Search Results (31)

Background and Objectives: Artificial intelligence (AI) tools promise efficiency gains in oncology, yet adoption depends on economic factors that remain under-characterized in Eastern European health systems. We quantified AI economic literacy, cost-effectiveness perceptions, reimbursement readiness, and return-on-investment (ROI) confidence among Romanian oncology professionals; we described candidate economic adoption profiles and examined whether sector was associated with the strength of the indirect association between literacy and willingness to invest via ROI confidence. Materials and Methods: A multicenter cross-sectional survey (N = 108) was conducted between September 2025 and April 2026 at “Victor Babes” University of Medicine and Pharmacy Timisoara and affiliated oncology services. Participants completed a 25-item AI Economic Literacy Index (AIELI; 0–25) plus 1–5 scales for ROI confidence, willingness to invest, perceived financial barriers, cost-effectiveness perception, and adoption intention. Analyses used Spearman correlations, multivariable logistic regression, k-means clustering, and covariate-adjusted moderated mediation with 5000 bootstrap resamples. Results: Mean age was 41.3 ± 10.7 years; 58.3% were female. AIELI was moderate (13.7 ± 4.6/25). Familiarity favored cost-effectiveness analysis (59.3%) over AI-specific reimbursement codes (16.7%). High willingness to invest occurred in 48.1% and was independently associated with higher AIELI (aOR 1.78 per +1 SD; 95% CI 1.17–2.71), higher ROI confidence (aOR 1.93; 1.24–3.02), lower perceived financial barriers (aOR 0.58; 0.37–0.91), and prior AI training (aOR 2.34; 1.13–4.86). Three exploratory profiles were identified: Cost-Conscious Adopters (n = 43), Reimbursement-Cautious (n = 37), and Budget-Constrained Skeptics (n = 28). Moderated-mediation models were consistent with a sector-conditional indirect association, largest in private clinics (β = 0.193; 95% CI 0.087–0.318) and weakest in public hospitals (β = 0.072; 0.014–0.158). Conclusions: In this exploratory cross-sectional sample, AI economic literacy and ROI confidence were associated with willingness to invest in oncology AI, interpreted as stated support for investment rather than an enacted procurement decision, since many respondents lacked formal budgetary authority. Because the design cannot establish temporal ordering, whether sector-tailored capability-building and reimbursement clarity would increase adoption remains a hypothesis for prospective evaluation. Full article

Oncology conditions are increasingly defined by their molecular profiles, and drug repurposing exploits this new evidence to identify new therapeutic uses of authorized/investigational medicinal products outside their original indication(s). This scoping review mapped original research published between January 2022 and December 2024 to determine the impact of automated processes and artificial intelligence in generating oncology candidates for drug repositioning, and 42 individual projects met the eligibility criteria and were analyzed. The included studies demonstrate extensive use of computational approaches for candidate prioritization, large-scale data integration, and hypothesis generation in oncology drug repurposing, creating opportunities for positive impact on efficiency. The included projects most commonly were target-oriented and disease-oriented and used multiple databases and computational validation procedures, while experimental and clinical validation were less frequently reported. The available open-access literature suggests substantial activity in China and India, which can support the notion that digitalization represents an important instrument in healthcare systems of low- and middle-income countries but should be interpreted cautiously. While the search was limited to PubMed and open-access English-language publications, we identified a relatively small number of drug-oriented projects, the importance of providing publicly accessible source code to reduce development costs, and the predominant role of academic institutions. Full article

We propose a navigation signal authentication scheme for civilian GNSS receivers that exploits the intermodulation components introduced by constant envelope modulation without relying on modernized authenticatable signals or direct access to authorized spreading codes. Since the product of multiple authorized spreading codes is unpredictable and does not reveal the original codes, the spreading code of the intermodulation component can serve as an authentication feature similar to that of authorized signals. The receiver obtains the intermodulation spreading code via a communication link, then authenticates the GNSS signal by detecting the presence of this code through a correlation-based hypothesis test. We analyze the scheme using the operational GPS L1 signal as an example and compare its performance with Chimera (proposed for GPS), OSNMA (proposed for Galileo), and authorized spreading code authentication in PROSPA. Results show that the proposed scheme achieves robustness and security comparable to Chimera, while avoiding the authorization restrictions associated with authorized codes. Under a 63.82 kbps communication rate and a civilian signal $C/N_0$ of 30 dB-Hz, its authentication efficiency exceeds Chimera’s fast channel, with substantially lower data storage requirements. Full article

