Search Results (215)

The antimicrobial resistance crisis necessitates novel therapeutics. Selenium nanoparticles (SeNPs) offer promise, but their precise bactericidal mechanism remains poorly defined. This study aimed to define the antibacterial action of SeNPs synthesized via a green method with ascorbic acid and sodium citrate. The resulting SeNPs were monodisperse (17.8 ± 0.66 nm), crystalline, and highly stable (zeta potential: −69.9 ± 4.3 mV), exhibiting potent bactericidal activity against multidrug-resistant E. coli. To generate a mechanistic hypothesis, we integrated phenotypic analyses with a preliminary, single-replicate proteomic profiling. Recognizing this as an exploratory step, we focused our analysis on proteins with the most substantial changes. This revealed a coherent pattern of a targeted dual assault on core cellular processes. The data indicate that SeNPs simultaneously induce oxidative stress while severely depleting key components of the primary antioxidant glutathione system; key detoxification enzymes—glutathione S-transferase and glutaredoxin 2—were depleted 18- to 19-fold, while the stress protein HchA was reduced by over 63-fold. Concurrently, the patterns point toward a crippling of central energy metabolism; iron–sulfur enzymes in the TCA cycle, including aconitate hydratase (8.1-fold decrease) and succinate dehydrogenase (13.9-fold decrease), were severely suppressed, and oxidative phosphorylation was impaired (e.g., 4.7-fold decrease in NADH dehydrogenase subunit B). We propose that this coordinated disruption creates a lethal feedback loop leading to metabolic paralysis. Consequently, this work provides a detailed and testable mechanistic hypothesis for SeNPs action, positioning them as a candidate for a potent, multi-targeted antimicrobial strategy against drug-resistant pathogens. Full article

Exercise training reduces metabolic dysfunction and improves neural function; however, its cortical molecular effects in diabetic–obese conditions remain unclear. Here, we aimed to identify transcriptional pathways by integrating physiological evaluation with an in silico analysis of cortical RNA-seq data from Zucker Fatty Diabetes Mellitus rats following a 12-week swimming training program. Exercise training reduced body weight and improved glucose control and blood pressure. RNA-seq analysis revealed 814 differentially expressed genes, with pathway enrichment highlighting glutamatergic synapse, retrograde endocannabinoid signaling, and oxytocin signaling pathways. These coordinated transcriptional shifts involved genes related to excitatory neurotransmission, neuromodulatory feedback, and calcium-dependent regulation. As hypothesis-generating models, these pathway-level patterns suggest that exercise training may modulate cortical signaling properties in diabetic–obese states and provide a conceptual framework for future mechanistic investigation. Full article

Decarbonizing heavy-duty freight transport is essential for achieving climate neutrality targets. Although internal combustion engine (ICE) trucks currently dominate logistics, they contribute substantially to greenhouse gas emissions. Zero-emission alternatives, such as battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (H2), provide different decarbonization pathways; however, their relative roles remain contested, particularly in small economies. While BEVs benefit from technological maturity and declining costs, hydrogen offers advantages for high-payload, long-haul operations, especially within energy-intensive cold supply chains. The aim of this paper is to examine the gradual transition from ICE trucks to hydrogen-powered vehicles with a specific focus on cold-chain logistics, where reliability and energy intensity are critical. The hypothesis is that applying a system dynamics forecasting approach, incorporating investment costs, infrastructure coverage, government support, and technological progress, can more effectively guide transition planning than traditional linear methods. To address this, the study develops a system dynamics economic model tailored to the structural characteristics of a small economy, using a European case context. Small markets face distinct constraints: limited fleet sizes reduce economies of scale, infrastructure deployment is disproportionately costly, and fiscal capacity to support subsidies is restricted. These conditions increase the risk of technology lock-in and emphasize the need for coordinated, adaptive policy design. The model integrates acquisition and maintenance costs, fuel consumption, infrastructure rollout, subsidy schemes, industrial hydrogen demand, and technology learning rates. It incorporates subsystems for fleet renewal, hydrogen refueling network expansion, operating costs, industrial demand linkages, and attractiveness functions weighted by operator decision preferences. Reinforcing and balancing feedback loops capture the dynamic interactions between fleet adoption and infrastructure availability. Inputs combine fixed baseline parameters with variable policy levers such as subsidies, elasticity values, and hydrogen cost reduction rates. Results indicate that BEVs are structurally more favorable in small economies due to lower entry costs and simpler infrastructure requirements. Hydrogen adoption becomes viable only under scenarios with strong, sustained subsidies, accelerated station deployment, and sufficient cross-sectoral demand. Under favorable conditions, hydrogen can approach cost and attractiveness parity with BEVs. Overall, market forces alone are insufficient to ensure a balanced zero-emission transition in small markets; proactive and continuous government intervention is required for hydrogen to complement rather than remain secondary to BEV uptake. The novelty of this study lies in the development of a system dynamics model specifically designed for small-economy conditions, integrating industrial hydrogen demand, policy elasticity, and infrastructure coverage limitations, factors largely absent from the existing literature. Unlike models focused on large markets or single-sector applications, this approach captures cross-sector synergies, small-scale cost dynamics, and subsidy-driven points, offering a more realistic framework for hydrogen truck deployment in small-country environments. The model highlights key leverage points for policymakers and provides a transferable tool for guiding freight decarbonization strategies in comparable small-market contexts. Full article

