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PubMed article 1 was ingested from pubmed.
pubmed
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Open Cathedral analysis
Artificial intelligence (AI) has become a major topic of public and scientific debate, whereas public perceptions of quantum computing (QC) remain less understood. In the present study, we aimed to understand if people have different views on AI vs. QC, while also asking them about their perceived knowledge levels in these two domains. Further, we investigated the construct of technology self-efficacy to consider how people in general think they can master new technology. In N = 475 German speaking participants from the general population (227 females, 248 males) we observed a) that people report on average more positive attitudes towards QC than towards AI, while b) at the same time reporting less knowledge on QC compared to AI. In mediation models, we observed that the link between technology self-efficacy and more positive AI / QC attitudes is mediated by knowledge levels about AI / QC. We also shed light on further mediation models with negative AI / QC attitudes and pessimism aversion in the context of AI / QC as outcomes. The present data could be interpreted in a way that the massive global discussions on AI technology might make people less positive towards this general-purpose technology compared to overall QC attitudes, whereas interestingly the reported knowledge levels about QC are lower than those regarding AI. This is noteworthy, as it is discussed that the fusion of QC and AI technology will represent a powerful merger likely leading to a dramatic increase of scientific discovery, an assumption that received moderate support in the present sample.
europe-pmc
2026-07-01
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Open Cathedral analysis
The exponential demand for energy-efficient and adaptive computing architectures drives the evolution of artificial intelligence (AI) and machine learning (ML). Neuromorphic computing, inspired by biological neural networks, overcomes the limitations of traditional von Neumann architectures, including high energy consumption and limited scalability. The introduction of two-dimensional (2D) materials, such as transition metal dichalcogenides, hexagonal boron nitride, black phosphorus, and tellurene, enables neuromorphic devices with unprecedented control over electronic and optoelectronic properties. These materials exhibit atomic-scale thickness, high carrier mobility, and tunable bandgaps, facilitating synaptic behaviours such as spike-timing-dependent plasticity and paired-pulse facilitation. This review describes the integration of 2D materials into neuromorphic systems, highlighting applications in wearable electronics, brain-machine interfaces, and quantum neuromorphic platforms. In wearable and edge computing, 2D-based devices enable localized, ultra-low-power data processing. In brain-machine interfaces, they enhance signal transduction and neural interfacing. Quantum effects in 2D materials further enable hybrid quantum-classical neuromorphic architectures for high-dimensional computational tasks. Despite significant advances, challenges in reproducibility, scalability, and stability remain. Addressing these limitations through innovations in synthesis and defect passivation is essential for practical application. This review underscores the transformative potential of 2D-material-based neuromorphic computing for energy-efficient AI. Integration of 2D materials into neuromorphic computing architectures offers a promising pathway toward energy-efficient and adaptive systems that bridge biological learning mechanisms with machine intelligence.
europe-pmc
2026-06-24
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Open Cathedral analysis
We investigate the capabilities of quantum generative adversarial networks (QGANs) in image generations tasks. Our analysis centers on fully quantum implementations of both the generator and discriminator. Through extensive numerical testing of current main architectures, we find that QGANs struggle to generalize across datasets, converging on merely the average representation of the training data. When the output of the generator is a pure-state, we analytically derive a lower bound for the discriminator quality given by the fidelity between the pure-state output of the generator and the target data distribution, thereby providing a theoretical explanation for the limitations observed in current models. Our findings reveal fundamental challenges in the generalization capabilities of existing quantum generative models. While our analysis focuses on QGANs, the results carry broader implications for the performance of related quantum generative models.