Background: Long non-coding RNAs (lncRNAs) are increasingly recognized as key regulators of gene expression, playing pivotal roles in diverse biological processes, including reproduction. This study identified and characterized lncRNAs located near fertility-associated genes in Retinta beef cattle, exploring their potential regulatory roles via DNA–RNA triplex formation using in silico approaches. Methods: We applied an integrative bioinformatics pipeline to identify potential triplex interactions, predicting structurally accessible regions within the lncRNAs and demonstrating the statistical enrichment of binding sites across known regulatory genomic elements. Results: Twelve protein-coding genes previously linked to female fertility or male scrotal circumference were analyzed, revealing 16 unique lncRNAs within ±50 kb windows, predominantly on BTA5. We predicted high-confidence triplex-forming oligonucleotides (TFOs) for most gene-lncRNA pairs. Our results suggest robustness and sequence specificity, as interactions were disrupted by sequence permutation or when a control background sequence was used. RNA secondary-structure analysis revealed that TFOs generally lie in exposed regions, supporting their accessibility for triplex formation. Furthermore, promoter and regulatory regions of fertility-associated genes were enriched in predicted triplex target sites (TTSs), with some overlapping CpG islands and enhancer regions, leading to the hypothesis that these lncRNAs might play a role in epigenetic regulation. Conclusions: Overall, these findings establish computationally derived hypotheses regarding the potential molecular mechanisms by which lncRNAs may modulate reproductive efficiency in cattle and highlight specific lncRNAs as promising targets for functional studies and marker-assisted breeding. Full article

Background: Identifying causal genes underlying quantitative trait loci (QTLs) remains challenging due to small effect sizes and the prevalence of non-coding variants. Although multi-omics integration frameworks such as eQTL- and epigenomic-based approaches and TWAS have advanced gene prioritization, their application in poultry and livestock is often constrained by limited reference panels and tissue resources. This review introduces a cost-effective F₂-based integrative framework and compares it with existing multi-omics strategies. Methods: The proposed framework combines QTL remapping, transcriptome analysis, haplotype frequency comparison, association analysis, and conditional correlation analysis within a single workflow. Causal analysis and quantitative complementation tests using knockout birds are incorporated to identify causal genes. Results: By reusing the original F₂ population employed for QTL mapping, this approach enables hypothesis-independent gene prioritization without requiring additional fine-mapping crosses. Its effectiveness is demonstrated through comparison with conventional multi-omics methods, and the integration of causal analysis and quantitative complementation testing provides robust genetic evidence for pinpointing causal genes. Conclusions: This F₂-based framework efficiently prioritizes and verifies causal gene candidates directly within the mapping population, offering a cost-effective alternative to multi-omics approaches that require large-scale resources. It is broadly applicable to diverse chicken crosses and readily transferable to other small livestock species and model organisms. Full article

The integration of Artificial Intelligence (AI) technologies in students’ lives necessitates the systematic incorporation of foundational AI literacy into educational curricula. Students are challenged to develop conceptual understanding of computational frameworks such as Machine Learning (ML) algorithms and Decision Trees (DTs). In this context, unplugged (i.e., computer-free) pedagogical approaches have emerged as complementary to traditional coding-based instruction in AI education. This study examines the pedagogical effectiveness of an instructional intervention employing unplugged activities to facilitate conceptual understanding of DT algorithms among 47 9th-grade students within a Computer Science (CS) curriculum in Greece. The study employed a quasi-experimental design, utilizing the Structure of Observed Learning Outcomes (SOLO) taxonomy as the theoretical framework for assessing cognitive development and conceptual mastery of DT principles. Quantitative analysis of pre- and post-intervention assessments demonstrated statistically significant improvements in student performance across all evaluated SOLO taxonomy levels. The findings provide empirical support for the hypothesis that unplugged pedagogical interventions constitute an effective and efficient approach for introducing AI concepts to secondary education students. Based on these outcomes, the authors recommend the systematic implementation of developmentally appropriate unplugged instructional interventions for DTs and broader AI concepts across all educational levels, to optimize AI literacy acquisition. Full article