Increasing aridity across the Middle East Region has intensified concerns about the impacts of drought in conflict-affected Northeast Syria (NES). In this study, drought dynamics and their drivers from 2000 to 2023 were analyzed by integrating ERA5-Land meteorological data, MODIS land-surface indicators, FLDAS soil moisture, and ISRIC soil properties at 250 m resolution. The integration of these multisource datasets contributes to a more comprehensive understanding of drought dynamics by combining information on weather conditions, vegetation status, and soil characteristics. The proposed drought analysis framework clarifies independent controls on meteorological, agricultural, and hydrological drought, underscoring the role of land-atmosphere feedback through soil temperature. This workflow provides a transferable approach for drought monitoring and hypothesis generation in arid regions. For this purpose, different XGBoost models were trained for the vegetation health index (VHI), the standardized precipitation-evapotranspiration index (SPEI), and surface soil-moisture anomalies, excluding target-related variables to prevent data leakage. Model interpretability was achieved using SHAP, complemented by time-series, trend, clustering, and spatial autocorrelation analyses. The models performed well (R² = 0.86–0.90), identifying soil temperature, SPEI, relative humidity, precipitation, and soil-moisture anomalies as key predictors. Regionally, soil temperature rose (+0.069 °C yr⁻¹), while rainfall (−1.203 mm yr⁻¹) and relative humidity (−0.075% yr⁻¹) declined. Spatial analyses demonstrated expanding heat hotspots and persistent soil moisture deficits. Although 2018–2019 were anomalously wet, recent years (2021–2023) exhibited severe drought. Full article

Melanoma cells in the brain may use similar mechanisms for adapting to injury and/or disease (that is, through continued reallocation of energy, matter, and information) as other cell types do to create an environment in which cancer cells can grow and sustain themselves within the confines of the brain. These adaptable mechanisms include the ability to reactivate dormant neural crest-derived migration and communication pathways. Unlike some other types of cancers that invade neural tissue as a simple invasion, melanomas are capable of achieving limited molecular, metabolic, and electrical similarity to the neural circuitry of the brain. Melanomas achieve this limited similarity through both vascular co-optation and mimicking synaptic functions, as well as through their engagement of redox-coupled metabolic pathways and feedback-regulated signal transduction pathways. The result is the creation of a metastable tumor–host system, where the relationship between tumor and host is defined by the interaction of stabilizing and destabilizing forces; forces that define the degree of coherence, vulnerability, and persistence of the tumor–host system. In this review, we integrate molecular, electrophysiological, and anatomical data to develop a single unifying hypothesis for the functional integration of melanoma cells into the neural tissue of the brain. Additionally, we describe how neural crest-based regulatory pathways are reactivated in the adult brain and how tumor–host coherence is developed as a function of the shared thermodynamic and informational constraints placed on both tumor and host. We also describe how our proposed conceptual model allows for the understanding of therapeutic interventions as selective disruptions of the neural, metabolic, and immunological couplings that support metastatic adaptation. Full article

