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This paper is focused on the problem of the pollutant source localisation in streams in other words the solution of the inverse problem of pollution spreading with in an extensive open channel network structure, i.e. in a complex system of rivers, channels and creeks in natural catchments or sewer systems in urban catchments. The design of the overall localisation procedure is based on the requirement that the entire localization system be operative and fast enough to enable quick operative interventions and help prevent the spread of pollution. The proposed model, as well as, the overall localisation procedure was calibrated and tested on a real sewer system, which represents in this case an extensive open channel network structure with free surface flow. The test results are successful and confirmed applicability of proposed localization tool in simple real conditions. However, the localisation procedure has pros and cons, which are discussed in the paper.

Nowadays 3D technologies are integrated into all aspects of modern lives. This integration of 3D technologies paved new paths for further expansion of other fields, most recently the field of arts. In this paper bridging the symmetry-related gap between physical and digital sculpting by application of reverse engineering modeling was presented. This approach enables artists to express their art in physical 3D object form and then transfer it into a digital world in order to satisfy various prerequisites (here, the final 3D model is to be ideally symmetrical). In order to describe this process, one case study is selected. With the use of close-range photogrammetry based on structure from motion, as a 3D digitization technique, a physical 3D model of a human head - manually sculpted in clay was digitized. In order to obtain a final physical or digital 3D model of sculpted head and to make it symmetrical, the symmetry analysis and correction was performed. Namely, by comparing digitized 3D model with its idealized 3D model (that was created based on symmetry-correction), symmetry analysis was carried out using computer-aided inspection. The results showed critical regions of the physical i.e. its digitized 3D model that, because of having no acceptable levels of dimensional deviations (regarding ideal symmetry), it must be corrected/minimized. This can be performed either by manual re-sculpting of the physical 3D model, or by modification of digitized 3D model (if there is no intention to use idealized 3D model for subsequent CNC-fabrication as an example).

Analytical solutions describing the 1D substance transport in streams have many limitations and factors, which determine their accuracy. One of the very important factors is the presence of the transient storage (dead zones), that deform the concentration distribution of the transported substance. For better adaptation to such real conditions, a simple 1D approximation method is presented in this paper. The proposed approximate method is based on the asymmetric probability distribution (Gumbel’s distribution) and was verified on three streams in southern Slovakia. Tracer experiments on these streams confirmed the presence of dead zones to various extents, depending mainly on the vegetation extent in each stream. Statistical evaluation confirms that the proposed method approximates the measured concentrations significantly better than methods based upon the Gaussian distribution. The results achieved by this novel method are also comparable with the solution of the 1D advection-diffusion equation (ADE), whereas the proposed method is faster and easier to apply and thus suitable for iterative (inverse) tasks.

Článok prezentuje výsledky technologických skúšok vykonaných v UV Kúty. Cieľom tejto práce bolo porovnať modifikované (povrchovo upravené) zeolity známe ako klinoptilolit (veľké nálezisko klinoptilolitu bolo objavené na Východnom Slovensku v 1980-tych rokoch) s dovážaným povrchovo upraveným zeolitom z náleziska v Maďarsku. Klinopur-Mn a Klinomangan boli použité pre odstraňovanie železa a mangánu z podzemnej vody na dosiahnutie limitných hodnôt pre pitnú vodu podľa Nariadenia vlády č. 496/2010 Z.z. Sledované materiály vykazovali rôznu účinnosť odstraňovania mangánu z vody, na účinnosť odstraňovania mala významný vplyv kvalita upravovanej vody (obsah kyslíka, hodnota pH). V prípade odstraňovania železa z vody kvalita surovej vody nie je limitujúcim faktorom, obidva materiály odstraňovali železo z vody pod limitnú hodnotu (0,2 mg.l-1).

