This article examines the appeal of Serbian nationalist ideology among the contemporary far right. We argue that the discursive othering of Bosnian Muslims as “Turks” as well as the Serbian grand narrative presenting the Bosnian War as a civilizational struggle between Christian Europe and Islam are uniquely resonant with the popular anti-Muslim and xenophobic discourses that are mobilizing right-wing extremists across the globe. Through an analysis of Serbian and far-right discourses, we demonstrate how the patterns of representation that were used to incite and justify the violence committed against Bosnian Muslims in the 1990s are being exported to remote corners of the world via the internet, where they merge with extraneous Islamophobic and racist ideologies to inspire a new generation of extremism, hatred, and violence.

Background/Objectives: The aim of this study was to evaluate brain metabolism using MR spectroscopy (MRS) after recovery from Coronavirus disease (COVID-19) and to test the impact of disease severity on brain metabolites. Methods: We performed MRS on 81 individuals (45 males, 36 females, aged 40–60), who had normal MRI findings and had recovered from COVID-19, classifying them into mild (17), moderate (36), and severe (28) groups based on disease severity during the acute phase. The study employed two-dimensional spectroscopic imaging above the corpus callosum, focusing on choline (Cho), creatine (Cr), and N-acetylaspartate (NAA). We analyzed Cho/Cr and NAA/Cr ratios as well as absolute concentrations using water as an internal reference. Results: Results indicated that the Cho/Cr ratio was higher with increasing disease severity, while absolute Cho and NAA/Cr ratios showed no significant differences across the groups. Notably, absolute Cr and NAA levels were significantly lower in patients with severe disease. Conclusions: These findings suggest that the severity of COVID-19 during the acute phase is associated with significant changes in brain metabolism, marked by an increase in Cho/Cr ratios and a reduction in Cr and NAA levels, reflecting substantial metabolic alterations post-recovery.

This research explores into the utilization of synthetic data within image classification tasks and evaluates its efficiency in comparison to the utilization of real data. To facilitate this investigation, we employ the CIFAKE dataset, comprising the well-established CIFAR10 dataset and an equivalent number of images synthetically generated using the Latent Diffusion Model (LDM). The increasing demand for diverse and abundant labeled datasets has prompted the emergence of synthetic data as a potential solution to address data scarcity. Within this study, we scrutinize the performance of image classification models trained on both real and synthetic datasets. To ensure a comprehensive evaluation, we alternately apply test data across different models. Our analysis encompasses diverse factors, including classification accuracy, generalization capabilities, and robustness in various scenarios. The findings provide valuable insights into the efficacy of synthetic data as a viable alternative or complement to real data in the realm of image classification.

Artificial intelligence, Machine Learning, and Deep Learning are increasingly making significant contributions to the field of medicine. Individual patient conditions, disease localization, and various influencing factors underscore the complexity of disease diagnosis and treatment planning. Introducing new technologies can revolutionize medical diagnostics, facilitating swift and accurate assessments. Among the noninvasive diagnostic methods, Magnetic Resonance Imaging (MRI) stands out, particularly in tumor diagnosis. UNet, renowned for its effectiveness in medical image analysis, serves as a robust model for semantic segmentation, as does DeepLabV3+. However, these models are inherently complex, and their inference process can be time-consuming. By leveraging the OpenVINO toolkit, the inference process is significantly reduced. In this study, nearly a 2-fold acceleration is achieved in inference time with the DeepLabV3+ model and a roughly 1.2-fold improvement with the UNet model on CPU. Moreover, when employing GPU with FP16 precision, the acceleration reached almost 2.5fold for UNet and nearly 3-fold for DeepLabV3+, showcasing the substantial performance enhancements attainable through optimized hardware utilization.

