Melanoma is characterized by significant intratumoral heterogeneity and complex evolutionary dynamics. This diversity in genomic alterations leads to the emergence of various subclonal populations within a single tumor. In our research, we established a new model system composed of 24 single-cell-derived clonal sublines (C1-C24), originating from the M4 melanoma model. This model was developed using a genetically engineered hepatocyte growth factor (HGF)-transgenic mouse. We employed Trisicell (Triple-toolkit for single-cell intratumor heterogeneity inference), a cutting-edge computational tool for scalable analysis of intratumor heterogeneity and evaluation based on single-cell RNA mutations. This enabled us to construct a phylogeny tree, revealing melanoma’s intricate branching evolutionary patterns. These patterns show ancestral clones evolving into genetically distinct subclones, which demonstrate varied phenotypic traits such as drug sensitivity or resistance, cellular plasticity, and immunogenicity. In our study, we conducted long-read sequencing on these clonal sublines in the phylogeny and identified structural variants (SVs) using Severus, a tool optimized for phasing in long-read sequencing. The types of SVs we discovered include deletions, insertions, amplifications, translocations, and inversions. We explored their roles in subclonal evolution, particularly focusing on how they disrupt genes and accumulate during melanoma progression. Our initial data from eleven sublines indicated a higher prevalence of ancestral SVs, shared by all sublines, compared to subline-specific SVs, representing later events. Notably, the individual sublines showed a higher rate of gene disruption by SVs, hinting at potential functional selection. Our analysis further revealed that SVs common to all sublines are linked with genes in key cell growth pathways, such as Rap1, Hippo, and calcium signaling pathways. In contrast, subline-specific SVs primarily affect genes involved in neurophysiological pathways, such as glutamatergic synapse and morphine addiction pathways. These findings suggest that different genes, associated with various pathways, are disrupted at distinct stages of melanoma progression, providing insights into the genetic factors that may predispose individual melanocytes to melanomagenesis. This methodology presents a comprehensive tool for characterizing tumor genomes and understanding their relationship with disease progression and therapy resistance. We are continuing our analysis to map SVs across the entire mutation-based phylogeny of all sublines, which will further our understanding of melanoma’s genomic landscape. Citation Format: Xiwen Cui, Ayse G. Keskus, Farid R. Mehrabadi, Salem Malikic, Mikhail Kolmogorov, Chi-Ping Day, Glenn Merlino, S. Cenk Sahinalp. Structural variant dynamics in melanoma: Unraveling tumor heterogeneity and evolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6924.

Melanoma is the most invasive skin cancer caused by the malignant melanocytes. The use of immune checkpoint blockade (ICB) improved the survival rate in advanced melanoma. Yet, the response rate to ICB varies across patients due to the highly heterogeneous nature of melanoma. Recent studies reported genomic and epigenetic factors contributing to the therapeutic response. Identifying these factors involved in clonal evolution in melanoma is a key to better understanding the tumor progression and divergence in the therapy response. To study melanoma heterogeneity, we generated twenty-four clonal sublines, each derived from a single cell isolated from a parental cell line derived from the M4 mouse melanoma model. DNA prepared from each subline and a spleen from a healthy mouse were subjected to long-read sequencing. Importantly, long-read sequencing allows direct detection of methylation states, small variants, and structural variants in the same run. The results thus provide excellent means to study genetic and epigenetic factors in clonal evolution. Each of these sublines was also implanted in distinct mice for survival and tumor growth analysis and further functional evaluation. We developed Severus, a structural variation (SV) caller for long reads, which works with single (germline), paired (tumor-normal), and multiple samples (e.g., multi-site or time series). Severus takes advantage of improved phasing in long-read sequencing, attributes somatic variants to germline haplotype, and builds a haplotype-specific breakpoint graph that is used to cluster multi-break rearrangements and represent the derived chromosomal structure. We benchmarked Severus using multiplatform validated COLO829 truthset and CHM1/CHM13 haploid genomes, and Severus outperformed all other methods in terms of recall and precision. The structural variation (SV) analysis using Severus with all the sublines showed enrichment of possibly clonal SVs in chr4, chr11, and mainly in chr13. Further investigation revealed SVs in chr13 were not distributed across the chromosome but clustered in certain regions. Analysis of somatic SNVs in chr13 corroborates the localized hypermutation profile, which indicates a possible kataegis. Interestingly, a loss of the chr13 copy affected by kataegis in one of the sublines coincides with a better survival rate, as compared to other sublines, in an allograft melanoma mouse model, which further supports the association between the kataegis and tumor progression. We also identified other clonal and subclonal events, including the homozygous deletion of Cdkn2a in most of the sublines, associated with their aggressiveness in vivo. Our analysis allowed us to identify clonal and subclonal genetic and epigenetic factors involved in tumor progression and evolution in a melanoma model, which can potentially translate to human disease. Citation Format: Ayse Keskus, Anton Goretsky, Yuelin Liu, Xiwen Cui, Tanveer Ahmad, Eva Perez Guijarro, Asher Bryant, Erin Malloy, Salem Malikic, Glenn Merlino, Chi-Ping Day, Cenk Sahinalp, Mikhail Kolmogorov. Melanoma clonal subline analysis reveals genetic factors driving intra-tumor heterogeneity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7407.