Oftentimes in a complex system it is observed that as a control parameter is varied, there are certain intervals during which the system undergoes dramatic change. In biology especially, these signatures of criticality are thought to be connected with efficient computation and information processing. Guided by the classical theory of rate–distortion (RD) from information theory, we propose a measure for detecting and characterizing such phenomena from data. When applied to RD problems, the measure correctly identifies exact critical trade-off parameters emerging from the theory and allows for the discovery of new conjectures in the field. Other application domains include efficient sensory coding, machine learning generalization, and natural language. Our findings give support to the hypothesis that critical behavior is a signature of optimal processing. Full article

The present Perspective analyzes the remarkable evolution of the Amyloid Cascade Hypothesis 2.0 (ACH2.0) theory of Alzheimer’s disease (AD) since its inception a few years ago, as reflected in the diminishing role of amyloid-beta (Aβ) in the disease. In the initial iteration of the ACH2.0, Aβ-protein-precursor (AβPP)-derived intraneuronal Aβ (iAβ), accumulated to neuronal integrated stress response (ISR)-eliciting levels, triggers AD. The neuronal ISR, in turn, activates the AβPP-independent production of its C99 fragment that is processed into iAβ, which drives the disease. The second iteration of the ACH2.0 stemmed from the realization that AD is, in fact, a disease of the sustained neuronal ISR. It introduced two categories of AD—conventional and unconventional—differing mainly in the manner of their causation. The former is caused by the neuronal ISR triggered by AβPP-derived iAβ, whereas in the latter, the neuronal ISR is elicited by stressors distinct from AβPP-derived iAβ and arising from brain trauma, viral and bacterial infections, and various types of inflammation. Moreover, conventional AD always contains an unconventional component, and in both forms, the disease is driven by iAβ generated independently of AβPP. In its third, the current, iteration, the ACH2.0 posits that proteolytic production of Aβ is suppressed in AD-affected neurons and that the disease is driven by C99 generated independently of AβPP. Suppression of Aβ production in AD seems an oxymoron: Aβ is equated with AD, and the later is inconceivable without the former in an ingrained Amyloid Cascade Hypothesis (ACH)-based notion. But suppression of Aβ production in AD-affected neurons is where the logic leads, and to follow it we only need to overcome the inertia of the preexisting assumptions. Moreover, not only is the generation of Aβ suppressed, so is the production of all components of the AβPP proteolytic pathway. This assertion is not a quantum leap (unless overcoming the inertia counts as such): the global cellular protein synthesis is severely suppressed under the neuronal ISR conditions, and there is no reason for constituents of the AβPP proteolytic pathway to be exempted, and they, apparently, are not, as indicated by the empirical data. In contrast, tau protein translation persists in AD-affected neurons under ISR conditions because the human tau mRNA contains an internal ribosomal entry site in its 5′UTR. In current mouse models, iAβ derived from AβPP expressed exogenously from human transgenes elicits the neuronal ISR and thus suppresses its own production. Its levels cannot principally reach AD pathology-causing levels regardless of the number of transgenes or the types of FAD mutations that they (or additional transgenes) carry. Since the AβPP-independent C99 production pathway is inoperative in mice, the current transgenic models have no potential for developing the full spectrum of AD pathology. What they display are only effects of the AβPP-derived iAβ-elicited neuronal ISR. The paper describes strategies to construct adequate transgenic AD models. It also details the utilization of human neuronal cells as the only adequate model system currently available for conventional and unconventional AD. The final alteration of the ACH2.0, introduced in the present Perspective, is that AβPP, which supports neuronal functionality and viability, is, after all, potentially produced in AD-affected neurons, albeit not conventionally but in an ISR-driven and -compatible process. Thus, the present narrative begins with the “omnipotent” Aβ capable of both triggering and driving the disease and ends up with this peptide largely dislodged from its pedestal and retaining its central role in triggering the disease in only one, although prevalent (conventional), category of AD (and driving it in none). Among interesting inferences of the present Perspective is the determination that “sporadic AD” is not sporadic at all (“non-familial” would be a much better designation). The term has fatalistic connotations, implying that the disease can strike at random. This is patently not the case: The conventional disease affects a distinct subpopulation, and the basis for unconventional AD is well understood. Another conclusion is that, unless prevented, the occurrence of conventional AD is inevitable given a sufficiently long lifespan. This Perspective also defines therapeutic directions not to be taken as well as auspicious ways forward. The former category includes ACH-based drugs (those interfering with the proteolytic production of Aβ and/or depleting extracellular Aβ). They are legitimate (albeit inefficient) preventive agents for conventional AD. There is, however, a proverbial snowball’s chance in hell of them being effective in symptomatic AD, lecanemab, donanemab, and any other “…mab” or “…stat” notwithstanding. They comprise Aβ-specific antibodies, inhibitors of beta- and gamma-secretase, and modulators of the latter. In the latter category, among ways to go are the following: (1) Depletion of iAβ, which, if sufficiently “deep”, opens up a tantalizing possibility of once-in-a-lifetime preventive transient treatment for conventional AD and aging-associated cognitive decline, AACD. (2) Composite therapy comprising the degradation of C99/iAβ and concurrent inhibition of the neuronal ISR. A single transient treatment could be sufficient to arrest the progression of conventional AD and prevent its recurrence for life. Multiple recurrent treatments would achieve the same outcome in unconventional AD. Alternatively, the sustained reduction/removal of unconventional neuronal ISR-eliciting stressors through the elimination of their source would convert unconventional AD into conventional one, preventable/treatable by a single transient administration of the composite C99/iAβ depletion/ISR suppression therapy. Efficient and suitable ISR inhibitors are available, and it is explicitly clear where to look for C99/iAβ-specific targeted degradation agents—activators of BACE1 and, especially, BACE2. Directly acting C99/iAβ-specific degradation agents such as proteolysis-targeting chimeras (PROTACs) and molecular-glue degraders (MGDs) are also viable options. (3) A circumscribed shift (either upstream or downstream) of the position of transcription start site (TSS) of the human AβPP gene, or, alternatively, a gene editing-mediated excision or replacement of a small, defined segment of its portion encoding 5′-untranslated region of AβPP mRNA; targeting AβPP RNA with anti-antisense oligonucleotides is another possibility. If properly executed, these RNA-based strategies would not interfere with the protein-coding potential of AβPP mRNA, and each would be capable of both preventing and stopping the AβPP-independent generation of C99 and thus of either preventing AD or arresting the progression of the disease in its conventional and unconventional forms. The paper is interspersed with “validation” sections: every conceptually significant notion is either validated by the existing data or an experimental procedure validating it is proposed. Full article

