Melanoma is the most serious form of skin cancer, developed by the malignant evolution of melanocytes. Malignant melanoma incidence is increasing faster than most other cancers. While stage zero melanoma is highly treatable, survivability dramatically decreases in its advanced stages. Melanoma has shown to be one of the most heterogeneous cancers from RNA and exome analyses by The Cancer Genome Atlas and other groups. A better understanding of the key genomic and epigenomic events that characterize the diverse subclonal populations in melanoma may reveal key insights into what drives its progression and therapeutic resistance. In this study, we leveraged Nanopore long-read sequencing to study the evolution of the mouse B2905 melanoma cell line. Twenty-four distinct clonal sublines were derived in vitro from single cells of the cell line, and the genetically homogeneous population from each subline was sequenced using PromethION R10 flow cells. Enabled by long reads to perform haplotype phasing and accurate structural variation detection, our goal is to integrate small and structural variants to better our understanding of melanoma evolution, and build upon prior analyses of short-read sequenced sublines. We employed multiple SNV calling approaches, including DeepVariant and Clair, in order to provide highly accurate variants for phylogeny reconstruction using Trisicell. We performed structural variant calling with our cancer somatic structural variant (SV) caller Severus as well as copy-number alteration (CNA) analysis with our method Wakhan. Lastly, we placed SNVs, SVs, and CNAs on our reconstructed phylogeny to examine the progression of different types of variants during subline evolution. We identified approximately 560k unique SNVs and around 2, 400 unique SVs. The majority of SNVs (19%) are either clonal or private (73%); however, a meaningful fraction of subclonal variants were available for phylogenetic tree reconstruction. SVs are distributed across the phylogenetic tree branches similarly to SNVs. We identified loss of heterozygosity (LOH) events throughout the subline evolution as well as subclonal CNAs resulting from chromosomal translocations. We find clonal and subclonal evidence of densely clustered SNVs and SV, resembling kataegis; however, our analysis of mutational signatures did not reveal APOBEC-mediated mutations. By analyzing mutational signatures within individual branches of the phylogenetic tree, we observed relative timing of different mutational processes, such as early clonal signatures of UV damage. By incorporating structural variations, copy number changes, and small variant data in the phylogenetic reconstruction, our analysis offers a better characterization of the genetic landscape of subclonal evolution in melanoma. Anton Goretsky, Yuelin Liu, Ayse Keskus, Tanveer Ahmad, Salem Malikic, Glenn Merlino, Chi-Ping Day, Erin Molloy, S. Cenk Sahinalp, Mikhail Kolmogorov. Nanopore sequencing of single-cell derived sublines provides insights into melanoma heterogeneity and evolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7497.

Melanoma, a highly heterogeneous cancer, evolves through a complex interplay of genetic alterations, including both single nucleotide variants (SNVs) and structural variants (SVs). To study the evolutionary trajectory of melanoma, we established a model system composed of 24 single-cell-derived clonal sublines (C1-C24) from the M4 melanoma model, developed in a genetically engineered hepatocyte growth factor (HGF)-transgenic mouse. While SNVs have been extensively used to construct phylogenetic trees using Trisicell (Triple-toolkit for single-cell intratumor heterogeneity inference), a tool that analyzes intratumor heterogeneity and single-cell RNA mutations, the role and timing of SVs in melanoma evolution remain less well understood. This study integrates SV data with an SNV-driven phylogeny to investigate whether SV patterns align with SNV-based evolutionary trajectories in the mouse melanoma model, providing insights into the functional impact of SVs during tumor progression. We performed long-read sequencing on the 24 clonal sublines and detected SVs using Severus, a tool optimized for phasing in long-read sequencing. The SVs were mapped to the SNV-driven phylogeny using R and classified as either concordant (aligning with the SNV-based tree) or discordant (deviating from the SNV phylogeny). Gene ontology enrichment analysis revealed that concordant SVs were significantly enriched in genes associated with the hepatocyte growth factor receptor signaling pathway and the negative regulation of peptidyl-threonine phosphorylation, both of which represent core drivers of tumor progression. In contrast, discordant SVs were associated with a broader range of functional pathways, including the positive regulation of antigen receptor-mediated signaling and the regulation of natural killer cell-mediated cytotoxicity, though the exact mechanisms underlying these associations remain unclear. By integrating these SVs with an established SNV-driven phylogeny, this study highlights the distinct and critical roles SVs play in melanoma evolution. Concordant SVs appear to drive core oncogenic processes, while discordant SVs may contribute to other aspects of tumor evolution. These findings underscore the importance of considering SVs alongside SNVs to fully capture the complexity of melanoma evolution. Ongoing investigations will continue to explore the functional implications of these SVs and how the gene disruption patterns they cause shape the evolutionary trajectory of melanoma, offering potential targets for future therapeutic strategies. Xiwen Cui, Ayse G. Keskus, Salem Malikic, Yuelin Liu, Anton Goretsky, Chi-Ping Day, Farid R. Mehrabadi, Mikhail Kolmogorov, Glenn Merlino, S. Cenk Sahinalp. Integrating structural variants and single nucleotide variants to uncover evolutionary trajectories in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3898.

