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Sudety w kenozoiku : od tektonicznej ekshumacji podłoża do współczesnej rzeźby – wnioski z termochronologii niskotemperaturowej

Sobczyk Artur

Przegląd Geologiczny

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2025

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Vol. 73, nr 9

804--812

EN

Low-temperature the rmochronology based on apatite and zircon (40-240°C) provides essential infor¬mation on the evolution of the Sudetes during the Mesozoic and Cenozoic. AFT data from various areas of the Sudetes indicate intense cooling and basement exhumation during the Late Cretaceous-Paleogene (~90-45 Ma). The estimated mean exhumation rate during this period varied between 1.0 and 0.04 km/Ma. In the Cenozoic, there was a significant slowdown in tectonic processes and a decrease in the exhumation rate to <0.01 km/Ma. Results from numerical modelling of AFT data suggest that the folding of Cretaceous sediments in the lntra-Sudetic Synclinorium and the Upper Nysa Kłodzka Graben took place between 75 and 70 Ma. Thermochronology has also provided evidence of deep burial of the Sudetes in the Late Cretaceous, with the observed thermal reset suggesting that the thickness of the sedimentary cover may have reached up to 6 km. The development of the Sudetic landscape into a form similar to the present one did not occur earlier than after the Eocene, when the last phase of basement rock cooling and associated denudation of the Me¬ta-Carpathian Swell was recorded. The final uplift of the Sudetic Block began in the Middle Miocene (15±5 Ma), and the total erosion over the past 90 million years reached 5-6 km.

2

Koncepcja autonomicznego systemu utrzymania klimatu w budynkach inwentarskich

Knaga Jarosław, Szul Tomasz

Przegląd Elektrotechniczny

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2025

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R. 101, nr 1

168--170

PL

W pracy przedstawiono koncepcję sytemu mającego na celu utrzymanie właściwego klimatu w budynkach inwentarskich, wykorzystując do tego energię promieniowania słonecznego. Układ wymuszający ruch powietrza składa się z wentylatora, napędzanego silnikiem DC i modułu fotowoltaicznego sterowanego układem analogowym, co zapewni bezawaryjność działania układu i przyczyni się do obniżenia kosztów ponoszonych na zakup energii elektrycznej.

EN

This paper presents the concept of a system aimed at maintaining a proper climate in livestock buildings, using the energy of solar radiation. The system forcing the air movement consists of a DC fan and a photovoltaic module controlled by an analogue system, which ensures failure-free operation of the system and contributes to reduction of the costs incurred for the purchase of electricity.

3

Różnorodność widzenia barwnego w świecie zwierząt

Grzesiak Zofia, Grzegorczyk Gabriela

Optyka

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2025

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Nr 1

66--69

PL

Artykuł przedstawia przegląd wiedzy na temat widzenia barwnego u różnych gatunków zwierząt, porównując zdolności percepcyjne ssaków, ptaków, płazów, gadów, owadów i skorupiaków. Widzenie kolorów zależy od liczby i rodzaju fotoreceptorów oraz kropli olejowych w oczach. U większości gatunków kluczową rolę odgrywają czopki, ale u niektórych, jak płazy, również pręciki mogą uczestniczyć w percepcji barw, co pozwala na widzenie w słabym świetle. Ssaki, takie jak psy i koty, są dichromatami i widzą świat w żółto-niebieskich barwach. Wyjątkiem są małpy Starego Świata, które są trójchromatyczne, widzą podobnie jak ludzie. Ptaki mają cztery rodzaje czopków i krople olejowe, co umożliwia tetrachromatyczne widzenie, w tym ultrafiolet. Gady również mają tetrachromatyczne widzenie. Płazy korzystają z dwóch rodzajów pręcików i czterech czopków, co pozwala na widzenie barw nawet w słabym oświetleniu. Ryby mogą mieć szerokie spektrum widzenia, obejmujące UV, ale niektóre mają jedynie pręciki.

EN

The article presents a review of current knowledge on color vision in various animal species, comparing the perceptual abilities of mammals, birds, amphibians, reptiles, insects, and crustaceans. Color vision depends on the number and type of photoreceptors and oil droplets present in the eyes. In most species, cones play a key role in color vision, but in some, such as amphibians, rods can also participate in color perception, allowing them to see colors even in low light. Mammals, such as dogs and cats, are dichromatic and see the world in a limited yellow-blue palette. The exception among mammals are Old World monkeys, which are trichromatic, seeing colors similarly to humans. Birds have four types of cones and oil droplets, enabling tetrachromatic vision, including ultraviolet detection. Reptiles also have tetrachromatic vision. Amphibians rely on two types of rods and four types of cones, which allow them to see colors even in dim lighting. Fish may have a wide spectrum of color vision, including UV, but some species have only rods. Mantis shrimp possess up to 12 types of cones, allowing them to perceive polarized and ultraviolet light.