In automated sorting and grasping of livestock meat cuts, the ideal assumption of symmetric mass distribution is often violated due to irregular morphology and soft tissue deformation. Under the combined effects of gripping forces and gravity, the originally balanced configuration evolves into an asymmetric state, resulting in dynamic shifts of the center of gravity (CoG) that undermine the stability and accuracy of robotic grasping. To address this challenge, this study proposes a CoG trajectory prediction method tailored for meat-cut grasping tasks. First, a dynamic model is established to characterize CoG displacement during grasping, quantitatively linking gripping force to CoG shift. Then, the prediction task is reformulated as a nonlinear state estimation problem, and a Small-Target Bayesian–Probability Hypothesis Density (STB-PHD) algorithm is developed. By incorporating historical error feedback and adaptive covariance adjustment, the proposed method compensates for asymmetric perturbations in real time. Extensive experiments validated the effectiveness of the proposed method: the Optimal Sub-Pattern Allocation (OSPA) metric reached 4.82%, reducing the error by 4.35 percentage points compared to the best baseline MGSTM (9.17%). The task completion time (TC Time) was 6.15 s, demonstrating superior performance in grasping duration. Furthermore, the Average Track Center Distance (ATCD) reached 8.33%, outperforming the TPMBM algorithm (8.86%). These results demonstrate that the proposed method can accurately capture CoG trajectories under deformation, providing reliable control references for robotic grasping systems. The findings confirm that this approach enhances both stability and precision in automated grasping of deformable objects, offering valuable technological support for advancing intelligence in meat processing industries. Full article

Porcelain-fused-to-metal (PFM) crowns continue to serve as a cornerstone of restorative dentistry owing to their strength, affordability, and esthetics. However, late-onset complications such as oral burning and lichenoid reactions have been observed in long-serving PFMs, suggesting complex host–material interactions that extend beyond simple mechanical wear. This Perspective introduces the Nallan–Nickel Effect, a theoretical model proposing that a host- and environment-dependent threshold of bioavailable nickel ions (Ni²⁺), once exceeded, may trigger a neuro-immune cascade culminating in a burning phenotype. Within this framework, slow corrosion at exposed PFM interfaces releases Ni²⁺ into saliva and crevicular fluid, facilitating epithelial uptake and activation of innate immune sensors such as TLR4 and NLRP3. The resulting cytokine milieu (IL-1β, IL-6, TNF-α) drives NF-κB, mediated inflammation and T-cell activation, while neurogenic mediators—including nerve growth factor (NGF), substance P, and CGRP—sensitize TRPV1/TRPA1 nociceptors, establishing feedback loops of persistent burning and neurogenic inflammation. Modifying factors such as low salivary flow, acidic oral pH, mixed-metal galvanic coupling, and parafunctional stress can lower this threshold, whereas replacement with high-noble or all-ceramic materials may restore tolerance. The model generates testable predictions: elevated local free Ni²⁺ levels and increased expression of TLR4 and TRPV1 in symptomatic mucosa, along with clinical improvement following substitution of nickel-containing restorations. Conceptually, the Nallan–Nickel Effect reframes PFM-associated burning and lichenoid lesions as threshold-governed, neuro-immune phenomena rather than nonspecific irritations. By integrating corrosion chemistry, mucosal immunology, and sensory neurobiology, this hypothesis offers a coherent, testable framework for future translational research and patient-centered management of PFM-related complications. Full article

Background/Objectives: Binaural beat audio has gained popularity as a non-invasive tool to promote relaxation and enhance cognitive performance, though empirical support has been inconsistent. We developed a novel algorithm integrating real-time electroencephalography (EEG) feedback to dynamically tailor binaural beats to induce relaxed brain states. This study aimed to examine the efficacy and feasibility of this algorithm in a clinical trial. Methods: In a randomized, double-blinded, sham-controlled crossover trial, 25 healthy adults completed two 30 min sessions (EEG-guided intervention versus sham). EEG (Fp1) was recorded using a consumer-grade single-electrode headset, with auditory stimulation adjusted in real time based on EEG data. Outcomes included EEG frequency profiles, stop signal reaction time (SSRT), and novelty encoding task performance. Results: The intervention rapidly reduced dominant EEG frequency in all participants, with 100% achieving <8 Hz and 96% achieving <4 Hz within median 7.4 and 9.0 min, respectively. Compared to the sham, the intervention was associated with an faster novelty encoding reaction time (p = 0.039, dz = −0.225) and trends towards improved SSRT (p = 0.098, dz = −0.209), increased boundary separation in stop trials (p = 0.065, dz = 0.350), and improved inhibitory drift rate (p = 0.067, dz = 0.452) within the limits of the exploratory nature of these findings. Twenty-four (96%) participants reached a target level of <4 Hz with the intervention, while none reached this level with the sham. Conclusions: Real-time EEG-guided binaural beats may rapidly induce low-frequency brain states while potentially preserving or enhancing aspects of executive function. These findings support the feasibility of personalized, closed-loop auditory entrainment for promoting “relaxed alertness.” The results are preliminary and hypothesis-generating, warranting larger, multi-channel EEG studies in ecologically valid contexts. Full article

