In the era of exponential data generation, a fast, consistent, and efficient string processing technique is necessary to represent extensive genomic data. One of the earliest string processing techniques, predating MinHash and minimizer-based sketching, is Locally Consistent Parsing (LCP). This technique partitions an input string and identifies short, exactly occurring substrings called cores, which collectively cover the input string while maintaining Partition and Labeling Consistency. The iterative application of LCP yields progressively longer cores in a compressed format, thereby substantially enhancing the efficiency of genomic sequence representation and subsequent downstream analysis. We have previously developed Lcptools as the first iterative implementation of LCP for the DNA alphabet and demonstrated its effectiveness in identifying cores with minimal collisions. Here, we introduce GenCore, a computational method that leverages LCP cores for the first time to sketch and estimate genomic distances for closely related large genomes, and successfully reconstruct simulated progression trees. GenCore also successfully recapitulates primate phylogeny using both telomere-to-telomere (T2T) assemblies and the PacBio HiFi reads for assembly-free comparisons. Availability GenCore is available at https://github.com/BilkentCompGen/gencore

Background/Objectives: The COVID-19 pandemic caused over seven million deaths globally as of July 2024. In an attempt to bring the pandemic under control, immunization was implemented as the main preventive strategy. This study aimed to investigate the knowledge, attitudes, and practices (KAP) of hospitalized patients and healthcare workers (HCWs) regarding COVID-19 vaccination, as well as the factors contributing to COVID-19 vaccination rates. Methods: This cross-sectional, survey-based KAP study was conducted between November 2024 and February 2025 in five hospitals across five cities of the Republic of Srpska, Bosnia and Herzegovina. Results: There were 571 respondents, 68% of whom were female, with an average age of 39.17 ± 14.74 years; one-third held a university degree. The study sample consisted of patients and healthcare workers (HCWs) (59% vs. 41%). During the pandemic period, 46.6% of respondents were diagnosed with COVID-19, with a higher prevalence among healthcare workers compared to patients (54.2% vs. 41.2%). Among the 55.2% of respondents who were vaccinated, HCWs were more often vaccinated than patients (70.9% vs. 44.2%) and more likely to know that vaccines protect against severe forms of disease and death (80.8% vs. 68.5%). Patients more often believed that vaccination against COVID-19 may lead to sterility in young patients (11.3% vs. 6%) and were more often afraid of vaccination compared to the occurrence of COVID-19 (35.6% vs. 24.8%). Regression analyses showed that independent predictors of COVID-19 vaccination were older age (p < 0.001), higher education level (p = 0.039), knowledge of vaccine production technology, and the belief that vaccinated individuals have milder symptoms of the disease (p = 0.002). Conversely, the belief that the COVID-19 situation was overblown was negatively associated with vaccination (p = 0.004). Conclusions: HCWs had better knowledge, more positive attitudes, and better vaccination practices against COVID-19 in comparison to patients. However, there are still certain dilemmas and hesitations among HCWs toward COVID-19 vaccination.

Scattered between many healthcare providers across the US, Electronic Health Records (EHR) are extensively used for research purposes. Collaboration and sharing of EHRs between multiple institutions often provide access to more diverse datasets and a chance to conduct comprehensive studies. However, these collaboration efforts are usually hindered by privacy issues that render the pooling of such data at a centralized database impossible. Furthermore, EHRs are often incomplete and require statistical imputation prior to the study. To enable collaborative studies on top of incomplete, private EHRs, here we provide a provably secure solution built with secure multiparty computation (SMC) that provides practical runtimes and accuracy on par with the state-of-the-art, non-secure equivalents. Our solution enables the utilization of distributed datasets as a whole to impute the missing data and conduct collective studies between non-trusting private data proprietors. We demonstrate its effectiveness on various synthetic and real-world datasets, and show that our solution can significantly improve the classification of high-risk patient outcomes during ICU admission.

Despite major advances in Connected and Autonomous Vehicles (CAVs), edge cases such as unmapped construction zones or dense urban areas still require human intervention. Teleoperation, often combined with Level 4 automation, addresses these situations but demands strict network performance i.e., latency below 5 ms, uplink throughput above 25 Mbps, and 99.999% reliability, to ensure safe, responsive control under heavy load. This paper presents a Proof-of-Concept (PoC) 5G Standalone (SA) network supporting end-to-end Network Slicing for teleoperation. The PoC achieves seamless slice isolation across the 5G Core (5GC), Transport Network (TN), and Radio Access Network (RAN), enabling dynamic and cross-domain Network Slicing to meet teleoperation requirements.