4

Nowoczesne metody badania deformacji rur podatnych

Kliszczewicz Barbara

Materiały Budowlane

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2025

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nr 2

40--42

5

Perceptually Optimised Swin-Unet for Low-Light Image Enhancement

Lehmann Tomasz M., Rokita Przemysław

Machine Graphics and Vision

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2025

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Vol. 34, No. 4

23--42

EN

In this paper we propose a novel approach to low-light image enhancement using a transformer-based Swin-Unet and a perceptually driven loss that incorporates Learned Perceptual Image Patch Similarity (LPIPS), a deep-feature distance aligned with human visual judgements. Specifically, our U-shaped Swin-Unet applies shifted-window self-attention across scales with skip connections and multi-scale fusion, mapping a low-light RGB image to its enhanced version in one pass. Training uses a compact objective - Smooth-L1, LPIPS (AlexNet), MS-SSIM (detached), inverted PSNR, channel-wise colour consistency, and Sobel-gradient terms - with a small LPIPS weight chosen via ablation. Our work addresses the limits of purely pixel-wise losses by integrating perceptual and structural components to produce visually superior results. Experiments on LOL-v1, LOL-v2, and SID show that while our Swin-Unet does not surpass current state-of-the-art on standard metrics, the LPIPS-based loss significantly improves perceptual quality and visual fidelity. These results confirm the viability of transformer-based U-Net architectures for low-light enhancement, particularly in resource-constrained settings, and suggest exploring larger variants and further tuning of loss parameters in future work.

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The impact of using multi-source remote sensing images on building segmentation with U-Net model

Pham Trung Dung, Pham Ngoc Hung

Inżynieria Mineralna

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2025

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Vol. 2, no. 1

art. no. 070

EN

The building extraction from remote sensing (RS) images has been a significant area of research in the photogrammetric and remote sensing communities, especially with the development of deep learning for over a decade. With the availability of multi-source data from RS images, accurately identifying buildings with different spatial image resolutions has become a challenging task. In this study, we assessed how the unalignment of image resolution between the training and testing datasets affects the ability to extract buildings. Image resolution plays a crucial role in the performance of building extraction. Our experiments found that as the image resolution decreased from 10 cm to 50 cm, the efficiency of building segmentation reduced from 0.759 to 0.585 according to the IoU metric. Besides, the ability and accuracy of building segmentation significantly decreased when the difference in image resolution between the training and testing datasets increased. In the case study, we use the model trained on a 10 cm resolution dataset to predict for 50 cm resolution data, the IoU drops significantly to 0.299. This research offers important insights into building segmentation tasks using multi-source data from satellite, airborne, and UAV images.

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Application of YOLO and U-Net models for building material identification on segmented images

Voronkov Ruslan, Bezuglyi Mykhailo

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2025

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T. 15, nr 2

13--17

EN

This paper is devoted to the analysis of existing convolutional neuralnetworks and experimental verification of the YOLO and U-Netarchitectures for the identification and classification of building materials based on images of destroyed structures. The aim of the study is to determinethe effectiveness of these models in the tasks of recognising materials suitable for reuse and recycling. This will help reduce construction wasteand introduce a more environmentally friendly approach to resource management. The study examined several modern deep learning models for image processing, including Faster R-CNN, Mask R-CNN, FCN (Fully Convolutional Networks), and SegNet. However, the choice was made on the YOLOand U-Netarchitectures. YOLO is used for fast object identification in images, which allows for quick detection and classification of building materials, and U-Netis used for detailed image segmentation, providing accurate determination of the structure and composition of building materials. Each of these models has been adapted to the specific requirements of building materials analysis in the context of collapsed structures. Experimental results have shown that the use of these models allows achieving high accuracy of segmentation of images of destroyed buildings, which makes them promising for usein automated resource control systems.