This study aims to analyze the opinions of Bosnia and Herzegovina (BiH) citizens regarding mandatory pension insurance and the possibility of incorporating private insurance in future reforms. The research involves evaluating the satisfaction of BiH residents with the current pension system, understanding their perception of the pension fund’s risks, and identifying their attitudes towards possible pension system reforms, including the potential involvement of private insurance. The study also seeks to highlight any differences in attitudes towards socio-demographic characteristics, such as gender, employment, length of service, professional qualification, and monthly income. A survey of 812 BiH adults (representative but potentially not fully capturing the entire population) explored these aspects. While acknowledging limitations, the study reveals significant differences in attitudes based on demographics. For example, men are more optimistic about future pensions, while employed individuals are more inclined towards reform. The findings suggest general public support for pension system reform and openness to private insurance. However, the study highlights the need to consider these varying attitudes across different population groups when designing future reforms. This research provides the first quantitative data on BiH residents’ views on private insurance reform, contributing to public discourse and informing future policy changes.

This paper explores the legal regulations on the termination of pregnancy in comparative law, a sensitive topic that, although it does represent the exclusive domain of state regulation, encroaches into human rights as well. The basic research question is how selected modern democratic states legally regulate the issue of the termination of pregnancy. Hence, the research goal is to prove that the trend of modern democratic states is to allow the termination of pregnancy even on request, but also to determine the existence of recent retrograde trends in this area. In this paper and research, except for the comparative method, the analytic, dogmatic, normative, and axiological methods are utilized. Although the core of the research is comparative legal, the historic and international legal segments are presented in short in this paper. In researching the following selected states, BiH, Serbia, Croatia, Germany, USA and Ireland, it is determined that the termination of pregnancy is currently largely allowed even on the request of a pregnant woman, especially for justified reasons, with regards to a specific legal regime (Germany), a sudden shift in complete liberalization (Ireland), and even for retrograde changes towards absolute prohibition (USA). In the argument section, the right of the state to ban a medical procedure out of arbitrary reasons (at least in modern discourse) is considered (even disputed). The conclusion is, considering the practice and development of democratic states, the trend of allowing the termination of pregnancy in early stages on demand of a pregnant woman without a reason, and in later stages with a reason, is evident. Concerning the region, the situation is relative satisfactory, although in greater parts of Bosnia and Herzegovina as well as Croatia the outdated legislation needs innovations, as well as certain improvements, which at this point is inevitable.

Unlike classical artificial neural networks, which require retraining for each new set of parametric inputs, the Deep Operator Network (DeepONet), a lately introduced deep learning framework, approximates linear and nonlinear solution operators by taking parametric functions (infinite-dimensional objects) as inputs and mapping them to complete solution fields. In this paper, two newly devised DeepONet formulations with sequential learning and Residual U-Net (ResUNet) architectures are trained for the first time to simultaneously predict complete thermal and mechanical solution fields under variable loading, loading histories, process parameters, and even variable geometries. Two real-world applications are demonstrated: 1- coupled thermo-mechanical analysis of steel continuous casting with multiple visco-plastic constitutive laws and 2- sequentially coupled direct energy deposition for additive manufacturing. Despite highly challenging spatially variable target stress distributions, DeepONets can infer reasonably accurate full-field temperature and stress solutions several orders of magnitude faster than traditional and highly optimized finite-element analysis (FEA), even when FEA simulations are run on the latest high-performance computing platforms. The proposed DeepONet model's ability to provide field predictions almost instantly for unseen input parameters opens the door for future preliminary evaluation and design optimization of these vital industrial processes.

