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Large gas bubbles can reach the surface of pools of mud and lava where they burst, often through the formation and expansion of circular holes. Bursting bubbles release volatiles and generate spatter, and hence play a key role in volcanic degassing and volcanic edifice construction. Here, we study the ascent and rupture of bubbles using a combination of field observations at Pâclele Mici (Romania), laboratory experiments with mud from the Imperial Valley (California, USA), numerical simulations and theoretical models. Numerical simulations predict that bubbles ascend through the mud as elliptical caps that develop a dimple at the apex as they impinge on the free surface. We documented the rupture of bubbles in nature and under laboratory conditions using high-speed video. The bursting of mud bubbles starts with the nucleation of multiple holes, which form at a near-constant rate and in quick succession. The quasi-circular holes rapidly grow and coalesce, and the sheet evolves towards a filamentous structure that finally falls back into the mud pool, sometimes breaking up into droplets. The rate of expansion of holes in the sheet can be explained by a generalization of the Taylor-Culick theory, which is shown to hold independent of the fluid rheology.

Petra Hartmann,1 Edina Butt,2 Ágnes Fehér,1 Ágnes Lilla Szilágyi,1 Kurszán Dávid Jász,1 Boglárka Balázs,3 Mónika Bakonyi,3 Szilvia Berkó,3 Gábor Erős,4 Mihály Boros,1 Gyöngyi Horváth,5 Endre Varga,2 Erzsébet Csányi3 1Institute of Surgical Research, University of Szeged, Szeged, Hungary; 2Department of Traumatology, University of Szeged, Szeged, Hungary; 3Department of Pharmaceutical Technology, University of Szeged, Szeged, Hungary; 4Department of Pathology, University of Szeged, Szeged, Hungary; 5Department of Physiology, University of Szeged, Szeged, Hungary Purpose: Since electroporation (EP) can increase the permeability of biological membranes, we hypothesized that it offers an opportunity to enhance the transdermal delivery of drugs for intra-articular indications. Our aim was to compare the anti-inflammatory and analgesic efficacy of EP-combined topical administration of diclofenac sodium hydrogel (50 mg mL-1 in 230 μL volume) with that of an equivalent dose of oral (75 mg kg-1) and simple topical administration. Methods: Arthritis was induced with the injection of 2% λ-carrageenan and 4% kaolin into the right knee joints of male Sprague Dawley rats. EP was applied for 8 min with 900 V high-voltage pulses for 5 ms followed by a 20 ms break. Drug penetration into the synovial fluid and plasma was detected by high-performance liquid chromatography. Leukocyte–endothelial interactions were visualized by intravital videomicroscopy on the internal surface of the synovium. Inflammation-induced thermal and mechanical hyperalgesia reactions, knee joint edema, and inflammatory enzyme activities were assessed at 24 and 48 h after arthritis induction. Results: EP significantly increased the plasma level of diclofenac as compared with the topical controls 10 min after the 2% λ-carrageenan and 4% kaolin injection. Increased leukocyte–endothelial interactions were accompanied by joint inflammation, which was significantly reduced by oral and EP diclofenac (by 45% and by 30%, respectively) and only slightly ameliorated by simple topical diclofenac treatment (by 18%). The arthritis-related secondary hyperalgesic reactions were significantly ameliorated by oral and EP-enhanced topical diclofenac treatments. The knee cross-section area (which increased by 35%) was also reduced with both approaches. However, simple topical application did not influence the development of joint edema and secondary hyperalgesia. Conclusion: The study provides evidence for the first time of the potent anti-inflammatory and analgesic effects of EP-enhanced topical diclofenac during arthritis. The therapeutic benefit provided by EP is comparable with that of oral diclofenac; EP is a useful alternative to conventional routes of administration. Keywords: diclofenac, transdermal delivery, HPLC, intravital videomicroscopy

Considering Gen Z's learning needs, primarily focusing on bachelor IT students, a STEAM-based methodology was developed and tested for teaching and learning the principal properties of the cardioid curve. The four-component methodology is based on visuality and combines frontal teaching techniques with educational robotics, dynamic geometry software and project-based learning elements. Interactive learning materials were set up to support independent learning, including tasks which follow the guidelines of recent research on task design. Moreover, a cardioid drawing LEGO robot model was designed and built to visualise the generation process of the curve and to provide first-hand experiences for students. The participants involved in testing the methodology had to solve two homework problems using dynamic geometry software. An assessment system was set up to evaluate students' solutions. After a statistical analysis of the data obtained, it could be concluded that the four-component methodology is an effective didactic technique to facilitate the learning of the cardioid curve.

