With the development and implementation of the 5G systems worldwide, the shortages and imperfections that limit the functions and services of 5G networks were made apparent. This generation should enable its users to access unseen services and applications and a better quality of service in comparison to its predecessors. However, aside from the apparent importance of 6G networks, the possibilities, use cases, applications, and services that this technology would provide are yet to be explored. Our intention with this paper is to research the papers and compare the differences in requirements, capabilities, services, and use cases between 5G and 6G.

In this study, the influence of the type of extract (leaf and rhizome of R. japonica) and the type of copper-based mordant (copper (I) oxide - Cu2O and copper (II) sulfate - CuSO4) on the spectroscopic and antimicrobial properties of dyed wool knitwear was investigated. The antimicrobial activity of all samples was tested against the bacteria Staphylococcus aureus and Escherichia coli and the yeast Candida albicans. It was observed that knitted fabrics dyed with R. Japonica leaf extract and Cu2O show a better antimicrobial effect on S. aureus bacteria compared to knitted fabrics dyed with the addition of CuSO4. However, in the case of knitwear dyed with R. Japonica rhizome extract, we have the opposite case; CuSO4 proved to be better as a mordant. Conductance was measured using the dielectric spectroscopy method in the frequency range from 20 Hz to 100 kHz. The highest increase in conductivity was observed in the sample dyed with the rhizome extract of R. Japonica and Cu2O, where the increase at the frequency of 24 kHz was 20 times higher compared to the initial sample. The obtained results of the coloration spectrophotometric analysis of the samples show that the highest colour strength (K/S) was achieved with the sample dyed with R. Japonica leaf extract and CuSO4. The obtained results indicate the possibility of using the tested knitwear for antimicrobial as well as electro protection.

Most current studies rely on short-read sequencing to detect somatic structural variation (SV) in cancer genomes. Long-read sequencing offers the advantage of better mappability and long-range phasing, which results in substantial improvements in germline SV detection. However, current long-read SV detection methods do not generalize well to the analysis of somatic SVs in tumor genomes with complex rearrangements, heterogeneity, and aneuploidy. Here, we present Severus: a method for the accurate detection of different types of somatic SVs using a phased breakpoint graph approach. To benchmark various short- and long-read SV detection methods, we sequenced five tumor/normal cell line pairs with Illumina, Nanopore, and PacBio sequencing platforms; on this benchmark Severus showed the highest F1 scores (harmonic mean of the precision and recall) as compared to long-read and short-read methods. We then applied Severus to three clinical cases of pediatric cancer, demonstrating concordance with known genetic findings as well as revealing clinically relevant cryptic rearrangements missed by standard genomic panels.

Quantum computing represents a paradigm shift in computation, offering the potential to solve complex problems intractable for classical computers. Although current quantum processors already consist of a few hundred of qubits, their scalability remains a significant challenge. Modular quantum computing architectures have emerged as a promising approach to scale up quantum computing systems. This paper delves into the critical aspects of distributed multi-core quantum computing, focusing on quantum circuit mapping, a fundamental task to successfully execute quantum algorithms across cores while minimizing inter-core communications. We derive the theoretical bounds on the number of non-local communications needed for random quantum circuits and introduce the Hungarian Qubit Assignment (HQA) algorithm, a multi-core mapping algorithm designed to optimize qubit assignments to cores with the aim of reducing inter-core communications. Our exhaustive evaluation of HQA against state-of-the-art circuit mapping algorithms for modular architectures reveals a 4.9 × and 1.6 × improvement in terms of execution time and non-local communications, respectively, compared to the best performing algorithm. HQA emerges as a very promising scalable approach for mapping quantum circuits into multi-core architectures, positioning it as a valuable tool for harnessing the potential of quantum computing at scale.

The capacity of transport infrastructure is one of the very important tasks in transport engineering, which depends mostly on the geometric characteristics of road and headway analysis. In this paper, we have considered 14 road sections and determined their efficiency based on headway analysis. We have developed a novel interval fuzzy-rough-number decision-making model consisting of DEA (data envelopment analysis), IFRN SWARA (interval-valued fuzzy-rough-number stepwise weight-assessment-ratio analysis), and IFRN WASPAS (interval-valued fuzzy-rough-number weighted-aggregate sum–product assessment) methods. The main contribution of this study is a new extension of WASPAS method with interval fuzzy rough numbers. Firstly, the DEA model was applied to determine the efficiency of 14 road sections according to seven input–output parameters. Seven out of the fourteen alternatives showed full efficiency and were implemented further in the model. After that, the IFRN SWARA method was used for the calculation of the final weights, while IFRN WASPAS was applied for ranking seven of the road sections. The results show that two sections are very similar and have almost equal efficiency, while the other results are very stable. According to the results obtained, the best-ranked is a measuring segment of the Ivanjska–Šargovac section, with a road gradient = −5.5%, which has low deviating values of headways according to the measurement classes from PC-PC to AT-PC, which shows balanced and continuous traffic flow. Finally, verification tests such as changing the criteria weights, comparative analysis, changing the λ parameter, and reverse rank analysis have been performed.