The targeted use of social robots for the family demands a better understanding of multiple stakeholders’ privacy concerns, including those of parents and children. Through a co-learning workshop which introduced families to the functions and hypothetical use of social robots in the home, we present preliminary evidence from 6 families that exhibits how parents and children have different comfort levels with robots collecting and sharing information across different use contexts. Conversations and booklet answers reveal that parents adopted their child’s decision in scenarios where they expect children to have more agency, such as in cases of homework completion or cleaning up toys, and when children proposed what their parents found to be acceptable reasoning for their decisions. Families expressed relief when they shared the same reasoning when coming to conclusive decisions, signifying an agreement of boundary management between the robot and the family. In cases where parents and children did not agree, they rejected a binary, either-or decision and opted for a third type of response, reflecting skepticism, uncertainty and/or compromise. Our work highlights the benefits of involving parents and children in child- and family-centered research, including parental abilities to provide cognitive scaffolding and personalize hypothetical scenarios for their children.

La cría de caracoles, también conocida como helicicultura, es un tipo de ganadería que implica la reproducción y cultivo de caracoles para consumo humano. Aunque la cría de caracoles sigue siendo relativamente rara en muchas partes del mundo, es una industria en crecimiento que ofrece una serie de beneficios, incluido el impacto ambiental pequeño, alto potencial de beneficios y ventajas nutricionales únicas. Entre varias especies comestibles, los caracoles como Cornu aspersum y Helix pomatia han demostrado un tremendo potencial como fuentes alternativas de proteínas en las dietas animales. Tienen un perfil nutricional favorable, que contiene aminoácidos esenciales, minerales y vitaminas, lo que los convierte en valiosos suplementos o sustitutos de fuentes de proteínas tradicionales. Las técnicas y prácticas de cría de caracoles han avanzado significativamente en los últimos años, lo que ha resultado en un aumento de la productividad y mejoras en los rasgos genéticos en las poblaciones de caracoles. Además, los esfuerzos de investigación en curso están explorando el desarrollo de protocolos de cría de caracoles adaptados a requisitos veterinarios específicos. La optimización de los métodos de cría, incluida la selección artificial, el apareamiento controlado y la mejora genética, tiene un enorme potencial para mejorar los rasgos deseables en las poblaciones de caracoles. Este progreso puede contribuir al desarrollo de razas de caracoles superiores con perfiles nutricionales mejorados, resistencia a enfermedades y propiedades terapéuticas. Además, la cría de caracoles presenta una oportunidad para abordar problemas ecológicos asociados con la producción tradicional de ganado. El uso eficiente de recursos, los requisitos mínimos de espacio y el bajo impacto ambiental hacen que la cría de caracoles sea una alternativa ecológica. Los caracoles también son menos susceptibles a ciertas enfermedades infecciosas que comúnmente afectan a los animales criados tradicionalmente, lo que reduce la necesidad de antimicrobianos y promueve prácticas sostenibles. La educación continua y la investigación sobre técnicas de cría de caracoles, perfiles nutricionales y aplicaciones terapéuticas allanan el camino para prácticas veterinarias innovadoras que priorizan la sostenibilidad, el bienestar animal y mejores resultados de salud. Una mayor investigación en este campo representa una nueva oportunidad para que veterinarios y agricultores contribuyan a la salud y el bienestar de los animales al tiempo que promueven prácticas sostenibles en la industria ganadera. Este artículo de revisión tiene como objetivo proporcionar una visión general de la literatura científica sobre la cría adecuada de caracoles comestibles y los posibles desafíos de los impactos antropogénicos y climáticos negativos como un desafío potencial para los criadores.

Tekst predstavlja prilagođeno izlaganje sa naučne konferencije ZAVNOBiH u retrospektivi: evaluacija historijske važnosti i savremene relevantnosti za društvo i državu koja je organizirana povodom 80. godišnjice od Prvog zasjedanja ZAVNOBiH-a. Konferencija je održana 20. novembra 2023. godine na Univerzitetu u Sarajevu - Fakultetu političkih nauka. The text represents an adapted presentation delivered at the scientific conference ZAVNOBiH in Retrospect – Evaluating Its Historical Importance and Contemporary Relevance for Society and the State, organized on the occasion of marking the 80th Anniversary of the First Session of ZAVNOBiH. The conference was held at the University of Sarajevo - Faculty of Political Sciences, on 20 November 2023.