The global health concern of pharmacoresistant epilepsy necessitates innovative therapeutic strategies. Drug resistance often arises due to complex pharmacokinetic challenges. Beta-cyclodextrin, known for enhancing drug solubility and stability, offers a potential solution for improving the efficacy of antiseizure medications. This study aims to investigate the impact of beta-cyclodextrin-complexed rufinamide on seizure-like activity using an in vitro model of temporal lobe epilepsy.

QT interval prolongation on the electrocardiogram is considered a precursory sign for imminent, potentially lethal ventricular arrhythmias. Beside the inherited condition of long QT syndrome, numerous drugs, certain electrolyte disturbances and early transmural ischemia have been identified to induce reversible prolongation of the QT interval, collectively called as acquired long QT syndrome. Herein we describe a case of a patient with transient QT prolongation and Takotsubo cardiomyopathy, a rather infrequent cause of long QT development. Serial changes of the repolarization pattern were documented to demonstrate progression and resolution of the abnormal QT interval.

Abstract Biological reproduction rests ultimately on chemical autocatalysis. Autocatalytic chemical cycles are thought to have played an important role in the chemical complexification en route to life. There are two, related issues: what chemical transformations allow such cycles to form, and at what speed they are operating. Here we investigate the latter question for solitary as well as competitive autocatalytic cycles in resource-unlimited batch and resource-limited chemostat systems. The speed of growth tends to decrease with the length of a cycle. Reversibility of the reproductive step results in parabolic growth that is conducive to competitive coexistence. Reversibility of resource uptake also slows down growth. Unilateral help by a cycle of its competitor tends to favour the competitor (in effect a parasite on the helper), rendering coexistence unlikely. We also show that deep learning is able to predict the outcome of competition just from the topology and the kinetic rate constants, provided the training set is large enough. These investigations pave the way for studying autocatalytic cycles with more complicated coupling, such as mutual catalysis.

Objective: Pharmacoresistant epilepsy represents a significant global health challenge, necessitating novel therapeutic approaches. Despite advances in antiseizure medications, many patients remain treatment-resistant partially due to complex pharmacokinetic issues. Beta-cyclodextrin, known for enhancing drug solubility and stability, offers potential solutions by forming inclusion complexes, thereby improving anti-seizure medication’s efficacy. This study aimed to investigate the effect of beta-cyclodextrin and beta-cyclodextrin-complexed carbamazepine on epileptiform activities, using an in vitro model of temporal lobe epilepsy.

Abstract For alkaline anion-exchange membrane electrolysers and fuel cells to become a technological reality, hydroxide-ion (OH−) conducting membranes that are flexible, robust, affording high OH− conductivity, and synthesised in a low-cost and scalable way must be developed. In this paper, we engineer a stable, self-supporting, and flexible fibre mat using a low-cost ZIF-8 metal–organic framework composited with ionic liquid tetrabutylammonium hydroxide and widely used polyacrylonitrile as polymeric backbone. We obtain mats with a high intrinsic OH− conductivity for a metal–organic framework-based material already at room temperature, without added ion-conductor polymers. This approach will contribute to the development of low-cost and tuneable ion-conducting membranes.

Background Faith and belief systems impact the emotional as well as immunological states of believers in ways that we are just beginning to understand. However, the clinical implications of prior studies are limited. The aim of the HEALING (Hospital-based Ecumenical and Linguistic Immuno-NeuroloGic) study is to examine immunological and neurological changes in hospitalized patients after meeting with a chaplain coupled with the study of biblical readings. Methods Hospitalized patients were pre-screened to identify those who were most in need of a spiritual intervention. A passage from the Bible was read to them during a meeting with the chaplain at bedside (n = 20) or in the chapel (n = 18). No meeting occurred in the randomized control group (n = 19). Blood samples were obtained 30 min prior and 60 min after the meeting to measure white blood cell (WBC) count, interferon-gamma (IFN-γ), immunoglobulin M (IgM), IgA, IgG, and complement 3 (C3). A subgroup of the visited patients was subjected to functional magnetic resonance imaging (fMRI), during which they listened to an audiotape of readings of the same biblical passage (n = 21). Results Immunological changes were not significant. Conversely, a significant (p fwe = 0.003) correlation was observed between lymphocyte changes and activation of the angular gyrus (left BA39) during fMRI, a brain area involved in word recognition. Conclusions This article contributes to the relevant literature by helping to create a realistic picture of the possibilities of neuroimmune modulation in clinical practice. Compared to healthy volunteers, the extent of short-term neuroimmunomodulation becomes narrower in a clinical setting. Although limited by the sample size and cohort study design, the findings suggest that the depth of psycho-immunological changes could depend on the degree to which the chaplain’s main message is understood.

