Advancing Open Science

Accurate node localization in wireless sensor networks (WSNs) is challenging under variable signal propagation and strict energy constraints. This paper presents a hybrid localization framework that combines a deep neural network (DNN) with particle swarm optimization (PSO) to improve accuracy while reducing energy consumption. The DNN learns the non-linear mapping from received signal strength indicator (RSSI) measurements to node coordinates, mitigating propagation effects. PSO jointly optimizes key DNN hyperparameters and selects a minimal subset of anchor nodes that preserve localization performance, thereby lowering communication overhead. Simulation results on 200-node networks show that the proposed DNN–PSO achieves a mean localization error (MLE) of 0.87 m, outperforming a standard DNN (1.32 m) and classical multilateration (3.84 m). The optimized anchor selection reduces per-cycle energy consumption by 23% (239 mJ to 184 mJ) while maintaining sub-meter accuracy. Performance remains stable across diverse propagation conditions and scales well with increasing network size. These results indicate that the proposed approach provides an effective accuracy–energy trade-off for resource-constrained IoT/WSN deployments requiring reliable localization.

Osteosarcoma (OS) is a highly aggressive bone cancer with limited therapeutic options. Carnosic acid (CA), a phenolic diterpene with well-established antioxidant properties, has shown anticancer activity, yet its mechanisms in OS remain unclear. In this study, we found that CA suppressed proliferation and induced apoptosis in human osteosarcoma cells in a dose-dependent manner. Mechanistically, CA activated the STING/IRF3 signaling pathway and enhanced nitric oxide (NO) production, factors closely linked to redox modulation and mitochondrial apoptotic signaling. Pharmacological inhibition or siRNA-mediated knockdown of STING, as well as blockade of NO synthesis, significantly reduced CA-induced apoptosis in vitro. In a xenograft mouse model, CA treatment suppressed tumor growth, and this effect was partially reversed by STING inhibition. These findings suggest that CA exerts antitumor effects in OS through modulation of innate immune and redox-related signaling pathways, supporting its potential as a therapeutic compound that links antioxidant and immunomodulatory actions.

Background/Objectives: Healthcare workers (HCWs) have a crucial role in addressing vaccine hesitancy in ethnic minority populations as they are a trusted source of information. The aim of this systematic review is to synthesise and evaluate behaviour change techniques (BCTs) and strategies in interventions aimed at HCWs to promote vaccine uptake among ethnic minority populations. Methods: The literature was systematically searched in peer-reviewed databases and the grey literature. Studies were included if they reported interventions for respiratory and routinely recommended vaccine-preventable diseases which were delivered by HCWs to increase vaccine uptake in ethnic minority groups. Interventions were coded using the Behaviour Change Wheel (BCW) and BCT Taxonomy. Results: From 7250 records identified, 14 studies were included in the review. Vaccines targeted by interventions included influenza, pneumococcal disease, pertussis, tetanus, diphtheria, meningitis and hepatitis B. Seven BCW intervention types, six policy options and 22 BCTs were identified. Main intervention types used were persuasion, enablement and education. Effective interventions had multi-components and were tailored to specific populations. Staff training to improve vaccine recommendation and dialogue with patients, and prompts/cues were associated with positive effects, but there was no strong evidence to recommend one specific intervention strategy over another as effectiveness was linked to a multitude of BCTs and intervention types. Conclusions: Several strategies aimed at HCWs can be used and tailored to increase vaccine uptake among ethnic minority communities; however, this does not address all issues related to low vaccine uptake. While HCWs are necessary, without system-level enablement, they cannot fully address barriers to vaccine uptake.

There are numerous operating parameters that affect the thermodynamic and thermoeconomic performance of gas turbine cycles, and many studies based on energy, exergy, and economic analyses have been conducted in the literature by considering these parameters. However, the order of importance and contribution ratios of key operating parameters such as ambient temperature, compressor pressure ratio, combustion efficiency, regenerator effectiveness, and compressor and turbine isentropic efficiencies with respect to thermal efficiency, exergy efficiency, and the levelized cost of electricity (LCOE) have not been sufficiently investigated using statistical methods. Accordingly, a thermodynamic model of a gas turbine cycle improved with intercooling, reheating, and regeneration processes was developed in the study, and thermal efficiency, exergy efficiency, and LCOE values were calculated under different parameter levels. Taguchi analysis was carried out by using the L27 orthogonal array, in which six operating parameters were evaluated at three levels, and optimum parameter levels were determined for each performance indicator. Next, the contribution ratios of the parameters to the objective functions were calculated using the ANOVA method. The results showed that turbine isentropic efficiency was the most influential parameter in terms of thermal and exergy efficiencies, while compressor pressure ratio played the dominant role in terms of LCOE. Additionally, to simultaneously achieve the goals of maximizing thermal and exergy efficiencies and minimizing the LCOE value, the grey relational analysis (GRA) method was applied as a multi-objective optimization approach, and the optimum operating conditions were determined based on a single performance indicator. According to the GRA results, under the optimum conditions, the thermal efficiency was calculated as 0.5533, its exergy efficiency was 0.5772, and the LCOE value was 0.01751 USD/kWh.