PL

Niniejszy artykuł poświęcony jest analizie istniejących konwolucyjnych sieci neuronowych i eksperymentalnej weryfikacji architektur YOLOi U-Net do identyfikacji i klasyfikacji materiałów budowlanych na podstawie obrazów zniszczonych konstrukcji. Celem badania jest określenie skuteczności tych modeli w zadaniach rozpoznawania materiałów nadających się do ponownego wykorzystania i recyklingu. Pomoże to zmniejszyć ilość odpadów budowlanych i wprowadzić bardziej przyjazne dla środowiska podejście do zarządzania zasobami. W badaniu przeanalizowano kilkanowoczesnych modeli głębokiego uczenia do przetwarzania obrazu, w tym Faster R-CNN, Mask R-CNN, FCN (Fully Convolutional Networks) i SegNet, jednak wybór padłna architektury YOLO i U-Net. YOLO służy do szybkiej identyfikacji obiektów na obrazach, co pozwala na szybkie wykrywanie i klasyfikację materiałów budowlanych, a U-Net służy do szczegółowej segmentacji obrazu, zapewniając dokładne określenie struktury i składu materiałów budowlanych. Każdyz tych modeli został dostosowany do specyficznych wymagań analizy materiałów budowlanych w kontekście zawalonych konstrukcji.Wyniki eksperymentów wykazały, żezastosowanie tych modeli pozwala osiągnąć wysoką dokładność segmentacji obrazów zniszczonych budynków, co czynije obiecującymi do wykorzystania w zautomatyzowanych systemach kontroli zasobów.

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River Area Segmentation Using Sentinel-1 SAR Imagery with Deep-Learning Approach

Dewi Ni Putu Karisma, Suputra Putu Hendra, Paramartha A.A. Gede Yudhi, Dewi Luh Joni Erawati, Varnakovida Pariwate, Aryanto Kadek Yota Ernanda

Geomatics and Environmental Engineering

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2025

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Vol. 19, no. 4

39--63

EN

River segmentation is important in delivering essential information for environmental analytics such as water management, flood/disaster management, observations of climate change, or human activities. Advances in remote-sensing technology have provided more complex features that limit the traditional approaches’ effectiveness. This work uses deep-learning-based models to enhance river extractions from satellite imagery. With Resnet-50 as the backbone network, CNN U-Net and DeepLabv3+ were utilized to perform the river segmentation of the Sentinel-1 C-Band synthetic aperture radar (SAR) imagery. The SAR data was selected due to its capability to capture surface details regardless of weather conditions, with VV+VH band polarizations being employed to improve water surface reflectivity. A total of 1080 images were utilized to train and test the models. The models’ performance was measured using the Dice coefficient. The CNN U-Net architecture achieved an accuracy of 0.94, while DeepLabv3+ attained an accuracy of 0.92. Although DeepLabv3+ showed more stability during the training and performed better on wider rivers, CNN U-Net excelled at identifying narrow rivers. In conclusion, a river-segmentation model was conducted using Sentinel-1 C-Band SAR data, with CNN U-Net outperforming DeepLabv3+; this enabled detailed river mapping for irrigationand flood-monitoring applications – particularly in cloud-prone tropical regions.

9

Insights into U-NET models with special focus on ultrasound and MRI medical image segmentation

Shereena V. B., Raju G.

Control and Cybernetics

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2025

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Vol. 54, No. 1

79--117

EN

The advent of deep learning enabled the extraction of complex feature representations from medical imaging data, which was considered impossible to be achieved with standard computer learning. The applications of deep learning in the field of medical image analysis afford significant results. A key feature of deep learn- ing techniques is their ability to automatically learn task-specific feature representations and extract relevant features without hu- man intervention. Various deep learning models, including CNN, AlexNet, ResNet, DenseNet and U-Net were developed for medical image analysis. Among these models, U-Net is a popular model, used for medical image segmentation. The present article provides a comprehensive review of the deep learning segmentation models, which use U-Net and its variants, applied in the domain of medical image segmentation, specifically tailored to medical imaging modal- ities, such as ultrasound and MRI, along with respective pros and cons in the field of image segmentation. The analysis reveals that the performance of different U-Net variants varies significantly based on imaging modality and segmentation complexity.

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Analysis on grading of lung nodule images with segmentation using u-net and classification with Convolutional Neural Network Fish Swarm Optimization

Sudha R., Uma Maheswari K.M.