Background: Endoscopic retrograde cholangiopancreatography (ERCP) is essential for the minimally invasive management of biliary and pancreatic disorders. Under certain indications, performing ERCP without delay during the weekend can be important for improving outcomes. Objectives: To compare the outcomes of ERCP performed on weekends and holidays with those of regular weekday ERCPs. Design: Propensity score match analysis of the data from the Hungarian ERCP Registry. Methods: A total of 116 ERCPs were performed during weekends or holidays, and 3144 during weekday working hours. The analyses were performed on 1:2 propensity-matched groups (116 weekend and 232 weekday cases). Results: Weekend ERCPs were mostly performed for acute cholangitis and acute biliary pancreatitis (70% of cases), whereas in the weekday group, only 32% of cases were performed for these indications. No significant difference was found between weekday and weekend ERCPs in terms of the rates of successful (91.38% vs 93.1%, p = 0.565) and difficult (33.62% vs 36.64%, p = 0.511) biliary cannulations. We found no significant differences in the number of adverse events (bleeding, post-ERCP pancreatitis, and 30-day mortality) in ERCPs performed during weekends or weekdays. Moreover, no significant differences in the aforementioned outcomes were detected between the propensity-matched groups. Conclusion: In this propensity-matched study, no significant differences were found in the outcomes of weekend and weekday ERCPs.

Abstract Gene regulatory networks (GRNs) fulfill the essential function of maintaining the stability of cellular differentiation states by sustaining lineage-specific gene expression, while driving the progression of development. However, accounting for the relative stability of intermediate differentiation stages and their divergent trajectories remains a major challenge for models of developmental biology. Here, we develop an empirical data-based associative GRN model (AGRN) in which regulatory networks store multilineage stage-specific gene expression profiles as associative memory patterns. These networks are capable of responding to multiple instructive signals and, depending on signal timing and identity, can dynamically drive the differentiation of multipotent cells toward different cell state attractors. The AGRN dynamics can thus generate diverse lineage-committed cell populations in a robust yet flexible manner, providing an attractor-based explanation for signal-driven cell fate decisions during differentiation and offering a readily generalizable modelling tool that can be applied to a wide variety of cell specification systems.

This article follows the way the Austrian composer Dieter Kaufmann has gone with his conceptions taken from musical and extra-musical domains, as literature, visual arts, religion and politics, describing some of his theatrical works. For him the function of music is to improve the society and therefore his works are a critic to politics or to religion. Having studied in Paris by Olivier Messiaen, Pierre Schaeffer, François Bayle and René Leibowitz, he contributed to the development of the electronic music in Austria with his creation and his teaching of electroacoustic composition at the University of Music and Performing Arts in Vienna. He went further than the Parisian ‘musique concrète’, which worked with sound objects, and consciously selected and worked with objects of sound ambient in order to manage the sound environment, to musically shape it. The acousmatic art deals with sound objects, which are recorded and projected in another space, the theatre scene being an ideal space of it. A large part of his works are included in the genre of New Music Theater of the 20th and 21st century, where sound (tone and word) and movement come together, each of them having the same weight.

Large gas bubbles can reach the surface of pools of mud and lava where they burst, often through the formation and expansion of circular holes. Bursting bubbles release volatiles and generate spatter, and hence play a key role in volcanic degassing and volcanic edifice construction. Here, we study the ascent and rupture of bubbles using a combination of field observations at Pâclele Mici (Romania), laboratory experiments with mud from the Imperial Valley (California, USA), numerical simulations and theoretical models. Numerical simulations predict that bubbles ascend through the mud as elliptical caps that develop a dimple at the apex as they impinge on the free surface. We documented the rupture of bubbles in nature and under laboratory conditions using high-speed video. The bursting of mud bubbles starts with the nucleation of multiple holes, which form at a near-constant rate and in quick succession. The quasi-circular holes rapidly grow and coalesce, and the sheet evolves towards a filamentous structure that finally falls back into the mud pool, sometimes breaking up into droplets. The rate of expansion of holes in the sheet can be explained by a generalization of the Taylor–Culick theory, which is shown to hold independent of the fluid rheology.