Introduction. An autologous bone (bone derived from the patient himself) is considered to be a “golden standard” in the treatment of bone defects and partial atrophic alveolar ridge. However, large defects and bone losses are difficult to restore in this manner, because extraction of large amounts of autologous tissue can cause donor-site problems. Alternatively, data from computed tomographic (CT) scan can be used to shape a precise 3D homologous bone block using a computer-aided design-computer-aided manufacturing (CAD-CAM) system. Case report. A 63-year old male patient referred to the Clinic of Dentistry of Vojvodina in Novi Sad, because of teeth loss in the right lateral region of the lower jaw. Clinical examination revealed a pronounced resorption of the residual ridge of the lower jaw in the aforementioned region, both horizontal and vertical. After clinical examination, the patient was referred for 3D cone beam (CB)CT scan that enables visualization of bony structures and accurate measurement of dimensions of the residual alveolar ridge. Considering the large extent of bone resorption, the required ridge augmentation was more than 3 mm in height and 2 mm in width along the length of some 2 cm, thus the use of granular material was excluded. After consulting prosthodontists and engineers from the Faculty of Technical Sciences in Novi Sad we decided to fabricate an individual (custom) bovine-derived bone graft designed according to the obtained 3D CBCT scan. Conclusion. Application of 3D CBCT images, computer-aided systems and software in manufacturing custom bone grafts represents the most recent method of guided bone regeneration. This method substantially reduces time of recovery and carries minimum risk of postoperative complications, yet the results fully satisfy the requirements of both the patient and the therapist.

Foundation models in deep learning are characterized by a single large-scale model trained on vast amounts of data serving as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labelled datasets are often scarce. Here, we developed a foundation model for cancer imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of cancer imaging-based biomarkers. We found that it facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed conventional supervised and other state-of-the-art pretrained implementations on downstream tasks, especially when training dataset sizes were very limited. Furthermore, the foundation model was more stable to input variations and showed strong associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering new imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2024.)

The application of next-generation sequencing (NGS) has transformed cancer research. As costs have decreased, NGS has increasingly been applied to generate multiple layers of molecular data from the same samples, covering genomics, transcriptomics, and methylomics. Integrating these types of multi-omics data in a combined analysis is now becoming a common issue with no obvious solution, often handled on an ad hoc basis, with multi-omics data arriving in a tabular format and analyzed using computationally intensive statistical methods. These methods particularly ignore the spatial orientation of the genome and often apply stringent p-value corrections that likely result in the loss of true positive associations. Here, we present GENIUS (GEnome traNsformatIon and spatial representation of mUltiomicS data), a framework for integrating multi-omics data using deep learning models developed for advanced image analysis. The GENIUS framework is able to transform multi-omics data into images with genes displayed as spatially connected pixels and successfully extract relevant information with respect to the desired output. We demonstrate the utility of GENIUS by applying the framework to multi-omics datasets from the Cancer Genome Atlas. Our results are focused on predicting the development of metastatic cancer from primary tumors, and demonstrate how through model inference, we are able to extract the genes which are driving the model prediction and are likely associated with metastatic disease progression. We anticipate our framework to be a starting point and strong proof of concept for multi-omics data transformation and analysis without the need for statistical correction., Competing Interests: MS, AK, LD, BH, HJ, NB No competing interests declared, (© 2023, Sokač et al.)