Most human cancers arise from somatic alterations, ranging from single nucleotide variations to structural variations (SVs) that can alter the genomic organization. Pathogenic SVs are identified in various cancer types and subtypes, and they play a crucial role in diagnosis and patient stratification. However, the studies on structural variations have been limited due to biological and computational challenges, including tumor heterogeneity, aneuploidy, and the diverse spectrum of SVs from simpler deletions and focal amplifications to catastrophic events shuffling large fragments from one or multiple chromosomes. Long-read sequencing provides the advantage of improved mappability and direct haplotype phasing. Yet, no tool currently exists to comprehensively analyze complex rearrangements within the cancer genome using long-read sequencing. Here, we present Severus, a tool for somatic SV calling and complex SV characterization using long reads. Severus first detects individual SV junctions from phased split alignments, then constructs a phased breakpoint graph to cluster junctions into complex rearrangement events. We first benchmarked the somatic SV calling performance using six tumor/normal cell line pairs (HCC1395, H1437, H2009, HCC1937, HCC1954, Hs578T). We sequenced all cell lines with Illumina, ONT, and PacBio HiFi. We then established a set of high-confidence calls supported by multiple technologies and tools. Severus consistently had the highest F1 scores compared to the HiFi, ONT, and Illumina methods against this high-confidence SV call set. We then extend our analysis to complex SVs. Severus accurately detected complex events, i.e., chromothripsis and chromoplexy, and templated insertion cycles/chains (TIC), reported for these cell lines. We then compared Severus’ performance with Jabba and Linx, two widely used tools for complex SV calling in short-read sequencing. Our comparison revealed that Severus showed higher agreement with Linx, while Jabba failed to detect most of the SV clusters identified by both Severus and Linx. Severus also outperformed the other tools in characterizing complex reciprocal translocations and TICs. Most of the junctions in complex SVs called by either of the tools but not Severus were either simple SVs with a single long-read junction or were not present in long-read sequencing. In contrast, Severus effectively resolved overlapping SVs by utilizing long-read connectivity, allowing for more accurate clustering of smaller genomic segments. We have also applied Severus to seventeen pediatric leukemia cases. Severus identified two chromoplexy and two cryptic translocations, which were missed by FISH and karyotype panels and were incomplete in Illumina SV calls, further validated by RNA-seq. This highlights the potential of the long-read whole genome sequencing approach for diagnosing complex cases driven by SVs. Ayse Keskus, Asher Bryant, Tanveer Ahmad, Anton Goretsky, Byunggil Yoo, Sergey Aganezov, Ataberk Donmez, Lisa A. Lansdon, Isabel Rodriguez, Jimin Park, Yuelin Liu, Xiwen Cui, Joshua Gardner, Brandy McNulty, Samuel Sacco, Jyoti Shetty, Yongmei Zhao, Bao Tran, Giuseppe Narzisi, Adrienne Helland, Daniel Cook, Pi-Chuan Chang, Alexey Kolesnikov, Andrew Carroll, Erin Molloy, Chengpeng Bi, Adam Walter, Margaret Gibson, Irina Pushel, Erin Guest, Tomi Pastinen, Kishwar Shafin, Karen Miga, Salem Malikic, Chi-Ping Day, Nicolas Robine, Cenk Sahinalp, Michael Dean, Midhat S. Farooqi, Benedict Paten, Mikhail Kolmogorov. Severus: A tool for detecting and characterizing complex structural variants in cancer using long-read sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2848.