This paper investigates the long-debated issue of technological knowledge and potters in the Early Copper Age (4500–4000/3900 cal BC) through the analysis of the forming technology of a typical, small-scale settlement at Polgár-Király-ér-part (Northeastern Hungary). A complex technological know- ledge of forming was identified, in which most of the vessel types have their own forming technique. This technological knowledge presumably originated in the local Neolithic. However, a few smaller technological differences nuance this picture, raising the possibility of distinct learning networks and multiple potters at the settlement.

Abstract The physical–chemical characteristics of different types of flours are essential for understanding their composition, nutritional value, and functional properties as well. The aim of this research was to identify the variability of the different wheat flours available in Romania. In this study 39 different wheat flours were selected and the following parameters were analyzed in the laboratory: moisture content, ash content, gluten content (wet and dry) and wet gluten spreading. The tested flours were classified into four different classes according to their ash content: 480 (ash content 0.48%) (N = 11), 550 (0.55%) (N = 9), 650 (0.65%) (N = 8), 1100 (1.1%) (N = 11). Mathematical and statistical methods were used to analyze the obtained results: descriptive statistics, box-plot, Spearman correlation and hierarchical cluster analysis. The results revealed that moisture content varied between 9.5 and 11.8%. In terms of ash content, the lowest and highest measured values were 0.427–2.04 g/100 g. The average wet gluten content of the studied flours varied between 30 and 32%, while the average dry gluten content was 12.8%. The findings indicate that the moisture content of all examined flour samples was within permissible levels for extended storage, aligning with established standards. Gluten is a key and essential parameter for bread making because influences the dough mixing and baking properties. The mineral content, represented by ash content, is influenced by cereal type and milling process, with wheat's ash content ranging between 1.5 and 2%. Flours with high wet gluten content (> 34%) can be used to improve the properties of lower quality flours. Further studies are necessary in order to determine the possible health effects of different cereal varieties.

The present study focuses on an Early Copper Age (4500–4000/3900 cal BC) atypical ceramic bowl from Tiszagyenda-Vágott-halom (Eastern Hungary). At first glance, the bowl was strikingly different from the other well-executed, often richly decorated bowls known from the site. Although such vessels can be found sporadically in other Early Copper Age sites, they have never been studied to see exactly how they differ from typical pottery. The aim of the present study is to understand the ideas, intentions, decisions, knowledge, and skills of the potter who made this vessel by reconstructing the chaîne opératoire of the making of the bowl. Technological and stylistic analyses suggest that the atypical pot was probably made by a beginner potter. The stylistic elements (form and decoration) are common in the pottery repertoire of the site and are not considered exceptional, apart from the quality of the execution. The deviation from the usual pottery-making techniques, however, suggests that the potter had probably not acquired all the pottery-making traditions of the community.

The paper examines the potential of artificial intelligence (AI) in parsing text and conducting sentiment analysis to identify early markers of mental health and neurodegenerative disorders. Through the analysis of textual data, we investigate whether AI can provide a noninvasive, continuous, and objective complement to traditional diagnostic practices.

The so-called fuzzy-possibilistic c-means (FPCM) algorithm was introduced as an early mixed-partition method aiming to eliminate some adverse effects present in the behavior of the fuzzy c-means (FCM) and the possibilistic c-means (PCM) algorithms. A great advantage of FPCM was the low number of its parameters, as it eliminated the possibilistic penalty terms used by PCM. Unfortunately, FPCM in its original formulation also has a weak point: the strength of the possibilistic term is in inverse proportion with the number of clustered data items, which makes FPCM act like FCM when clustering large sets of data. This paper proposes a modification of the FPCM algorithm by introducing an extra coefficient into the possibilistic term that allows us to control the strength of the possibilistic effect within the mixture model. The modified clustering model will be referred to as generalized FPCM, since a certain value of the extra parameter reduces it to the original FPCM, or in other words, FPCM is a special case of the proposed algorithm. The proposed method is evaluated using noise-free and noisy data as well.

This paper was set to address to what extent intellectual property law is prepared for keeping pace with the rapid development of artificial intelligence and digitalization? We examined, by way of using empirical methods, the relationship between artificial intelligence, its regulation and the current trends in intellectual works. The overall conclusion of our research findings demonstrates that current legislation is well suited to support the requirements of technological progress. Nevertheless, there are some new dimensions in terms of worthy proposals that may also be considered. This paper stresses that given AI is capable of bringing significant transformations in our daily lives about, a major paradigm shift in legislation may soon be inevitably required.