Noise removal in image processing and computer vision is a crucial preprocessing step employing a spectrum of techniques. In recent years, autoencoders exhibit remarkable efficacy in mapping noisy images to clean counterparts, capturing intricate relationships for effective noise removal. Motivated by the challenges posed by noise in real-world images, this research focuses on the denoising preprocessing step, crucial for tasks like object detection and segmentation. The study explores the application of autoencoders in removing artificially added noise from images within the MNIST dataset. The MNIST dataset’s simplicity and historical significance facilitate focused examinations on specific aspects, such as the impact of different types and levels of noise. The efficacy of autoencoders for noise removal is assessed through the evaluation of results using various metrics, including SSIM, PSNR, MSE, and RMSE. In one remarkable instance, the reconstruction process achieved an impressive peak SSIM score of 99.06%, showcasing the efficacy of the method in preserving image fidelity despite the challenging presence of noise. This comprehensive analysis provides valuable insights into the performance and effectiveness of autoencoders in the context of noise reduction in various domains.

Abstract Let S be a groupoid (magma) with zero 0, and let R=⊕s∈SRs be a contracted S-graded ring, that is, an S-graded ring with R0=0. By G(HR) we denote the undirected power graph of a multiplicative subsemigroup HR=∪s∈SRs of R, and by G*(HR)a graph obtained from G(HR) by removing 0 and its incident edges. If Re is a nonzero ring component of R, then G*(Re) denotes a subgraph of G*(HR), induced by Re*. In this paper we address a problem raised in [Abawajy, J., Kelarev, A., Chowdhury, M.: Power Graphs: A Survey. Electron. J. Graph Theory Appl. 1(2), 125–147 (2013)]. Namely, let S be torsion-free, that is, sn=tn implies s = t for all s, t∈S, and all positive integers n, and let S be 0-cancellative, that is, for all s, t, u∈S,su=tu≠0 implies s=t, and us=ut≠0 implies s=t. Also, let R be semisimple Artinian. We prove that if G*(Re) is connected for every nonzero ring component Re of R, then the connected components of G*(HR) are precisely the graphs G*(Re).

To maximize the impact of precision medicine approaches, it is critical to identify genetic variants underlying disease and to accurately quantify their functional effects. A gene exemplifying the challenge of variant interpretation is the von Hippel–Lindautumor suppressor (VHL). VHL encodes an E3 ubiquitin ligase that regulates the cellular response to hypoxia. Germline pathogenic variants in VHL predispose patients to tumors including clear cell renal cell carcinoma (ccRCC) and pheochromocytoma, and somatic VHL mutations are frequently observed in sporadic renal cancer. Here we optimize and apply saturation genome editing to assay nearly all possible single-nucleotide variants (SNVs) across VHL’s coding sequence. To delineate mechanisms, we quantify mRNA dosage effects and compare functional effects in isogenic cell lines. Function scores for 2,268 VHL SNVs identify a core set of pathogenic alleles driving ccRCC with perfect accuracy, inform differential risk across tumor types and reveal new mechanisms by which variants impact function. These results have immediate utility for classifying VHL variants encountered clinically and illustrate how precise functional measurements can resolve pleiotropic and dosage-dependent genotype–phenotype relationships across complete genes. Saturation genome editing characterizes von Hippel–Lindau (VHL) coding variants and their associations with diseases. Function scores for 2,268 VHL single-nucleotide variants (SNVs) classify pathogenic alleles driving renal cell carcinoma and suggest new mechanisms by which variants impact function.

The aim of this study was to analyze various environmental factors influencing dyslexia to enhance our understanding of its risk factors, including the exposure of mothers of dyslexic children to potential negative developmental influences, perinatal and postnatal developmental characteristics of dyslexic children, genetic predisposition, socioeconomic status, and reading exposure in dyslexic children.Mothers of both dyslexic and non-dyslexic children took part in the study. The home literacy environment and the development of motor skills emerge as significant risk indicators for dyslexia. These findings hold profound implications for public health, emphasizing the critical importance of early childhood in providing children with the best possible educational opportunities.Key words:risk factors, dyslexia, child development, developmental influences, home literacy environment