Biocybernetics and Biomedical Engineering

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2025

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Vol. 45, iss. 1

90--104

EN

Lung malignant tumors are abnormal growths of cells in the lungs that have the potential to invade nearby tissues and spread to other parts of the body. Early detection of these malignant lung tumors is crucial to avoid complications and improve patient outcomes. However, manual processing consumes time and is a tedious process. This might result in poor estimation on cancer-prognosis, leading the patients into a higher risk of mortality. Many existing literatures have detected the malignant tumors, yet, found certain difficulties with the identification of size, appearance and spread of cancerous-cells in lung region to determine how far it has been occupied. Hence, the present study aims to overcome the existing complications through Deep Learning based Swarm Intelligence Algorithms. Implementation of the proposed work is involved with three stages such as preprocessing, segmentation and classification. Besides, CT scan possess the capability for giving a comprehensive view than X-rays. Data are collected from LIDC-IDRI (Lung Image Database Consortium-Image Database Resource Initiative) with lung CT-images and accomplishes pre-processing by removing noise efficiently using wiener filter. Further, changes in soft tissues of lungs are identified and segmented in the subsequent phase using U-Net and finally classification is performed using CFSO (Convolutional Neural Network Fish Swarm Optimization) to overcome the slight chance of misclassification error as proposed CFSO can lead to more efficient computational processes since FSO algorithms are designed to minimize computational costs while maximizing performance through their metaheuristic nature. This efficiency is particularly beneficial when dealing with large datasets typical in medical imaging, allowing faster processing times without sacrificing accuracy. Hence, amalgamation of CFSO can reduce the number of features, thus speeding up training and inference times. Through the performance assessment, IoU (Intersection over Union) value attained through the analysis is found to be 0.7822. Further, accuracy obtained by the proposed model is 97.80%, recall is 98.49%, precision is 96.8% and F1-score is 97.32%. Findings of the study exhibits the purposefulness of the study in clinical settings by potentially reducing false negatives in lung cancer screening, ultimately improving patient survival rates through earlier detection and treatment.

11

Deep learning model for ECG reconstruction reveals the information content of ECG leads

Gradowski Tomasz, Buchner Teodor

Bio-Algorithms and Med-Systems

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2025

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Vol. 21, no 1

13--23

EN

Objective: This study aimed to evaluate the information content of individual electrocardiogram (ECG) leads and their inter-lead correlations using a deep learning approach. Specifically, we investigated the capability of a neural network to reconstruct missing ECG leads from reduced-lead configurations, thereby revealing each lead's unique and shared informational value. Methods: We developed a U-net convolutional neural network to reconstruct missing leads in 12-lead ECG recordings. The model was trained using the PTB-XL dataset and tested on the PTB dataset. We trained the model with varying combinations of input leads, including single limb leads, combinations of two limb leads and configurations, including one or two precordial leads. We evaluated the model's performance using mean squared error (MSE) between the reconstructed and actual signals. Results: The models demonstrated varying reconstruction accuracy depending on the input lead configuration. Precordial leads V1 and V6 showed the highest reconstruction fidelity from limb leads alone, while V3 consistently exhibited the lowest, indicating its unique informational content. Conclusions: The proposed method effectively quantifies the informational value of ECG leads. This has significant implications for optimizing lead selection in diagnostic scenarios, particularly in settings where complete 12-lead ECGs are impractical. In addition, the study provides insights into the physiological underpinnings of ECG signals and their propagation. The findings pave the way for advances in telemedicine, portable ECG devices and personalized cardiac diagnostics by reducing redundancy and improving signal interpretation.

12

Advancing flood disaster management: leveraging deep learning and remote sensing technologies

Roohi Mohammad, Reza Ghafouri Hamid, Mohammad Ashrafi Seyed

Acta Geophysica

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2025

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Vol. 73, no. 1

557--575

EN

Floods are among the most widespread and devastating natural disasters, accounting for 47% of all weather-related events and affecting over 2.3 billion people, particularly in Asia. Assessing flood-prone areas is crucial for effective disaster risk reduction, but existing flood damage estimation methods, such as depth-damage functions, often lack regional adaptability and accuracy. This study addresses this gap by integrating geospatial data, remote sensing, and artificial intelligence (AI) to identify flood-affected areas in the Kan basin, Tehran. We applied deep learning methods, specifically U-Net and fully convolutional neural network (FCN) algorithms, to optical and radar images from four flood events. Our results demonstrate that the U-Net model achieves significantly higher accuracy (88%) in identifying flood-affected areas compared to the FCN model (55% accuracy). This superior performance is further supported by the mean intersection over union (mIoU) values, with U-Net achieving 0.65, compared to 0.55 for FCN. The key message of this investigation is that deep learning, particularly the U-Net model, applied to remote sensing data holds significant promise for enhancing flood monitoring, early warning systems, and disaster management strategies by enabling more accurate and timely flood assessments.