Cyber threats have become increasingly prevalent and sophisticated. Prior work has extracted actionable cyber threat intelligence (CTI), such as indicators of compromise, tactics, techniques, and procedures (TTPs), or threat feeds from various sources: open source data (e.g., social networks), internal intelligence (e.g., log data), and “first-hand” communications from cybercriminals (e.g., underground forums, chats, darknet websites). However, “first-hand” data sources remain underutilized because it is difficult to access or scrape their data. In this work, we analyze (i) 6.6 million posts, (ii) 3.4 million messages, and (iii) 120,000 darknet websites. We combine NLP tools to address several challenges in analyzing such data. First, even on dedicated platforms, only some content is CTI-relevant, requiring effective filtering. Second, “first-hand” data can be CTI-relevant from a technical or strategic viewpoint. We demonstrate how to organize content along this distinction. Third, we describe the topics discussed and how “first-hand” data sources differ from each other. According to our filtering, 20% of our sample is CTI-relevant. Most of the CTI-relevant data focuses on strategic rather than technical discussions. Credit card-related crime is the most prevalent topic on darknet websites. On underground forums and chat channels, account and subscription selling is discussed most. Topic diversity is higher on underground forums and chat channels than on darknet websites. Our analyses suggest that different platforms may be used for activities with varying complexity and risks for criminals.

The ubiquity of mobile applications has increased dramatically in recent years, opening up new opportunities for cyber attackers and heightening security concerns in the mobile ecosystem. As a result, researchers and practitioners have intensified their research into improving the security and privacy of mobile applications. At the same time, more and more mobile applications have appeared on the market that address the aforementioned security issues. However, both academia and industry currently lack a comprehensive overview of these mobile security applications for Android and iOS platforms, including their respective use cases and the security information they provide. To address this gap, we systematically collected a total of 410 mobile applications from both the App and Play Store. Then, we identified the 20 most widely utilized mobile security applications on both platforms that were analyzed and classified. Our results show six primary use cases and a wide range of security information provided by these applications, thus supporting the core functionalities for ensuring mobile security.

Ethnic villages are examples of tourism products based on historical representations of the region. Within these villages, tourists participate in various customs and traditions, gaining insights into the heritage of local communities. As heritage should be the basis for improving rural tourism, this research was conducted to investigate the extent to which ethnic villages safeguard their heritage. The examination of cultural heritage was carried out by experts who evaluated the importance of the criteria for assessing heritage and the application of cultural heritage in these ethnic villages. A fuzzy approach was used to assess the criteria and ethnic villages using fuzzy Logarithm Methodology of Additive Weights (LMWA) and fuzzy Additive Ratio Assessment (ARAS). The sampling process included an initial pool of 28 ethnic villages identified through various associations and agencies. The villages included in this research were chosen by randomly selecting eight villages using a random number generator. Through collaboration with experts and thorough literature research, 12 criteria were established for evaluating heritage use degree in these villages. Results highlighted tourist participation as the most significant criterion, with the Lubac Valley ethnic village demonstrating superior performance. As this research has shown, applying heritage in tourism provides a unique experience, which is the base for developing rural tourism, and the Lubac Valley could serve as an example to other ethnic villages on building a tourist offer based on heritage. In addition, this research contributes to understanding the current landscape and strengthening the promotion of heritage in ethnic villages by developing a sustainable tourist offer.

The widespread deployment of AI systems has led to overlapping concerns around technological impact and governance, often resulting in conceptual ambiguities and policy confusion. We propose a structured and context-sensitive framework for addressing the ethical implications of artificial intelligence. We argue that ethical frameworks must distinguish between the intended domain of AI deployment and the scale of its societal effects.To resolve these tensions, we introduce a two-dimensional matrix based on (1) the extent of AI’s impact and (2) the scope of its governance, which together form four distinct ethical contexts. Within each quadrant, we explore specific risks, values, and regulatory considerations. This matrix not only clarifies the conceptual terrain of AI ethics but also offers a practical roadmap for anticipating ethical risks, developing normative guidance, and informing domain-specific governance strategies.Our goal is not to prescribe a single ethical doctrine but to provide decision-makers with a structured lens through which AI systems can be evaluated in context. This approach promotes adaptive and anticipatory governance while remaining responsive to local, institutional, and cultural variations.