This study explores the relationship between and within Hausa and English letter sound knowledge and word decoding skills among children studying in early years settings in northern Nigeria. There is a lack of correlational studies as well as causal evidence in the African context to indicate any transfer of language skills from L1 and L2 and vice versa. Test scores from 851 children studying in 158 government provided pre-primary schools took tests in letter sound (phoneme) and reading (word) decoding skills. Through bivariate correlations and a just-identified feedback path model, the results support Cummins’ interdependence hypothesis. Hausa and English word scores are bidirectionally associated, and the data reveal very strong significant positive correlations between Hausa and English letter sound scores and Hausa and English word scores. With the language policy set to change in Nigeria concerning the use of the language of the immediate community becoming a possible medium of instruction, these results, supporting bidirectionality and linguistic interdependence, provide evidence for the teaching of L1 and L2 in pre-primary settings in northern Nigeria. Full article

This paper examines how social movements influenced Greece’s macroeconomic adjustment during the financial crisis and austerity period (2010–2015). The purpose is to identify the channels through which mobilizations—anti-austerity protests, general strikes, youth actions, and solidarity networks—interacted with the economy. The main hypothesis is that social protest operates as an economic force via three mechanisms: expectations (shifts in household and firm beliefs affecting consumption, confidence, and investment), disruption (coordination and operating costs from strikes and stoppages affecting output and employment), and institutional feedback (policy sequencing and credibility under EU–IMF conditionality shaping behavior). Using a theoretical, literature-based methodology—a structured narrative review of peer-reviewed studies, policy documents, and historical syntheses—we map these mechanisms onto outcomes (GDP, unemployment, investment, consumer confidence). The findings support the hypothesis: expectations and feedback dominate the transmission to investment and confidence, while repeated disruption is most salient for labor-market dynamics; solidarity infrastructures cushion social costs but have ambiguous aggregate effects. The scope is interpretive and Greece-specific, yielding testable propositions for future causal work. Limitations follow from the design: the study does not estimate effect sizes or establish causality; conclusions are analytically persuasive rather than statistically demonstrative. The contribution is a mechanism map that integrates social-movement theory with crisis political economy and clarifies where empirical identification should focus. Full article

This study examines the Enron collapse through an integrated theoretical framework combining the financial saturation paradox with the dynamics of a naturally occurring Ponzi process. The central objective is to evaluate whether endogenous market mechanisms—beyond managerial misconduct—played a decisive role in the emergence and breakdown of the Enron stock bubble. A logistic-growth-based saturation model is formulated, incorporating positive feedback effects and bifurcation thresholds, and applied to Enron’s stock price data from 1996 to 2001. The computations were performed using LogletLab 4 (version 4.1, 2017) and Microsoft^® Excel^® 2016 MSO (version 2507). The model estimates market saturation ratios (P/Pp) and logistic growth rate (r), treating market potential, initial price, and time as constants. The results indicate that Enron’s share price approached a saturation level of approximately 0.9, signaling a hyper-accelerated, unsustainable growth phase consistent with systemic overheating. This finding supports the hypothesis that a naturally occurring Ponzi dynamic was underway before the firm’s collapse. The analysis further suggests a progression from market-driven expansion to intentional manipulation as the bubble matured, linking theoretical saturation stages with observed price behavior. By integrating behavioral–financial insights with saturation theory and Natural Ponzi dynamics, this work offers an alternative interpretation of the Enron case and provides a conceptual basis for future empirical validation and comparative market studies. Full article