This demonstration paper presents a real-life 5G Standalone proof-of-concept showcasing end-to-end network slicing with both inter-slice and intra-slice isolation across the 5G Core, Transport Network, and Radio Access Network domains. The system, based on a smart hospital scenario, highlights a decentralized 5G architecture that aligns slice selection, controlplane, and data-plane mechanisms to guarantee that network resources are securely partitioned across the entire infrastructure. The demonstration highlights the technical implementation of slice orchestration and performance isolation, showing how critical services such as robotic-surgery control or vital-sign sensors maintain guaranteed quality of service even under high load, while less critical services coexist without interference. The proof-of-concept confirms the practical viability of complete end-to-end network slicing for mission-critical applications that demand reliability, confidentiality, and predictable performance.

The exponential growth in connectivity and computing demand has made Network Function Virtualization Infrastructure (NFVI) a major contributor to global energy consumption. Conventional network and service deployments, whether based on legacy hardware appliances or NFVI stacks, struggle to dynamically provision resources for peak data traffic demand. This results in nearly constant energy consumption, even during low-traffic periods, leading to inefficient resource use. Network softwarization and virtualization have enabled flexible and programmable service deployments, which are beneficial for the rapid and dynamic scaling of network functions. This paper validates energy-aware service and network orchestration with a Zero-touch Network and Service Management (ZSM) framework for the autonomous optimization of computing, network, and power resources from NFVI in a use case focused on connected mobility, and in particular, smart traffic management. By modeling road traffic based on vehicle count and type and 3rd Generation Partnership Project (3GPP) profiles for data formats in vehicular communication scenarios, the ZSM framework adjusts services and resources to service requirements and to actual demand. Experimental validation on the real-life Smart Highway testbed in Antwerp, Belgium, demonstrates a strong correlation between vehicular traffic and power consumption, supporting the hypothesis that adaptive compute and network resource management reduces unnecessary energy use and advances the vision of sustainable and self-optimizing Sixth-Generation (6G) networks.

This paper presents a comprehensive benchmark study of a real-life 5G Standalone (SA) deployment with different Multiple Input Multiple Output (MIMO) configurations (1x1, 2x2, and 4x4) in an indoor office environment. We evaluate the impact of distance, obstacles, and material composition on key performance metrics, including throughput, Reference Signal Received Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), and Rank Indicator (RI). The results demonstrate that while higher-order MIMO configurations can deliver substantial throughput gains under favorable conditions, their effectiveness is fundamentally constrained by environmental factors such as signal attenuation, multipath propagation, and material-induced losses. Our results provide practical guidelines for indoor network planning and optimization, establishing concrete performance baseline for open-source 5G systems in typical office scenarios highlighting the critical importance of site selection.

Morus alba L., or white mulberry, is a small deciduous shrub from the Moraceae family, with well-known medicinal properties and beneficial effects on human health. Recent studies have confirmed the bioactive potential of this plant, including its antimicrobial effects, primarily against bacteria and viruses. In contrast, data regarding its antifungal activity are scarce. The main goal of this study was to evaluate the antifungal and antibiofilm capacity of white mulberry ethanolic and methanolic extracts derived from leaves and fruits against Candida albicans, through the measuring of fungal growth inhibition in the agar diffusion method; determining the minimum inhibitory concentration (MIC) by the broth microdilution method; defining the minimum fungicidal concentration (MFC); evaluation of the antibiofilm capacity of the extracts through the tissue culture plate method; and calculation of the biofilm-inhibition percentage. The fruit methanolic extract achieved the largest inhibition zones (31.00±1.00 mm), followed by the fruit ethanolic extract (30.00±1.00 mm). MIC values for all tested samples were defined at 125 µg/ml, while MFC values were determined at 250 µg/ml. In this study, C. albicans was evaluated as a strong biofilm former, and white mulberry extracts showed the potential to change the biofilm-forming capacity in subinhibitory concentrations. Fruit methanolic extract decreased this capacity in a wide range of concentrations, with biofilm inhibition up to 92.61%. Results indicate that white mulberry represents a strong candidate for further investigation in phytotherapy.

