Functional Safety system (software & hardware) development is typically a V-Model process, which is governed by strenuous regulations & norms. This, along with use case specificity, and the scrupulous nature of functional safety creates various bottlenecks across the V-Model, i.e., redundant aspects of functional safety system development. To alleviate these bottlenecks, we introduce two LLM assistants designed to support key V-Model phases. The first assistant, the Digital Safety Assistant (DSA), provides safety engineers with general knowledge of functional safety norms through Retrieval Augmented Generation, thus decreasing norm and application domain adaptation overhead. We benchmark various models and assess the DSA using an official functional safety Certification exam, where the DSA achieves up to 70%, surpassing typical performance levels. A second assistant, the Automated Testing Assistant, developed through Parameter Efficient Fine-tuning to support the V-Model verification phase, is capable of correctly generating and debugging PLC test code with 93% correctness.

The β-catenin destruction complex (BDC) is a central node in WNT/β-catenin signaling, governing embryonic development and adult tissue homeostasis. Although recognized as a prime therapeutic target in colorectal cancer (CRC) for three decades, its dynamic architecture and biochemical complexity have hindered mechanistic understanding. Here, we systematically mapped the sequence-function landscape of the BDC using tiled base editor screens across four endogenous components—CTNNB1, AXIN1, APC, and GSK3B. Validation studies identified ∼150 previously unreported mutations across these genes that affected WNT/β-catenin signaling. In addition to known cancer-associated mutations, we discovered rare gain-of-function and separation-of-function alleles of AXIN1 and CTNNB1 that provide mechanistic insights into complex assembly and regulation. We describe a region in β-catenin that regulates its binding to TCF/LEF transcription factors and demonstrate that the AXIN1–β-catenin interface is critical for controlling signaling flux through the oncogenic BDC. Mechanistic studies revealed that assembly of the oncogenic BDC is scaffolded by its own substrate β-catenin, establishing an autoregulatory mechanism that represents an unexploited vulnerability in cancers harboring common APC truncations. Our comprehensive mutational resource provides a foundation for understanding WNT/β-catenin signaling mechanisms in health and disease, while revealing strategies for therapeutic intervention in WNT-driven cancers.

Extracellular vesicles (EVs) transport biomolecules that could serve as biomarkers for disease diagnosis and monitoring. The clinical utility of EVs derived from cerebrospinal fluid (CSF) in patients with intradural spinal tumors (IST) has not yet been investigated. Here, we obtained EVs from CSF of adult patients with intraspinal ependymoma (n = 9), meningioma (n = 9), hemangioma (n = 4) and schwannian tumors (n = 7), as well as comparison group (‘CG’, normal pressure hydrocephalus, n = 7), by ultrafiltration. CSF-EVs were characterized by electron microscopy and nanoparticle tracking analysis. EV populations according to the presence of tetraspanins (CD9, CD63, CD81) were measured by imaging flow cytometry (IFCM). CD81+ EVs were more prevalent in the comparison group, meningioma, ependymoma WHO grade 2, and hemangioma, whereas CD9+ EVs were predominant in ependymoma grade 1 and Schwannian tumors. CD63+ EVs per milliliter/CSF differed between ependymoma WHO grades 1 and 2 (FC = 24.6, AUC = 90%, p < 0.05). Based on results from a bead-based multiplex profiling, we selected ITGB1, CD44, CD133 and HLA-DR/DQ/DP for further phenotyping in CSF-EVs using IFCM, in combination with each tetraspanin as double-positive subpopulations. Compared to CG, CD44+ EVs were the most relevant population in CSF from IST patients, followed by ITGB1. Notable differences in absolute (EVs/mL CSF) and relative (percentages of CSF-EVs) levels were: CD44+/CD81+ for ependymoma grade 1 (FC = 196.5 and 34.5; p < 0.01) and grade 2 (%FC = 6.1, p < 0.05); CD44+/CD63+ for meningioma (abs. and %FC > 1000, p < 0.05); ITGB1+/CD81+ for hemangioma (%FC = 4.8, p < 0.05); and ITGB1+/CD9+ for schwannian tumors (abs.FC = 19.8, p < 0.01). In conclusion, we identified distinct EV subpopulations in the CSF of IST patients, potentially facilitating tumor classification.

Quercus petraea (Matt.) Liebl. holds significant ecological and economic value, yet phenotypic variation within its natural populations remains only partially explored. The main objectives of the study were: (1) to quantify the phenotypic variation in the analyzed leaf and acorn traits; (2) to determine the proportion of phenotypic variation occurring among and within populations; (3) to examine the pattern of population-level variation and its relationship with bioclimatic factors. Material and methods A total of 13 leaf traits and 10 acorn traits were analyzed in 64 sampled trees from seven natural populations. Descriptive statistics and population differentiation coefficients were calculated. The significance of differences in traits mean among populations and among trees within populations were assessed. Principal component analysis (PCA) was conducted at both the tree and population levels. Correlations between morphological principal components and bioclimatic variables were examined, and regression models were developed to evaluate relationships between significantly correlated morphological traits and bioclimatic factors. Results The coefficients of variation for the 23 traits ranged widely, from 12.08% for cupule width to 36.61% for petiole length, with an average of 20.36%. Nested ANOVA revealed substantial phenotypic variations both within and among populations. The primary source of variation was at the intra-population level, accounting for an average of 68.25%, which exceeded the inter-population variation (30.34%). The analyzed traits were reduced to six principal components, which together explained 88% of the phenotypic variation among trees. A statistically significant correlation between morphological principal components and bioclimatic variables was found for the first component, specifically with temperature related variables. Conclusions The study confirms that Q. petraea in Bosnia and Herzegovina exhibits considerable morphological variability, primarily within populations, and that leaf traits are strongly influenced by climatic gradients. Conservation strategies should prioritize the preservation of population integrity and patterns of local adaptation patterns, particularly in ecologically marginal or historically disturbed sites.