europe-pmc
2026-06-09
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Open Cathedral analysis
Cloud computing environments face persistent structural challenges in cost control, dynamic resource allocation, and security risk management, which traditional infrastructure approaches fail to address adequately. This systematic literature review aimed to synthesize empirical evidence on the application of artificial intelligence (AI) and machine learning (ML) models for cost optimisation, resource management, and security enhancement in cloud computing environments. Following the PRISMA 2020 guidelines and the Kitchenham-Charters methodology, a structured search was conducted across IEEE Xplore, Web of Science, ScienceDirect, and the ACM Digital Library, covering the period 2020-2025. From an initial pool of 216 records, 18 primary studies were selected after applying the PICOC framework, predefined inclusion and exclusion criteria, and a dual-reviewer quality assessment process yielding substantial inter-rater agreement (Cohen's κ = 0.86). The synthesized evidence demonstrates that predictive provisioning systems and intelligent load-balancing mechanisms reduce operational costs by up to 85%, metaheuristic algorithms such as the Whale Optimization Algorithm and Particle Swarm Optimization improve energy efficiency by 30%-40% and increase resource utilization by up to 80%, and deep learning-based intrusion detection systems achieve accuracy levels exceeding 92%. These findings confirm that AI constitutes a structural mechanism for strengthening economic efficiency, operational resilience, and the sustainability of cloud infrastructures. However, heterogeneity in simulation environments, limited validation in production-scale deployments, and insufficient coverage of virtual machine migration dynamics represent critical gaps requiring standardized benchmarking frameworks and empirical validation in hybrid and multicloud architectures. A quantitative synthesis (Table 1) reveals that metaheuristic algorithms achieve 30%-40% cost and energy efficiency improvements, while ensemble deep learning approaches attain >97% security threat detection rates.
europe-pmc
2026-04-30
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Open Cathedral analysis
Artificial intelligence is typically formulated as an information-processing system composed of artificial neurons, where computation is understood as recursive operations connecting inputs and outputs. However, real neural systems are materially embodied and continuously reconfigured by metabolic and physical processes, suggesting that computation cannot be reduced to fixed causal structures. In this paper, we propose a theoretical framework that captures the interplay between informational and material processes as the interaction between two computational schemes: a vertical scheme, representing fixed cause-effect relations, and a horizontal scheme, representing transformations between such relations. We show that the vertical scheme corresponds to Bayesian inference, which updates probability distributions over a fixed hypothesis space, and is consistent with the free-energy minimization principle. In contrast, the horizontal scheme is formalized as inverse Bayesian inference, which modifies the hypothesis space itself by updating likelihood structures based on experienced data. We further demonstrate that the interplay between these schemes can be expressed algebraically as a process of continuously gluing Boolean algebras. This construction yields a non-distributive orthomodular lattice, i.e., quantum logic, without invoking Hilbert space formalism. In this view, quantum logic emerges not as a static logical system but as a structural consequence of dynamically reconfiguring causal contexts. This framework provides a unified perspective in which inference is understood not only as optimization within a fixed model but also as a process that generates and transforms the model itself. It offers a formal basis for describing open-ended computation and suggests a connection to approaches such as unconventional computing and Natural Born Intelligence, where computational structures evolve through interaction with material processes. Unlike existing approaches, this framework derives quantum-logic-like structure from the continual reconfiguration of causal contexts rather than from Hilbert-space assumptions or optimization within a fixed hypothesis space.