This empirical pilot study explored the use of wearable eye-tracking technology to gain objective insights into interpersonal interactions, particularly in healthcare provider training. Traditional methods of understanding these interactions rely on subjective observations, but wearable tech offers a more precise, multimodal approach. This multidisciplinary study integrated counseling perspectives on therapeutic alliance with an empirically motivated wearable framework informed by prior research in clinical psychology. The aims of the study were to describe the complex data that can be achieved with wearable technology and to test our primary hypothesis that the therapeutic alliance in clinical training interactions is associated with certain behaviors consistent with stronger interpersonal engagement. One key finding was that a single multimodal feature predicted discrepancies in client versus therapist working alliance ratings (b = −4.29, 95% CI [−8.12, −0.38]), suggesting clients may have perceived highly structured interactions as less personal than therapists did. Multimodal features were more strongly associated with therapist rated working alliance, whereas linguistic analysis better captured client rated working alliance. The preliminary findings support the utility of multimodal approaches to capture clinical interactions. This technology provides valuable context for developing actionable insights without burdening instructors or learners. Findings from this study will motivate data-driven methods for providing actionable feedback to clinical trainees. Full article

Background: Preclinical dental training requires simulation-based tools to develop fine motor skills, but traditional models like plastic teeth often lack realistic tactile feedback, and systematic evaluations of multi-layered drilling plates are scarce. This study aimed to evaluate the educational utility and perceived realism of a novel multi-layered drilling plate designed to simulate enamel, dentin, and pulp, with null hypotheses that it would not differ in realism from natural dental tissues or in educational utility from existing tools. Methods: Seventy dental educators (mean preclinical teaching experience: 112.9 ± 116.7 months) from 14 institutions across four continents assessed the plates using standardized protocols. Statistical analysis (Mann–Whitney U Test) was performed to analyze the results. Results: Quantitative ratings (1–10 scale) showed high mean scores for drilling quality (enamel: 7.80 ± 1.55, dentin: 7.27 ± 1.94, pulp: 7.48 ± 2.33), surface smoothness (enamel: 8.17 ± 1.55, dentin: 8.17 ± 1.57), and ergonomic visibility (8.56 ± 1.58), with 90% passing grades, rejecting the null hypothesis of no difference in educational utility. Tissue transition scores (enamel/dentin: 7.09 ± 2.56; dentin/pulp: 6.86 ± 2.46) showed significant differences (p < 0.05) in realism from natural tissues, rejecting the null hypothesis of no difference. Inter-rater reliability was poor (Krippendorff’s alpha: 0.449 for failing scores, 0.211 for passing scores). Qualitative feedback praised ease of use but noted limitations in dentin haptic simulation. Conclusions: The drilling plate shows promise for skill development, though without controlled comparisons to existing tools, its relative efficacy remains preliminary. Further research on student outcomes and tool refinement is needed to validate its use in dental education. Full article

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

Fungi, from saprophytes to pathogens, face predictable daily fluctuations in light, temperature, humidity, and nutrient availability. To cope, they have evolved an internal circadian clock that confers a major adaptive advantage. This review critically synthesizes current knowledge on the molecular architecture and physiological relevance of fungal circadian systems, moving beyond the canonical Neurospora crassa model to explore the broader phylogenetic diversity of timekeeping mechanisms. We examine the core transcription-translation feedback loop (TTFL) centered on the FREQUENCY/WHITE COLLAR (FRQ/WCC) system and contrast it with divergent and non-canonical oscillators, including the metabolic rhythms of yeasts and the universally conserved peroxiredoxin (PRX) oxidation cycles. A central theme is the clock’s role in gating cellular defenses against oxidative, osmotic, and nutritional stress, enabling fungi to anticipate and withstand environmental insults through proactive regulation. We provide a detailed analysis of chrono-pathogenesis, where the circadian control of virulence factors aligns fungal attacks with windows of host vulnerability, with a focus on experimental evidence from pathogens like Botrytis cinerea, Fusarium oxysporum, and Magnaporthe oryzae. The review explores the downstream pathways—including transcriptional cascades, post-translational modifications, and epigenetic regulation—that translate temporal signals into physiological outputs such as developmental rhythms in conidiation and hyphal branching. Finally, we highlight critical knowledge gaps, particularly in understudied phyla like Basidiomycota, and discuss future research directions. This includes the exploration of novel clock architectures and the emerging, though speculative, hypothesis of “chrono-therapeutics”—interventions designed to disrupt fungal clocks—as a forward-looking concept for managing fungal infections. Full article