Traditional fault allocation methods often struggle in networks with multiple branching points, leading to inaccurate fault distance estimates. This problem not only undermines network reliability, but also forces distribution system operators to spend extra time and resources tracing faults in the field, which can compromise both worker safety and service continuity. This paper introduces a novel fault allocation method that leverages routinely available network data from electricity distribution companies-namely their existing network models and fault-impedance measurements recorded by IEDs. By integrating detailed network calculations under fault conditions, the proposed approach locates ground fault point even in complex branching network configurations. The simulation results - derived from multiple ground fault tests in a small segment of a real Bosnian distribution system (names omitted) - demonstrate accuracy and computational efficiency of the method, making it a valuable tool for supporting operator workflows, reducing outage durations, and strengthening the resilience of tomorrow’s smart grids.

This paper proposes the use of the Linux kernel’s ftrace framework, particularly the function_graph tracer, to generate informative system-level data for machine learning (ML) applications. Experiments on a real-world encryption detection task demonstrate the efficacy of using the proposed features across several learning algorithms. The learner is subjected to the problem of detecting encryption activities across a large dataset of files, where function call traces and graph-based features are used. Empirical results highlight an outstanding accuracy of $99.28 \%$ on the task at hand, underscoring the efficacy of features derived from the function_graph tracer. The results were further validated using an additional experiment targeting a multi-label classification problem by identifying the running programs based on trace data. This work provides comprehensive methodologies for preprocessing raw trace data and extracting graph-based features, offering significant advancements in applying ML to system behavior analysis, program identification, and anomaly detection. By bridging the gap between system tracing and ML, this paper paves the way for innovative solutions in performance monitoring and security analytics.

We consider a large-scale data center where a fleet of heterogeneous mobile robots and human workers collaborate to handle various installation and maintenance tasks. We focus on the underlying multi-agent task assignment problem which is crucial to optimize the overall system. We formalize the problem as a Markov Decision Process and propose an end-to-end learning approach to solve it. We demonstrate the effectiveness of our approach in simulation with realistic data and in the presence of uncertainty.

ObjectiveTo evaluate the predictive value of LA strain parameters and LASI for AF recurrence following electrical CV, and to compare them to conventional echocardiographic, biochemical, and clinical markers.MethodsIn this prospective, observational pilot study, 31 patients with persistent AF underwent electrical CV and were followed for six months. Echocardiographic evaluation included LA reservoir, conduit, and contractile strain, left atrial stiffness index, left atrial volume index (LAVI), left atrial appendage (LAA) morphology, left ventricular ejection fraction (LVEF), right atrial (RA) area, and right ventricular systolic pressure (RVSP). AF recurrence was assessed at three and six months.ResultsAt three months post-CV, LA reservoir, conduit, and contractile strain values were significantly negatively associated with AF recurrence (p < 0.001), while LASI and E/E' ratios were positively associated (p < 0.001). At six months, only contractile strain retained prognostic significance (p = 0.008). LVEF showed a positive correlation with recurrence at six months (p = 0.003), potentially reflecting the role of diastolic dysfunction.ConclusionLA strain parameters and LASI are valuable tools for predicting AF recurrence after CV, particularly in the early post-procedural period. Contractile strain may serve as a more reliable long-term predictor, emphasizing the importance of longitudinal atrial function assessment in rhythm outcome prediction. However, given the small sample size and single-center design, these results should be considered hypothesis-generating, requiring validation in larger studies.

Aim: This manuscript summarizes the key scientific and practical outcomes of the #DHPSP2024 digital networking event, focusing on emerging trends in digital health technologies, innovations in patient safety, and their implications for improving healthcare delivery. Methods: The #DHPSP2024 event was held from June 18 to 20, 2024, on X (formerly Twitter) and LinkedIn, connecting professionals and stakeholders in digital health and patient safety from different sectors. Data from posts on X and LinkedIn were analyzed for geographical distribution, engagement metrics (impressions, likes, shares), top hashtags, and frequently used terms. A qualitative analysis of the central themes and key online messaging discussions of the network event was also conducted. Results: On X, 2,329 posts by 179 participants from 38 countries generated over 231,000 impressions, with the most activity in Austria, China, and India. LinkedIn engagement included 3,475 likes, 217 comments, and 2,030 shares. Both platforms highlighted core themes such as digital health, patient safety, treatment quality, research on natural compounds, and interdisciplinary collaboration. Online messaging discussions emphasized technologies like telemedicine and artificial intelligence as critical tools for enhancing care delivery and patient safety. Participants also promoted special issues of scientific journals and explored collaborative research opportunities. Conclusions: The #DHPSP2024 event underscored the pivotal role of digital technologies in transforming healthcare, particularly in improving the quality and safety of interventions. The findings demonstrate how digital networking events, grounded in open innovation, foster global research communities, accelerate knowledge exchange, and support the integration of clinically relevant digital solutions. The strong engagement reflects growing interest in leveraging digital platforms to advance health outcomes and professional development. Overall, the event contributed to greater visibility of ongoing research, encouraged interdisciplinary cooperation, and may positively influence both the adoption of innovations in healthcare practice and the dissemination of scientific knowledge.