Abstract By providing important indicators, financial indices help investors make educated judgements regarding their assets, much like vital sign monitors for the financial markets. The best way for investors to keep up with the market and make strategic adjustments is to keep an eye on these indexes. Researching the most important financial indexes for making educated investing decisions is, thus, quite relevant. Finding the most essential financial indices from an investing standpoint and assigning a weight to each of those indexes are the main goals of this research. A weighted score is derived by combining four financial indices in a Multi-Criteria Decision-Making (MCDM) technique. These objectives are then pursued. Triangular Fuzzy Numbers (TFNs) and the Fuzzy Analytic Hierarchy Process (F-AHP) are used to determine the weights of criteria in this technique. Using these methods together, the research hopes to provide a thorough analysis of the role that different financial indexes have in informing investment choices. This study emphasizes the paramount importance of considering the Price Earning to Growth (PEG) ratio when making investment decisions, followed by the Debt Equity Ratio. Price to Book Value and Dividend Yield, while relevant, carry comparatively less weightage in the overall assessment. Investors are advised to use these insights as a guideline in their financial analysis and decision-making processes.

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.

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.

Background: Cancer research autopsy genomic studies offer insight into the metastatic cancer landscape but come with complexities that relate to the sampling and processing of post-mortem tissue. Clarifying the effect of autopsy variables on pre- and post-sequencing quality control (QC) is an unmet need that may inform tissue collection strategies. Methods: The effect of age, sex, post-mortem interval (PMI), and sample type (primary, metastatic, or normal) on pre-sequencing QC (nucleic acid concentration and integrity) was examined in 2678 samples (88.6% metastatic, 8.0% primary, 3.4% normal) from 83 patients with melanoma, lung, renal, or prostate cancer in the PEACE study. In the lung cohort, 160 surgical samples were also included through the TRACERx study, allowing surgery-autopsy tissue comparisons. Post-sequencing QC metrics were evaluated for lung samples that underwent DNA (n=522) or RNA (n=366) sequencing. Results: RNA concentration and RIN were greater in surgical samples than those collected at autopsy. Across cohorts, metastatic autopsy samples had greater nucleic acid concentrations than primary or normal autopsy samples, but not integrity. DNA and RNA concentration and integrity differed significantly between primary tumor types. When comparing samples of different metastatic sites from the whole cohort, concentration was lowest in bone (DNA) or the digestive tract (RNA), while integrity was greatest in the brain and lowest in the digestive tract (DIN, RIN). Although autopsy variables like age, sex and PMI correlated with pre-sequencing QC metrics in univariate analysis, they were not found to significantly correlate with these metrics in multivariate analysis, which identified that only primary cancer type and metastatic site were independent determinants of concentration and integrity. Similarly, for post-DNA (whole exome) sequencing QC, only the metastatic site was found to independently influence sequencing QC metrics like total number of sequences, average sequence length, and FastQC score. For RNA sequencing, only the metastatic site was found to influence sequencing QC metrics like total number of sequences, percentage of non-duplicated sequences, one hit-one genome percentage, and the alignment percentage on the human genome. Discussion: The lack of influence of PMI on QC in the largest QC-focused autopsy cancer study to date suggests that quality tissue can be obtained from non-rapid autopsy programs, which are more feasible and less resource-intensive than rapid programs. Citation Format: Petros Fessas, Sonya Hessey, Corentin Richard, Cristina Naceur-Lombardelli, Sophia Ward, David A. Moore, Karolina Nowakowska, Blanca Trujillo, Irene Lobon, Scott T. Shepherd, Fiona Byrne, Samra Turajlic, Gerhardt Attard, Charles Swanton, Mariam Jamal-Hanjani. The effect of cancer research autopsy parameters on DNA and RNA sequencing quality [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 2926.