The Australian Open, together with Wimbledon, Roland Garros and the US Open, is one of the four biggest tournaments known as Grand Slams. As the first major tournament of the year, it is a very important and prestigious event. Since it is held immediately after the preparation period, players are expected to show a high level of physical, technical-tactical and psychological preparedness. It is played on a hard surface, which additionally increases the physical demands placed on tennis players. The aim of this research is to determine whether there is a statistically significant difference in the “winner” points achieved between winning and losing tennis players at the 2024 Australian Open. The study analyzed situational efficiency parameters between winners and losers. The sample of variables consisted of 246 players in 123 matches played at the 2024 Australian Open. All matches that were completed were included in the research; four matches ended with the retirement of one of the players and were therefore not included in order to obtain valid data in the final statistical analysis. The results were obtained based on the analysis of points won during the match, using seven (7) different tennis strokes. Discriminant analysis of the obtained data showed that there are statistically significant differences between winning and losing tennis players in certain tennis strokes. The results showed that the greatest difference was in the “overhead” stroke, in favor of the winners.Keywords:tennis, tennis strokes, situational efficiency parameters, Australian Open, discriminant analysis.

BACKGROUND AND HYPOTHESIS Kidney replacement therapy (KRT) practices in Europe are heterogeneous, with apparent differences between Western and Central/Eastern Europe. However, time trends in KRT incidence and prevalence in Central and Eastern Europe are not previously reported. Therefore, we aimed to describe trends in incidence and prevalence of KRT in Central and Eastern Europe from 2010 to 2021. METHODS Data on incident and prevalent KRT patients from 19 Central and Eastern European countries between the years 2010 and 2021 were derived from the European Renal Association (ERA) Registry. Time trends were calculated using JoinPoint regression. RESULTS Overall, KRT incidence increased with 1.5% (95% confidence interval (CI): +0.7 to + 2.6%) per year from 106.3 per million population (pmp) in 2010 to 119.6 pmp in 2019. However, trends differed within the region. While in Bosnia and Herzegovina KRT incidence significantly decreased from 2010 to 2019, it remained stable in nine and increased in eight countries. The overall KRT prevalence increased with 5.1% (95% CI: +4.5 to + 5.7%) per year from 426.2 pmp in 2010 to 651.2 pmp in 2019. KRT prevalence increased in all countries, except for Belarus where it remained stable, and was mainly attributable to increases in the prevalence of kidney transplantation. The COVID-19 pandemic did not have a major impact on KRT incidence and prevalence in the region, as most trends remained until 2021. CONCLUSIONS Although we found an overall increase in KRT incidence and prevalence in the region, large country variations remain, much larger than observed in Western Europe. The results of this study can help to define country-specific priorities for the optimization of KRT care in Central and Eastern Europe.

Non-small cell lung cancer (NSCLC) remains the leading cause of cancer mortality worldwide; however, precision oncology has fundamentally transformed its treatment landscape. In 2025, seven approvals by the U.S. Food and Drug Administration (FDA) further accelerated biomarker-driven care across critical molecular subsets. These include MET-directed and trophoblast cell-surface antigen-2 (TROP-2) antibody-drug conjugates (ADCs), expanded strategies targeting epidermal growth factor receptor (EGFR), notably those addressing exon 20 insertion mutations, a ROS proto-oncogene 1 (ROS1) inhibitor, and various human epidermal growth factor receptor 2 (HER2) options that encompass both tumor-agnostic and mutation-selected approaches. These advancements underscore the necessity for integrated diagnostics-such as next-generation sequencing (NGS), fluorescence in situ hybridization (FISH), and immunohistochemistry (IHC)-while also emphasizing ongoing challenges in biomarker selection, therapeutic sequencing, and equitable global implementation.

The growth of medical tourism has contributed to the expansion of dental tourism. With the rise of tourism in Albania, dental tourism has similarly progressed. This research aims to evaluate the quality of dental tourism services in Tirana, applying fuzzy methods such as CRITIC (CRiteria Importance Through Intercriteria Correlation) and CoCoSo (Combined Compromise Solution). These two methods were integrated into a hybrid approach, as described in this paper. Seven experts evaluated dental practices using linguistic values. The CRITIC method results indicated that, among the ten criteria, the most significant was the variety of services. According to the CoCoSo method, Biodent Denti e Sorrisi, Diamond Dental, and Dental Med Austria practices demonstrated the highest quality in dental tourism services. As research results indicate, these clinics could serve as models for improving dental tourism in Albania through a focus on quality development.