europe-pmc
2026-04-28
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Open Cathedral analysis
Emerging infectious diseases have risen significantly in the twenty-first century as ecological disruption, climate change, expanding human-animal interfaces, and global mobility intensify opportunities for pathogen transmission. This review synthesizes historical and contemporary evidence across viral, bacterial, fungal, and parasitic threats to characterize how diverse pathogens emerge and spread. Foundational events such as the 1918 influenza pandemic, mid-century influenza pandemics, the emergence of HIV/AIDS, and the eradication of smallpox provide context for understanding modern disease dynamics. In recent decades, coronaviruses including SARS, MERS, and SARS-CoV-2, pandemic H1N1, avian influenza subtypes, and major arboviruses such as dengue, chikungunya, Zika, West Nile virus, and yellow fever have demonstrated the rapidity with which zoonotic pathogens can disseminate globally. Viral hemorrhagic fevers including Ebola, Marburg, Lassa, and Crimean-Congo hemorrhagic fever remain critical threats, especially in regions with limited health-care capacity. Concurrently, antimicrobial resistance, the emergence of Candida auris, and the climate-driven expansion of endemic mycoses involving Histoplasma, Coccidioides, and Blastomyces highlight the increasing importance of fungal pathogens. Parasitic diseases such as artemisinin-resistant malaria, zoonotic trypanosomiasis, and expanding Leishmania transmission reflect shifting ecological conditions. These patterns are shaped by intersecting drivers including deforestation, wildlife trade, agricultural intensification, urban crowding, conflict, and rapid microbial evolution that enable spillover and sustained transmission. Although advances in genomic surveillance, metagenomic diagnostics, mRNA vaccines, monoclonal antibodies, and broad-spectrum antivirals have strengthened global response capacity, substantial gaps persist in equity, surveillance, and access to countermeasures. Strengthening One Health systems and resilient public health infrastructures is essential to anticipate and mitigate emerging infectious threats.
pubmed
2026-03-31
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Open Cathedral analysis
Background Malaria continues to pose a major public health challenge in sub-Saharan Africa (SSA), where more than 95% of global cases and deaths occur. Despite WHO Global Technical Strategy for Malaria (GTS) targeting a 90% reduction in malaria mortality by 2030, progress is hindered by persistent transmission conditions, fragile health systems, and emerging resistance to drugs and insecticides. Epidemiological models are increasingly applied to guide malaria control, yet their diversity and complexity present both opportunities and limitations for policy use. The aim of the proposed systematic review is to explore the epidemiological models that have been applied to malaria transmission in SSA, discuss how these models have been applied in informing malaria control measures as well as long-term elimination planning, and address the methodological strengths, limitations, challenges in implementing them, and their policy and strategic implications. Methods This study presents a systematic review of malaria modeling efforts in SSA, with a focus on the strengths, weaknesses, and practical applications of different approaches. Following PRISMA 2020 guidelines and a PROSPERO-registered protocol, we searched five databases (PubMed, Scopus, LILACS, Web of Science, and African Medicus Index) for studies published up to December 31, 2024. Eligible articles were screened by three independent reviewers using predefined PECO criteria, and data were extracted on study context, model type, interventions, populations, and outcomes. The quality of the methodology used in the modelling studies that were included was determined using the ISPOR-SMDM good research practices framework. This framework assesses major areas of model structure, assumptions, transparency, validation and reporting. Since most of the included studies represented mechanistic epidemiologic transmission models, rather than clinical prediction, studies, a formal risk-of-bias instrument, like PROBAST, was not utilized. Risk of bias was actually not measured, the modelling quality and reporting practices were instead appraised with the help of the ISPOR-SMDM assessment. Results Following systematic screening, a total of 102 studies met the inclusion criteria. The most prevalent models were transmission-focused models (52.9%, 54 articles), which involved disease dynamics. Intervention models contributed 21.6% (22 articles), optimal control models 9.8% (10 articles) and combined optimal control-cost-effectiveness models 15.7% (16 articles). Key gaps include limited incorporation of drug and insecticide resistance, migration dynamics, and climate variability. Conclusion We conclude that future modeling for SSA must be better tailored to local transmission patterns, age-specific vulnerabilities, and programmatic needs, while promoting open access, transparent methods, and collaborative use cases. Strengthening the alignment between modeling outputs and policy priorities will be critical for achieving effective and sustainable malaria control in the region.