Water planning and governance strategies must adapt to challenges associated with population growth, climate change, and projected water shortages. In the Western United States, agriculture is the dominant water use, and agricultural water users are being asked to conserve or share their water with other uses. Managing scarce water supplies at the local level often involves creative solutions, many of which are not well documented, especially in the agricultural sector. It is therefore critical to understand ideas to manage scarce water resources from the perspective of agricultural water users and those who work with them. In our research, we used interviews to explore how agricultural water users are managing increasing water scarcity in the Middle Rio Grande basin of central New Mexico and what enables or prevents them from taking innovative action to manage water scarcity. We hypothesized that we would find undocumented water use innovations born out of water users’ responses to lower and more variable water availability in recent years. We primarily recruited interviewees through snowball sampling, with a total of 42 (47%) agricultural water users, decision makers, and non-profit leaders influencing agricultural water governance in the basin accepting our invitation to participate. Our approximately one-hour, semi-structured and open-ended interviews explored agricultural water users’ lived experiences with water governance and opportunities to manage water scarcity. The interviews were recorded, transcribed, coded, and analyzed using HyperRESEARCH software (version 4.5.4). Our results did not support our hypothesis. Instead, we found that agricultural water users struggled to implement well-known innovations amid the pressures of water scarcity, supply uncertainty, administrative complexity, and constraints on their time, labor, and money. Water users and decision makers were mutually interested in implementing innovations in crop choice, flexibility in water storage, use, and management, stricter enforcement of water use efficiency, and access to more efficient irrigation equipment. However, high costs, a lack of knowledge, education, and training, and challenges related to water distribution and scheduling prevented agricultural water users from accessing these and other innovations. Recommendations include incentive-based policies to promote agricultural water use innovations that require high initial costs, improved water accounting at the basin and regional levels to promote flexible and reliable access to agricultural water, targeted education and outreach programming on alternative irrigation methods and cropping patterns, and improved access to irrigation scheduling information to support agricultural water users in planning for water scarcity. Full article

