine the effectiveness of intraoral thermoformed splint with a magnet device over a period of 3 months, that is, to assess acceptability of this method of treatment using both objective and self-reported measures. MATERIALS AND METHODS This study was designed as a prospective clinical pilot trial. 22 patients with xerostomia and hyposalivation were included. Xerostomia was assessed using a 4-question xerostomia questionnaire score and a test for unstimulated salivary flow rates. Evaluations were performed before the treatment and 3 months after the treatment using a thermoformed splint with a magnet device. RESULTS There was a significant reduction in subjective symptoms after using a thermoformed splint with a magnet device for 3 months. For all 4 scored items, there was a statistically significant difference (P

In many graph signal processing applications, finding the topology of a graph is part of the overall data processing problem rather than a priori knowledge. Most of the approaches to graph topology learning are based on the assumption of graph Laplacian sparsity, with various additional constraints, followed by variations of the edge weights in the graph domain or the eigenvalues in the graph spectral domain. These domains are high-dimensional, since their dimension is at least equal to the order of the number of vertices. In this paper, we propose a numerically efficient method for estimating of the normalized Laplacian through its eigenvalues estimation and by promoting its sparsity. The minimization problem is solved in quite a low-dimensional space, related to the polynomial order of the underlying system on a graph corresponding to the the observed data. The accuracy of the results is tested on numerical example.

The aim of this study was to evaluate the effects of pneumoperitoneum on liver function during and after laparoscopic cholecystectomy. This prospective study comprised a total of 165 patients, who were divided into two groups: The first group had low-pressure pneumoperitoneum (12 mm Hg; N = 78) and the second group had high-pressure pneumoperitoneum (14 mm Hg; N = 87). A detailed statistical analysis included sex, age, operation time, and liver function tests including total bilirubin, gamma-glutamyl transferase (GGT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin, fibrinogen, and lactate dehydrogenase (LDH), which were obtained preoperatively and 24 h, 7 days, and 30 days postoperatively. The statistical hypotheses were tested with a t test, Mann–Whitney test, chi-square test, Friedman test, and Wilcoxon’s test. There was no statistical difference between the two groups considering age, gender, and operation time (p = 0.740, p = 0.255, and p = 0.480, respectively). There was also no statistical difference in the median values of bilirubin, AST, GGT, LDH, albumin, and fibrinogen between the two groups. There was a significant statistical difference between the two groups in the median values of ALT on the 30th postoperative day (p = 0.045). There was a statistical difference for all hematochemical parameters as a function of time, independent of the level of intra-abdominal pressure (IAP). There were no statistically significant differences in the values of parameters of structural damage to the liver between the two groups, but within the groups themselves. From this we conclude that both values of elevated IAP cause microstructural and functional damage to the liver.

Background/Aim. Microstructural diversity of brain astrocytomas makes their diagnostics and differentiation by using the diffusion weighted imaging (DWI) difficult. In this study we used the histogram-based positioning of regions of interests on the apparent diffusion coefficient (ADC) maps in order to restrict the determination of diffusion parameters to regions of interest (ROI) corresponding to maximum cellularity. Success of ADC standard deviation (ΔADC) and kurtosis (K) in differentiation of brain astrocytomas was evaluated. Methods. The thirtyone patients (16 women and 15 men, median age 37 years, age range 6–72 years) with suspected supratentorial astrocytomas were included in the retrospective study. The magnetic resonance imaging (MRI) examinations were performed using the 1.5 T MR system (Avanto; Siemens, Erlangen, Germany) and 8-channel phased array head coil. The DWI images were acquired in three orthogonal directions for the b-values 0, 500 and 1000 s mm-2. The histogram calculations and determination of diffusion parameters were performed using the MIPAV software package and the statistical analysis was done in the Openstat software. Results. The ADC values enabled differentiation of diffuse astrocytomas (DA) from a high-grade astrocytoma (HGA), but not between the classes of HGA. In addition, the ΔADC value provided discrimination between the anaplastic astrocytoma (AA) and glioblastoma multiforme (GBM) with 100% of sensitivity and 89% of specificity . The kurtosis value can also differentiate between the grades AA and GBM although with the lower sensitivity and specificity. Conclusion. The histogram analysis of tumor region on the ADC maps can provide a guidance for an appropriate choice of the ROIs. The parameters which characterize diffusion of such defined ROIs, as well as their combination can improve differentiation of brain astrocytomas. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. III41005]

Binary compressive sensing (CS) is a relatively new idea in the theory of sparse signal reconstruction. Under this framework, the signal is reconstructed based on the sign of the available measurements. This paper analyzes basic one-bit CS concepts for the case of complex valued random Gaussian measurement matrices. The reconstruction is compared with the B-bit quantized measurements. The concept of binary CS-based reconstruction is generalized by applying a sigmoid function to the measurements. Noise influence is also considered. The reconstruction is performed using a simple iterative thresholding algorithm.

Background/Aim. Lower-leg septocutaneous system of perforating blood vessels represents the vascular basis of fasciocutaneous flaps. Additionally, it is of a particular importance when designing distally based fasciocutaneous flaps which represent the ?workhorse? in the reconstruction of the distal third of the lower leg and foot. The aim of this study was to analyse the vascular anatomy of posterior tibial artery and its septocutaneous (fasciocutaneous) perforating arterial vessels. Methods. The dissection was conducted on 20 fetuses of both sexes and of gestational age from 20 to 28 weeks. Cluster analysis was applied to the data on vascular anatomy of posterior tibial artery and its septocutaneous performating arterial vessels. Results. A total of 212 perforating arterial vessels was identified. The average number of perforating arterial vessels was 5.32 (ranging from 4 to 7). It was identified that septocutaneous perforating blood vessels are more likely to be found at certain levels (?safe levels of finding perforators?). These are: second, third, fifth and sixth tenth (measured as a distance from intermalleolar line to popliteal crease). Conclusion. The presence of septocutaneous system of perforating blood vessels and reliability of their localization even in the fetal period allows application of these findings in the lower leg reconstructions in children of early age. It also contributes to the greater level of understanding of anatomy of the lower-leg vascular system. Finally, it provides a basis for understanding the development of this system as it is now possible to compare results obtained on fetuses with those obtained on adults.