Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein–Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.

Abstract The ODYSSEY OUTCOMES trial, comprising over 47 000 patient-years of placebo-controlled observation, demonstrated important reductions in the risk of recurrent ischaemic cardiovascular events with the monoclonal antibody to proprotein convertase subtilisin/kexin type 9 alirocumab, as well as lower all-cause death. These benefits were observed in the context of substantial and persistent lowering of low-density lipoprotein cholesterol with alirocumab compared with that achieved with placebo. The safety profile of alirocumab was indistinguishable from matching placebo except for a ∼1.7% absolute increase in local injection site reactions. Further, the safety of alirocumab compared with placebo was evident in vulnerable groups identified before randomization, such as the elderly and those with diabetes mellitus, previous ischaemic stroke, or chronic kidney disease. The frequency of adverse events and laboratory-based abnormalities was generally similar to that in placebo-treated patients. Thus, alirocumab appears to be a safe and effective lipid-modifying treatment over a duration of at least 5 years.

Placing wind turbines within large migration flyways, such as the North Sea basin, can contribute to the decline of vulnerable migratory bird populations by increasing mortality through collisions. Curtailment of wind turbines limited to short periods with intense migration can minimize these negative impacts, and near‐term bird migration forecasts can inform such decisions. Although near‐term forecasts are usually created with long‐term datasets, the pace of environmental alteration due to wind energy calls for the urgent development of conservation measures that rely on existing data, even when it does not have long temporal coverage. Here, we use 5 years of tracking bird radar data collected off the western Dutch coast, weather and phenological variables to develop seasonal near‐term forecasts of low‐altitude nocturnal bird migration over the southern North Sea. Overall, the models explained 71% of the variance and correctly predicted migration intensity above or below a threshold for intense hourly migration in more than 80% of hours in both seasons. However, the percentage of correctly predicted intense migration hours (top 5% of hours with the most intense migration) was low, likely due to the short‐term dataset and their rare occurrence. We, therefore, advise careful consideration of a curtailment threshold to achieve optimal results. Synthesis and applications: Near‐term forecasts of migration fluxes evaluated against measurements can be used to define curtailment thresholds for offshore wind energy. We show that to minimize collision risk for 50% of migrants, if predicted correctly, curtailments should be applied during 18 h in spring and 26 in autumn in the focal year of model assessments, resulting in an estimated annual wind energy loss of 0.12%. Drawing from the Dutch curtailment framework, which pioneered the ‘international first’ offshore curtailment, we argue that using forecasts developed from limited temporal datasets alongside expert insight and data‐driven policies can expedite conservation efforts in a rapidly changing world. This approach is particularly valuable in light of increasing interannual variability in weather conditions.

Local markets have been a special setting throughout human history. Apart from their important social role, they had immeasurable economic importance as primary forms of exchange of goods (trade). Nonetheless, they experienced numerous transformational changes that affected their functioning. Like other countries, Serbia has a long tradition of market activity. However, several novelties have been introduced in recent years. Among many, the process of e-fiscalization is the main issue. Therefore, the focus of our research is based on a qualitative analysis of online media content (news and comments) related to the fiscalization of market activity. The attitudes of different categories of participants (state authorities, vendors, and customers) were analyzed. LIGRE open-access software was used for this purpose. The results of the analysis showed conflicting parties. Legislators emphasize the exclusive positive effects, while vendors point to the negative side of fiscalization. As a third party, customers (service users) have an undefined attitude in relation to fiscalization (pros/cons/neutral). There is an agreement to introduce market activity into legal flows. However, the key prerequisite is the prior resolution of a number of problems (working conditions, business costs, market monopoly, etc.).

Requirements elicitation has since long been recognized as critical to the success of requirements engineering, hence also to the success of systems engineering. Achieving sufficient scope and quality in the requirements elicitation process poses a great challenge, given the limited slices of budget and time available for this relatively sizeable activity. Among all predominant requirements elicitation techniques and approaches, operational scenarios development has a special standing and character. The set of operational scenarios is acknowledged as a constituent deliverable in the requirements engineering process, serving many purposes. Hence, ensuring success in the development of operational scenarios constitutes a consequential area of research. In this paper we present the results from an industrial survey on experienced and presumptive success factors in the development of operational scenarios. The survey was done using a strength-based approach, involving engineers and managers in two organizations developing cyber-physical systems in the transportation and construction equipment businesses. Our results suggest that operational scenarios reusability and a collaborative operational scenarios development environment are two prime areas for success. Our study provides two contributions. First, we provide an account of success factors in the view of practitioners. This is fundamental knowledge, since a successful deployment of any state-of-the-art approach and technology in a systems engineering organization needs to take the views of the practitioners into consideration. Second, the study adds input to the body of knowledge on requirements elicitation, and can thereby help generate suggestions on direction for future work by researchers and developers.

Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical domain. This paper introduces the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions.

Introduction: Social support is not a one-way relationship but is based on the connections people have with other people, groups, and the wider community. This study aimed to assess the perception of social support by people in the third age and to investigate the correlation of social support with the sociodemographic characteristics of the respondents. Methods: A quantitative cross-sectional study was conducted with 147 elderly people who actively use the services of the Center for Health Promotion and Improvement “Generacija” in Sarajevo. The Multidimensional Scale of Perceived Social Support (MSPSS) was used to assess social perceptions. Results: The results show a weak negative relationship between age and the total scale (r = −0.199, p = 0.05), with older people having lower scores on the total scale. A significant relationship was found between the subscale other factors and age (r = −0.202, p = 0.05). The evaluation of the performance of daily activities correlates weakly with the evaluation of the friend’s subscale (r = 0.186, p = 0.05). The friend’s subscale correlates significantly with the quality of social life (r = 0.227, p = 0.05). The subjective assessment of the quality of social life after arriving at the center showed a correlation with the overall scale score (r = 0.182, p = 0.05) and especially with the friend subscale (r = 0.219, p = 0.05), with the increase in social life and the subscales examined in both cases. Conclusion: Users of the “Generacija” center rate social support on the MSPSS with high scores, with users receiving the most support from family. The sociodemographic characteristics of the respondents have an impact on the perception of social support by the users of the Center for Health Promotion and Improvement “Generacija,” more specifically; they were statistically significantly influenced by age, the way of performing daily activities, the quality of social life and the quality of social life after arrival at the Center.

Accurate Throughput Prediction (TP) represents a cornerstone for reliable adaptive streaming in challenging mediums, such as cellular networks. Challenged by the highly dynamic wireless medium, recent state-of-the-art solutions adopt Deep Learning (DL) models to improve TP accuracy. However, these models perform poorly in critical, rare network conditions, leading to degraded user Quality of Experience (QoE). Such performance results from depending solely on the model's capacity and power of learning, without integrating system knowledge into the design. In this paper, we propose MATURE, a novel multi-stage DL-based TP model designed to capture network operating context to improve prediction accuracy and user experience. MATURE's operation involves characterising the operating context before estimating the network throughput. Our performance evaluation shows that MATURE improves the average user QoE by 4% - 90% in critical network conditions when compared to state-of-the-art.

With the low integration costs and quick development cycle of all-IP-based 5G+ technologies, it is not surprising that the proliferation of IP devices for residential or industrial purposes is ubiquitous. Energy scheduling/management and automated device recognition are popular research areas in the engineering community, and much time and work have been invested in producing the systems required for smart city networks. However, most proposed approaches involve expensive and invasive equipment that produces huge volumes of data (high-frequency complexity) for analysis by supervised learning algorithms. In contrast to other studies in the literature, we propose an approach based on encoding consumption data into vehicular mobility and imaging systems to apply a simple convolutional neural network to recognize certain scenarios (devices powered on) in real time and based on the nonintrusive load monitoring paradigm. Our idea is based on a very cheap device and can be adapted at a very low cost for any real scenario. We have also created our own data set, taken from a real domestic environment, contrary to most existing works based on synthetic data. The results of the study’s simulation demonstrate the effectiveness of this innovative and low-cost approach and its scalability in function of the number of considered appliances.

Clusters of neurons generate electrical signals which propagate in all directions through brain tissue, skull, and scalp of different conductivity. Measuring these signals with electroencephalography (EEG) sensors placed on the scalp results in noisy data. This can have severe impact on estimation, such as, source localization and temporal response functions (TRFs). We hypothesize that some of the noise is due to a Wiener-structured signal propagation with both linear and nonlinear components. We have developed a simple nonlinearity detection and compensation method for EEG data analysis and utilize a model for estimating source-level (SL) TRFs for evaluation. Our results indicate that the nonlinearity compensation method produce more precise and synchronized SL TRFs compared to the original EEG data.