Background: Brain tumors are highly complex, making their detection and classification a significant challenge in modern medical diagnostics. The accurate segmentation and classification of brain tumors from MRI images are crucial for effective treatment planning. This study aims to develop an advanced neural network architecture that addresses these challenges. Methods: We propose L-net, a novel architecture combining U-net for tumor boundary segmentation and a convolutional neural network (CNN) for tumor classification. These two units are coupled such a way that the CNN classifies the MRI images based on the features extracted by the U-net while segmenting the tumor, instead of relying on the original input images. The model is trained on a dataset of 3064 high-resolution MRI images, encompassing gliomas, meningiomas, and pituitary tumors, ensuring robust performance across different tumor types. Results: L-net achieved a classification accuracy of up to 99.6%, surpassing existing models in both segmentation and classification tasks. The model demonstrated effectiveness even with lower image resolutions, making it suitable for diverse clinical settings. Conclusions: The proposed L-net model provides an accurate and unified approach to brain tumor segmentation and classification. Its enhanced performance contributes to more reliable and precise diagnosis, supporting early detection and treatment in clinical applications.

Robotics offers innovative possibilities at all levels of education, which should be considered when planning the teaching process for certain engineering mathematics topics in higher education. This paper introduces a new teaching–learning technique that utilizes STEAM-based methods to explore cycloidal curves for Computer Science Engineering BSc students. Traditional frontal teaching has been enhanced with methods addressing the generational needs of students, including problem-based learning, STEAM integration, and project-based learning. We developed a methodological model that merges traditional teaching advantages with practical modern approaches suitable for Generation Z. The four-stage model for learning cycloidal curves employs various didactical approaches, utilizing different learning techniques at each stage to create an engaging and effective learning experience. A vital component of this model is the use of Desmos dynamic geometry software to create animations alongside educational robotics to aid visualization. We conducted quantitative studies with 98 first-year Computer Science Engineering students using a quasi-experimental research design to evaluate the new teaching technique’s effectiveness. Results from the Mann–Whitney U test indicated that the experimental group significantly outperformed the control group. Additionally, the Kruskal–Wallis test confirmed that the four-stage model for learning cycloidal curves enhances learning achievement for all students, regardless of their prior knowledge.

Retinal ischemia–reperfusion (I/R) injury is a critical pathogenic mechanism in various eye diseases, and an effective therapeutic strategy remains unresolved. Natural derivatives have recently reemerged; therefore, in our present study, we examined the potential therapeutic effects of a stilbenoid that is chemically related to resveratrol. Pterostilbene, recognized for its anti-inflammatory, anti-carcinogenic, anti-diabetic, and neuroprotective properties, counteracts oxidative stress during I/R injury through various mechanisms. This study explored pterostilbene as a retinoprotective agent. Male Sprague Dawley rats underwent retinal I/R injury and one-week reperfusion and were treated with either vehicle or pterostilbene. After this functional electroretinographical (ERG) measurement, Western blot and histological analyses were performed. Pterostilbene treatment significantly improved retinal function, as evidenced by increased b-wave amplitude on ERG. Histological studies showed reduced retinal thinning and preserved the retinal structure in the pterostilbene-treated groups. Moreover, Western blot analysis revealed a decreased expression of glial fibrillary acidic protein (GFAP) and heat shock protein 70 (HSP70), indicating reduced glial activation and cellular stress. Additionally, the expression of pro-apoptotic and inflammatory markers, poly(ADP-ribose) polymerase 1 (PARP1) and nuclear factor kappa B (NFκB) was significantly reduced in the pterostilbene-treated group. These findings suggest that pterostilbene offers protective effects on the retina by diminishing oxidative stress, inflammation, and apoptosis, thus preserving retinal function and structure following I/R injury. This study underscores pterostilbene’s potential as a neuroprotective therapeutic agent for treating retinal ischemic injury and related disorders.

Background/Objectives: Managing blood glucose levels effectively remains a significant challenge for individuals with diabetes. Traditional methods often lack the flexibility needed for personalized care. This study explores the potential of reinforcement learning-based approaches, which mimic human learning and adapt strategies through ongoing interactions, in creating dynamic and personalized blood glucose management plans. Methods: We developed a mathematical model specifically for patients with type IVP diabetes, validated with data from 10 patients and 17 key parameters. The model includes continuous glucose monitoring (CGM) noise and random carbohydrate intake to simulate real-life conditions. A closed-loop system was designed to enable the application of reinforcement learning algorithms. Results: By implementing a Policy Optimization (PPO) branch, we achieved an average Time in Range (TIR) metric of 73%, indicating improved blood glucose control. Conclusions: This study presents a personalized insulin therapy solution using reinforcement learning. Our closed-loop model offers a promising approach for improving blood glucose regulation, with potential applications in personalized diabetes management.