Cancer-associated cachexia (CAC) is a major contributor to morbidity and mortality in individuals with non-small cell lung cancer. Key features of CAC include alterations in body composition and body weight. Here, we explore the association between body composition and body weight with survival and delineate potential biological processes and mediators that contribute to the development of CAC. Computed tomography-based body composition analysis of 651 individuals in the TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy (Rx)) study suggested that individuals in the bottom 20th percentile of the distribution of skeletal muscle or adipose tissue area at the time of lung cancer diagnosis, had significantly shorter lung cancer-specific survival and overall survival. This finding was validated in 420 individuals in the independent Boston Lung Cancer Study. Individuals classified as having developed CAC according to one or more features at relapse encompassing loss of adipose or muscle tissue, or body mass index-adjusted weight loss were found to have distinct tumor genomic and transcriptomic profiles compared with individuals who did not develop such features. Primary non-small cell lung cancers from individuals who developed CAC were characterized by enrichment of inflammatory signaling and epithelial-mesenchymal transitional pathways, and differentially expressed genes upregulated in these tumors included cancer-testis antigen MAGEA6 and matrix metalloproteinases, such as ADAMTS3. In an exploratory proteomic analysis of circulating putative mediators of cachexia performed in a subset of 110 individuals from TRACERx, a significant association between circulating GDF15 and loss of body weight, skeletal muscle and adipose tissue was identified at relapse, supporting the potential therapeutic relevance of targeting GDF15 in the management of CAC., (© 2023. The Author(s) under exclusive license to Springer Nature America, Inc.)

Immunotherapy has revolutionised cancer treatment. However, not all cancer patients benefit, and current stratification strategies based primarily on PD1 status and mutation burden are far from perfect. We hypothesised that high activation of an innate response relative to the adaptive response may prevent proper tumour neoantigen identification and decrease the specific anticancer response, both in the presence and absence of immunotherapy. To investigate this, we obtained transcriptomic data from three large publicly available cancer datasets, the Cancer Genome Atlas (TCGA), the Hartwig Medical Foundation (HMF), and a recently published cohort of metastatic bladder cancer patients treated with immunotherapy. To analyse immune infiltration into bulk tumours, we developed an RNAseq-based model based on previously published definitions to estimate the overall level of infiltrating innate and adaptive immune cells from bulk tumour RNAseq data. From these, the adaptive-to-innate immune ratio (A/I ratio) was defined. A meta-analysis of 32 cancer types from TCGA overall showed improved overall survival in patients with an A/I ratio above median (Hazard ratio (HR) females 0.73, HR males 0.86, P < 0.05). Of particular interest, we found that the association was different for males and females for eight cancer types, demonstrating a gender bias in the relative balance of the infiltration of innate and adaptive immune cells. For patients with metastatic disease, we found that responders to immunotherapy had a significantly higher A/I ratio than non-responders in HMF (P = 0.036) and a significantly higher ratio in complete responders in a separate metastatic bladder cancer dataset (P = 0.022). Overall, the adaptive-to-innate immune ratio seems to define separate states of immune activation, likely linked to fundamental immunological reactions to cancer. This ratio was associated with improved prognosis and improved response to immunotherapy, demonstrating potential relevance to patient stratification. Furthermore, by demonstrating a significant difference between males and females that associates with response, we highlight an important gender bias which likely has direct clinical relevance., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Ahrenfeldt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Cancer metastasis is the lethal developmental step in cancer, responsible for the majority of cancer deaths. To metastasise, cancer cells must acquire the ability to disseminate systemically and to escape an activated immune response. Here, we endeavoured to investigate if metastatic dissemination reflects acquisition of genomic traits that are selected for. We acquired mutation and copy number data from 8332 tumours representing 19 cancer types acquired from The Cancer Genome Atlas and the Hartwig Medical Foundation. A total of 827,344 non-synonymous mutations across 8332 tumour samples representing 19 cancer types were timed as early or late relative to copy number alterations, and potential driver events were annotated. We found that metastatic cancers had a significantly higher proportion of clonal mutations and a general enrichment of early mutations in p53 and RTK/KRAS pathways. However, while individual pathways demonstrated a clear time-separated preference for specific events, the relative timing did not vary between primary and metastatic cancers. These results indicate that the selective pressure that drives cancer development does not change dramatically between primary and metastatic cancer on a genomic level, and is mainly focused on alterations that increase proliferation.