The global adoption of battery electric vehicles (EVs) and hybrid electric vehicles (HEVs) as a substitute for internal combustion engine cars (ICEs) in various nations offers a substantial opportunity to reduce carbon dioxide (CO₂) emissions from land transportation. EVs are fitted with an energy conversion system that efficiently converts stored energy into propulsion, referred to as “tank-to-wheel (TTW) conversion”. Battery-electric vehicles have a significant advantage in that their exhaust system does not produce any pollutants. This hypothesis is equally relevant to public transport. Despite their higher upfront cost, electric buses contribute significantly to environmental sustainability during their operation. This study aimed to evaluate the environmental sustainability of electric buses during their operational phase by utilizing the life cycle assessment (LCA) technique. This paper used the MATLAB R2021b code to ascertain the mean load of the buses during their operation. The energy consumption of battery electric and hybrid electric buses was evaluated using the WLTP Class 2 standard, which refers to vehicles with a power-to-mass ratio between 22 and 34 W/kg, overing four speed phases (low, medium, high, extra high) with speeds up to 131.3 km/h. The code was used to calculate the energy consumption levels for the complete test cycle. The code adopts an idealized rectangular blind box model, disregarding the intricate design of contemporary buses to streamline the computational procedure. Simulating realistic test periods of 1800 s resulted in an average consumption of 1.451 kWh per km for electric buses and an average of 25.3 L per 100 km for hybrid buses. Finally, through an examination of the structure of the Hungarian power system utilization, it was demonstrated that electrification is a more appropriate method for achieving the emission reduction goals during the utilization phase. Full article

Background/Objectives: microRNAs are small non-coding RNAs that regulate gene expression by inducing mRNA degradation or inhibiting translation. A growing body of evidence suggests that miRNAs may be utilized as anti-cancer therapeutics by targeting expression of key genes involved in cancerous transformation and progression. Renal cell cancer (RCC) is the most common kidney malignancy. The most efficient RCC treatments involve blockers of immune checkpoints, including antibodies targeting PD-L1 (Programmed Death Ligand 1). Interestingly, recent studies revealed the cross-kingdom horizontal transfer of plant miRNAs into mammalian cells, contributing to the modulation of gene expression by food ingestion. Here, we hypothesized that PD-L1 expression may be modulated by miRNAs originating from edible plants. Methods: To verify this hypothesis, we performed bioinformatic analysis to identify mes-miR395e from Manihot esculenta (cassava) as a promising candidate miRNA that could target PD-L1. To verify PD-L1 regulation mediated by the predicted plant miRNA, synthetic mes-miR395 mimics were transfected into cell lines derived from RCC tumors, followed by evaluation of PD-L1 expression using qPCR and Western blot. Results: Transfection of mes-miR395e mimics into RCC-derived cell lines confirmed that this miRNA decreases expression of PD-L1 in RCC cells at both mRNA and protein levels. Conclusions: This preliminary study shows the promise of plant miRNA as potential adjuvants supporting RCC treatment. Full article

The ability of AI to process vast datasets can enhance creativity, but its rigid knowledge base and lack of reflective thinking limit sustainable design. Generative Design Thinking (GDT) integrates human cognition and machine learning to enhance design automation. This study aims to explore the cognitive mechanisms underlying GDT and their impact on design efficiency. Using behavioral coding and quantitative analysis, we developed a three-tier cognitive model comprising a macro-cycle (knowledge acquisition and expression), meso-cycle (creative generation, intelligent evaluation, and feedback adjustment), and micro-cycle (knowledge base and model optimization). The findings reveal that increased task complexity elevates cognitive load, supporting the hypothesis that designers need to allocate more cognitive resources for complex problems. Knowledge base optimization significantly impacts design efficiency more than generative model refinement. Moreover, creative generation, evaluation, and feedback adjustment are interdependent, highlighting the importance of a dynamic knowledge base for creativity. This study challenges traditional design automation approaches by advocating for an adaptive framework that balances cognitive processes and machine capabilities. The results suggest that improving knowledge management and reducing cognitive load can enhance design outcomes. Future research should focus on developing flexible, real-time knowledge repositories and optimizing generative models for interdisciplinary and sustainable design contexts. Full article