Quantitative polymerase chain reaction (qPCR) is a widespread method to quantify RNA or DNA. The results are reported as cycle of quantification (Cq), scaled to absolute numbers of copies or relative to reference genes. The reported Cq values of the same reaction vary between different machines and cannot be compared between different laboratories. This study shows that the third derivative zero (TD0) method is machine independent and more reproducible than the classic Cq calculations. Together with the mean PCR efficiency it allows the calculation of the number of copies initially present (Ncopy), a parameter easy to interpret. A large dataset was created for the evaluation of this method including amplicons with different length, primer concentrations, reaction mixes, and fluorescence reporter systems. Furthermore, the calculated Ncopy values can be corrected at the same time using known concentrations of a standard and for the expression of reference genes and combining absolute and relative quantification. The algorithms were implemented in the open-source program RDML-Tools, which can perform all steps of a qPCR analysis using the raw fluorescence amplification data and is available on the internet. We conclude that qPCR analysis today should widen its focus and include the three essential parameters, TD0, mean PCR efficiency and Ncopy.

Macrophages differentiated with macrophage colony-stimulating factor (M-CSF) (M-Mac) are widely used as an experimental model. Interleukin 27 (IL-27)-polarized M-Mac (27M-Mac) suppresses HIV replication; however, the effects of IL-27 polarization on granulocyte-macrophage colony-stimulating factor (GM-CSF)-induced macrophages (GM-Mac) remain less investigation. Here, we compare multiple functional properties and gene expression profiles of 27M-Mac and IL-27-polarized GM-Mac (27GM-Mac). M-Mac and GM-Mac were generated from monocytes of healthy donors and subsequently treated with IL-27 for three days. HIV replication in 27M-Mac, GM-Mac, and 27GM-Mac was suppressed to nearly 10% of that in M-Mac; however, single-cell RNA sequencing showed that M-Mac clustered with GM-Mac, and 27M-Mac clustered with 27GM-Mac. Expression of CD38 and secretion of CXCL9 and C1q were significantly increased in 27M-Mac and 27GM-Mac compared with M-Mac and GM-Mac. Although CD16 and CD64 expression increased in 27M-Mac and 27GM-Mac relative to their respective controls, phagocytic activity in 27M-Mac and 27GM-Mac was 30% of that in M-Mac. Autophagy was promoted 3.7-fold more strongly in 27M-Mac than in M-Mac, reaching levels comparable to those in GM-Mac and 27GM-Mac. Collectively, these findings indicate that IL-27 polarizes M-Mac and GM-Mac toward transcriptionally and functionally similar subtypes, providing insight into the role of IL-27 in macrophage polarization and plasticity.

The loss of estrogen following menopause is associated with a marked increase in cardiometabolic risk, accompanied by adverse changes in lipid metabolism, insulin sensitivity, vascular function, and systemic inflammatory tone. Emerging evidence suggests that estrogen signaling interacts with chromatin regulatory mechanisms, including DNA methylation, histone modifications, and chromatin remodeling, across multiple metabolic tissues. In this review, we examine current evidence linking estrogen receptor signaling to epigenetic modulation in cardiovascular, hepatic, adipose, vascular, and immune systems. We propose that epigenetic remodeling represents a plausible and testable mechanistic framework connecting estrogen depletion to cardiometabolic disease progression, while acknowledging that much of the mechanistic evidence derives from preclinical and in vitro systems and that direct longitudinal validation in human cardiovascular tissues remains limited. We further explore how this framework may contribute to understanding the “estrogen paradox” and the heterogeneous outcomes of hormone replacement therapy (HRT), particularly within the context of the timing hypothesis. Finally, we evaluate pharmacologic and lifestyle interventions, including structured exercise, dietary modulation, and cardiometabolic therapeutics, through the lens of potential epigenetic influence. Clarifying tissue-specific and immune-integrated chromatin responses to estrogen loss will be essential for advancing precision strategies aimed at improving cardiometabolic health in postmenopausal women.

Climate change is reshaping global hydrological cycles, intensifying scarcity and heightening health risks in vulnerable regions. This study examines the health impacts of climate-driven water scarcity across the Middle East, South Asia, and Sub-Saharan Africa using data on water availability, climate variability, and health outcomes. The study uses a multi-regional mixed methods approach that brings together climate, hydrology, governance, and health data to explore how climate-driven water scarcity affects public health in South Asia, Sub-Saharan Africa, and the MENA region. It combines quantitative climate and health indicators with qualitative evaluations of water system vulnerability to compare exposure pathways and health outcomes across regions. Findings show that rising temperatures, altered rainfall, declining groundwater, and recurrent droughts undermine water security, leading to increased disease burdens through four pathways: (1) waterborne illnesses from unsafe or insufficient supplies; (2) reduced hygiene due to limited access; (3) food insecurity from crop failures; and (4) mental health stress, conflict, and displacement from water competition. Women, children, and low-income households face disproportionate impacts. Current adaptation measures are fragmented, highlighting the need for integrated water governance to build climate resilience. Recommended strategies include community-based water safety planning, digital water monitoring, and embedding health metrics in climate–water policies. This cross-regional analysis supports equitable, climate-resilient health systems and informs interventions to mitigate water scarcity under accelerating climate change. This study directly supports global policy agendas by providing evidence that advances the objectives of the Sustainable Development Goals and international frameworks on climate resilience, water security, and food and health protection.