Morus alba L. is a plant with a long history of dietary and medicinal uses. We hypothesized that M. alba possesses a significant biological potential. In that sense, we aimed to generate the chemical, antimicrobial, toxicological, and molecular profile of M. alba leaf and fruit extracts. Our results showed that extracts were rich in vitamin C, phenols, and flavonoids, with quercetin and pterostilbene concentrated in the leaf, while fisetin, hesperidin, resveratrol, and luteolin were detected in fruit. Extracts exhibited antimicrobial activity against all tested bacteria, including multidrug-resistant strains. The widest inhibition zones were in Staphylococcus aureus ATCC 33591. The values of the minimum inhibitory concentration ranged from 15.62 μg/ml in Enterococcus faecalis to 500 μg/ml in several bacteria. Minimum bactericidal concentration ranged from 31.25 μg/ml to 1000 μg/ml. Extracts impacted the biofilm formation in a concentration-dependent and species-specific manner. A significant difference in the frequency of nucleoplasmic bridges between the methanolic extract of fruit (0.5 μg/ml, 1 μg/ml, 2 μg/ml), as well as for the frequency of micronuclei between ethanolic extract of leaf (2 μg/ml) and the control group was observed. Molecular docking suggested that hesperidin possesses the highest binding affinity for multidrug efflux transporter AcrB and acyl-PBP2a from MRSA, as well as for the SARS-CoV-2 Mpro. This study, by complementing previous research in this field, gives new insights that could be of great value in obtaining a more comprehensive picture of the Morus alba L. bioactive potential, chemical composition, antimicrobial and toxicological features, as well as molecular profile.

Background. Autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and schizophrenia (SCZ) are highly heritable and linked to disruptions in foetal (neuro)development. While epigenetic processes are considered an important underlying pathway between genetic susceptibility and neurodevelopmental conditions, it is unclear (i) whether genetic susceptibility to these conditions is associated with epigenetic patterns, specifically DNA methylation (DNAm), already at birth; (ii) to what extent DNAm patterns are unique or shared across conditions, and (iii) whether these neonatal DNAm patterns can be leveraged to enhance genetic prediction of (neuro)developmental outcomes. Methods. We conducted epigenome-wide meta-analyses of genetic susceptibility to ASD, ADHD, and schizophrenia, quantified using polygenic scores (PGSs) on cord blood DNAm, using four population-based cohorts (npooled=5,802), all North European. Heterogeneity statistics were used to estimate DNAm pattern overlap between PGSs. Subsequently, DNAm-based measures of PGSs were built in a target sample, and used as predictors to test incremental variance explained over PGS in 130 (neuro)developmental outcomes spanning birth to 14 years. Outcomes. Probe-level analyses showed SCZ-PGS associated with neonatal DNAm at 246 loci (p<9x10-8), predominantly in the major histocompatibility complex. Functional characterization of SCZ-PGS loci confirmed strong genetic effects, significant blood-brain concordance and enrichment for immune-related pathways. 8 loci were identified for ASD-PGS (mapping to FDFT1 and MFHAS1), and none for ADHD-PGS. Regional analyses indicated a large number of differentially methylated regions for all PGSs (SCZ-PGS: 157, ASD-PGS: 130, ADHD-PGS: 166). DNAm signals are largely unique for individual PGSs. Finally, a DNAm-based measure of genetic susceptibility at birth nominally increased explained variance for several child cognitive and motor outcomes above PGS, but not after multiple testing correction. Interpretation. Genetic susceptibility for neurodevelopmental conditions, particularly schizophrenia, is detectable in cord blood DNAm at birth in a population-based sample, with largely distinct DNAm patterns between PGSs. These findings support the early-origins perspective on schizophrenia. Funding. HorizonEurope; European Research Council Keywords. Population-based; Genetic susceptibility; DNA methylation; Epigenetics; Neurodevelopmental conditions; Generation R Study; PREDO; ALSPAC; MoBa