What Politicians Must Do Now was ingested from crossref.
crossref
2026-03-24
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Open Cathedral analysis
The modern antibiotic era began in the early twentieth century, but humans have long used materials from the natural world to attempt to treat the symptoms of infection. In this primer, we will discuss the rationale for attempting to reconstruct historical infection remedies in order to assess their antimicrobial activity and how this approach could aid the discovery of molecular cocktails with potential for development into novel treatments for infection.
europe-pmc
2026-01-01
0 citations
Open Cathedral analysis
De novo enzyme design seeks to build proteins containing ideal active sites with catalytic residues surrounding and stabilizing the transition state(s) of the target chemical reaction 1-7 . The generative artificial intelligence method RFdiffusion 8,9 solves this problem, but requires specifying both the sequence position and backbone coordinates for each catalytic residue, limiting sampling. Here we introduce RFdiffusion2, which eliminates these requirements, and use it to design zinc metallohydrolases starting from quantum chemistry-derived active site geometries. From an initial set of 96 designs tested experimentally, the most active has a catalytic efficiency (k cat /K M ) of 16,000 M -1  s -1 , orders of magnitude higher than previously designed metallohydrolases 6,7,10,11 . A second round of 96 designs yielded 3 additional highly active enzymes, with k cat /K M values of up to 53,000 M -1  s -1 and a catalytic rate constant (k cat ) of up to 1.5 s -1 . The design models of the four most active designs differ from known structures and from each other, and the crystal structure of the most active design is very close to the design model, demonstrating the accuracy of the design method. The most active enzymes are predicted by PLACER 12 and Chai-1 (ref. 13 ) to have preorganized active sites that effectively position the substrate for nucleophilic attack by a water molecule activated by the bound metal. The ability to generate highly active enzymes directly from the computer, without experimental optimization, should enable a new generation of potent designer catalysts 14,15 .
pubmed
2025-12-31
0 citations
Open Cathedral analysis
Background Malaria remains a major public health burden in Tanzania, where Plasmodium falciparum accounts for 96% of infections and children under five years experience the highest mortality. The emergence of kelch13 mutations and partial resistance to artemisinin derivatives poses a threat to the sustainability of artemisinin-based combination therapy (ACT). Reliance on artemether-lumefantrine (ALU) as the dominant first-line treatment increases selective pressure. The World Health Organization recommends multiple first-line therapies (MFT) to slow the development of resistance. This study examined the barriers, facilitators, and potential strategies for adopting MFT for uncomplicated malaria in Tanzania. Methods A multi-method qualitative study was conducted. A desk review of national malaria data (2020-2024) was conducted to examine trends in incidence, mortality, and treatment outcomes. Data on importation from the national medicine regulatory authority (2021-2025) were analysed to assess the ACT importation pattern. The desk review provided epidemiological and pharmaceutical context for qualitative enquiry. Semi-structured interviews were conducted with purposively selected participants, including policymakers, regulators, supply chain managers, and frontline healthcare providers. Interviews were transcribed and thematically analysed using Braun and Clarke's framework with NVivo software. Results From the NMCP desk review data between 2020 and 2024, malaria cases declined from approximately 8.9 million to 7.2 million before resurging to 8.1 million in 2023; deaths followed a similar trend. Children under five consistently bore a higher burden, representing 34% of all cases and 46% of deaths. While case incidence in this age group declined significantly (p = 0.011), mortality showed no improvement (p = 0.802). Importation data revealed ALU comprised 73.5% of all antimalarial imports, compared to 12% for artesunate, 6% for artemether, and only 2.6% for dihydroartemisinin-piperaquine, highlighting limited diversification. A total of 12 Qualitative interviews identified barriers, including the high cost of alternative artemisinin-based combinations, limited provider training, and weak supply chains. Facilitators, on the other hand, demonstrated a strong political commitment, engaged in capacity-building initiatives, and relied on therapeutic efficacy and pharmacovigilance data to inform their approach. Conclusion MFT presents a promising strategy for prolonging ACT efficacy and enhancing malaria case management in Tanzania. However, financial constraints, import dependence on ALU, and inadequate provider preparedness limit implementation. Successful adoption will require diversification of ACT imports, strengthening supply chains, sustained capacity-building, and embedding surveillance and regulatory data into policy decision-making.
europe-pmc
2026-03-24
0 citations
Open Cathedral analysis
europe-pmc
2025-12-04
0 citations
Open Cathedral analysis