13

Psychological determinants of efficiency loss: attachment styles and burnout

Dyląg Anna

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2024

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z. 200

69--82

EN

Purpose: The aim of this article is to present the documented relationships between individual attachment styles reported by adults, and job burnout defined as a syndrome of work efficiency loss. In addition to highlighting the role of childhood patterns and their impact on adult professional life, the article also discusses direction of practical use of this interdisciplinary framework in the field of HR. Design/methodology/approach: The study of the literature and research findings related to the theory of attachment and its correlates with burnout, as well as the study of the current reports on burnout scale, definition and legal regulations. Findings: Evaluation of the research findings revealed the gap in theoretical knowledge and practical application of the attachment concept in organizations, especially with regard to Polish workplaces and burnout problem. Research limitations/implications: Application of attachment concept in burnout research may require a dedicated tool with reference to work setting, as proposed by Leier et al., 2015. Practical implications: The article promotes interdisciplinary approach, also with possibility of use in multicultural work settings; introduces a relatively new framework for stress/burnout intervention and prevention aimed at increasing quality of social environment and support systems. Research on the relationship between attachment style and occupational burnout has implications for HRM. Understanding employees' attachment types can assist organizations in adapting stress management and burnout prevention strategies by creating a supportive work environment, attentive recruitment and leadership, adjusting coaching and mentoring techniques, and promoting autonomy and extensive support for employees. Social implications: In the face of currently observed burnout scale, both in Poland and globally, it is crucial to undertake actions toward reducing its spread. Building employees’ awareness and stress resilience through better self-knowledge and modification of less effective operational models (i.e. non-secure attachment style), may be valuable for employees and managers. Research on relationship between attachment styles and burnout also provides a solid basis for organizational level interventions, as a better care and investments in more balanced and harmonious work environments. Originality/value: Application of the psychological concept of attachment style typology to the field of work psychology, especially in Polish context; identification of the possible areas of implementation with the use of HR tools in reducing burnout.

14

Magnetic hysteresis analysis for non-destructive evaluation of aircraft structural steels

Solomakha Roman, Uchanin Valentyn

Transactions on Aerospace Research

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2024

|

Nr 3 (276)

1--12

EN

This paper introduces non-destructive testing (NDT) techniques centered on measuring magnetic properties, offering insight into evaluating structural steels throughout aircraft production and service life. It highlights the method’s utility in quality control of steel components, particularly in detecting variations in microstructure affecting mechanical properties. The NDT method correlates material structural state with magnetic properties, utilizing parameters such as coercive force, remanence, and hysteresis loop area. Developed instruments, like the MA-05 Magnetic Analyzer and KRM-Ts coercive force meter, enable precise measurements in both closed and open magnetic circuits. Applications range from assessing heat treatment quality to monitoring materials degradation in challenging conditions. A set of 12 gas containers that have been in service in aircraft for more than 40 years are tested as a case study, demonstrating the method's efficacy in evaluating damage accumulation. Future prospects include other potential applications in testing aircraft landing gear components.

15

Skład izotopowy (U-Pb, O) cyrkonów z waryscyjskich skał magmowych w strefie uskokowej Kraków–Lubliniec i obszarach przyległych w badaniach mikrosondą jonową SHRIMP IIe/MC

Mikulski Stanisław Z., Krzemińska Ewa, Czupyt Zbigniew J.

Przegląd Geologiczny

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2024

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Vol. 72, nr 12-1

735--739

EN

In situ analyses of zircon oxygen isotope compositions integrated with U-Pb dating have been used as a tracer of igneous processes on the Małopolska and Upper Silesia Blocks in the Kraków-Lubliniec Fault Zone (KLFZ). This integration provides one of the most robust records of primary magmatic oxygen isotope ratios, making them an important archive for crustal evolution considerations. The sensitive SHRIMP IIe/MC high-resolution microprobe was used to distinguish differences in melt components related to the Carboniferous-Permian magmatism and Mo-Cu (W) mineralization. The compilation of zircon oxygen isotope ratios from several samples from the KLFZ reveals variable magmatic δ18O values, interpreted as mixing of the mantle (δ18O ~ 5.3 ±0.6%) and crustal melts (δ18O >6%), with no contamination by sediments (i.e. δ18O >10%o). There is also a systematic record of the influence of hydrothermal processes with δ18O values <4%%. These results can be potentially used as a database, presented as a map of characteristic δ18O values collected as part of the tasks of the Polish Geological Survey. The starting point for this database could be a collection of about 600 oxygen isotope analyses of zircons from 20 samples of previously dated zircons from the MB in the KLFZ, accompanied by about 260 oxygen isotope analyses from 14 samples from Variscan rocks of the Sudetic area (southern Poland).