In diffusion research, journalistic coverage is acknowledged as a significant factor in spreading awareness and fostering knowledge about innovation, potentially accelerating or impeding the adoption process. With regards to AI-related innovations, this dynamic has largely been studied within the context of Western developed countries. There is far less understanding of how this process unfolds in the news ecosystem of post-communist countries, particularly those with lower democratic standards and weaker economic development, such as Bosnia and Herzegovina. With the intention of gaining preliminary insights, this study investigated how the journalistic organizations in Bosnia and Herzegovina covered the emergence and societal adoption of ChatGPT, a novel form of generative AI, during the initial six-month period following its widespread availability. The content analysis of relevant news messages (N=542) published by 40 legacy and digital- only news outlets was used to explore the key characteristics of journalistic coverage, the attention given to the issue over time and the media depictions of this innovative AI technology. Results indicate that a small group of news outlets, predominantly legacy news organizations, provided significantly more content on ChatGPT than others, particularly public broadcasting services. Findings highlight a tendency among news outlets to focus on either the risks or benefits of ChatGPT and similar AI-based products and amplify sources associated with the business sector and high-tech industry, overrepresented by male voices.

Mining of the thick coal layers that include roof caving operation can results in residual coal quantities in the gob as a potential threat causing occurence of spontaneous oxidation process, smoldering, and endogenous mine fire that can affect the safety and regular mine operations. Endogeneous fire occurences in Zenica coal mines are directly linked to complex natural conditions reflecting in complex geological conditions, great depth of mining, high methane content in coal seams, and tendency of coal to spontaneous oxidation process. The subject of the paper is the analysis of endogenous fire supression method applyed in conditions of complete coal thickness longwall mining in Raspotočje mine, that has been rehabilitiated upon the endogeneous fire and then reactivated. The following methods were used in fire fighting: passive fire fighting methods (sealing of the area affected by the fire), active method (injection of electrofilter ash) and ventilation methods. Furthermore additional data (position of gob area and sealing objects, air flow regulators, routes of possible air migration, suggested technical solutions, etc) were added in the linear and canonic schemes for the purpose of defining efficient solutions for fire fighting. Key words: endogenous fire, longwall mining, advancing mining, fire fighting.

Introduction. At the end of 2019 and the year before, there was a significant spread of measles in the World Health Organization (WHO) European Region.Gap statement. Among the countries that reported, a measles outbreak was Bosnia and Herzegovina (BiH).Aim. To describe the measles outbreak in BiH (an entity of the Federation of BiH, FBiH) in 2019.Methodology. Confirmatory IgM serology, measles nucleic acid detection by real-time RT-PCR and virus genotyping were done in the WHO-accredited laboratory for measles and rubella at the Clinical Center of the University of Sarajevo, Unit for Clinical Microbiology. Genotype was determined in all measles-RNA-positive cases by sequence analysis of the 450 nt fragment coding the C-terminal of measles virus nucleoprotein (N).Results. From 1 January to 31 December 2019, 1332 measles cases were reported, with the peak observed in April 2019 (413/1332, 31.01 %). Sarajevo Canton had the highest incidence, number of cases and percentage (206.4; 868/1332; 65.17 %) of measles cases. Around four-fifths of infected persons were unvaccinated (1086/1332, 81.53 %), while 4.58 % of the patients (61/1332) were immunized with one dose of measles-containing vaccine. The highest proportion of cases was found in children 0-6 years of age (738/1332, 55.41 %). Measles IgM positivity was determined in 75.88 % (346/456), while virus RNA was detected in 82.46 % (47/57) of the swab samples. All measles virus sequences belonged to genotype B3. SNP (position 216: C=>T) was detected in 1 of the 40 sequences obtained during this outbreak.Conclusion. Due to suboptimal immunization coverage, BiH belongs to countries at a high risk for measles outbreaks. Post-COVID-19 (coronavirus disease 2019) pandemic, targeted and tailored strategies are required to ensure routine vaccination demand and acceptance and broad partner and stakeholder group participation.