Metastasis is the main cause of cancer death, yet the evolutionary processes behind it remain largely unknown. Here, through analysis of large panel-based genomic datasets from the AACR Genomics Evidence Neoplasia Information Exchange project, including 40,979 primary and metastatic tumors across 25 distinct cancer types, we explore how the evolutionary pressure of cancer metastasis shapes the selection of genomic drivers of cancer. The most commonly affected genes were TP53, MYC, and CDKN2A, with no specific pattern associated with metastatic disease. This suggests that, on a driver mutation level, the selective pressure operating in primary and metastatic tumors is similar. The most highly enriched individual driver mutations in metastatic tumors were mutations known to drive resistance to hormone therapies in breast and prostate cancer (ESR1 and AR), anti-EGFR therapy in non-small cell lung cancer (EGFR T790M), and imatinib in gastrointestinal cancer (KIT V654A). Specific mutational signatures were also associated with treatment in three cancer types, supporting clonal selection following anticancer therapy. Overall, this implies that initial acquisition of driver mutations is predominantly shaped by the tissue of origin, where specific mutations define the developing primary tumor and drive growth, immune escape, and tolerance to chromosomal instability. However, acquisition of driver mutations that contribute to metastatic disease is less specific, with the main genomic drivers of metastatic cancer evolution associating with resistance to therapy., Significance: This study leverages large datasets to investigate the evolutionary landscape of established cancer genes to shed new light upon the mystery of cancer dissemination and expand the understanding of metastatic cancer biology., (©2022 American Association for Cancer Research.)

The cGAS-STING pathway serves a critical role in anticancer therapy. Particularly, response to immunotherapy is likely driven by both active cGAS-STING signaling that attracts immune cells, and by the presence of cancer neoantigens that presents as targets for cytotoxic T cells. Chromosomal instability (CIN) is a hallmark of cancer, but also leads to an accumulation of cytosolic DNA that in turn results in increased cGAS-STING signaling. To avoid triggering the cGAS-STING pathway, it is commonly disrupted by cancer cells, either through mutations in the pathway or through transcriptional silencing. Given its effect on the immune system, determining the cGAS-STING activation status prior to treatment initiation is likely of clinical relevance. Here, we used combined expression data from 2,307 tumors from five cancer types from The Cancer Genome Atlas to define a novel cGAS-STING activity score based on eight genes with a known role in the pathway. Using unsupervised clustering, four distinct categories of cGAS-STING activation were identified. In multivariate models, the cGAS-STING active tumors show improved prognosis. Importantly, in an independent bladder cancer immunotherapy-treated cohort, patients with low cGAS-STING expression showed limited response to treatment, while patients with high expression showed improved response and prognosis, particularly among patients with high CIN and more neoantigens. In a multivariate model, a significant interaction was observed between CIN, neoantigens, and cGAS-STING activation. Together, this suggests a potential role of cGAS-STING activity as a predictive biomarker for the application of immunotherapy., Significance: The cGAS-STING pathway is induced by CIN, triggers inflammation and is often deficient in cancer. We provide a tool to evaluate cGAS-STING activity and demonstrate clinical significance in immunotherapy response., Competing Interests: M. Sokač reports grants from Lundbeck Foundation and Aarhus University Research Foundation during the conduct of the study. K. Litchfield reports personal fees from Monopteros therapeutics; grants from Ono pharmaceuticals and Genesis therapeutics outside the submitted work. L. Dyrskjøt reports grants from Ferring, Natera, C2i Genomics, AstraZeneca, Photocure, and personal fees from Ferring outside the submitted work. M.R. Jakobsen reports other from Stipe Therapeutics outside the submitted work. N.J. Birkbak reports grants from The Lundbeck Foundation, Aarhus University Research Foundation, and Danish Cancer Society during the conduct of the study; in addition, N.J. Birkbak has a patent to quantifying homologous recombination deficiency issued, licensed, and with royalties paid and a patent to a prognostic gene expression signature for lung cancer pending. No disclosures were reported by the other authors., (© 2022 The Authors; Published by the American Association for Cancer Research.)