Background: Genetic evolution of clear cell renal cell carcinoma (ccRCC) follows distinct trajectories, with varying levels of intratumor heterogeneity (ITH) and chromosomal complexity (WGII). While these patterns associate with clinical outcomes, it remains unknown whether they fully reconcile tumor behavior and how genetic and transcriptional features co-evolve in relation to the tumor microenvironment (TME). Methods: To analyze the patterns of transcriptional and TME heterogeneity, we performed bulk whole-transcriptome sequencing on 244 samples, including 22 metastatic and 12 tumor-adjacent normal samples, from 79 ccRCC patients recruited to the TRACERx Renal study. We integrated transcriptional data with previously published genetic, phylogenetic, spatial and clinical information. Results: Transcriptional distances between paired samples from the same primary tumor mirrored but were not fully determined by genetic distance (p-value < 0.001); and increased from primary-primary to primary-metastasis and primary-normal pairs. Within primary-metastasis pairs, metastasis-seeding primary tumor regions were transcriptionally closest to their matched metastasis (p-value < 0.001), suggesting that an important fraction of metastatic transcriptional traits were acquired in the primary tumor. Regarding the tumor clonal structure, transcriptional evolution followed a conserved path through increasing cell proliferation and oxidative phosphorylation and downregulating DNA repair from earlier to later clones. Further, within tumors with increasing WGII we observed upregulation and downregulation of repressors and downstream effectors, respectively, of the canonical cGAS-STING pathway. Combining the presence of this transcriptional pattern with WGII predicted shorter PFS in TRACERx Renal (p-value < 0.001) and in TCGA-KIRC (p-value < 0.001). Clonal evolution was also linked to changes in TME, with each of the previously defined genetic evolutionary trajectories associated to a specific TME (p-value < 0.001). For example, ccRCCs on a PBRM1-SETD2 trajectory demonstrated increased infiltration of cytotoxic immune cells. TME ITH was pervasive and associated with shorter PFS (p-value = 0.03). A recurrent trend from earlier to later clones was progressive T cell depletion (p-value < 0.001). The evolution of the TCR repertoire mirrored the tumor clonal structure (p-value = 0.002), suggesting the thus far elusive antigenic source in ccRCC is heritable. Accordingly, the TCR repertoire in metastasis-seeding primary tumor regions resembled the closest the TCR repertoire of matched metastasis (p-value = 0.06). Conclusion: Integrated analysis of genetic and transcriptional data in TRACERx Renal showed i) transcriptional and TME ITH not fully recapitulated by genetic ITH, ii) conserved paths of transcriptional and TME evolution and iii) a heritable nature of part of the ccRCC antigen source. Citation Format: Ángel Fernández Sanromán, Lewis Au, Benjy Jek Yang Tan, Charlotte Spencer, Anne-Laure Catin, Irene Lobon, Husayn Pallikonda, Kevin Litchfield, Fiona Byrne, James Larkin, Annika Fendler, Samra Turajlic. Integrated analysis of genetic, transcriptional and TME evolution of ccRCC: TRACERx Renal [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 1621.

Diverse clinical presentations of clear cell renal cell carcinoma (ccRCC) confound clinical decision making, leading to over and undertreatment. Clonal evolution of ccRCC proceeds through distinct trajectories characterised by differing levels of genomic intratumoral heterogeneity (gITH) and chromosomal complexity (weighted genomic instability index, wGII). However, accurate evaluation of these indices requires multiregional profiling of fresh tumour; cost prohibitive and logistically challenging in the clinical setting. Clinical histopathology workflows routinely capture multiple tumour areas enabling the use artificial intelligence (AI) to predict tumour evolutionary features directly from clinical grade H&E whole slide image (WSIs). ccRCC displays profound genetic and histological ITH but the link between these entities remains unclear. We leverage the TRACERx Renal cohort, comprising 1485 WSIs from 81 tumours to predict WGII and gITH and to gain insights into the relationship between genetic and histological ITH. Critically, each WSI is associated with a wGII and gITH label derived from a closely linked fresh tumour sample. For both prediction tasks, we extracted meaningful features for each WSI using self-supervised representation learning “MoCo”. Since high wGII confers poor prognosis we focussed on predicting binary stratification label of high wGII or low wGII (relative to the cohort median). First we predicted wGII as a continuous variable using a supervised multiple instance learning regression model trained on the MoCo features, and then classified the predicted wGII into “high” or “low” achieving 0.80 AUROC. To predict gITH we postulated that the degree of gITH would correlate with histological ITH. Using an unsupervised clustering of refined MoCo features we defined 24 histological clusters. The number of computationally derived histological clusters within a single tumour positively correlated with gITH (pearson’s 0.56). We used the number of clusters to classify WSIs into prognostic binary groups of high or low gITH (relative to the cohort median) achieving an AUROC of 0.80. To understand the biological relationship between histological and genetic ITH we pathologically characterised the histological clusters: a pathologist annotated WSIs with tumour architecture and cytomorphology. Image tiles were associated with the annotations using spatial coordinates, illuminating phenotypic traits of different evolutionary trajectories and providing an interpretability framework for our AI pipelines. Since the tumour evolutionary course dictates disease progression tempo, applying evolutionary classification in clinic can fundamentally improve patient care. Here, for the first time, we provide a framework to translate fundamental evolutionary principles underpinning tumour biology and clinical progression into a prognostic computational pathology biomarker possible to clinically implement. Citation Format: Charlotte E. Spencer, Axel Camara, Auriane Riou, Lewis Au, Jose I. Lopez, Zayd Tippu, Charles Maussion, Kenneth Ho, Amy Strange, Emma Nye, Veronique Birault, Lydwine Van-praet, Kim Edmonds, Eleanor Carlyle, Steve Hazell, Sarah Rudman, James Larkin, Samra Turajlic. Predicting tumor evolution from digital histology using AI [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 4298.