BACKGROUND HER2-positive breast cancer (BC) is highly aggressive with a poor prognosis. It is driven by HER2 oncoprotein activation/crosstalk with other receptors like EGFR/(HER1), HER3, and HER4, in addition to IGF-1R, making these receptors ideal therapeutic targets as they are expressed/overexpressed in this subtype. We postulated that targeting HER2 and IGF-1R together is a promising therapy for HER2-positive BC. Thus, we explored the outcome of a novel combination treatment using neratinib, a pan-HER inhibitor, and metformin, an IGF-1R inhibitor, on HER2-positive BC cells. METHODS In this investigation, we used cellular and molecular biology techniques in addition to an angiogenesis model and tissue microarray analysis. RESULTS Our data revealed that this combination therapy significantly reduced cell viability compared to individual treatments and exhibited a synergistic effect in HER2-positive BC cells. Moreover, the combination disrupted cell cycle progression and inhibited colony formation, and invasion of HER2-positive BC cells; this is accompanied by the deregulation of HER1-3 and IGF-1R expression patterns, in addition to Caspase-3, BCL2, Fascin, and Vimentin. Moreover, key regulator molecular pathways, including, ERK1/2, AKT, p38 MAPK, and mTOR, were significantly downregulated upon treatment with neratinib and metformin combination. Additionally, our data pointed out that neratinib and metformin combination inhibited angiogenesis, in-ovo, an important biological event in cancer progression. Finally, using a cohort of 55 HER2-positive BC samples, we revealed that HER2 and IGF-1R are co-expressed in most of the cases. CONCLUSIONS These findings suggest that neratinib and metformin combination can present a promising strategy for targeting multiple pathways in HER2-positive BC.

This article situates itself in the theoretical space between world-systems theory and postcolonial theory, exploring how the state of peripherality and concomitant dependency is reproduced in Bosnia and Herzegovina during the Covid-19 pandemic. The dependent position of the Bosnian protectorate in the world-system, its heritage of colonial rule and peripherality, as well as post-colonial influences of Pax-Americana on state constitution and state capture, have all contributed to the inability of the divided state to adequately respond to the pandemic. This article reveals a multifaceted dependence of Bosnia and Herzegovina on the Western core economies in relation to aid, equipment and vaccines as well as its gradual move towards China as a new opportunity. The pandemic also becomes the stage for competition between the Eastern and Western companies for mining concessions needed to secure the green transition in the respective economies, as a new wave of primitive accumulation ravages the European periphery. As a result of this new scramble for the Balkans, and amidst the global shift towards multipolarity, we see a stable reproduction of peripherality in Bosnia and Herzegovina and the Western Balkans, and re-emergence of ethnic conflict in previously disputed areas, where ethnic groups identify with the interests of their respective hegemons.

Background and aim Public health and social measures (PHSM) are critical aspects of limiting the spread of infections in pandemics. Compliance with PHSM depends on a wide range of factors, including behavioral determinants such as emotional response, trust in institutions or risk perceptions. This study examines self-reported compliance with PHSM during the COVID-19 pandemic in the Federation of Bosnia and Herzegovina (FBIH). Materials and methods We analyze the association between compliance and behavioral determinants, using data from five cross-sectional surveys that were conducted between June 2020 and August 2021 in FBIH. Quota-based sampling ensured that the 1000 people per wave were population representative regarding age, sex, and education level based on the data from the latest census in Bosnia and Herzegovina. One-way analysis of variance (ANOVA) was used to identify significant changes between studies on determinants and PHSM measures. Regression was used to find relations between behavioral determinants and PHSM. Results Participants reported strong emotional responses to the rapid spread of the virus and its proximity to them. Risk perception was spiking in December 2020 when rates of infection and death were particularly high. Trends in policy acceptance were divergent; participants did not rate PHSM as exaggerated, but perceived fairness was low. Trust in institutions was low across all waves and declined for specific institutions such as the health ministry. In five wave-specific regression analyses, emotional response (βmin/max = .11*/.21*), risk perception (βmin/max = .06/.18*), policy acceptance (βmin/max = .09/.20*), and trust in institutions (βmin/max = .06/.21*) emerged as significant predictors of PHSM. Conclusions This study contributes to the body of research on factors influencing compliance with PHSM. It emphasizes the importance of behavioral monitoring through repeated surveys to understand and improve compliance. The study also affirms the impact of public trust on compliance, the risk of eroding compliance over time, and the need for health literacy support to help reinforce protective behaviors.