Introduction Hepatitis E virus (HEV) is foodborne zoonotic pathogen widespread among European swine yet unstudied in Bosnia and Herzegovina (B&H). We estimated HEV seroprevalence in domestic pigs in Federation of B&H (FB&H) and assessed farm-level risk factors for exposure. Methods Cross-sectional survey sampled 437 pigs from 87 farms across seven cantons via two-stage random design. Serum anti-HEV IgG measured by commercial indirect ELISA; managers completed standardized biosecurity/management questionnaire. Apparent seroprevalence calculated with 95% CIs. Univariable screening (α = 0.10) informed multivariable logistic regression with farm-level clustering; collinearity checked (Phi), AIC-guided forward selection applied. Results Animal-level seroprevalence 77.1% (95% CI 73.0–81.0%); herd-level 95.4% (88.9–98.7%). Adults showed higher seropositivity than growers (91.0% vs. 71.7%; p < 0.001). Significant factors: wild-boar proximity (adjusted POR 3.11; p = 0.04), small farm size (18.35; p < 0.001), swill feeding (5.70; p = 0.03). Cleaning ≥5×/month strongly protective (0.01; p < 0.001). All surveyed cantons had positives; no equivocal ELISA results. Discussion Findings indicate widespread HEV in FB&H swine with environmental, food-safety, and occupational implications. Older-animal pattern reflects cumulative exposure; small-farm context and wildlife interface likely sustain transmission, whereas frequent cleaning reduces risk. Strengthened biosecurity, wildlife exclusion, feed oversight (including prohibition/monitoring of swill feeding), and improved hygiene, should form basis of One Health interventions to mitigate potential zoonotic transmission via the pork production chain.

Energy security is currently one of the most important topics worldwide. Maintaining a reliable energy supply is one of the biggest challenges in security science. Additionally, defending energy infrastructure from cyberattacks is an ongoing issue. Understanding the vulnerabilities of energy infrastructure, especially the Smart Grid, which relies on information technology and communications, is a significant advantage. Understanding which system vulnerabilities lead to specific cyber threats presents a significant opportunity, enhancing the defence of energy infrastructure. This paper uses a systematic literature review to identify the most common cyber threat and Smart Grid vulnerability mentioned and researched in the literature from 2018 to 2025. This paper also aims to map the vulnerabilities that allow for cyber threats to occur, with the idea that if we know what causes a weak spot, we can effectively prevent it. Identifying specific weaknesses that could lead to cyber threats allows us to mitigate these dangers by addressing and correcting those vulnerabilities.

Research on mental health has traditionally separated the study of ill-being, including clinically defined mental and behavioural disorders and subthreshold problems, from the study of well-being, which encompasses factors such as life satisfaction and positive affect. Although previous reviews of studies primarily using self-report scales indicate that ill-being and well-being are distinct yet interconnected constructs, a deeper examination of their relationship is lacking. In this Perspective, we synthesize genetic, biological, developmental, psychosocial, societal, cultural and clinical research on ill-being and well-being. Our review reveals substantial genetic overlap and similar biological underpinnings for ill-being and well-being. By contrast, environmental factors and societal changes often exert divergent influences. We propose a differentiated multidisciplinary framework in which the shared and unique determinants, predictors, mechanisms and consequences of mental ill-being and well-being vary across levels of analysis, offering a more nuanced understanding of the interconnections. Tamnes et al. explore the complex relationship between ill-being—including mental and behavioural disorders—and mental well-being.

In this study, ethanolic and petroleum ether extracts of commercial cloves were prepared and analyzed. Soxhlet extraction (SE) and ultrasonic extraction (UAE) were used to prepare the extracts. Phytochemicals from plant material were extracted for 2 hours, in the case of both extraction techniques. The content of total polyphenols and flavonoids, as well as their antioxidant and antibacterial activity, were analyzed in the extracts. The results show an extremely high content of total polyphenols, which was also confirmed by the high antioxidant capacity, confirmed by DPPH and FRAP methods. Antibacterial screening on reference bacterial strains from the WDCM collection shows high potency of clove extracts at the tested concentration. In terms of the efficiency of extraction of bioactive components, petroleum ether in combination with ultrasonic extraction is the most effective. These results may contribute to further research and optimization of the extraction of biologically active components from cloves and related plant materials.