16

SHRIMP wspiera szczegółową kartografię geologiczną w Sudetach

Cymerman Zbigniew, Krzemińska Ewa

Przegląd Geologiczny

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2024

|

Vol. 72, nr 12-1

719--722

EN

For the first time, at the beginning of the 2nd edition of the Detailed Geological Map of the Sudetes 1 : 25,000, new, extended geochronological studies using the U-Pb method on zircons by means of the modern SHRIMP device were planned. They were completed by chemical dating of monazites with the WDS Cameca Sx100 electron microprobe. As a result of age studies about 920 new U-Pb zircon analyses were obtained from 28 samples of the several important rock-types of the Góry Sowie Metamorphic Complex, which significantly expands the scope of radiometric age record in this area. Apart from age, other diagnostic parameters were also used, such as Th/U ratios in zircon. They allow better understanding of the extremely complicated tectonometamorphic evolution of the Góry Sowie Metamorphic Complex during the complex orogenic processes in the Paleozoic in the NE part of the Bohemian Massif.

17

Datowanie cyrkonów z anortozytów : misja niemożliwa?

Garbarczyk-Gurba Anna, Krzemińska Ewa, Wiszniewska Janina, Gurba Dominik

Przegląd Geologiczny

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2024

|

Vol. 72, nr 12-1

723--729

EN

Despite the low content of Zr element (<100 ppm) in the anorthosites from the Suwałki Massif and Sejny Intrusion, it was necessary to undertake U-Pb age investigation on zircons. It was a technical challenge, which required a modification of typical separation procedures. In anorthosites associated with Fe-Ti ores, zircon is rich in Fe-Ti oxide inclusions and it often ends up in the magnetic fraction. The drillcore samples were selected from four different drillings located in the Suwałki Massif and nearby Sejny Intrusion, where a dominant rock type is anorthosites. In these rocks, zircon crystallizes as a late phase, which is reflected by its interstitial morphology. The samples were collected from depth intervals spanning multiple sections of the core, therefore a large number of U-Pb SHRIMP measurements (n = 50-97) has been performed for each sample. Age results, presented as a weighted average (Mean age), range from ~1510Ma (Jezioro Szlinokiemskie IG 1) to ~1505 Ma (Sejny IG 2). The SHRIMP data provided evidence of a distinct magma composi¬tion reflected by diversity of zircon chemical composition. The later evolution of Fe-Ti oxides is recorded as zircon lamellae being an effect of the subsolidus Zr precipitation from the ilmenite network at 1482 Ma.

18

Cyrkony nie są wieczne. Badania izotopowe O-U-Pb cyrkonów ze skał alkalicznych

Krzemińska Ewa, Poprawa Paweł

Przegląd Geologiczny

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2024

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Vol. 72, nr 12-1

730--–734

EN

Despite the common belief that zircons are nearly indestructible i.e. zircons last forever, regardless of the geological evolution experienced by their host rocks, there is one hostile environment in which decomposition of this incredibly resistant mineral may occur. An alteration of the natural zircon by coupled dissolution-reprecipitation or by ion-exchange with an aqueous fluid are common for alkaline rocks. In those zircons, the abundance of non-formula elements increase and textural changes are frequently observed. These symptoms are accompanied by the disturbance of primarily isotopic signatures. The extent of these processes can be well inferred from the oxygen (δ18O) isotopic composition of zircon. A large contrast of the δ18O between values of the normal mantle/magmatic zircons (>5.3 ±0.6%o) and the results obtained from the porous zircons from the Elk syenite massif have been detected by SHRIMP IIe/MC analyses using a~20 fim Cs+ beam. Most of the grains had reduced δ 1SO, up to prominently negative δ18O values of-7.46 ±0.27%. These δ 18O-depleted signatures resulted from high-temperature alkaline fluids - zircon interaction after foid syenite emplacement. Then this would imply that porous textures, as illustrated in Fig. 1, could be induced by alkaline fluids and thus these grains could be used to date solely the post-emplacement, i.e. hydrothermal, stage of evolution.