This study compares two titrimetric methods for quantifying acetylsalicylic acid (ASA) in aspirin tablets stored under different environmental conditions. ASA stability can be influenced by factors such as temperature, humidity, and light exposure. The two titrimetric methods used are acid-base titration with hydrochloric acid (HCl) and sodium hydroxide (NaOH). Aspirin tablets were stored for 30 days under controlled conditions simulating varying environmental factors, and both methods were evaluated for accuracy, precision, and reliability. The results show a strong  correlation between the two methods, with a Pearson correlation coefficient of 0.937 and a high Intraclass Correlation Coefficient (ICC), indicating consistency and reliability. However, the paired t-test revealed a statistically significant difference (r = 0.937, p = 0.001) between the  methods, suggesting small but meaningful discrepancies in their results. The Bland-Altman   analysis demonstrated that Method I consistently provided higher values than Method II, while the linear regression analysis indicated that Method II slightly underestimates values compared to Method I. Overall, both methods were found to be highly reliable and interchangeable within certain limits, but the small systematic differences between them should be considered when interpreting results. This study provides valuable insights into the performance of titrimetric   methods for ASA quantification, contributing to the optimization of pharmaceutical analysis techniques.

BACKGROUND AND AIMS Adults with congenital heart disease (ACHD) knowledge regarding their heart condition is crucial for optimal long-term outcome. Previous studies from North-Western Europe showed that important gaps in ACHD knowledge still exist. This study evaluates ACHD patients' knowledge in Central and South-eastern Europe (CESEE) and aims to identify opportunities for improving life-long ACHD care and outcomes in this region. METHODS A structured survey regarding the baseline heart condition knowledge was prospectively distributed to stable ACHD patients over a one-year period (2021-2022). Patients' responses were verified by their ACHD physicians to ensure accurate background information. RESULTS Among 1650 patients (age 34.5 ±14) across 14 CESEE countries the majority 1023(62.0%) had simple congenital heart disease with at least one previous heart procedure performed 1201(72.8%); 1060(64.2%) were asymptomatic and 875(53.8%) had secondary school education. Overall, 576(34.9%) did not have basic knowledge regarding their congenital heart disease and 146(12.2%) did not have basic understanding regarding their previous heart procedure/s. Patients considered their life expectancy similar to the general population (p=0.039). Encouragingly, 962(59.5%) expressed a desire to learn more, and 929(58.1%) favoured technological integration in their care. CONCLUSIONS Significant knowledge gaps exist amongst CESEE ACHD patients regarding their heart condition. Better ACHD patient education on current health and prospects is urgently needed. The results of this study should serve for developing congenital heart disease structured transitional and educational programmes in CESEE incorporating technology for their ACHD care and education to enhance patients' health knowledge and healthy life-behaviours to positively influence their life-long prospects.

BackgroundDialysis is a very complex treatment that is received by around 3 million people annually. Around 10% of the death cases in the presence of the dialysis machine were due to the technical errors of dialysis devices. One of the ways to maintain dialysis devices is by using machine learning and predictive maintenance in order to reduce the risk of patient's death, costs of repairs and provide a higher quality treatment.ObjectivePrediction of dialysis machine performance status and errors using regression models.MethodThe methodology includes seven steps: data collection, processing, model selection, training, evaluation, fine-tuning, and prediction. After preprocessing 1034 measurements, twelve machine learning models were trained to predict dialysis machine performance, and temperature and conductivity error values.ResultsEach model was trained 100 times on different splits of the dataset (80% training, 10% testing, 10% evaluation). Logistic regression achieved the highest accuracy in predicting dialysis machine performance. For temperature predictions, Lasso regression had the lowest MSE on training data (0.0058), while Linear regression showed the highest R² (0.59). For conductivity predictions, Lasso regression provided the lowest MSE (0.134), with Decision tree achieving the highest R² (0.2036). SVM attained the lowest MSE on testing dataset, with 0.0055 for temperature and 0.1369 for conductivity.ConclusionThe results of this study demonstrate that clinical engineering (CE) and health technology management (HTM) departments in healthcare institutions can benefit from proposed automated systems for advanced management of dialysis machines.