We introduce both conceptual and empirical findings arising from the amalgamation of a robotics cognitive architecture with an embedded physics simulator, aligning with the principles outlined in the intuitive physics literature. The employed robotic cognitive architecture, named CORTEX, leverages a highly efficient distributed working memory known as deep state representation. This working memory inherently encompasses a fundamental ontology, state persistency, geometric and logical relationships among elements, and tools for reading, updating, and reasoning about its contents. Our primary objective is to investigate the hypothesis that the integration of a physics simulator into the architecture streamlines the implementation of various functionalities that would otherwise necessitate extensive coding and debugging efforts. Furthermore, we categorize these enhanced functionalities into broad types based on the nature of the problems they address. These include addressing challenges related to occlusion, model-based perception, self-calibration, scene structural stability, and human activity interpretation. To demonstrate the outcomes of our experiments, we employ CoppeliaSim as the embedded simulator and both a Kinova Gen3 robotic arm and the Open-Manipulator-P as the real-world scenarios. Synchronization is maintained between the simulator and the stream of real events. Depending on the ongoing task, numerous queries are computed, and the results are projected into the working memory. Participating agents can then leverage this information to enhance overall performance. Full article

This paper aims to test the hypothesis that the quality of social media bot detection systems based on supervised machine learning may not be as accurate as researchers claim, given that bots have become increasingly sophisticated, making it difficult for human annotators to detect them better than random selection. As a result, obtaining a ground-truth dataset with human annotation is not possible, which leads to supervised machine-learning models inheriting annotation errors. To test this hypothesis, we conducted an experiment where humans were tasked with recognizing malicious bots on the VKontakte social network. We then compared the “human” answers with the “ground-truth” bot labels (‘a bot’/‘not a bot’). Based on the experiment, we evaluated the bot detection efficiency of annotators in three scenarios typical for cybersecurity but differing in their detection difficulty as follows: (1) detection among random accounts, (2) detection among accounts of a social network ‘community’, and (3) detection among verified accounts. The study showed that humans could only detect simple bots in all three scenarios but could not detect more sophisticated ones (p-value = 0.05). The study also evaluates the limits of hypothetical and existing bot detection systems that leverage non-expert-labelled datasets as follows: the balanced accuracy of such systems can drop to 0.5 and lower, depending on bot complexity and detection scenario. The paper also describes the experiment design, collected datasets, statistical evaluation, and machine learning accuracy measures applied to support the results. In the discussion, we raise the question of using human labelling in bot detection systems and its potential cybersecurity issues. We also provide open access to the datasets used, experiment results, and software code for evaluating statistical and machine learning accuracy metrics used in this paper on GitHub. Full article

Viral populations are large and highly heterogeneous. Despite the evolutionary relevance of such heterogeneity, statistical approaches to quantifying the extent to which viruses maintain a high genotypic and/or phenotypic diversity have been rarely pursued. Here, we address this issue by analyzing a nucleotide-to-protein sequence map through deep sequencing of populations of the Q$\beta$ phage adapted to high temperatures. Tens of thousands of different sequences corresponding to two fragments of the gene coding for the viral replicase were recovered. A diversity analysis of two independent populations consistently revealed that about $40\%$ of the mutations identified caused changes in protein amino acids, leading to an almost complete exploration of the protein neighborhood of (non-silent) mutants at a distance of one. The functional form of the empirical distribution of phenotype abundance agreed with analytical calculations that assumed random mutations in the nucleotide sequence. Our results concur with the idea that viral populations maintain a high diversity as an efficient adaptive mechanism and support the hypothesis of universality for a lognormal distribution of phenotype abundances in biologically meaningful genotype–phenotype maps, highlighting the relevance of entropic effects in molecular evolution. Full article