Working with different DBMS for programmers in their daily work represents a significant challenge in terms of choosing the appropriate way of connecting to the DBMS for the appropriate needs, given that a significant number of factors can influence the same. Although experience is usually one of the important elements that has influence on the selection of the appropriate way to connect to a DBMS, the choice can still vary from system to system and from situation to situation. For this reason, it is necessary to conduct appropriate analysis and research in accordance with various factors that can be an indicator of whether a connection with a DBMS is good or bad. In this research, an analysis was performed between the two leading methods of interaction between Java Spring Boot applications and PostgreSQL databases, namely Spring JDBC and Spring Hibernate. The results of the analysis indicate that there are certain differences in the speed of query execution in certain situations, which Java programmers should pay special attention to when choosing one of the two mentioned technologies to achieve more complex functionalities.

This paper aimed to explore ways to organize Spotify playlists, relying on clustering algorithms. Clustering algorithms were performed on playlists with extracted and standardized audio features obtained from the Spotify API, and the algorithms used were KMeans, DBSCAN, Affinity Propagation, and Spectral Clustering. Their performances were measured with the silhouette score, execution time, and inspection of clustered tracks, where it was determined that KMeans was the best algorithm in this case. Even though the execution time of KMeans is the third best, its silhouette score is the highest with 0.263. With this model, it is possible to effectively perform a mood-based organization of one's Spotify playlist, by dividing it into multiple smaller ones that share similar audio features.

Abstract Histologic transformation to small cell lung cancer (tSCLC) is a rare but increasingly recognised mechanism of acquired resistance to tyrosine kinase inhibitors (TKI) in patients with epidermal growth factor receptor (EGFR)-positive non-small cell lung cancer (NSCLC). Beyond its acknowledged role in TKI resistance, histologic transformation to SCLC might be an important, yet under-recognised, mechanism of resistance in NSCLC treated with immunotherapy. Our review identified 32 studies that investigated tSCLC development in patients with EGFR-mutated NSCLC treated with TKI therapy and 16 case reports of patients treated with immunotherapy. It revealed the rarity of tSCLC, with a predominance of EGFR exon 19 mutations and limited therapeutic options and outcomes. Across all analysed studies in EGFR-mutated NSCLC treated with TKI therapy, the median time to tSCLC development was ∼17 months, with a median overall survival of 10 months. Histologic transformation of EGFR-mutated NSCLC to SCLC is a rare, but challenging clinical problem with a poor prognosis. A small number of documented cases of tSCLC after immunotherapy highlight the need for rebiopsies at progression to diagnose this potential resistance mechanism. Further research is needed to better understand the mechanisms underlying this phenomenon and to develop more effective treatment strategies for patients with tSCLC.

With the growing requirements to keep the security of supply higher than ever the room for failures is getting smaller in today's power systems, while the increased integration of distributed renewable energy sources is additionally complicating fault detection. By using big data that is collected in modern power systems, artificial intelligence algorithms can significantly improve the capabilities of traditional protection schemes. However, the choice of the artificial intelligence algorithm can significantly impact the scheme accuracy. This paper analyses a novel approach for power system fault detection and classification by using automated machine learning procedure that iterates over different data transformations, machine learning algorithms, and hyperparameters to select the best model. By simulating and testing tens of thousands of fault scenarios on a realistic test system, the suggested approach resulted with robustness and high accuracy.

Recognizing the increasing importance of renewable energy sources, specifically wind farms, in today's power environments, this paper aims to clarify the complex interactions between these renewable energy facilities and distribution grids functioning under low-demand conditions. This particular case comes with inherent limitations that must be considered by taking into account all the factors that can influence the performance of the wind farm under these conditions. The modelling procedure and the simulation of the connection of the wind farm to the power system in rural area was performed using EMTP-RV software. The mean annual production of the wind power plant and the behaviour of the wind power plant in the event of failure in a real power system were calculated. Also, the power quality was examined in agreement with the Network Code of the transmission system of Bosnia and Herzegovina.