19

Late Cretaceous age of magmatism at Vršatec in the Pieniny Klippen Belt, Western Carpathians, Slovakia

Bazarnik Jakub, Matejová Marína Molčan, Lenik Piotr, Plašienka Dušan, Potočný Tomáš, Pańczyk Magdalena

Mineralogia

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2024

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Vol. 55, iss. 1

40--47

EN

Volcanic rocks in the Pieniny Klippen Belt (PKB) of the Western Carpathians have been the focus of geologists for over a century (e.g. Uhlig, 1890; Małkowski, 1921). Miocene volcanism is most common in the PKB. However, there are infrequent occurrences of Cretaceous volcanic rocks. Several magmatic bodies of Cretaceous age have already been described in the PKB, including basalts at Hanigovce and Biała Woda, as well as peperites at Vršatec, and Velykyi Kamenets. The magmatic body in Vršatec occurs within the Upper Cretaceous marlstones of the Lalinok Formation, the age of which was previously determined to be younger than 100 Ma (Spišiak et al., 2011). Our new U-Pb zircon dating indicates the magmatic age to be ca. 80 Ma. This new age can be used as a benchmark for the forthcoming provenance studies of the surrounding clastic rocks in the PKB and the Outer Carpathians flysch.

20

Guz Wilmsa u chłopca – opis przypadku

Cieplińska Aleksandra, Kowalczyk Katarzyna, Ożóg Katarzyna, Klęba Joanna

Inżynier i Fizyk Medyczny

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2024

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Vol. 13, nr 1

59--64

PL

Guz Wilmsa jest najczęstszym pierwotnym nowotworem złośliwym nerki u dzieci, a 95% guza Wilmsa występuje u pacjentów w wieku poniżej 10 lat. Wstępne obrazowanie guza nerek zwykle obejmuje badanie USG jamy brzusznej w celu identyfikacji punktu wyjścia zmiany, a następnie wykonuje się badania TK i MR celem oceny stopnia zaawansowania. Przedstawiono przypadek 3,5-letniego chłopca zgłaszającego ból brzucha z widoczną asymetrią powłok brzucha w badaniu fizykalnym. Po przeprowadzeniu badań obrazowych postawiono rozpoznanie guza Wilmsa i wdrożono przedoperacyjną chemioterapię, która po miesięcznej kontroli w badaniu TK spowodowała redukcję objętości guza o 91%. Podjęto decyzję o resekcji guza wraz z lewostronną nefrektomią. U chłopca wystąpiły dwie wznowy nowotworu, które leczono chemioterapią, resekcją i radioterapią. Obecnie pacjent, po wykonanych badaniach kontrolnych, ma podejrzenie trzeciej wznowy guza Wilmsa. Długoterminowe przeżycie pacjentów z nerczakiem zarodkowym stale się poprawia w ciągu ostatnich kilku dekad i obecnie przekracza 85%. Podanie chemioterapii neoadjuwantowej w przypadku guza Wilmsa może być dobrą metodą leczenia, pozwalającą na zmniejszenie wielkości guza i zachowanie funkcji nerek. Obrazowanie odgrywa zasadniczą rolę we wstępnej diagnostyce, określaniu stopnia zaawansowania i monitorowaniu guza Wilmsa.

EN

Wilms tumor (WT) is the most common primary renal malignancy in children. 95% of Wilms tumors occur in patients less than 10 years of age. Initial imaging of a renal tumor includes an abdominal ultrasound examination, followed by CT and MRI examinations to assess the stage of advancement. We present the case of a 3.5-year-old boy reporting abdominal pain with visible asymmetry of the abdominal wall during physical examination. After performing imaging tests, the diagnosis of Wilms’ tumor was made and treatment was implemented, which reduced the tumor volume by 91%, followed by nephrectomy. The boy had two recurrences of the cancer, which were treated, but a third recurrence occurred. The long-term survival of patients with nephroblastoma has steadily improved over the past decades and now exceeds 85%. Neoadjuvant chemotherapy for Wilms tumor reduces tumor size and preserves renal function. Imaging plays an essential role in the initial diagnosis, staging, and monitoring of Wilms’ tumor.